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Retail Price Promotions and Retailer Financial Performance: The Roles of Expandable Purchases, Inventory Turnover Acceleration, and
Competitive Intensity
by
Jared M. Hansen, B.S. in Engineering,
MBA
A Dissertation
In
BUSINESS ADMINISTRATION - MARKETING
Submitted to the Graduate Faculty of Texas Tech University in
Partial Fulfillment of the Requirements for
the Degree of
DOCTOR OF PHILOSOPHY
Approved
Shelby D. Hunt Chairman of the Committee
James B. Wilcox
Dale F. Duhan
Steve Buchheit
Fred Hartmesiter Dean of the Graduate School
December, 2007
Copyright 2008, Jared Michael Hansen
Texas Tech University, Jared M. Hansen, December 2007
ACKNOWLEDGMENTS
I extend appreciation to all those who have assisted me as I completed this
dissertation, including the professors and doctoral students of the Texas Tech
University marketing department who have been willing, insightful sounding boards
for the refinement of many of the ideas presented in this dissertation. In particular, I
thank Dr. Shelby Hunt for being a mentor and friend, in addition to chairman of my
dissertation, and for the learning I have gained through daily interactions I have been
privileged to have with him during the last two and a half years that I have been his
teaching assistant at Texas Tech. I also thank Dr. Jim Wilcox for his Aristotelian
council on how I might improve measurement in the dissertation. Likewise, I
appreciate the passion that Dr. Dale F. Duhan exhibited on the subject of market
basket analysis. Our many conversations on the subject have been most helpful. I also
thank Dr. Steve Buchheit for helping me attempt to bridge research from accounting,
finance, and marketing.
I give a very deep thanks to the several unnamed friends and associates in
business practice who have made this research possible by providing data. Without the
contributions of these executives and senior managers, the hypotheses could not ever
be explored in their current forms.
I also thank several individuals who have steered me to where I am today.
First, I thank my parents for teaching me to love learning. I thank Ms. Evelynn
Bassutt and Dr. Jeff Campbell who were great examples of teaching in excellence and
motivated me to excel in my studies. I thank Chris Gould and Chris Black who hired
me to be a corporate buyer at Wal-Mart, and Alan Epler, Tom Daugherty, Terry Clark,
and others who were mentors and friends while I was in Bentonville. In all, my
teaching and research compliment each other, and both draw heavily from my industry
experiences. Last, and most important, I thank my wife and best friend Tracy who,
along with my children, made many sacrifices so that I could complete this
dissertation.
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Texas Tech University, Jared M. Hansen, December 2007
TABLE OF CONTENTS
ACKNOWLEDGEMENTS .................................................................................... ii
ABSTRACT ........................................................................................................... iv
LIST OF TABLES .................................................................................................. v
LIST OF FIGURES ............................................................................................... vi
CHAPTER 1. INTRODUCTION ........................................................................... 1
CHAPTER 2. A REVIEW OF THE LITERATURE ........................................... 26
Overview ............................................................................................................... 26
Price Promotions and Definition ........................................................................... 26
Price Promotions and Expandable Purchasing ...................................................... 30
Price Promotions and Return Calculation ............................................................. 41
Moderations of the Promotion-Performance Relationships .................................. 50
CHAPTER 3. RESEARCH DESIGN ................................................................... 57
Samples ................................................................................................................. 57
Data Collection ..................................................................................................... 60
Measurement of Variables .................................................................................... 62
CHAPTER 4. DATA ANALYSIS ........................................................................ 69
CHAPTER 5. DISCUSSION AND CONCLUSION ........................................... 77
REFERENCES ...................................................................................................... 96
APPENDIX ......................................................................................................... 105
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Texas Tech University, Jared M. Hansen, December 2007
ABSTRACT This dissertation investigates the effects of pure, retail price promotions on
product performance, market basket performance, and shareholder investment in the
retailer’s product-level inventory. By pure, I refer to the absence of any additional
displays, features, coupons, advertisements, or other intentional activities by the
retailer that could confound the effects of the price promotions, themselves. The
underlying research question of this study is: What is the impact of retail price
promotions on retailer performance? Specifically, I focus on price promotions and
two measures of performance: (1) the retailer’s total market basket performance (i.e.,
market basket sales, profitability, and inventory turnover), and (2) the price-promoted
products’ financial return on shareholder investment in product-level inventory.
Furthering the goal of understanding the effect of promotional activities on
retailer performance, this study is the first study to (1) measure, instead of estimate,
the effect of individual price promotions on the actual, total-market-basket
performance, and (2) quantify the impact of retailer promotional activity on the
retailer’s shareholders by introducing and using a new metric, the gross margin return
on shareholder investment (or GMROSI) in the retailer’s product-level inventory. The
study also examines several moderating variables, including competitive intensity.
The sample for this dissertation consists of weekly, point-of-sale, stock-
keeping unit, store scanner data for manufacturer-branded products collected in all of
the approximately 450 stores of an every-day-low-price retailer operating in the
northeastern and midwestern United States.
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Texas Tech University, Jared M. Hansen, December 2007
LIST OF TABLES 1. Literature Related to the Effects of Price Promotions on Market Basket
Performance .................................................................................................. 83
2. The Effects of Pure Price Promotions on Product Performance ................... 84
3. Effects of Pure Price Promotions on Market Basket Performance ............... 85
4. Standardized Ridge Regression Coefficients-GMROII Response Surface .. 86
5. The Effects of Pure Price Promotions on GMROII and GMROSI ............... 87
6. The Moderating Effects on H3-H7 ............................................................... 88
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Texas Tech University, Jared M. Hansen, December 2007
vi
LIST OF FIGURES
1. A Causal Model of The Effects of Retail Price Promotions ..................... 89
2. Retailers’ GMROII Response Surface Map ............................................. 90
3. Retailers’ GMROII Response Surface Map with Promotion Data Overlay ............................................................................................. 91
4.a. Response Surface Map of Sample A Marketi Basket
Profitability ............................................................................................... 92
4.b. Response Surface Map of Sample B Marketi Basket
Profitability ............................................................................................... 93
4.c. Response Surface Map of Sample C Marketi Basket
Profitability ............................................................................................... 94
4.d. Response Surface Map of Sample D Marketi Basket
Profitability ............................................................................................... 95
CHAPTER 1
INTRODUCTION
The concept of retailer price promotions has been an important topic in
marketing practice and academe for at least a century. A major impetus for retailer
price promotions was an early US Supreme Court ruling that manufacturers cannot set
retail minimum markup prices (Miles v. Park and Son 1911). A more recent impetus
has been the ongoing shift in relational influence from manufacturers to retailers in
supply chains (e.g., Fishman 2006; Hansen 2007; Mulhern and Leone 1991; Useem,
2003). These forces have afforded retailers the opportunity to control their market
offering retail prices. As Walters (1991) points out, “the primary purpose of retail
prices is to increase retailer sales, and, in turn, retailer profitability.” As a
consequence, retail price promotions have become an “important tool for the modern
marketing managers in stimulating sales” (Goodman and Moody 1970, p. 31).
Retail price promotions can affect the promoted product’s inventory turnover,
as well as influence the merchandise category level performance (e.g., sales,
markdowns, receipts, turns, gross profit, and gross margin return on inventory
investment or “GMROII”) (Hansen, Raut, and Swami 2006, forthcoming). For
example, some price-promoted products may increase category sales but decrease the
category-level (average) initial margin. Other product price promotions may increase
the category initial margin percentage, but retard category turnover. Thus, successful
retailers must adjust to their changing environments, or change them, by deciding (1)
how much to price promote (i.e., how deep to discount), (2) which products to price
Texas Tech University, Jared M. Hansen, December 2007
promote, (3) where to price promote (i.e., in which stores), and (4) when to price
promote.
The purpose of this research is to increase our understanding of the impact of
pure, retailer price promotions (hereafter, price promotions). By pure, I refer to the
absence of any additional displays, features, coupons, advertisements, or other
intentional activities by the retailer that could confound the effects of price promotions
(see Van Heede, Leeflang, and Wittink 2004, for additional discussion on “pure”
promotions). Thus, they are distinct from products that are retailer-advertised but not
retailer-price-discounted (e.g., to create awareness of the product availability at the
store). They are also distinct from products that are retailer-advertised and retailer-
priced-discounted (e.g., to increase customers visiting the store). (Pure) price
promotions, in contrast, are used by retailers in an attempt to increase purchasing
volume across customers and over time (e.g., Blattberg and Wisniewski 1987;
Duncan, Hollander, and Savitt 1983; Kuehn and Rohloff 1967; Mason and Mayer
1984; Moriarty 1985; Woodside and Waddle 1975), and are often the most frequent
type of promotion used by retailers (e.g., Gedenk, Neslin, and Ailawadi 2006),
especially for every-day-low-price retailers such as Wal-Mart that dominant the retail
industry (Heller 2001).1
1 Consider, for instance, that while Wal-Mart reported 718 price promotions (i.e., “rollbacks”) in May 2007 (Wal-Mart 2007), only 31 of the price promotion were included in their printed monthly advertisement circular—in addition to the 70 items advertised in the same circular that were not being price promoted. Combining the promotional counts, 9% of promotions were advertised, but not price discounted, 4% were advertised and price discounted, and 87% were price discounted and not advertised.
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Texas Tech University, Jared M. Hansen, December 2007
The underlying research question of this study is: What is the impact of
retailer price promotions on retailer performance? Specifically, I focus on price
promotions and two measures of retailer performance: (1) the retailer’s total market
basket performance (i.e., market basket sales, profitability, and inventory turnover),
and (2) the price-promoted products’ financial return on shareholder (inventory)
investment. A process model indicating the hypotheses to be explored is presented in
Figure 1. The uniqueness of the research sample design is presented in Table 1.
This dissertation proceeds as follows. First, hypotheses are presented regarding
the effect of a product’s price promotions on that product’s sales, inventory turnover,
and profitability. These hypotheses are intended to ensure that the samples used in this
research are consistent with prior retail price promotion research samples. Therefore,
these hypotheses replicate previous research on price promotions. Second, a rationale
of price promotions is presented that is consistent with a theory of (what I label)
expandable purchases. This view of purchasing and consumption is used to develop
hypotheses related to the first investigated measure of retailer profitability, that is, the
impact of price promotions on the total market basket. Third, the chapter presents an
overview of a new financial performance metric for evaluating price promotions. This
new metric incorporates the effects price promotions have on the shareholder return on
investment. Fourth, the chapter presents an initial discussion on how competitive
intensity affects the hypotheses on product performance, market basket performance,
and shareholder return on investment. Last, the chapter outlines the research design of
the dissertation.
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Texas Tech University, Jared M. Hansen, December 2007
Replication Hypotheses
Prior literature has found that retail price promotions have a positive effect on
product sales volume (i.e., units sold) and dollars (i.e., net sale dollars) during the
promotional period (e.g., Ailawadi and Neslin 1998; Van Heerde, Gupta, and Wittink
2003). Related, it seems intuitive to hypothesize that the increase in sales volume also
typically accelerates the inventory turnover rate. However, when retail price
promotions increase sales volume, how often (or when) is the increase sufficient to
improve profitability (i.e., initial gross margin dollars)? For instance, a price
promotion of Oreo’s could result in $50 sales growth, a $10 gross margin loss, and a
one-fold increase in inventory turnover. Or, it could result in a $100 sales growth, a
$20 gross margin growth, and a two-fold increase in inventory turnover. Given the
substantial, previous research on the effects of price promotions on sales volume and,
in contrast, the paucity of previous research on the effects of price promotions on
profitability, I hypothesize (and ask):
H1: Price promotions of a product will have a positive effect on that product’s sales (at the product-store level).
H2: Price promotions of a product will have a positive effect on that product’s
inventory turnover (at the product-store level).
RQ1: What is the effect of price promotions of a product on that product’s profitability (at the product-store level)?
Expandable Purchases and Market Basket Performance
There is a growing stream of literature that finds that retailer price promotions
can not only increase the purchase of promoted product(s), but also can result in
changes in the purchases of nonpromoted, substitutable and complementary products
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Texas Tech University, Jared M. Hansen, December 2007
(e.g., Ailawadi et al. 2006; Ailawadi and Neslin 1998; Chandon and Wansink 2002,
Chintagunta and Haldar 1998; Janakiraman, Meyer, Morales 2006; Mulhern and
Leone 1991; Walters and MacKenzie 1988; Walters 1991). Consequently, there are
calls for price-promotion research to move from a product or brand focus to a category
management level of analysis to account for customer product choice substitutes (e.g.,
from focusing on Oreos to focusing on cookies; see Nijs et al. 2001). Even further,
some researchers argue for price-promotion research to shift from category
management to investigating total store purchases to account for customer product
choice complements (e.g., Bucklin and Gupta 1999; Manchanda, Ansari, and Gupta
1999; Shocker, Bayus, and Kim 2004). In particular, two recent articles have provided
initial evidence that price promotions can increase total shopping cart (i.e., store-level,
market basket) purchases per shopping visit (Ailawadi et al 2006; Janakiraman,
Meyer, Morales 2006).
The proposed research advances the literature on the price promotion-retailer
performance relationship. Specifically, I develop and test hypotheses relating the price
promotions of a product to changes in the customer’s market basket. Further, I
quantify the impact of retailer promotional activity on the retailer’s shareholders by
introducing and discussing a new financial performance metric. Moreover, I
investigate the role of competitive intensity as a moderator of the relationship between
product price promotions and retailer profitability, identifying when promotions work
well for retailers (i.e., customer, demographic-based, market segments).
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Texas Tech University, Jared M. Hansen, December 2007
As indicated in the research question, one contribution of this research is to
provide the first documented analysis of the impact of pure price promotions of
individual products on the total purchase of the customer per shopping visit (hereafter,
market basket) using store-level scanner data. According to Hansen, Raut and Sumit
(forthcoming, p.1), “research is needed that investigates the effect of price promotions
…on market basket performance.” While some research has begun to explore the
potential, particular product, complement (e.g., inter-category performance) and
product substitute (e.g., intra-category performance) effects in market baskets (e.g.,
Shocker, Bayus, Kim 2004), no study has looked at the impact of individual product
pure price promotions on total-market-basket sales, profitability, and inventory
turnover. See Table 1. The absence of research on total-market-basket effects could be
because of the difficulty of acquiring data, which is usually available only through
proprietary, store-level scanner data.
For example, the recent work by Ailawadi et al. (2006, p.526) is “the first to
estimate halo [i.e., market basket expansion] rates.” However, they use a regression
based estimate of the quantity of items in the market basket based on a sample of store
loyalty card information because they do not have access to market basket information
for the store-level scanner data. They then estimate the market basket sales dollars
and gross profit dollar by multiplying the estimated change in units by the total store
sales and profits averages. There are several limitations to such store-level estimations,
one of which is the inability to calculate the market basket metrics for each individual
promotion (and individual promotions are, indeed, the specified unit level of analysis).
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Texas Tech University, Jared M. Hansen, December 2007
Therefore, Ailawadi et al (2006, p. 531; emphasis added) call for future researchers to
“validate our work, find new ways of obtaining more disaggregate estimates of the
halo effect, and study how and why halo effects vary across categories and retail
formats.”
Furthering the goal of understanding the effect of promotional activities on
retailer performance, this study is the first study to measure, instead of estimate, the
effect of individual pure price promotions on the actual, total-market-basket
performance. See Table 1. Synthesizing the findings of prior research on the subject
of market basket (e.g., Ailawadi et al 2006; Janakiraman, Meyer, and Morales 2006;
Mulhern and Padgett 1995), this research proposes that purchase and consumption are
expandable. One example is the “halo effect” proposed by Ailawadi et al. (2006).
Another example is the “spillover effect” proposed by Janakiraman, Meyer, and
Morales (2006). Other examples are found in the works of Chandon, Wansink,
Laurent (2000), who identify six major multiple consumer benefits from sales
promotions: opportunities for value expression, entertainment, exploration, savings,
higher product quality, improved shopping convenience. Also, McCracken (1999)
presents several other potential reasons for consumption, including identity, emotional
fulfillment, and a consistent product constellation. This dissertation proposes that
(what I refer to as) expandable purchasing can occur as a result of any of these, or
perhaps others, individually or in combination.
The word “expandable” indicates that, contrary to the commonly-accepted
“market pie” metaphor in marketing (e.g., Carson et al. 1999; Chakravarti and
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Texas Tech University, Jared M. Hansen, December 2007
Janiszewski 2004; Day and Montgomery 1999; Jap 1999; Nason 2006; Rokkan,
Heide, and Wathne 2003; Weitz and Bradford 1999), consumption is not like a static
“market pie” to be divided (i.e., maintained). This usage of the pie metaphor is an
adaptation of the neoclassical economic pie metaphor. Speaking on the original usage
of the metaphor in macroeconomics, Swinnerton (1997, p. 75) states, “To develop a
better intuition for the definitions of efficiency and their equivalence, it is useful to
think of a pie as a metaphor for the output of the economy.” In the pie metaphor in
marketing research, however, researchers have equated pie size with “primary
demand” (instead of labor allocation) and pie distribution with “market share” (instead
of wealth distribution). The pie metaphor, as currently applied to purchasing-related
research, implies that all consumers go to a store with a budget and they spend the
budget. This adaptation of the metaphor has resulted in interpretations of price
promotions as zero-sum games of product switching (e.g., Dodson, Tybout, and
Sternthal 1978).
The growing literature converging on a theory of expandable purchasing does
not support this metaphor or its resulting interpretation of price promotion research.
Instead, this growing stream of research is more consistent with a balloon metaphor,
where purchasing can be grown similar to the inflating of a balloon (Hansen and
McGinty, 2007). Rather than assuming that all consumers go to a store with a budget
and spend the budget limit, this approach assumes that consumers may visit with a
budget in mind, but retain the flexibility to adjust spending. For instance, a consumer
may plan on spending 100 dollars as he has done in the past, but decides to spend
8
Texas Tech University, Jared M. Hansen, December 2007
more because of the effect of the pure price promotion. Similar to the balloon analogy,
a shallow discount will not result in much of an effect on overall purchase behavior.
However, the effect grows nonlinearly with the depth of the price promotion. That is,
as the size of the price promotion for a product increases, there is expanded purchasing
of other products. These products may be complements or may be completely
unrelated. Indeed, according to Walters and MacKenzie (1988, p. 54), “Retailers and
marketing researchers generally believe one of the primary benefits of price
promotions is that they stimulate sales not only for the lower price, lower margin
promoted items but also for higher margin goods that are not being promoted.”
The motivation for expanded purchasing may be economic utility
maximization, hedonic enjoyment, a reward to the retailer, or a combination of these
and other consumer motivations. I propose that there is a point (though it might be
difficult to locate precisely) where a very deep price promotion might result in
negative reaction by consumers who begin to doubt the quality of the product given
the large price reduction (similar to the balloon popping). See Kirmani and Rao (2000)
for discussion on signaling unobservable product quality. Most retailers, however, do
not engage in this kind of price promotion activity, with the exception of occasional
loss leaders (e.g., milk, bread, crayons, beer). Rather, the relevant range of the type of
retail price promotions being investigated (i.e., context) is expected to be in the “front
half,” or positive slope area, of the proposed balloon effect. Based on the preceding
discussion, I hypothesize:
H3: Price promotions of a product will have positive effects on market basket sales.
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Texas Tech University, Jared M. Hansen, December 2007
H4: Price promotions of a product will have positive effects on market basket
item count (i.e., number of products in the market basket). H5: Price promotions of a product will have positive effects on market basket
profitability.
Shareholder Return on Investment Performance
Another contribution of this research is that it investigates the effect of price
promotions on shareholder investment. There have been calls (e.g., Lehmann 2004;
Rust et al. 2004) for marketing research to build bridges between marketing activities
and financial (e.g., shareholder) outcomes. Further, recent accounting research
recognizes and calls for research that addresses the difference between ROI and the
real economic profitability of an organization (see Rajan, Reichelstein, and Soliman,
forthcoming). This research will be the first study, based on a review of the literature
and discussion with several thought leaders in the area, to quantify the impact of
retailer promotional activity on the retailer’s shareholders by introducing and using a
new metric.
Early works on valuation often used return on investment (ROI).
Unfortunately, the “investment” in ROI varies by research study. The formula
variation (in studies purporting to use ROI) has resulted in several noted potential
problems in interpretation. Most often ROI appears to become return on sales—
which should be identified as ROS, not ROI. ROS is calculated as:
(1) ROS = Net Income (Before Interest & Tax) ÷ Sales Dollars
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Texas Tech University, Jared M. Hansen, December 2007
Practitioners are usually more interested in measuring performance within their
organization (e.g., particular product SKU price promotions) versus measuring
performance across organizations. Attempting to apply ROI (or ROS) to product (or
even category) merchandise decisions is, as indicated by Sweeney (1973, p. 61),
“fraught with cost measurement and allocation problems.” Further, price promotions,
by mathematical rule, decrease the unit margin in order to stimulate sales growth. In
the context of ROS, the growth in the numerator (i.e., net income) is always less than
the growth in the denominator. Take, for example, a box of Tide detergent that is
regularly prices at $6, which generates $2 in net income and a ROS of 2/6=33%. The
Tide is price promoted at $5 per box, which decreases net income to $1 and results in
a ROS of 1/5=20%. While there may be a ten-fold increase in sales volume, increasing
both the sales dollars and income dollars, the ROS ratio decreases. Thus, neither ROI
nor ROS is well equipped to measure product-level, operational performance.
However, both the allocation and dollars versus percentage problems can be overcome
when “the performance measure is used exclusively for planning and controlling
merchandising inventory investment” (Sweeney 1973, p. 61).
Consequently, a few academic researchers and many practitioners have
adopted a more specific measure of return, the gross margin return on inventory
investment (hereafter, GMROII) to plan and control (e.g., Ahern and Romano 1979;
Dunne and Lusch 2005; Kravitz 1977; Leeds 1976; Sweeney 1973; Tolle 1976;
Warrington 1982). GMROII is calculated as:
(2) GMROII ( % ) = Gross Margin (%) ÷ [1 - Gross Margin (%)] x Inventory Turnover
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Texas Tech University, Jared M. Hansen, December 2007
One advantage of GMROII is that it does not require any approximations from
aggregated activities such as labor costs. Thus, it can be applied at the product,
category, or overall retailer level of analysis. Another advantage of GMROII (over
ROS) is that GMROII accounts for inventory turnover. Also, while other return
metrics (e.g., ROI, ROS) do involve a time component (i.e., it is the return on
investment or sales for a given period of time), GMROII emphasizes the importance
of time. That is, it shows how time can provide a strategic advantage/disadvantage as
an organization manages its inventory (i.e., cost of goods sold) better/worse than its
competitors. Despite these advantages, there is a dearth of academic literature that
uses GMROII. Perhaps one reason why few academic researchers use GMROII (in
contrast to many practitioners) is access to data on both profitability and inventory
turnover rates. One result is that the effect of the price promotions of a product on
GMROII has not been established in the literature—an important gap that needs
addressing. This effect could be either positive or negative, depending on how the
gross margin percent and the inventory turnover are conjointly affected by the price
promotions.
For example, a price promotion of widgets might result in both a ten percent
decrease in gross margin percent (i.e., from 50 percent to 40 percent) and a three fold
increase in inventory turnover (i.e., from two to six turns annually). Or, a price
promotion of the same widgets might result in both a five percent decrease in gross
margin percent (i.e., from 50 percent to 45 percent) and a one fold increase in
inventory turnover (i.e., from two to three turns annually). The first scenario (smaller
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Texas Tech University, Jared M. Hansen, December 2007
gross margin, larger turnover) results in a GMROII of 400 percent return, whereas the
second scenario (larger gross margin, smaller turnover) results in a GMROII of 245
percent return. In this example, the price promotion that resulted in less of a margin
decrease did not result in a higher GMROII (because of the relative inventory turnover
impact). Thus, in hypothesizing the impact of price promotions on GMROII, a
researcher is implicitly hypothesizing about the relative change in gross margin versus
inventory turnover rate. The neoclassical economic “pie” metaphor (presented earlier
in this chapter) maintains that the inventory rate is maintained over time or across
products (i.e., substitution effects). In contrast, a theory of expandable purchasing
suggests that the inventory turnover rate would be accelerated due to the product’s
price promotion. Based on the preceding discussion, and consistent with the growing
literature supporting the phenomenon that I refer to as expandable purchasing, I
hypothesize:
H6: Price promotions of products, in a majority of cases, will have positive effects on promoted products’ GMROII.
As a derivation of ROI, though, GMROII does not consider the financing of
the cost component, and thus retains the inability to document the impact of price-
promotions on an organization’s owners. The cost component consists of both
financing and operating responsibilities, and operational metrics such as ROI and
GMROII follow managerial accounting guidelines that have traditionally held that:
Managers have both financing and operating responsibilities. Financing responsibilities relate to how one obtains the funds needed to provide for the assets in an organization. Operating responsibilities related to how one uses the assets once they have been obtained. Both are vital to a well-managed firm.
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Texas Tech University, Jared M. Hansen, December 2007
However, care must be taken not to confuse or mix the two when assessing the performance of a manager. (Garrison and Noreen 2003, p.773)
The foundational logic of this research agrees with the premises of these
quoted guidelines. That is, there are financing and operating responsibilities (i.e.,
premise 1) and both responsibilities are vital to the organization (i.e., premise 2).
However, it is proposed that the inventory turnover rate does have an effect on the
accounts payables. That is, the inventory turnover rate can change how one obtains the
funds (e.g., what is funded internally, or funded externally, or, even, never requires
funding at all). In contrast to ROI, return on equity (ROE) considers the financial
leveraging of funding (see Block and Hirt 2000, p. 56):
(3) Return on equity = Return on assets (investment) ÷ (1-Debt/Assets)
However, the ROE metric requires assumptions about dividing the costs of
labor, space, and other activities to arrive at net income that are not easily allocated to
product level analysis. At the same time, the cost of financing versus investing is
being debated (see Baker, Ruback, and Wurgler 2007). Thus, an alternative metric in
use is proposed here that is similar to the Du Pont model of ROE (see Garrison and
Noreen 2003, p. 70), and can be used as a framework for discussing promotional
impact on the organization. This metric replaces net income with gross margin (i.e.,
the purchased cost of goods, prior to allocating building and other costs) and, in
accounting for the change in the financing plan, also incorporates the internal rate of
return on the unused assets. Thus, the metric becomes the gross margin return on
shareholder investment (hereafter, GMROSI) of the product.
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Texas Tech University, Jared M. Hansen, December 2007
In relation to GMROII, the GMROSI metric replaces the “inventory”
investment with “shareholder” investment by accounting for the change in the cost due
to changes in account payables that are, in turn, due to changes in the contracted terms
of payment (e.g., 2-10-net 60) between the retailer and the manufacturer of the
product, and the actual (i.e., “positive,” not “normative”) internal rate of return in the
organization. By doing so, this planning and controlling metric accounts for the
relative return on each shareholder dollar invested in the organization (i.e., capital cash
flow productivity). See Appendix 1 for an example. Also, while GMROSI provides an
operational accounting of shareholder inventory investment productivity, it should not
be confused with shareholder market return. (Shareholder market return--as
traditionally used in accounting and finance--accounts for the variance in stock price.)
The relevant formulas for GMROSI computation are:
(4) GMROSI ( % ) = Adjusted Gross Margin (%) ÷ [1 – Adjusted Gross Margin (%)] x Inventory Turnover, where
(5) Adjusted Gross Margin = (Sales $ - Adjusted Cost $) ÷ Sales $, where
(6) Adjusted Cost $ = (Cost Schedule * Cost%) - ( IRR * Cost Schedule
* [1-Cost%] )
Taking a shareholder perspective, it is proposed that many promotions that
decrease the percent profitability of the product for the retailer may, in fact, increase
the percent profitability of the product for the shareholder. The argument here runs
contrary to the findings of Srinivasan et al. (2004, p. 617) that promotions are not
beneficial (for retailers) because price promotions reduce retailer category margins.
This difference in interpretation is understandable (i.e., explainable) given that the
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margins in their equations, consistent with the literature, are not the marginal return to
shareholders. Consistent with the theory of expandable purchasing, I argue that price
promotions of a product will accelerate inventory turnover. The inventory turnover
acceleration, in turn, results in less internal capital being used over the time of the
payable schedule. Thus,
H7: Price promotions of products, in a majority of cases, will have positive effects on promoted products’ GMROSI.
As a proposed metric, GMROSI is consistent with dynamic competition
theory, as exemplified by the resource-advantage theory of competition (hereafter, R-
A theory). As indicated by Hunt (2000, p.123), “The “superior” in superior financial
performance equates with both more than and better than” in R-A theory. By
mathematical rule, increases in GMROSI often involve financial performance that is
better than and may involve financial performance that is more than GMROII. That
is, GMROSI accounts for the funding origination in the equation, and, by so doing,
permits a more accurate picture of the return to the shareholder. In GMROII, all
funding is always from internal capital. Because this is not truly the case, GMROII
presents a negatively biased view of the return to the shareholder’s inventory
investment. Thus, GMROSI will often present a “better than” scenario that more
accurately reflects the business practice of leveraging funding across different sources
(i.e., changes in the denominator). It should be noted though that because of this
combination (of activity and investment measures), the interpretation of changes in
GMROSI (vs. GMROII) is more complex and requires careful attention.
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Texas Tech University, Jared M. Hansen, December 2007
GMROSI may involve financial performance that is “more than” because there
is no guarantee that the top line growth will occur (i.e., changes in the numerator).
Because of this, GMROSI as a measurement of organizational performance is
consistent with the relative resource costs and relative resource-produced value axis of
the competitive position matrix of comparative advantage (Hunt and Morgan 1997).
Thus, in adopting GMROSI as a dependent variable, the research also adopts a
dynamic competition perspective that can account for it.
Competitive Intensity
In regards to a dynamic competition perspective, this research will advance
understanding of the potential moderating role of competition on the relationship
between retailer promotions and retailer performance. Ailawadi et al (2006) use two
indicator (i.e., dummy) variables to represent the presence of either same-format
retailer competitors or alternative format retailer competitors. They find the two
effects to be different. This study would replicate their test in a new setting—an
every-day-low-price, general-merchandise retailer. More importantly, and drawing
upon R-A theory (e.g., Hunt and Morgan 1996; Hunt 2000), the proposed research
would investigate competitive intensity at a more detailed level, accounting for the
potential varying impact of approximately 30 different competitors that compete at
different levels (e.g., category, total-store) with the retailer.
Also, product competition occurs within a store (e.g., choosing Regular Oreos
vs. Chips Ahoy vs. Reduced Fat Oreos vs. a Snickers Candy Bar). Thus, research
should take into account the level of heterogeneity of the merchandise assortment in
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the store in which the item is price promoted. As the competitive intensity increases
either within or across stores, consumer price sensitivity increases. With increased
acuteness, consumers are better able to process price promotion information, thereby
making more informed decisions. In turn, the enhanced decision-making ability
permits consumers to expand purchasing and consumption, leading to improved
quality of life. Following the preceding discussion, I hypothesize:
H8a to H8k: Increases in merchandise heterogeneity will strengthen the relationships in hypotheses 1-7.
H9a to H9k: Increases in the number of total competitors will strengthen the
relationships in hypotheses 1-7. H10a to H10k: Increases in the number of primary competitors will strengthen
the relationships in hypotheses 1-7. H11a to H11k: Increases in the number of secondary competitors will strengthen
the relationships put forth in hypotheses 1-7. Lodish (2007, p.24) proposes that “for practitioners, the geographic differences
in market response are much more important than share differences because they are
directly actionable and can affect profitability.” The geographic differences in market
response are due, in large part, to differences in customers. According to Ailawadi et
al. (2006), the demographic customer differences include education levels, income
levels, and ethnic diversity levels. In their analysis, they find that education has a
negative effect on price elasticity, high income has a negative effect on price elasticity,
and minority ethnicity (i.e., Hispanic or Black) has a negative effect on price elasticity.
Yet, they provide no rationale for why any of these effects might occur. Recall that in
a theory of expandable consumption, there are multiple motives, one of which is
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identity. One reason why Ailawadi et al. (2006) find these effects might be that higher
income or more educated customers do not wish to be identified with advertising or
price discounting). Controlling for the effects of income and education, one potential
reason why customers of particular ethnicities might have been found to be less price-
sensitive in prior studies might be that they have less societal access to act on the price
promotions. That is, they have less capacity to consume that might be due, in part, to
their geography. For instance, they might live in areas with higher public
transportation usage (where additional product purchasing is more difficult to
transport to their residences) or higher housing density (where, as a result, house or
apartment sizes are smaller, resulting in less capacity to stockpile merchandise).
Regardless of whether capacity to consume does, perhaps, explain this ethnic, or
cultural effect, this research incorporates measurement of customer ethnicity,
consistent with prior research findings.
In summary, while the setting of this research is an every-day-low price
(EDLP), general-merchandise retailer, and not a hi-low price, drug-store retailer, there
is no reason based on the prior discussion as to why the relational signs between the
study of Ailwadi et al. (2006) and this study should be different. Rather, for a general-
merchandise retailer, these relationships should be stronger, if any differences do
exist, given that the limited, prior research finds shoppers in EDLP chains to have
higher regular or long run price sensitivities (e.g., Shankar and Krishnamurthi 1996).
Thus,
H12ato H12k: Increases in the level of customer education will have a negative effect on the relationships in hypotheses 1-7.
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Texas Tech University, Jared M. Hansen, December 2007
H13a to H13k: Increases in the amount of customer income will have a negative
effect on the relationships in hypotheses 1-7. H14a to H14k: Increases in the percentages of African American customers will
have a negative effect on the relationships in hypotheses 1-7. H15a to H15k: Increases in the percentages of Hispanic customers will have a
negative effect on the relationships in hypotheses 1-7.
Another potential moderator of the promotion-performance relationships stated
in this research is the product’s purchase frequency. For instance, razorblades are
purchased more frequently than toasters. In either situation, this often corresponds
with, but yet is distinct from, the product durability. It is possible for durable products
(e.g., toasters) to become commodities. It is proposed here that the perceived
consumability of these products is affected by price promotions. No evidence is found
in the literature to propose that the preceding hypotheses will be different (e.g.,
positive effect, negative effect) for more frequent (i.e., health and beauty aid) products
vs. less frequent (i.e., small appliance) products. However, will the effect size be
similar, or will the effect size of one type of product be larger? Thus,
RQ2: Is the effect of price promotions in H1 through H7 greater for more frequently purchases products or for less frequently purchased products?
Last, this research project investigates when (i.e., under what competitive
conditions) customers are “cherry picking” shopping due to the price promotion versus
“self-indulging” or “hedonic” shopping due to the price promotion. That is, under
what conditions is it more likely that consumers will respond to the price promotion
by purchasing more reduced price products—reducing profitability growth versus
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sales growth (i.e., a reduction in profitability margin: sales minus profitability, divided
by sales)? Alternatively, under what conditions is it more likely that consumers will
respond the price promotions by purchasing more regularly priced products—
increasing profitability growth versus sales growth (i.e., an increase in profitability
margin)? Both situations are consistent with the theory of expandable purchasing and
consumption. The conditions investigated in this research include changing
competitive intensity and customer demographics. However, there is no research
published (based on an in-depth review of the literature) to indicate when one effect
over the other effect will be found in the data analysis of this research study. Thus, the
investigation takes a research question form:
RQ3a: When does the moderating effect of H8a to H14k decrease profitability margin for either the product or market basket?
RQ3b: When does the moderating effect of H8a to H14k increase profitability
margin for either the product or market basket?
Overview of the Research Design
The sample for this research consists of weekly, point-of-sale, stock-keeping
unit (hereafter, SKU), scanner data for manufacturer-branded products collected in all
of the approximately 450 stores of an every-day-low-price, general-merchandise
retailer operating in the northeastern and midwestern United States. Several filters
were applied to available product SKUs (that were price-promoted by the retailer) to
arrive at the final sample that is consistent with the stated research purpose to
investigate the impact of retailer price promotions on retailer performance.
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Texas Tech University, Jared M. Hansen, December 2007
First, and central to the stated research purpose, product samples are limited to
particular products where pure price promotions (i.e., no additional promotional
activities for the investigated products) were taken at some point during the period
June 2004 to July 2006. By using only pure price promotions, the first instance of
general customer price-reduction awareness is in-store when the customer views the
shelf label (which states the prior and current price, both in dollars). This setting
contains the advantaging of preventing a needed simultaneous investigation of
customer price knowledge and the source of customer price comparison (e.g., see
Vanhuele and Dreze 2002).
Second, following the finding of Ailawadi et al (2006) that the greatest
variance (in their study) is in the general merchandise and health and beauty
merchandise categories, the product SKUs in this study are chosen from these same
merchandise categories. In contrast to both the drug store setting of Ailawadi et al
(2006) and the grocery setting of other related research (e.g., Janakiraman, Meyer,
Morales 2006; Mulhern and Leone 1991; Walters 1991; Van Heerde, Leeflang, and
Wittink 2004), however, this research uses samples drawn from an every-day-low
price, general-merchandise retailer in the United States, which permits investigation of
the generalization of their findings (to a general-merchandise store context), in
addition to the new contributions on total shopping cart consumption, shareholder
return, and competitive intensity.
Third, products are selected for which the product market baskets do not
include any of the other products being investigated in the study (i.e., unique market
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baskets). Unique market baskets are operationalized here as market baskets where the
overlap (in investigated products) is less than 1 in a 100 common occurrences. It
should be noted that while the filters decrease the level of “noise” in the sample data,
there is always the possibility that some other products may be price-promoted that
customers bought as part of some of the market baskets because of the natural field
experimental setting of this research. This does not imply, however, that the findings
of this research are overstated. Rather, it suggests that any corroboration of hypotheses
in the data analysis is understated (because the relationships are significant even in the
presence of the potential marketing mix activities on other products in the market
baskets).
In all, four product SKU samples, each of N = approximately 450 (same)
stores, are found that match the pure promotion, timeframe, merchandise category, and
unique market basket filters. Two of the product SKU samples are consumable
products from the health and beauty aid category. The other two of the product SKU
samples are more durable products from the small appliances category.
This research uses the retail store’s physical size (e.g., 122,000 square feet) as
a proxy for merchandise assortment, or the intra-organization competitive intensity,
because the number of product combinations at the store level is astronomical, making
the use of all product combinations beyond available degrees of freedom for statistical
testing. This proxy has been used in prior literature (e.g., Boatwright, Dhar, and Rossi
2004), and has been found to work because the larger the store, the larger the number
of merchandise categories and the deeper the penetration within categories.
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Texas Tech University, Jared M. Hansen, December 2007
Competitor stores of 30 potential, major competitors are geographically
identified for each store in the samples, first by census data, and then confirmed by the
store manager. This store-level competitor information is documented and retrieved
from the retailer’s scanner database. Some of the competitors compete in particular
categories, while others compete across many categories. Some of the competitors
carry higher priced products, while others carry lower priced products. This research
will account for these complexities using R-A theory as a foundation. Adopting this
foundation, the research will distinguish between category and store competitors in
modeling the complexities by (1) using indicator (i.e., dummy) variables for each
competitor and (2) identifying whether a given competitor is a total store competitor or
a category competitor. The increased sensitivity of these tests will advance
understanding of the moderating effects of competitive intensity on the relationship
between retailer promotional activities and retailer performance.
The price promotion’s effect on performance is measured against two baselines
for each product SKU sample. The first baseline consists of a moving average baseline
(e.g., Abraham and Lodish 1993). Because sufficient prior data is available, a second
baseline, consisting of prior-year, same-period data, is also used. Paired samples T-
test procedures are used to compare the difference between each baseline, in turn, and
the customer purchase behavior (i.e., product and market basket performance) after the
price promotion occurs.
In regards to comparing changes in GMROII and GMROSI, first, a simulated
response surface is constructed to provide a theoretical range of GMROII and
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GMROSI values that retailers, in general, might expect to achieve under different
variable (i.e., gross margin, inventory turnover, contracted terms of payment, and
internal rate of return) combinations. See Myers (1971) and Myers and Montgomery
(1995) for overviews of response surface methodology. Ranges for the inventory
turnover, gross margin, account payable (terms of payment), and internal rate of return
variables are developed based on analyses of several retailers’ annual reports. The
response surface permits investigation of the cost of capital while avoiding entry into
the debate over its calculation (see Chen, Dhaliwal, and Xie 2006). Second, GMROII
and GMROSI metrics are calculated for the baseline and post-promotion periods of
the four natural field experiments (each of N = approximately 450 stores). Paired
sample T-tests are used to compare pre- and post-promotion GMROII and GMROSI.
Third, these results are discussed in terms of their location on the generated response
surface.
In regards to the hypotheses related to competitive intensity, the market basket
performance (sales, profits, inventory turnover) and product GMROSI data are entered
as dependent variables in a multivariate general linear model, with competitive
intensity scenarios as fixed factors and customer demographic variables as covariates.
See Figure 1. Multiple comparisons tests (i.e., Bonferroni and Tukey honestly
significant difference) are run to investigate all pairwise comparisons between
competitive intensity scenarios, permitting corroboration of the auxiliary hypotheses
on competitive intensity.
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CHAPTER 2
A REVIEW OF THE LITERATURE
Overview
This chapter presents a review of the extant literature to the current research.
The chapter is structured as follows. Section 1, entitled “Price Promotions and
Definition,” identifies what type of promotional activity (and thus related literature),
either is or is not under inquiry in this research study. Section 2, entitled “Price
Promotions and Expandable Purchasing,” provides a general synopsis of the literature
concerning the effects of price promotions on the promoted product and its associated
market basket. Section 3, entitled “Price Promotions and Return Calculation,”
discusses the literature on return on investment metrics, as applied to retail product-
level analysis. Section 4, entitled “Moderators of the Promotion-Performance
Relationships” reviews the literature on competitive intensity, including intra-store
competition, inter-store competition, customer demographics, and product durability.
Hypotheses and research questions are presented at the end of each section. A
summary of all hypotheses is presented at the end of the chapter.
Price Promotions and Definition
Promotions can take several forms, from pure price promotions to sales
promotions (i.e., advertising). The purpose of this research is to increase our
understanding of the impact of pure, retail price promotions on retailer financial
performance. In this dissertation, a pure, retail price promotion (hereafter, price
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promotion) is defined as the temporary discounting of a product at retail where the
only indication of the discount to the customer is the stated change on the shelf label
adjoining the product (Blattburg, Briesch, and Fox 1995).
Thus, “pure” (in pure, retail price promotion) refers to the absence of any
additional displays, features, coupons, advertisements, or other intentional activities by
the retailer that could confound the effects of price promotions (see Van Heede,
Leeflang, and Wittink, 2004, for additional discussion on “pure” promotions).
“Retail” (in pure, retail price promotion) refers to price promotions taken by retailers
to consumers, not manufacturers to retailers, on products. Consequently, the analysis
adopts a retailer view of price promotions. A retailer orientation is, according to
Mulhern and Leone (1991, p. 64), “vastly different from the brand manufacturer
orientation that dominates the marketing literature.” They (1991, p. 64) state that
“typical depiction of retailers in the marketing literature as intermediaries that
distribute products from manufacturers to consumers” is an “inappropriate framework
for many marketing channels because of the relative power of manufacturers and
retailers has changed dramatically in the past few decades.” “Price” (in pure, retail
price promotion) indicates that the promotional activity is not to be confused with
sales promotion, or advertising, which includes activities such as “contests, point of
purchase displays, sampling premiums, coupons, multi-package price deals, incentive
programs, tie-in sales, and certain forms of direct mail” (Adler, 1963, p. 69).
“Promotion” (in pure, retail price promotion) refers to the dissemination of
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information from retailer to customers through the shelf label that the product has been
temporarily discounted to the retail customer to further the goals of the organization.
Thus, pure, retail price promotions are distinct from products that are retailer
advertised but not retailer price discounted (e.g., to create awareness of the product
availability at the store). They are also distinct from products that are retailer
advertised and retailer priced discounted (e.g., to increase customers visiting the
store). Pure, retail price promotions, in contrast, increase purchasing volume across
customers and time (e.g., Blattberg and Wisniewski 1987; Duncan, Hollander, and
Savitt 1983; Kuehn and Rohloff 1967; Mason and Mayer 1984; Moriarty 1985;
Woodside and Waddle 1975), and are usually the most frequent type of promotion
used by retailers (e.g., Gedenk, Neslin, and Ailawadi 2006), especially for every-day-
low-price retailers (e.g., Walton and Huey 1992).
As by indicated by Blattberg, Briesch, and Fox (1995, p. 122), “the price
promotions literature is new relative to other research in marketing, having been
developed primarily since the early 1980s.” Despite its relative newness, the subject of
price promotions is an important, and consequently, extensively researched area of
marketing thought. In the words of Dekimpe et al. (2005, p. 409), “Price promotions
are the most often used form of promotional support. As such, it should come as no
surprise that the effectiveness of price promotions has been studied extensively in the
marketing literature.” However, as noted by Assuncao and Meyer (1993, p. 517),
although the question “How should price promotions affect the purchase and
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consumption of goods…lies at the heart of much of the modern literature on sales
promotions, it is one for which we currently do not have a complete answer.”
Consistent with both the (1) the importance of this research stream and (2) the
paucity of current information about how price promotions affect customer
purchasing, the purpose of this research is to increase our understanding of the impact
of pure, retailer price promotions. The underlying research question of this study is:
What is the impact of retailer price promotions on retailer performance? Specifically,
I focus on price promotions and two types of retailer performance: (1) the retailer’s
total market basket performance (i.e., market basket sales, profitability, and turnover),
and (2) the price-promoted products’ financial return on shareholder investment. A
causal model indicating the hypotheses to be explored is presented in Figure 1.
Prior literature has found that retail price promotions have a positive effect on
product sales volume (i.e., units sold) and dollars (i.e., gross sale dollars) during the
promotional period (e.g., Ailawadi and Neslin 1998; Blattberg and Wisniewski 1987;
Moriarty 1985; Van Heerde, Gupta, and Wittink 2003; Woodside and Waddle 1975).
For listings of the many articles reporting the elasticities and cross elasticities of
product price promotions on products/brands and substitutes in same categories
(secondary demand) and longitudinal/early purchasing (primary demand), see table
summaries in Bell, Chiang, and Padmanabhan (1999, p. 510), Mulhern and Leone
(1991, p. 67), or Van Heerde, Gupta, and Wittink (2003, p. 483).
The increase in sales volume (due to the price promotion), in turn, also
typically accelerates the inventory turnover rate. However, when retail price
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promotions increase sales volume, how often (or when) is the increase sufficient to
improve profitability (i.e., initial gross margin dollars)? For instance, a price
promotion of Oreo’s could result in $50 sales growth, a $10 gross margin loss, and a
one-fold increase in inventory turnover. Or, it could result in a $100 sales growth, a
$20 gross margin growth, and a two-fold increase in inventory turnover. Given the
substantial, previous research on the effects of price promotions on sales volume and,
in contrast, the paucity of previous research on the effects of price promotions on
profitability, I hypothesize (and ask):
H1: Price promotions of a product will have a positive effect on that product’s sales (at the product-store level).
H2: Price promotions of a product will have a positive effect on that product’s
inventory turnover (at the product-store level).
RQ1: What is the effect of price promotions of a product on that product’s profitability (at the product-store level)?
Price Promotions and Expandable Purchasing
As indicated in the research question, one contribution of this research is to
provide the first documented measurement of the impact of pure price promotions of
individual products on the total purchase of the customer per shopping visit—or
market basket. The market basket is defined here as the total products acquired by a
customer during a single purchase event at the store checkout register (i.e., the total
“shopping cart” purchased). According to Hansen, Raut and Sumit (forthcoming, p.1),
“research is needed that investigates the effect of price promotions and advertising on
market basket performance.” While some research has begun to explore the potential,
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particular product, complement (e.g., inter-category performance) and product
substitute (e.g., intra-category performance) effects in market baskets (e.g.,
Janakiraman, Meyer, and Morales 2006; Shocker, Bayus, Kim 2004), no study has
looked at the impact of individual product pure price promotions on retailer total-
market-basket sales, profitability, and inventory turnover. See Table 1. The absence of
research on total-market-basket effects could be because of the difficulty of acquiring
data, which is usually available only through proprietary, store-level scanner data (see
Bucklin and Gupta 1999). Consider, for instance, the research sample design of the
following studies.
There is a growing stream of literature that finds that retailer price promotions
can not only increase the purchase of promoted product(s), but also can result in
changes in the purchases of nonpromoted, substitutable and complementary products
(e.g., Ailawadi et al. 2006; Ailawadi and Neslin 1998; Chandon and Wansink 2002,
Chintagunta and Haldar 1998; Janakiraman, Meyer, and Morales 2006; Mulhern and
Leone 1991; Walters and MacKenzie 1988; Walters 1991). This research stream is
recent, with many researchers citing MacKenzie and Walters (1998), Mulhern and
Leone (1991), and Walters (1991) as the first to investigate the potential effects of
product price promotions on other products.
Walters and MacKenzie (1988) state that in-store price promotions have no
effect on the sales “traffic” (i.e., the number of transactions), using store scanner data
from two stores of a large midwest supermarket chain on sales of cake and ready
made frosting and spaghetti and spaghetti sauce. They state “(1988, p. 52), “Unlike
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manufacturers, grocery retailers are not as interested in the effect of price promotions
on individual product performance as they are in the effect of marketing and product
mix changes on overall store performance.” Their findings should be interpreted in
view of their product sample selection criteria. First, “the product must have received
a reasonable amount of promotional attention during the data collection period. For
example, gourmet candy, private label crackers, and oven cleaner were promoted only
once or twice a year so few observations were available on those items. Consequently
such products were excluded from the analysis” (p. 56). In contrast, the sample design
of this research uses product SKUs that are at most promoted once or twice a year.
Also, they use structural equation analysis of instore price promotions to explain
variance in total store sales by week.
Mulhern and Leone (1991) analyze weekly POS (sales & profits figures) for
cake and cake frosting categories for a regional grocery store chain. They estimate the
cross category effect using cross-elasticity parameter estimations. Walters (1991)
investigates complementary relationships between cakes and cake frosting and
spaghetti and spaghetti sauce and the substitutes in each category in (8 pairs in 8
markets) 64 stores in the midwestern US, developing cross elasticities measuring total
category sales for each category (i.e., not necessarily same basket purchases). There
is, consistent with most other research in this area, no control for advertising or other
marketing mix activities.
Mulhern and Padgett (1995), survey shoppers to determine whether advertising
products (of a flyer containing 200 featured products) drive sales and profits dollars
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for two stores of a home improvement chain of a dozen stores. Retail clerks administer
the surveys to customer in two of 11 stores in a home improvement company. They
analyze 412 surveys and find that the surveyed promotion shoppers spend more
money on regular priced merchandise than on promotion merchandise. According to
their analysis, shoppers who did not visit the home improvement retailer because of
the promotions but did purchase promoted products, purchased $33.90 of merchandise
(resulting in $5.74 in profit for the retailer), on average. In contrast, shoppers who did
not purchase promoted products purchased $23.10 of merchandise (resulting in $5.85
in profit for the retailer), on average. According to their analysis, 13 of 56 (23.2%)
shoppers buying promoted products “cherry picked.” That is, they only purchased the
promoted products (and nothing else). However, the profitability in their study is
“computed by subtracting unit cost from unit price and summing unit margins across
all products purchased by each customer.”
Chintagunta and Haldar (1998) use household level data to investigate
complementary purchases of two categories using three “consumable” pairs: pasta and
sauce, liquid and powder detergents, and soup and yogurt (with both promoted), as
well as two “durable” pairs: and clothe washers and clothe dryers, and dishwashers
and air conditioners. They (p. 53) state that “we recognize that we have merely
scratched the surface in terms of investigating cross-category effects. It would be
necessary to extend our analysis to multivariate hazards (more than two
categories)…Furthermore, it would be important to examine individual brands rather
than the category as a whole.” In the same year, Ailawadi and Nelson (1998, p. 396)
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look at price, but not price promotions, using household scanner data. There is no
control for other promotional activities.
Nijs et al. (2001) use IRI household scanner data covering 560 categories in
the Netherlands to investigate the effects of different marketing mix elements,
including price promotions and advertising, all present at the same time, of two
categories on 79 other food categories using household scanner data. They (p. 10) state
that competitive structure is “measured by the number of brands in each category. This
is one of the best known measures of competitive market structure.” In contrast, by
adopting a retailer perspective, competitive structure is investigated in this dissertation
at the retailer level (as opposed to the brand level).
Bell, Chiang, Padmanabhan (1999, p. 514) use a sample of 250 IRI panelists in
three supermarkets over 78 weeks to review 13 categories of grocery and consumable
paper goods. Likewise, Van Heerde et al (2004, p. 326) use ACNielson store level
scanner data at the brand level for four product categories (i.e., tuna, tissue, shampoo,
and peanut butter) in stores in the US and two categories (i.e., shampoo and peanut
butter) in stores in Denmark to estimate price promotion effects. Across the categories,
they find an own-brand effect of .44, cross-brand effect of .35, cross period effect of
.44, and category expansion effect of .2. They (p.. 332) conclude that “From a retailer
perspective, a 100-unit increase for the promoted brand causes on average a net 33-
unit decrease for other brands in the category and a net 32-unit decrease in pre- and
post-promotion category sales (Table 4). This leaves a potentially beneficial 35-unit
increase for the retailer due to category expansion effects.”
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Bell, Iyer, and Padmanabhan (2002) use store level scanner data from several
hundred products spread across eight product categories in five stores in one market.
They find that consumption can be flexible, incremental as people stockpile due to
price promotion. Shocker, Bayus, and Kim (2004, p. 32) propose that “in-use” and
“occasional” substitutes and complements are connected and that price promotions
have an effect on them. They (p. 37) propose that one future research direction is to
address “What are additional managerial implications from influences of ‘other
products’?” One managerial implication of the current research study is that market
baskets are comprised of the product, its complements, its substitutes, and ‘other
products’ (or in the words of Shocker et al. “occasional” substitutes and complements)
that are random components. Other products may, in fact, represent much of the
potential market basket increase that comes from hedonic shopping. Thus, a total
market basket orientation, versus an orientation toward category substitutes or
commonly occurring complements, provides a more accurate view of the effect of
price promotions on retailer performance.
As a consequence of this new and growing literature on price promotions and
market basket analysis, there are calls for price-promotion research to move from a
product or brand focus to a category management level of analysis (e.g., from focusing
on Oreos to focusing on cookies; see Nijs et al. 2001). “The primary implication of
category management,” according to Bucklin and Gupta (1999, p. 265), “is a shift in a
retailer’s attitude from a buyer orientation (where money is made on how the product
is bought), to a buyer and merchandiser orientation (where the focus is on category
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profitability.” Research on, and discussion of, category management typically refers to
intra-category management, or management of one category of market offerings. This
type of category management is a part of, but not equivalent to, market basket
management.
Market Basket Management
Market basket management involves both intra-category and inter-category
performance management. In regards to intra-category performance, a product price
promotion can affect, for instance, substitute products (e.g., competing brands of hot
dogs). In regards to inter-category performance, the product price promotion can
affect, for instance, complementary products (e.g., hot dogs, hot dog buns, and
condiments). While a retail manager (based on typical reward systems) is most
interested in category analysis, the retailer (as an organization) is most interested in
market basket effects that are closer to representing the total store performance.
Consequently some researchers argue for price-promotion research to shift
even further, from category management to investigating total store purchases (e.g.,
Bucklin and Gupta 1999; Shocker, Bayus, and Kim 2004). In particular, two recent
articles have provided initial evidence implying that price promotions can increase
total shopping cart (i.e., store-level, market basket) purchases per shopping visit
(Ailawadi et al. 2006; Janakiraman, Meyer, Morales 2006).
For example, the recent work by Ailawadi et al. (2006, p.526) is “the first to
estimate halo [i.e., market basket expansion] rates.” However, they use a regression
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Texas Tech University, Jared M. Hansen, December 2007
based estimate of the quantity of items in the market basket based on a sample of store
loyalty card information because they do not have access to market basket information
for their store-level scanner data. They then estimate the market basket sales dollars
and gross profit dollar by multiplying the estimated change in units by the total store
sales and profits averages. There are several limitations to such store-level estimations,
one of which is the inability to calculate the market basket metrics for each individual
promotion (and individual promotions are, indeed, the specified unit level of analysis).
Therefore, Ailawadi et al (2006, p. 531; emphasis added) call for future researchers to
“validate our work, find new ways of obtaining more disaggregate estimates of the
halo effect, and study how and why halo effects vary across categories and retail
formats.”
In contrast, Janakiraman, Meyer, and Morales (2006, p. 363) use an
experimental design, involving the participation of 150 undergraduate students in an
computerized shopping simulation of 12 products over 35 weeks. They find positive
“spillover effects” of discounted products on the purchase of other products during the
same simulated shopping trip. Combined with the findings of Ailawadi et al. (2006),
these results indicate that price promotions affect more than the promoted product, and
thus, should be analyzed, and perhaps managed, at a more strategic level. However,
despite the importance of investigating more strategic perspectives of retailer
performance, “relatively few studies have focused on cross-category price-promotion
effects, especially at the retail store level” (Kamakura and Kang (2006, p. 159).
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Texas Tech University, Jared M. Hansen, December 2007
The purpose of this research is to model the effects of the price promotions on
the retailer (i.e., the organization)—not the individual retail manager. Adopting this
more strategic perspective, I focus on the broader market basket perspective as
opposed to the narrower category management perspective. Thus, furthering the goal
of understanding the effect of promotional activities on retailer performance, this
study is the first study to measure, instead of estimate, the effect of individual pure
price promotions on the actual, total-market-basket performance. See Table 1. Prior
studies, in contrast, must estimate dependencies across products and categories using,
for example, elasticities (e.g., Manchanda et al. 1999; Mulhern and Leone 1991;
Walters 1991) hazard models (e.g., Chintagunta and Haldar, 1998), or multiple
equation time series models (Dekimpe and Hanssens 1995; Nijs et al. 2001; Srinivasan
et al. 2000).
Toward a Theory of Expandable Purchasing and Consumption
Synthesizing the findings of prior research on the subject of market basket
(e.g., Ailawadi et al 2006; Janakiraman, Meyer, and Morales 2006; Mulhern and
Padgett 1995), this research proposes that purchase and consumption are expandable.
One example is the “halo effect” proposed by Ailawadi et al (2006). Another example
is the “spillover effect” proposed by Janakiraman, Meyer, and Morales 2006. Other
examples are found in the works of Chandon, Wansink, Laurent (2000), who identify
six major multiple consumer benefits from sales promotions (that could also apply to
price promotions): opportunities for value expression, entertainment, exploration,
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savings, higher product quality, improved shopping convenience. Also, McCracken
(1999) presents several other potential reasons for consumption, including identity,
emotional fulfillment, and a consistent product constellation. This dissertation
proposes that (what I refer to as) expandable purchasing can occur as a result of any of
these, or perhaps others, individually or in combination.
The word “expandable” indicates that, contrary to the commonly-accepted
“market pie” metaphor in marketing (e.g., Carson et al. 1999; Chakravarti and
Janiszewski 2004; Day and Montgomery 1999; Jap 1999; Nason 2006; Rokkan,
Heide, and Wathne 2003; Weitz and Bradford 1999), consumption is not like a static
“market pie” to be divided (i.e., maintained). This usage of the pie metaphor is an
adaptation of the neoclassical economic pie metaphor. Speaking on the original usage
of the metaphor in macroeconomics, Swinnerton (1997, p. 75) states, “To develop a
better intuition for the definitions of efficiency and their equivalence, it is useful to
think of a pie as a metaphor for the output of the economy.” In the pie metaphor in
marketing research, however, researchers have equated pie size with “primary
demand” (instead of labor allocation) and pie distribution with “market share” (instead
of wealth distribution). The pie metaphor, as currently applied to purchasing-related
research, implies that all consumers go to a store with a budget and they spend the
budget. This adaptation of the metaphor has resulted in interpretations of price
promotions as zero-sum games of product switching (e.g., Dodson, Tybout, and
Sternthal 1978).
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Texas Tech University, Jared M. Hansen, December 2007
The growing literature converging on a theory of expandable purchasing does
not support this metaphor or its resulting interpretation of price promotion research.
Instead, this growing stream of research is more consistent with a balloon metaphor,
where purchasing can be grown similar to the inflating of a balloon (Hansen and
McGinty, 2007). Rather than assuming that all consumers go to a store with a budget
and spend the budget limit, this approach assumes that consumer may visit with a
budget in mind, but retain the flexibility to adjust spending. For instance, a consumer
may plan on spending 100 dollars as he has done in the past, but decides to spend
more because of the effect of the pure price promotion. Similar to the balloon analogy,
a shallow discount will not result in much of an effect on overall purchase behavior.
However, the effect grows nonlinearly with the depth of the price promotion. That is,
as the size of the price promotion for a product increases, there is expanded purchasing
of other products. These products may be complements or may be completely
unrelated. Indeed, according to Walters and MacKenzie 1988, p. 54), “Retailers and
marketing researchers generally believe one of the primary benefits of price
promotions is that they stimulate sales not only for the lower price, lower margin
promoted items but also for higher margin goods that are not being promoted.” While
the effect is proposed to be nonlinear, hypotheses here are only directional due to
normal data limitations. That is, longitudinal data on particular products over several
promotional prices is needed to compute the nonlinear elasticities. Such data is rare,
not present in the literature, and not available for this research study.
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Texas Tech University, Jared M. Hansen, December 2007
The motivation for expanded purchasing may be economic utility
maximization, hedonic enjoyment, a reward to the retailer, or a combination of these
and other consumer motivations. I propose that there is a point (though it might be
difficult to locate precisely) where a very deep price promotion might result in
negative reaction by consumers who begin to doubt the quality of the product given
the large price reduction (similar to the balloon popping). See Kirmani and Rao (2000)
for discussion on signaling unobservable product quality. Most retailers, however, do
not engage in this kind of price promotion activity, with the exception of occasional
loss leaders (e.g., milk, bread, crayons, beer). Rather, the relevant range of the type of
retail price promotions being investigated (i.e., context) is expected to be in the “front
half,” or positive slope area, of the proposed balloon effect. Based on the preceding
discussion, I hypothesize:
H3: Price promotions of a product will have positive effects on market basket sales.
H4: Price promotions of a product will have positive effects on market basket
item count (i.e., number of products in the market basket).
H5: Price promotions of a product will have positive effects on market basket profitability.
Price Promotions and Return Calculation
Another contribution of this research is that it investigates the effect of price
promotions on shareholder investment. There have been calls (e.g., Lehmann 2004;
Rust et al. 2004) for marketing research to build bridges between marketing activities
and financial (e.g., shareholder) outcomes. According to Srivastava, Shervani, and
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Fahey (1998, p. 2), marketers must “understand the financial consequences of
marketing decisions, expanding the external stakeholders of marketing to include
explicitly the shareholders and potential shareholders of the firm.” In their words,
moving from the tradition assumption of sales, ROS, and equity as operational
measures toward the emerging assumptions of net present value of cash flow and
shareholder value.
It is argued here that price promotions of a product can make other resources
(i.e., internal capital) of an organization more productive. While Kumar, Madan, and
Srinivasan (2004, p. 933) state that “Price promotions commonly increase
manufacturer revenue and depress retailer revenue in the short term but have no
persistent effect. Promotions are tactical, not strategic, and they need to be managed
that way.” While the effect of a promotion is, according to the literature, often short-
term, the usage of promotions over time carries strategic implications. As indicated
prior, by adopting a retailer—rather than a retail manager—perspective on the
effectiveness of price promotions, this research advances the price promotions
literature toward strategy formulation, answering the calls of researchers such as Day
(1992) and Webster (1981, 1992).
Further, recent accounting research recognizes and calls for research that
addresses the difference between ROI and the real economic profitability of an
organization (see Rajan, Reichelstein, and Soliman, forthcoming). This research will
be the first study, based on a review of the literature and discussion with several
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thought leaders in the area, to quantify the impact of retailer promotional activity on
the retailer’s shareholders by introducing and using a new metric.
Early works on valuation often used return on investment (ROI).
Unfortunately, the “investment” in ROI varies by research study. The formula
variation (in studies purporting to use ROI) has resulted in several noted potential
problems in interpretation. Most often ROI appears to become return on sales—
which should be identified as ROS, not ROI. ROS is calculated as:
(1) ROS = Net Income (Before Interest & Tax) ÷ Sales Dollars
Practitioners are usually more interested in measuring performance within their
organization (e.g., particular product SKU price promotions) versus measuring
performance across organizations. Attempting to apply ROI (or ROS) to product (or
even category) merchandise decisions is, as indicated by Sweeney (1973, p. 61),
“fraught with cost measurement and allocation problems.” Further, price promotions,
by mathematical rule, decrease the unit margin in order to stimulate sales growth. In
the context of ROS, the growth in the numerator (i.e., net income) is less than the
growth in the denominator. Take, for example, a box of Tide detergent that is regularly
prices at $6, which generates $2 in net income and a ROS of 2/6=33%. The Tide is
price promoted at $5 per box, which decreases net income to $1 and results in a ROS
of 1/5=20%. While there may be a ten-fold increase in sales volume, increasing both
the sales dollars and income dollars, the ROS ratio decreases. Thus, neither ROI nor
ROS is well equipped to measure product-level, operational performance. However,
both the allocation and dollars versus percentage problems can be overcome when
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“the performance measure is used exclusively for planning and controlling
merchandising inventory investment” (Sweeney 1973, p. 61).
Consequently, a few academic researchers and many practitioners have
adopted a more specific measure of return, the gross margin return on inventory
investment (hereafter, GMROII) to plan and control (e.g., Ahern and Romano 1979;
Dunne and Lusch 2005; Kravitz 1977; Leeds 1976; Sweeney 1973; Tolle 1976;
Warrington 1982). GMROII is calculated as:
(2) GMROII ( % ) = Gross Margin (%) ÷ [1 - Gross Margin (%)] x Inventory Turnover
One advantage of GMROII is that it does not require any approximations from
aggregated activities such as labor costs. Thus, it can be applied at the product,
category, or overall retailer level of analysis. Another advantage of GMROII (over
ROS) is that GMROII accounts for inventory turnover. Also, while other return
metrics (e.g., ROI, ROS) do involve a time component (i.e., it is the return on
investment or sales for a given period of time), GMROII emphasizes the importance
of time. That is, it shows how time can provide a strategic advantage/disadvantage as
an organization manages its inventory (i.e., cost of goods sold) better/worse than its
competitors. Despite these advantages, there is a dearth of academic literature that
uses GMROII. Perhaps one reason why few academic researchers use GMROII (in
contrast to many practitioners) is access to data on both profitability and inventory
turnover rates. Thus, many studies have incorporated the effects of price promotions
on consumer inventory (e.g.., Assuncao and Meyer 1993; Bucklin and Lattin 1991;
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Texas Tech University, Jared M. Hansen, December 2007
Chintagunta 1993; Guadagni and Little 1987; Gupta 1988, 1991; Nelson and Stone
1996), but none of these studies analyzes, incorporates, or even mentions the potential
effects of price promotions on retailer inventory. One result is that the effect of the
price promotions of a product on GMROII has not been established in the literature.
This effect could be either positive or negative, depending on how the gross margin
percent and the inventory turnover are conjointly affected by the price promotions.
For example, a price promotion of widgets might result in both a ten percent
decrease in gross margin percent (i.e., from 50 percent to 40 percent) and a three fold
increase in inventory turnover (i.e., from two to six turns annually). Or, the same price
promotion of widgets might result in both a five percent decrease in gross margin
percent (i.e., from 50 percent to 45 percent) and a one fold increase in inventory
turnover (i.e., from two to three turns annually). The first scenario (smaller gross
margin, larger turnover) results in a GMROII of 400 percent return, whereas the
second scenario (larger gross margin, smaller turnover) results in a GMROII of 245
percent return. . In this example, the price promotion that resulted in less of a margin
decrease did not result in a higher GMROII (because of the relative inventory turnover
impact). Thus, in hypothesizing the impact of price promotions on GMROII, a
researcher is implicitly hypothesizing about the relative change in gross margin versus
inventory turnover rate. The neoclassical economic “pie” metaphor (presented earlier
in this chapter) maintains that the inventory rate is maintained over time or across
products (i.e., substitution effects). In contrast, a theory of expandable purchasing
suggests that the inventory turnover rate would be accelerated due to the product’s
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price promotion. Based on the preceding discussion, and consistent with the growing
literature supporting the phenomenon that I refer to as expandable purchasing, I
hypothesize:
H6: Price promotions of products, in a majority of cases, will have positive effects on promoted products’ GMROII.
As a derivation of ROI, though, GMROII does not consider the financing of
the cost component, and thus retains the inability to document the impact of price-
promotions on an organization’s owners. The cost component consists of both
financing and operating responsibilities, and operational metrics such as ROI and
GMROII follow managerial accounting guidelines that have traditionally held that:
Managers have both financing and operating responsibilities. Financing responsibilities relate to how one obtains the funds needed to provide for the assets in an organization. Operating responsibilities related to how one uses the assets once they have been obtained. Both are vital to a well-managed firm. However, care must be taken not to confuse or mix the two when assessing the performance of a manager. (Garrison and Noreen 2003, p.773)
The foundational logic of this research agrees with the premises of these
quoted guidelines. That is, there are financing and operating responsibilities (i.e.,
premise 1) and both responsibilities are vital to the organization (i.e., premise 2).
However, Anderson (1979, p. 325?) states that too often marketing tends to “fail to
recognize the impact of marketing decision on such variables as inventory levels,
working capital needs, financing costs, debt-to-equity-rations, and stock prices.”
According to Srivastava, et al. (1998, p. 11), “the impact of marketing activities on the
fixed and working capital requirements of the firm, though it has received some
attention lately, general is not well understood.” It is proposed here that the inventory
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turnover rate does have an effect on the accounts payables. That is, the inventory
turnover rate can change how one obtains the funds (e.g., what is funded internally, or
funded externally, or, even, never requires funding at all).
In contrast to ROI, return on equity (ROE) considers the financial leveraging
of funding (see Block and Hirt 2000, p. 56):
(3) Return on equity = Return on assets (investment) ÷ (1-Debt/Assets)
However, the ROE metric requires valuation assumptions about dividing the costs of
labor, space, and other activities to arrive at net income that are not easily allocated to
product level analysis. Indeed, as indicated by Srivastava, Shervani, and Fahey (1998,
p. 8), “the valuation of assets is controversial.” At the same time, the cost of financing
versus investing is being debated (see Baker, Ruback, and Wurgler 2007).
Thus, an alternative metric in use is proposed here that is similar to the Du
Pont model of ROE (see Garrison and Noreen 2003, p. 70), and can be used as a
framework for discussing promotional impact on the organization. This metric
replaces net income with gross margin (i.e., the purchased cost of goods cost, prior to
allocating labor, building, and other costs) and, in accounting for the change in the
financing plan, also incorporates the internal rate of return on the unused assets. Thus,
the metric becomes the gross margin return on shareholder investment (hereafter,
GMROSI) of the product. As a planning and controlling metric, GMROSI could be
used as a working template for managers interested in attempting to strategize on the
shareholder value-planning approach (e.g., Day and Fahey 1988; Kim, Mahajan, and
Srivastava, 1995; Rappaport, 1986).
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Texas Tech University, Jared M. Hansen, December 2007
In relation to GMROII, the GMROSI metric replaces the “inventory”
investment with “shareholder” investment by accounting for the change in the cost due
to changes in account payables that are, in turn, due to changes in the contracted terms
of payment (e.g., 2-10-net 60) between the retailer and the manufacturer of the
product, and the actual (i.e., “positive,” not “normative”) internal rate of return in the
organization. By doing so, this planning and controlling metric accounts for the
relative return on each shareholder dollar invested in the organization. See Appendix 1
for an example. Also, while GMROSI provides an operational accounting of
shareholder inventory investment productivity, it should not be confused with
shareholder market return. The relevant formulas for GMROSI computation are:
(4) GMROSI ( % ) = Adjusted Gross Margin (%) ÷ [1 – Adjusted Gross Margin (%)] x Inventory Turnover, where
(5) Adjusted Gross Margin = (Sales $ - Adjusted Cost $) ÷ Sales $, where
(6) Adjusted Cost $ = (Cost Schedule * Cost%) - ( IRR * Cost Schedule
* [1-Cost%] )
Taking a shareholder perspective, it is proposed that many promotions that
decrease the percent profitability of the product for the retailer may, in fact, increase
the percent profitability of the product for the shareholder. The argument here runs
contrary to the findings of Srinivasan et al. (2004, p. 617) that promotions are not
beneficial (for retailers) because price promotions reduce retailer category margins.
This difference in interpretation is understandable (i.e., explainable) given that the
margins in their equations, consistent with the literature, are not the marginal return to
shareholders. Consistent with the theory of expandable purchasing, I argue that price
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promotions of a product will accelerate inventory turnover. The inventory turnover
acceleration, in turn, results in less internal capital being used over the time of the
payable schedule. Thus,
H7: Price promotions of products, in a majority of cases, will have positive effects on promoted products’ GMROSI.
As a proposed metric, GMROSI is consistent with the resource-advantage
theory of competition (hereafter, R-A theory). As indicated by Hunt (2000, p.123),
“The “superior” in superior financial performance equates with both more than and
better than” in R-A theory. By mathematical rule, increases in GMROSI often
involve financial performance that is better than and may involve financial
performance that is more than GMROII. That is, GMROSI accounts for the funding
origination from in the equation, and, by so doing, permits a more accurate picture of
the return to the shareholder. In GMROII, all funding is always from internal capital.
Because this is not truly the case, GMROII presents a negatively biased view of the
return to the shareholder’s investment. Thus, GMROSI will often present a “better
than” scenario that more accurately reflects the business practice of leveraging funding
across different sources (i.e., changes in the denominator). It should be noted though
that because of this combination (of activity and investment measures), the
interpretation of changes in GMROSI (vs. GMROII) is more complex and requires
careful attention.
GMROSI may involve financial performance that is “more than” because there
is no guarantee that the top line growth will occur (i.e., changes in the numerator).
Because of this, GMROSI as a measurement of organizational performance is
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consistent with the relative resource costs and relative resource-produced value axis of
the competitive position matrix of comparative advantage (Hunt and Morgan 1997).
Thus, in adopting GMROSI as a dependent variable, the research also adopts a
dynamic competition perspective that can account for it.
Moderators of the Promotion-Performance Relationships
In regards to a dynamic competition perspective, this research will advance
understanding of the potential moderating role of competition on the relationship
between retail price promotions and retailer performance. According to Walters (1991,
p. 18, emphasis added), “Though little is known about the breadth and depth of brand
substitution and complementarity across product categories, even less is known about
promotional effects across stores.” Related, Boatwright, Dhar, Rossi (2004), propose
that retail competition, account retail strategy (e.g., EDLP pricing strategy, store size,
chain size), and customer demographics are important factors in investigating
promotional response. They use account-level (i.e., store-market combinations, as
opposed to individual stores in markets) data representing 35 Nielson SCANTRACK
markets (because, in their words (2004, p. 173), “it is the policy of both IRI and
Nielson not to release store level information”). Despite the absence of store-level
data, they (p. 182) still that “consumer demographics are very important in explaining
variation in promotion response.” They also find that retail competition accounts, in
contrast, for only three to four percent of the variation in price sensitivity in their data
(Boatwright, Dhar, and Rossi (2004, p. 186). Recall, however, that they only had
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access to account-level data. Taking a different approach, Ailawadi et al. (2006) use
two indicator (i.e., dummy) variables to represent the presence of either same-format
retailer competitors or alternative format retailer competitors. They find the two
effects to be different. This study will replicate their test in a new setting—an every-
day-low-price, general-merchandise retailer.
More importantly, and drawing upon R-A theory (e.g., Hunt and Morgan 1996;
Hunt 2000), the proposed research would investigate competitive intensity at a more
detailed level, accounting for the potential varying impact of approximately 30
different competitors that compete at different levels (e.g., category, total-store) with
the retailer. I propose that the effect of retail competition will be different here than in
prior work because (1) store-level scanner data is used (vs. account-level scanner
data), and (2) the model does aggregate all of the competitors together (vs. aggregating
competitors to either a primary and secondary indicator category, limiting any
investigation into the intensity, or count, of the number of competitors). Competitors
are also identified as primary competitors or secondary competitors in the model. A
primary competitor competes with the retailer across most product categories, or the
total store (e.g., Sam’s Club competing with Costco or J.C.Pennys competing with
Dillards). A secondary competitor competes with the retailer mostly within the
category of the product under investigation (e.g., Barnes and Nobles competing with
Target in books).
Also, competition occurs within a store (e.g., choosing Regular Oreos vs.
Chips Ahoy vs. Reduced Fat Oreos vs. a Snickers Candy Bar). Thus, research should
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take into account the level of heterogeneity of the merchandise assortment in the store
in which the item is price promoted. As the competitive intensity increases either
within or across stores, consumer price sensitivity increases. With increased acuteness,
consumers are better able to process price promotion information, thereby making
more informed decisions. In turn, the enhanced decision-making ability permits
consumers to expand purchasing and consumption. Following the preceding
discussion, I hypothesize:
H8a to H8k: Increases in merchandise heterogeneity will strengthen the relationships in hypotheses 1-7.
H9a to H9k: Increases in the number of total competitors will strengthen the
relationships in hypotheses 1-7. H10a to H10k: Increases in the number of primary competitors will strengthen
the relationships in hypotheses 1-7. H11a to H11k: Increases in the number of secondary competitors will strengthen
the relationships put forth in hypotheses 1-7. Lodish (2007, p.24) proposes that “for practitioners, the geographic differences
in market response are much more important than share differences because they are
directly actionable and can affect profitability.” The geographic differences in market
response are due, in large part, to differences in customers. According to Ailawadi et
al. (2006), the demographic differences include education levels, income levels, and
ethnic diversity levels. In their analysis, they find that education has a negative effect
on price elasticity, high income has a negative effect on price elasticity, and minority
ethnicity (i.e., Hispanic or Black) has a negative effect on price elasticity. Yet, they
provide no rationale for why any of these effects might occur. Recall that in a theory
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of expandable consumption, there are multiple motives, one of which is identity. One
reason why Ailawadi et al. (2006) find these effects might be that higher income or
more educated customers do not wish to be identified with advertising or price
discounting. Through this behavior, and, related, by avoiding shopping at price
discount stores, they might feel that they have achieved some degree of success or
financial independence in life (e.g., they don’t have to shop at, for example, a Wal-
Mart). Alternatively, Boatright, Dhar, and Rossi (2004), finding similar effects from
proxies of household wealth proxies, attribute it to differences in customer time
valuation (e.g., customers that earn more place a greater dollar figure on time, and thus
are less price sensitive. The conditions under which one rationale (over the other) is
present cannot be measured through the store-level scanner data in this study or prior
work, and is left for future research to investigate.
In regard to customer ethnicity, while Ailawadi et al. (2006) describe an effect
without discussion, Boatwright Dhar, and Rossi (2004, p. 188) state, “We have not
elected to include ethnicity in our results reported above as there is no real theory that
can account for effects of ethnicity once wealth and household composition is
controlled for. In specifications not reported here, we found that Hispanic and black
consumers are less price responsive.” Controlling for the effects of income and
education, one potential reason why customers of particular ethnicities might have
been found to be less price-sensitive in prior studies might be that they have less
societal access to act on the price promotions. That is, they have less capacity to
consume that might be due, in part, to their geography—which is related to, but
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distinct from the other variables (i.e., income, education, home value, and household
size).
For instance, they might live in areas with higher public transportation usage
(where additional product purchasing is more difficult to transport to their residences)
or higher housing density (where, as a result, house or apartment sizes are smaller,
resulting in less capacity to stockpile merchandise). Regardless of whether capacity to
consume does, perhaps, explain this ethnic, or cultural effect, this research
incorporates measurement of customer ethnicity, consistent with prior research
findings.
In summary, while the setting of this research is an every-day-low-price,
general-merchandise retailer (vs. a hi-low price, drug-store retailer), there is no reason
based on the prior discussion as to why the relational signs between their study and
this study should be different. Rather, for a general-merchandise retailer, these
relationships should be stronger, if any differences do exist, given that the limited,
prior research finds shoppers in EDLP chains to have higher regular or long run price
sensitivities (e.g., Shankur and Krishnamurthi 1996). Thus,
H12ato H12k: Increases in the level of customer education will weaken the relationships in hypotheses 1-7.
H13a to H13k: Increases in the amount of customer income will weaken the
relationships in hypotheses 1-7. H14a to H14k: Increases in the percentages of African American customers will
weaken the relationships in hypotheses 1-7. H15a to H15k: Increases in the percentages of Hispanic customers will have a
weaken the relationships in hypotheses 1-7.
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Another potential moderator of the promotion-performance relationships stated
in this research is the product’s purchase frequency. For instance, razorblades are
purchased more frequently than toasters. In either situation, this often corresponds
with, but yet is distinct from, the product durability. It is possible for durable products
(e.g., toasters) to become commodities. It is proposed here that the perceived
consumability of these products is affected by price promotions. No evidence is found
in the literature to propose that the preceding hypotheses will be different (e.g.,
positive effect, negative effect) for more frequent (i.e., health and beauty aid) products
vs. less frequent (i.e., small appliance) products. However, will the effect size be
similar, or will the effect size of one type of product be larger? Thus,
RQ2: Is the effect of price promotions in H1 to H7 greater for more frequently purchased products than for less frequently purchased products?
Last, this research project investigates when (i.e., under what competitive
conditions) customers are “cherry picking” shopping due to the price promotion versus
“self-indulging” or “hedonic” shopping due to the price promotion. That is, under
what conditions (i.e., competitive intensity, customer demographics) is it more likely
that consumers will respond to the price promotion by purchasing more reduced price
products—reducing profitability growth versus sales growth (i.e., a reduction in
profitability margin)? Alternatively, under what conditions is it more likely that
consumers will respond to the price promotions by purchasing more regularly priced
products—increasing profitability growth versus sales growth (i.e., an increase in
profitability margin)? Both situations are consistent with the theory of expandable
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purchasing and consumption. However, there is no research published (based on an in-
depth review of the literature) to indicate when one effect over the other effect will be
found in the data analysis of this research study. Thus, the investigation takes a
research question form:
RQ3a: When does the moderating effect of H8a to H14k decrease profitability margin for either the product or market basket?
RQ3b: When does the moderating effect of H8a to H14k increase profitability
margin for either the product or market basket?
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CHAPTER 3
RESEARCH DESIGN
Samples
The samples for this research consists of weekly, point-of-sale, stock-keeping
unit (hereafter, SKU), scanner data for manufacturer-branded products collected in all
of the approximately 450 stores of an every-day-low-price, general-merchandise
retailer operating in the northeastern and midwestern United States. Several reasons
exist as to why this sample kind is appropriate for the study of retail price promotions.
First, retailer store-level scanner data provides a way to track purchase
behavior that is (1) store specific and (2) more likely to be error free than either
household scanner data or surveys of customers. According to Bucklin and Gupta
(1999), store level scanner data is less prone to sample selection bias than household
level (or survey) scanner (panel) data. Further, as indicated by Sriram, Balachander,
and Kalwani (2007, p. 61), because “store-level data can be obtained across several
retailers in various geographic regions, managers can use these data to track the health
of their brands across geographic regions.” This is important because, in the words of
Kamakura and Kand (2007, p. 160), “while managers of retail chains develop price-
promotions policies that are consistent with the marketing strategy at the chain level,
they should implement theses policies in a way that is most effective at each store.”
Second, demand for manufacturer-branded products has been shown to be
more price-elastic than demand for non-branded or private-label products (e.g.,
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Sethuraman 1995). For example, Sethuraman (1995), finds that “national brands with
large market share have significant influence on private-label sales but they are less
likely to be affected by private-label price cuts.” The increased probabilty that the
demand for branded products will be more elastic increases the opportunity to
examine hypotheses regarding market basket, shareholder inventory investment, and
moderator effects.
Third, a general-merchandise retail store carries a greater variety in product
categories than other retailer type stores modeled in the literature (e.g., grocery stores,
drug stores). The increased variety makes it “easier for consumers to combine multiple
visits to multiple stores” (Dellaert, et al. 1998, p. 177). Indeed, customers can
normally combine apparel, home (e.g., furniture, bed and bath, kitchen), health and
beauty aids, grocery, and pharmacy product purchases at a general merchandise retail
store (e.g., Big Lots, B.J.’s, Costco, Dollar General, K-Mart, Shopko, Target, Wal-
Mart). The increased quantity/variety of categories permit greater investigation into
the effects of price promotions on customer market basket choices.
Fourth, as indicated by Hock, Dreze, and Purk (1994, p. 16) “the ‘every day
low price’ (EDLP) format…has experienced rapid growth and media popularity.” The
increase in customer acceptance and trust in EDLP claims permits a more transparent
view of the effectiveness of price discounts by decreasing the potential for customers
doubting price claims. Price discount claims are made by low-consistency cues (e.g.,
“was” or “regularly priced” on the shelf product-label). See Grewal, Marmorstein, and
Sharma (1996) for more discussion on low-consistency cues. For example, a “was
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$4.00” in a “was $4.00, now $3.00” claim in an EDLP store is likely to be more
trusted by customers versus claims in other stores using other pricing strategies (where
customers would be more likely to raise doubts as to the motives behind the “was
$4.00.”) In contrast, to what extent does the customer percieve that the non-EDLP
store is inflating the price to make the “now” price more attractive? This perception
seems less likely to occur by customers in EDLP stores (e.g., Fishman 2006; Hock,
Dreze, and Purk 1994). Also, price promotions are less frequent in EDLP stores.
Blattburg, Briesch, and Fox (1995, p. 124), citing Bolton (1989), Raju (1992), Winer
(1986), and Putler (1992), state “the greater the frequency of deals, the lower the
height of the deal spike.” Thus, the potential effects of price promotions should be
more evident in EDLP stores.
Fifth, Kamakura and Kang (2007, p. 161) call for price promotion research that
uses more than “a single store or small set of competing stores (e.g., two stores)” retail
store locations. Indeed, the approximately 450 store sample for each product SKU in
this research study permits investigation into the “possibility that each store in a retail
chain serves a distinctive trade areas responding different to price promotions,” and
does not “restrict cross-elasticities to be that same for all stores” (Kamakura and Kang
2007, p. 161). These several reasons provide description of why this sample kind is
appropriate for the study of retail price promotions. In addition to the preceding
reasons, several filters are applied to potential product SKU’s to increase the
appropriateness of the research setting for investigating the effects of price promotions
on retailer profitability.
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Data Collection
Several filters were applied to available product SKUs (that were price-
promoted by the retailer) to arrive at the final sample that is consistent with the stated
research purpose to investigate the impact of retailer price promotions on retailer
performance.
First, and central to the stated research purpose, product samples are limited to
particular products where pure price promotions (i.e., no additional promotional
activities for the investigated products) were taken at some point during the period
June 2004 to July 2006. By using only pure price promotions, the first instance of
general customer price-reduction awareness is in-store when the customer views the
shelf label (which states the prior and current price, both in dollars). This setting
contains the advantaging of preventing a needed simultaneous investigation of
customer price knowledge and the source of customer price comparison. Customers
can refer to different references when making price comparisons, including external
(e.g., shelf label, advertisement) or internal (i.e., memory based) references. Because
this research controls for advertising (through exclusion), all customers first see the
price change at the shelf. Thus, we do not need to investigate their ability to recall and
advertised price promotion (because there is no advertising involved), etc. Rather, all
customers are exposed to and using the same price comparison (i.e., the shelf label).
For additional discussion on the different types of external price references, see
Vanhuele and Dreze (2002).
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Second, following the finding of Ailawadi et al. (2006) that the greatest
variance (in their study) is in the general merchandise and health and beauty
merchandise categories, the product SKUs in this study are chosen from these same
merchandise categories. In contrast to both the drug store setting of Ailawadi et al
(2006) and the grocery setting of other, related research (e.g., Janakiraman, Meyer,
Morales 2006; Mulhern and Leone 1991; Walters 1991; Van Heerde, Leeflang, and
Wittink 2004), however, this research uses samples drawn from an every-day-low
price, general-merchandise retailer in the United States, which permits investigation of
the generalization of their findings (to a general-merchandise store context), in
addition to the new contributions on total shopping cart consumption, shareholder
return, and competitive intensity.
Third, products are selected for which the product market baskets do not
include any of the other products being investigated in the study (i.e., unique market
baskets). Unique market baskets are operationalized here as market baskets in which
the overlap (in investigated products) is less than 1 in a 100 common occurrences.
This filter was accomplished through analyzing system generated reports of the most
commonly occurring items in market baskets for each potential price-promoted
product over the course of a year. It should be noted that while the filters decrease the
level of “noise” in the sample data, there is always the possibility that some other
products may be price-promoted that customers bought as part of some of the market
baskets because of the natural field experimental setting of this research. This does not
imply, however, that the findings of this research are overstated. Rather, it suggests
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that any corroboration of hypotheses in the data analysis is understated (because the
relationships are significant even in the presence of the potential marketing mix
activities on other products in the market baskets).
In all, four product SKU samples, each of N = approximately 450 (same) retail
stores, are found that match the pure promotion, timeframe, merchandise category, and
unique market basket filters. The data were made available by the respective products’
manufacturers. Vice presidents of marketing or sales departments for manufacturing
firms identified product SKUs that match all of the specified filter criteria. The
manufacturer contacts then queried the data for the variables described in the
measurement section from the retailer scanner database for each product SKU
matching the filter constraints. Per their request, the products, brands, and retailer
names are not disclosed. Two of the product SKU samples are frequently purchased
products from the health and beauty aid category. The other two of the product SKU
samples are more infrequently purchased products from the small appliances category.
Measurement of Variables
All of the construct are measured at the product-store level. All pricing,
product performance, and market basket variables are measured for each product by
retail store, aggregated at a weekly level. These data come directly from the retail
scanners in each store location that is maintained is a centralized, corporate database.
All competitive intensity and customer demographic variables are, likewise, calculated
for each store, as described next.
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Product Sales
Product Sales is measured as net sales dollars. Net sales dollars is calculated as
the total sales dollars, minus return dollar figures, retained by the store (Risch, 1987,
p. 116). According to Walters and MacKenzie (1988, p. 56), “Using dollars instead of
units is more managerially relevant because retailers measure the success or failure of
their decisions, including the implementation of promotional activities, in dollars and
not in units.” It is aggregated at the weekly level by store.
Product Profitability
Product profitability is measured as gross margin dollars. Gross margin is
calculated as the difference between net sales and the billed cost price (before shrink
and allowances) of the merchandise sold, taking into account any reductions due to
sales discounts plus markdowns (e.g., Ailawadi et al. 2006). This definition of gross
margin is closely related to the definition of Risch (1987, p. 296) in which gross
margin is equal to the maintained markon minus the cost of alteration and cash
discounts. The two approaches only differ on the inclusion of cash discounts (as there
are no alteration costs for the general merchandise products studied here). The more
commonly used definition, as adopted here, excludes cash discounts (placing them in
gross profit or maintained profit), and is more often used by practitioners (e.g., Dunne
and Lusch 2006).
Gross profit, in comparison, is equal to gross margin minus operating expenses
(see Risch 1987, p. 290), and it is not calculated here. While the cash discount
component of gross profit can be calculated (combining available information on the
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inventory turnover and the retailer’s accounting method [e.g., LIFO]), the allocation of
operating expenses across products is not known. Indeed, the retailer has never
attempted to allocate it across its products. Given that there is no consensus on how to
allocate operating expenses (e.g., how much does a can of tuna really cost? Or, how
much store labor is allocated to a can of tuna?), gross margin preferred to gross profit
(due to avoidance of product-operating expense allocations). The gross margin is
aggregated at the weekly level by store.
Product Inventory Turnover
Product inventory turnover is measured as net sales dollars divided by average
inventory dollars at billed cost to the retailer (Risch 1987, p. 322). Average inventory
dollars are calculated in this research study as the averages of beginning inventory (at
billed cost dollars) and ending inventory (at billed cost dollars) for a given week and
then annualizing the figure. While average inventory could be computed for any
period of time, it is calculated here by week to be consistent with the other metrics,
permitting other calculations (e.g., GMROII, GMROSI).
Market Basket Sales
Market basket sales is measured as the aggregated net sales dollars for all of
the products acquired by a customer during a single purchase event at the store register
(i.e., the total shopping cart purchased). The calculation is the same as the product net
sales dollars calculation. The measure is aggregated at the weekly level by store for
market baskets that contained the product SKU under investigation.
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Market Basket Profitability
Market basket profitability is measured as the aggregated gross margin dollars
for all of the products acquired by a customer during a single purchase event at the
store register (i.e., the total shopping cart purchased). The calculation is the same as
the product gross margin dollars calculation. It is aggregated at the weekly level by
store for market baskets that contained the product SKU under investigation.
Market Basket Item Count
Market basket item count is measured as the number of products in a particular
market basket. It is a proxy for market basket inventory turnover—which would be
nearly impossible to calculate (i.e., it would require separate reports on every item that
is located in any of the market baskets in any store during any week for the price-
promoted product SKUs). The number of system queries would be astronomical. The
number of items in the market basket, in contrast, is calculable, and gives a rough
estimation of whether the store is selling more products per period of time (increasing
overall inventory turnover). The market basket item count is aggregated at the weekly
level by store for market baskets that contained the product SKU under investigation.
Gross Margin Return on Inventory Investment (GMROII)
GMROII is a calculation—shown in Chapter 2 on page 42. It combines
inventory turnover and gross margin percentage (each defined earlier).
Gross Margin Return on Shareholder Investment (GMROSI)
GMROSI is a calculation—shown in Chapter 2 on page 46. It combines
inventory turnover, gross margin percentage, and adjusted product cost. The adjusted
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product cost reflects the internal rate of return. Several potential internal rates of
return from the response surface are used in calculating potential GMROSI scenarios.
Store Assortment
Store assortment is measured through a proxy, the store’s actual physical size
(e.g., 122,000 square feet), because the number of product combinations at the store
level is astronomical, making the use of all product combinations beyond available
degrees of freedom for statistical testing. However, prior literature has shown store
size to be an adequate proxy for store assortment (e.g., Kamakura and Kang 2007), as
larger stores do, typically, carry additional breadth and depth of products, increasing
the assortment.
Competitive Intensity
Competitor stores of 30 potential, major competitors are geographically
identified for each store in the product SKU samples, first by census data, and then
confirmed (as actual store competitors) by the store manager. This store-level
competitor information is documented and retrieved from the scanner database. Some
of the competitors compete in particular categories, while others compete across many
categories. Some of the competitors carry higher priced products, while others carry
lower priced products. This research will account for these complexities using R-A
theory as a foundation. Adopting this foundation, the research will distinguish between
category and store competitors in modeling the complexities by (1) using indicator
(i.e., dummy) variables for each of the 30 competitor and (2) identifying whether a
given competitor is a total store competitor or a category competitor (as indicated by
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the retailer and confirmed through a expert panel survey conducted by the author). The
increased sensitivity of these tests (over prior work) will advance understanding of the
moderating effects of competitive intensity on the relationship between retailer
promotional activities and retailer performance.
Customer Education Level
Customer education is reported at the store level. It is computed by the retailer
using census and other third party data for the store selling area and adjusted, as
deemed appropriate, by the store manager. Percentages are given for the following
categories: some high school, high school, some college.
Customer Age
Customer age is reported at the store level. It is computed by the retailer using
census and other third party data for the store selling area and adjusted, as deemed
appropriate, by the store manager. Percentages (of total customer population for each
store) are given for each of the following categories: 18 to 34, 35 to 54, 55 to 64, 65
and older.
Customer Ethnicity
Customer ethnicity is reported at the store level. It is computed by the retailer
using census and other third party data for the store selling area and adjusted, as
deemed appropriate, by the store manager. Percentages (of total customer population
for each store) are given for each of the following ethnicities: Caucasian, African
American, Latin.
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Customer Income Level
Customer income level is reported at the store level. It is computed by the
retailer using census and other third party data for the store selling area and adjusted,
as deemed appropriate, by the store manager. Percentages (of total customer
population for each store) are given for each of the following household income
ranges: under $30,000; $30,000 to $49,999; $50,000 to $99,999; $100,000 or more.
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CHAPTER 4
DATA ANALYSIS
In all, four product SKU samples, each of N = approximately 450 (same) retail
stores, are found that match the pure promotion, timeframe, merchandise category, and
unique market basket filters. Samples A and B are frequently purchased products from
the health and beauty aid category. Samples C and D are less frequently purchased
products from the small appliances category. Sample A is a loss leader. That is, the
item was price promoted at a retail price below its cost to the retailer (known from
examination of the cost and price information). There is no evidence that customers
were generally aware of this (as markup information is confidential), and so while it
changes the interpretation of the profitability and cash flow analysis for the product, it
should not impact consumer behavior in a manner different from the other products.
That is, customers only saw the differences in retail prices (e.g., was/now) on the shelf
labels.
The price promotion’s effect on performance is measured against two baselines
for each product SKU sample. The first baseline, following Abraham and Lodish
(1993), Ailawadi et al (2006), and Kopalle et al. (1999), consists of a moving average
baseline. Because sufficient prior data is available, a second baseline, consisting of
prior-year, same-period data, is also used. The second baseline is more consistent with
practice. That is, firms publicly report comparisons to prior year, same time period
data. Moreover, they typically do not report comparisons to the prior month or quarter.
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Paired samples T-test procedures are used to compare the difference between each
baseline, in turn, and the customer purchase behavior after the price promotion occurs.
Data Analysis of H1 to H5: Product and Market Basket Performance
Table 2 summarizes the paired T-test comparison results of differences in
product sales, product inventory turnover, and product gross margin for each of the
four samples. The data provide strong support for hypotheses H1 and H2. That is, (1)
pure price promotions of a product do have a positive effect on that product’s dollar
sales (at the product-store level), and (2) pure price promotions of a product do have a
positive effect on that product’s inventory turnover (at the product-store level). Also,
with the understandable exception of the loss leader sample, pure price promotions of
a product do have a positive effect on that product’s total dollar profitability for the
analyzed time frame (at the product-store level). That is, the aggregated, incremental
increase in gross profit dollars more than offsets the per item decrease in gross profit
dollars (that happens because the item is price reduced, decreasing gross margin per
unit).
Table 3 summarizes the paired T-test comparison results of differences in
market basket variables. The data provide strong support for hypotheses H3, H4a,
H4b, and H5. That is, (1) pure price promotions of a product do have positive effects
on market basket sales, (2) pure price promotions of a product do have positive effects
on the number of market baskets (i.e., an increase in the number of people buying the
product—rather than just the same number of people buying more of the product), (3)
pure price promotions of a product do have positive effects on market basket item
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count (i.e., number of products in the market basket), and perhaps most importantly to
overall store and company performance (4) pure price promotions of a product do
have positive effects on market basket profitability. Indeed, the increase in market
basket profitability more than offsets the loss in item profitability for the loss leader
sample store group.
Data Analysis of H6 to H7: GMROII and GMROSI
In regards to comparing changes in GMROII and GMROSI, first, a simulated
response surface is constructed to provide a theoretical range of GMROII and
GMROSI values that retailers, in general, might expect to achieve under different
variable (i.e., gross margin, inventory turnover, contracted terms of payment, and
internal rate of return) combinations. See Myers (1971) and Myers and Montgomery
(1995) for overviews of response surface methodology.
The response surface methodology (RSM) has been used in marketing
research, for instance, to model interdependence in supplier-distributor channel
relationships (Kim and Hsieh 2003), customer service satisfaction (Danaher 1997),
and, more related to this research, congruence between pricing strategies and venture
strategies (Myers 2004). While response surfaces can investigate the relationship
between multiple independent variables and a response (i.e., dependent) variable (Box
and Wilson 1951), the RSM has occasionally been criticized because the optimization
is typically done in a model where the coefficients are hypothetical estimations, rather
than real-world measurements. In this research, the model coefficients are not
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estimations. Rather, the coefficients are data values from quarterly and annual reports
of the thirty publicly-held retailers in CNUM 5331 (e.g., Dollar Tree, Target, Wal-
Mart), 5411 (e.g., Kroger, Safeway), 5912 (e.g., CVS, Rite Aid, Walgreens), and 5399
(e.g.., BJ’s, Costco) to establish parameter boundaries and scales. These thirty firms
are selected because they are also the same thirty competitors being included in the
competitive intensity analysis. Specifically, ranges for the inventory turnover, gross
margin, account payable (terms of payment), and internal rate of return variables are
developed based on analyses of the retailers’ quarterly and annual reports. Using the
public report data permits the advantages of RSM while avoiding potential criticism of
common estimation practices (of how one defines the theoretical ranges).
One significant advantage of the response surface methodology is that it
permits investigation of the cost of capital while avoiding entry into the debate over its
calculation (see Chen, Dhaliwal, and Xie 2006). Instead, practitioners can identify
their organizations’ cost of capital (as they compute it) and then find the "path of
steepest ascent" in the direction of maximum GMROII or GMROSI on the response
surface (which indicates the best "direction" for management to incorporate into their
pricing and inventory strategies).
The response surface analysis indicates quadratic modeling for both the
“GMROII” and “Net Change in Market Basket Profitability” models. Canonical
analysis shows that the eigenvectors’ parameters are all positive for both models,
indicating directions of upward curvature shape in the response surfaces. However,
there is no apparent single optimum. See Figure 2.
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<< Insert Figure 2 About Here >>
As a result, ridge regression analysis was performed to determine what
direction should be searched on the grid to locate a superior gain. Ridge regression
reduces the standard errors by adding a degree of bias to the regression estimates. The
superior k value (bias addition) to the ridge regression is k = 0.019564. The resulting
ridge regression parameter estimates are found in Table 4
<< Insert Table 4 About Here >>
GMROII and GMROSI metrics are calculated (using the equations in Chapter
2 on pages 36 and 40) for the post promotion period and both the first (i.e., same year,
prior period) and second (i.e., prior year, same period data) baselines, in turn, for each
of the four natural field experiments. Paired sample T-tests are used to compare pre-
promotion and post-promotion GMROII and GMROSI figures. These results are then
discussed in terms of their location on the generated response surface.
Table 5 summarizes the paired T-test comparison results of differences in
GMROII and GMROSI variables. The data provide strong support for hypotheses H6
and H7. That is, (1) pure price promotions of products, in a majority of cases, have
positive effects on promoted products’ GMROII, and (2) pure price promotions of
products, in a majority of cases, have positive effects on promoted products’
GMROSI.
<< Insert Table 5 About Here >>
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The GMROII results from Table 5 are mapped on the response surface in
Figure 2, resulting in Figure 3. As seen in Figure 3, the locations of pre-promotional
period values are in the normal range of response surface values. Indeed, they appear
to be in the “Big Middle” where most values are located. The promotional timeframe
data points are all located along ascension paths on or above the surface. Thus, the
impact of price promotions on the gross margin percentage and the average inventory
turnover is such that it positively impacts the GMROII in all cases investigated.
< Insert Figure 3 about Here >>
Comparing the GMROII and GMROSI figures for the eight comparisons, the
difference between the two metrics (i.e., GMROSI change minus the GMROII
change) results in an equal interpretation in three of the eight comparisons and a
stronger confirmation in five of the eight comparisons (where the difference was .1, .1,
.1, .2, an .4). Note that a difference of .1 is interpreted as an additional 10% return on a
dollar invested in inventory. Thus, a range of zero to .4 suggests that GMROII does, in
all explored cases, underestimate the impact of marketing activities on shareholder
investment.
Analysis of Hypotheses 8a to RQ3b: Moderating Variables
In regards to the hypotheses related to competitive intensity and customer
demographics, correlations are constructed for the potential moderator variables and
the net change in values of the variables from hypotheses 3 through 7, consistent with
prior literature (e.g., Ailawadi et al. 2006, p. 528-9).
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Table 6 presents the correlates of the potential moderating variables on the net
market basket performance (sales, profits, inventory turnover) and net product
GMROII and GMROSI data. The results indicate that increases in merchandise
heterogeneity strengthen the relationships in hypotheses 3-5 on market basket
performance, consistent with the moderator effects proposed in hypotheses 8, but
weaken the relationships in H6 and H7 on GMROII and GMROSI.
In regards to the potential roles of competitive intensity, the results indicate
that increases in the number of total competitors strengthens the impact of the pure
price promotion on the gap between current market basket sales and prior year (same
period) market basket sales; they support moderator effects proposed in hypotheses 9.
Interestingly, though, an effect was not statistically significant for the number of
primary competitors or for the number of secondary competitors (i.e., hypotheses 10
and 11). One feasible explanation is that the effect of competition occurs more at a
local level (versus the analysis here spanning several states). That is, aggregating only
a particular set of category competitors and a separate set of other competitors does
not account for the fact that not all competitors are present in all markets, and thus
either the total number of competitors should be used, or the data should be modeled
at a more disaggregate level.
As to the moderating effects of consumer demographics, increases in the level
of customer education do have a positive effect on three of six of the relationships in
hypotheses 3-7. Increases in the age of customers do have a negative effect on five of
six of the relationships in hypotheses 3-7 (with the other effect being not statistically
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significant). Also, increases in the amount of customer income do have a positive
effect on the relationships in hypotheses 6 and 7. The results on market basket net
performance are mixed. Thus, mixed support is found for the proposed moderating
effects in hypotheses 13. Likewise, increases in the percentages of African American
customers do not have a statistically significant effect on the relationships in
hypotheses 1-7; support is not found for hypotheses 14. However, increases in the
percentages of Hispanic customers do have a positive effect on five of six of the
relationships in hypotheses 1-7 (with the other effect being not statistically
significant). Thus, support is found for hypotheses 15.
As to the second, general research question, the data show that the effect of
price promotions in H1 through H7 are greater for more frequently purchases products
(i.e., products A and B) than for less frequently purchased products (i.e., products C
and D).
As to the third, general research question, the data show that the moderating
effect of H8a to H14k increase the profitability margin for the market basket when there
is increased (1) merchandise heterogeneity, (2) competitors operating stores, (3)
household size, and (4) percentage of customers who are Latin American.
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CHAPTER 5
DISCUSSION AND CONCLUSION
Discussion
Prior literature suggests that price promotions are not beneficial because, at
least in part, the increased sales from the promotion are the result of product switching
or stockpiling. Such arguments are grounded in metaphors and logic that are derived
from an equilibrium neoclassical economic research tradition. Adopting a dynamic
competition perspective based on resource advantage theory, this research study
proposes that price promotions can be beneficial to companies who market and sell
products to consumers. The phrase “who market” is not equivalent to the phrase “who
sell.” Marketing involves pricing, advertising, promotion, and so forth. Selling does
not, by definition, necessarily involve these elements. The phrase “who market and
sell products to consumers” addresses the generalizability of this research study.
Indeed, it both includes and excludes certain manufacturers and retailers. Some
manufacturers such as Nike or Sony both market and sell directly to consumers
through their own consumer retail outlet stores or online. In contrast, a few small
retailers may not have enough relational control in their purchasing relationships to
change prices. Thus they may sell, but do not market (e.g., they cannot change or
control marketing mix activities such as pricing). Thus, the findings of this study apply
to most retailers as well as to a number of manufacturers. Therefore, some
manufacturers could be included (or substituted) in the phrases on retailers
performance.
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In particular, this research proposes that price promotions have a positive
effect on (1) the retailer’s total market basket performance (i.e., market basket sales,
profitability, and inventory turnover), and (2) the price-promoted products’ financial
return on shareholder investment in product-level inventory.
The analysis of four unique samples, each comprised of product and related
market basket and moderator data for 450 stores, supports the hypotheses. That is,
consumers, on average, do purchase more of the product and of other items. See
Figures 4a to 4d. The result is that while product gross margin percent decreases,
product gross margin dollars increases in each of the four samples—an effectiveness
gain. Furthermore, the decrease in margin percent is offset by the inventory
acceleration that results in a positive gain on the profit generated per dollar invested
by companies’ shareholders—an efficiency gain. Also, the market basket profit gains
are much greater than the product profitability decreases. Moreover, the GMROII and
GMROSI metrics indicate a positive gain. Thus, product price promotions can be
beneficial to retailers and to the retailers’ shareholders.
<< Insert Figure 4a to 4d about here >>
Implications for Practice
The results indicate that, at least for the 450 retail stores investigated here, pure
price promotions—simply reducing the price of the item without any advertising or
displays—can result in changed consumer purchasing behavior. That is, consumers as
a whole both purchase more often (e.g., the number of market basket carts) and they
purchase more (e.g., market basket sales dollars). Indeed, the results indicate that pure
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price promotions can provide both an effectiveness (e.g., product and market basket
sales and profit dollar performance) and an efficiency (e.g., inventory turnover,
GMROII, GMROSI) advantage to the retailers. Indeed, the gain of market basket
profit more than offsets the loss of product profit—even for the analyzed loss leaders
in Sample A.
The results provide reasons for retailers to be strategic in their approach to
price promotions, and to use care in how they measure their impact, both in the metric
and the baseline. As espoused in R-A theory, “All strategies (at the business-unit
level) involve, at the minimum, the identification of (1) market segments, (2)
appropriate market offerings, and (3) the resources required to produce the offerings”
(Hunt 2000, p. 131). For instance, given that some customer segments will cherry pick
items while other segments will expand their purchasing, merchandise assortments and
other activities could be customized (e.g., the identification and making of
“appropriate” market offerings) for identified market segments that participate more in
expandable purchasing activities, resulting in firm superior financial performance.
Part of the strategy could also include determination of the implications for logistics
(flow through constraints) and operations (potential out of stocks or merchandise
presentation effects). Care should be taken to distinguish between whether
observations that customers report having had to park farther away from the stores,
more merchandise offerings are out-of-stock, the restrooms are more dirty, and the
check-out lines are longer are (as they are commonly assumed to be) indeed evidences
of poor management or, in contrast, are simply (and to some extent uncontrollably) the
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result of increased inventory turnover. Indeed, retailers should take care in how much
they increase the patronage of customers through their pricing strategies so as not to
reach or exceed a “critical mass” of customer traffic within a store. Increased
“shopping momentum” can be good (see Dhar. Huber, and Khan 2007)—until it
approaches or exceeds critical mass. Price promotions, as shown here, increase
inventory turnover as more customers buy and customers buy more. Increased
customer patronage can affect the store atmospherics, having unintended
consequences on many customers.
Limitations and Implications for Future Research
In regards to the marketing literature on promotions, while this study provides
a unique, important first view of the effects of pure, retail price promotions on
consumer behavior at the market basket level, in so doing it excludes investigation of
the effects of promotions where the products are retailer advertised but not retailer
price discounted (e.g., to create awareness of the product availability at the store) or
retailer advertised and retailer priced discounted (e.g., to increase customers visiting
the store). Thus, future research is needed that investigates these other promotional
activities, including comparisons across different types of promotions.
As the study shows that there is, indeed, an effect from pure price promotions
on consumer purchasing behavior, future research is needed that investigates how
consumers mentally make such changes. To what extent is it due to shopping
momentum (see Dhar. Huber, and Khan 2007), hedonic enjoyment, or utilitarian
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savings? Or, recognizing differences in customer segment profitability, for which
customers segments might it be more of one of these or other motivations?
Possibly one of the most important areas for future research is investigation of
promotional competition at the local (geographical) level. This research has advanced
knowledge on how competition can affect consumers’ response to pure price
promotions. The results of the four sample analyses indicate that the total competitive
intensity has a positive effect on marketing mix activity effectiveness and efficiency
for the 450 stores of a major retail firm. This finding can be explained through
resource advantage theory (as described in Chapter 2). Equilibrium-based economic
theory cannot account for the results. Indeed, equilibrium-based economic theory
indicates that there should be a negative (or opposite) effect. Thus, additional research
is needed that evaluates how different configurations of marketplace competitive
intensity affect consumers’ buying behavior. That is, to what extent are there result
differences between stores where there are no identified local competitors versus
stores facing limited competitive intensity versus stores facing extensive competitive
intensity? Further, is the effect difference negative (in support of equilibrium-based
economic theory) or is it positive (in support of dynamic competition theory)?
Moreover, if competition does have a positive impact on the firm’s price promotions
effects (as in the current study), is it due more to competitors that are more similar to
the firm (i.e., Porter 5-Forces rivalry) or is it due to all competitors (i.e., R-A theory
rivalry)? Such research will provide confirmation as to which theories of competition
best represent actual consumer purchasing behavior, as the results here provide initial
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support that dynamic competition theory is better able to explain the moderating,
positive impact of competitive intensity on the price promotions effects on retailer
financial performance.
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Table 1. Literature Related to the Effects of Price Promotions on Market Basket Performance
Intra-Category Performance
Inter-Category Performance
Total Market Basket Performance
Store-Level Scanner Data
Van Heerde Leeflang, Wittink (2004, p. 326) *; Van Heerde, Gupta, Wittink (2003, p. 486); Kamakura and Kang (2006, p. 163)
Mulhern and Leone (1991, p. 67) 2 cat; Walters (1991, p.20) 2 pair of 2 cat; Kamakura and Kang (2006, p. 163) 2 cat
This
research study *
Household-Level Scanner Data
Nijs et al. (2001, p. 7-8); Bell, Chiang, Padmanabhan (2002, p. 514)
Chintagunta and Haldar (1998, p. 44) 5 pair of 2 cat
Ailawadi et al. (2006, p. 520)
Survey Shoppers
Van Trijp et al. (1996, p. 285)
Mulhern and Padgett (1995, p. 85)
Experimental (students)
Janakiraman, Meyer, Morales (2006, p. 363), 12 categories *
Read: store-level scanner data is acquired directly from retail point of purchase (POS) scanning of products at checkout in stores. Household-level scanner data is acquired from panels of customers self-reporting purchasing behavior (post-purchase). Total Market Basket Performance is analysis of the entire shopping cart purchased by a customer. Inter-Category Performance refers to analysis of products in different categories—usually two complementary categories (e.g., cake and frosting). Intra-Category refers to analysis of products within the same product categories (e.g., multiple products or brands of cookies). The two prior studies that are asterisked (*) included some products where only pure price promotions were present. All other studies investigated price promotions where advertising and other sales promotion activities were present.
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Table 2. The Effects of Pure Price Promotions on Product Performance Hypothesis Baseline Sample µ-Chg p H1 (Product Sales) Prior Yr, Same Time A 18.1 < 0.001 H1 Prior Yr, Same Time B 65.6 < 0.001 H1 Prior Yr, Same Time C 4.8 < 0.001 H1 Prior Yr, Same Time D 5.9 < 0.001 H1 Same Yr, Prior Time A 20.1 < 0.001 H1 Same Yr, Prior Time B 104.9 < 0.001 H1 Same Yr, Prior Time C 8.4 < 0.001 H1 Same Yr, Prior Time D 10.2 < 0.001 H2 (Inv Turnover) Prior Yr, Same Time A 4.7 < 0.001 H2 Prior Yr, Same Time B 5.3 < 0.001 H2 Prior Yr, Same Time C 2.3 < 0.001 H2 Prior Yr, Same Time D 2.9 < 0.001 H2 Same Yr, Prior Time A 5.9 < 0.001 H2 Same Yr, Prior Time B 7.1 < 0.001 H2 Same Yr, Prior Time C 3.5 < 0.001 H2 Same Yr, Prior Time D 4.4 < 0.001 RQ1 (Product Profit) Prior Yr, Same Time A (12.4) < 0.001 RQ1 Prior Yr, Same Time B 8.9 < 0.001 RQ1 Prior Yr, Same Time C 0.4 < 0.001 RQ1 Prior Yr, Same Time D 0.5 < 0.001 RQ1 Same Yr, Prior Time A (12.2) < 0.001 RQ1 Same Yr, Prior Time B 28.4 < 0.001 RQ1 Same Yr, Prior Time C 1.8 < 0.001 RQ1 Same Yr, Prior Time D 2.0 < 0.001 Read: Sample size = 450 stores for each sample. Sample A was the loss leader. µ-Chg is measured in retail sales dollars for H1, in annualized inventory turns for H2, in retail gross margin profit dollars for RQ1.
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Table 3. The Effects of Pure Price Promotions on Market Basket Performance Hypothesis Baseline Sample µ-Chg p H3 (MB Sales) Prior Yr, Same Time A 746.6 < 0.001 H3 Prior Yr, Same Time B 627.6 < 0.001 H3 Prior Yr, Same Time C 197.9 < 0.001 H3 Prior Yr, Same Time D 229.6 < 0.001 H3 Same Yr, Prior Time A 665.7 < 0.001 H3 Same Yr, Prior Time B 854.4 < 0.001 H3 Same Yr, Prior Time C 296.6 < 0.001 H3 Same Yr, Prior Time D 369.0 < 0.001 H4a (# Baskets) Prior Yr, Same Time A 7.5 < 0.001 H4a Prior Yr, Same Time B 4.8 < 0.001 H4a Prior Yr, Same Time C 0.9 < 0.001 H4a Prior Yr, Same Time D 1.6 < 0.001 H4a Same Yr, Prior Time A (7.7) < 0.001 H4a Same Yr, Prior Time B (6.9) < 0.001 H4a Same Yr, Prior Time C (2.6) < 0.001 H4a Same Yr, Prior Time D (3.5) < 0.001 H4b (Item count) Prior Yr, Same Time A 210.0 < 0.001 H4b Prior Yr, Same Time B 77.8 < 0.001 H4b Prior Yr, Same Time C 49.6 < 0.001 H4b Prior Yr, Same Time D 58.7 < 0.001 H4b Same Yr, Prior Time A 197.0 < 0.001 H4b Same Yr, Prior Time B 95.0 < 0.001 H4b Same Yr, Prior Time C 83.5 < 0.001 H4b Same Yr, Prior Time D 102.4 < 0.001 H5 (MB Profit $) Prior Yr, Same Time A 172.4 < 0.001 H5 Prior Yr, Same Time B 84.6 < 0.001 H5 Prior Yr, Same Time C 32.8 < 0.001 H5 Prior Yr, Same Time D 43.6 < 0.001 H5 Same Yr, Prior Time A 165.1 < 0.001 H5 Same Yr, Prior Time B 141.5 < 0.001 H5 Same Yr, Prior Time C 58.9 < 0.001 H5 Same Yr, Prior Time D 77.0 < 0.001 Read: Sample size = 450 stores for each sample. Sample A was the loss leader. µ-Chg is measured in total market basket retail sales dollars for H3, the number of occurring market baskets for H4a, the total number of products in the basket for H4b, and the total market basket retail gross margin profit dollars for H5.
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Table 4. Standardized Ridge Regression Coefficients-GMROII Response Surface
Variable RegressionCoefficient
Standard Error
StandardizedCoefficient VIF
Intercept -0.895 GM 2.903 0.551 0.170 4.773 INV 0.075 0.011 0.238 5.264 GM^2 1.858 1.087 0.058 5.279 INV^2 -0.001 0.001 -0.041 6.964 GM x INV 1.089 0.042 0.807 4.485
Read: The K= 0.019564. The K value search was performed using the Hoerl's (1976) algorithm. GM = Gross Margin Percentage, INV = Inventory Turnover.
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Table 5. The Effects of Pure Price Promotions on GMROII and GMROSI
Hypothesis Baseline Sample µ-Chg p H6 (GMROII) Prior Yr, Same Time A (2.4) < 0.001 H6 Prior Yr, Same Time B 0.8 < 0.001 H6 Prior Yr, Same Time C 0.9 < 0.001 H6 Prior Yr, Same Time D 1.3 < 0.001 H6 Same Yr, Prior Time A (2.2) < 0.001 H6 Same Yr, Prior Time B 2.5 < 0.001 H6 Same Yr, Prior Time C 1.6 < 0.001 H6 Same Yr, Prior Time D 2.0 < 0.001 H7 (GMROSI) Prior Yr, Same Time A (2.4) < 0.001 H7 Prior Yr, Same Time B 0.8 < 0.001 H7 Prior Yr, Same Time C 1.0 < 0.001 H7 Prior Yr, Same Time D 1.4 < 0.001 H7 Same Yr, Prior Time A (2.2) < 0.001 H7 Same Yr, Prior Time B 2.6 < 0.001 H7 Same Yr, Prior Time C 1.8 < 0.001 H7 Same Yr, Prior Time D 2.4 < 0.001 Read: Sample size = 450 stores for each sample. Sample A was the loss leader. µ-Chg is measured in unit change of the Gross Margin Return on Inventory Investment for H6 (GMROII) and in the unit change in the Gross Margin Return on Shareholder Investment for H7 (GMROSI).
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Table 6. The Moderating Effects on H3-H7.
Basket Sales
Basket Profit
# Items
# Baskets GMROII GMROSI
Store Assortment 0.24*** 0.17*** 0.16*** 0.28*** -0.07*** -0.07*** Total Competition 0.18*** 0.06*** 0.23*** 0.11*** 0.05** 0.05* Cat Competition n.s. n.s. n.s. n.s. n.s. n.s. Other Competition n.s. n.s. -0.04* -0.09* 0.04* 0.04* Higher Education n.s. n.s. 0.08*** n.s. 0.09*** 0.08*** Age 65+ -0.07*** n.s. -0.09*** -0.05** -0.07*** -0.07*** Household Size 0.07*** 0.05** 0.07** n.s. 0.12*** 0.11*** < $50,000 n.s. n.s. n.s. 0.05** -0.11*** -0.11*** $100,000 + n.s. n.s. 0.05** -0.04* 0.11*** 0.10*** African American n.s. n.s. n.s. n.s. n.s. n.s. Latin 0.08*** 0.04* 0.11*** n.s. 0.06*** 0.06* Read: Market Basket refers to the aggregated volume of the variable (whether it be retail sales dollars, gross profit dollars, total number of items, or the total number of baskets) for all of the products acquired by a customer during a single purchase event at the store register (i.e., the total shopping cart purchased). Gross Margin Return on Inventory Investment (GMROII) is a calculation—shown in Chapter 2 on page 42. It combines inventory turnover and gross margin percentage. Gross Margin Return on Shareholder Investment (GMROSI) is a calculation—shown in Chapter 2 on page 46. It combines inventory turnover, gross margin percentage, and adjusted product cost. Intra-store competition, or store assortment, is measured through a proxy, the store’s actual physical size (e.g., 122,000 square feet). Total Competition Intensity is the aggregation of the number of competitor stores (up to 30 potential, major competitors) that are geographically identified for each store in the product SKU samples, first by census data, and then confirmed (as actual store competitors) by the store manager. Category competition refers to the total number of competitors who compete with the retailer on the product(s) under investigation, but not at the total store (e.g., Target and Staples competing on 3 ring binders). Other Competition refers to stores that compete with firm in other areas, but not for the product(s) under investigation (e.g., Walgreens competes with Best Buy, but not for laptop computers). Higher Customer Education is the percentage of customers who have attended at least some college. Customer Age (Age 65+) is the percentages of total customer population for each store that are age 65 and older. Income Level is the percentages of total customer population for each store with total household income ranges of less than 50,000 or, for the higher end, $100,000 or more. Customer ethnicity is measured as the percentages of total customer population for each store that are African American or Latin.
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Figure 1: A Causal Model of the Effects of Retail Price Promotions
Product • Sales • Inventory Turnover• Profitability
Market Basket • Sales • Item Count & Freq.• Profitability
Product Price Promotion
Return Metrics• GMROII• GMROSI
Competitive Intensity• Industry Based• Category Based• Demographic Based
H1 H2 RQ1
H3 to H5
H6 to H7H8a to H14k,
RQ2 to RQ3b
Product • Sales • Inventory Turnover• Profitability
Market Basket • Sales • Item Count & Freq.• Profitability
Product Price Promotion
Return Metrics• GMROII• GMROSI
Competitive Intensity• Industry Based• Category Based• Demographic Based
H1 H2 RQ1
H3 to H5
H6 to H7H8a to H14k,
RQ2 to RQ3b
Read: The research investigates what the effect of retail price promotions is on measures of product performance (H1, H2, RQ1), market basket performance (H3 to H5), and shareholder investment in the retailer (H6 and H7). Further, the research investigates the moderating effects of competitive intensity and consumer demographics (H8a to Hk14). The dashed line from “market basket” to “return metrics” indicates that while this path is not computable in this project’s acquired database, the return metrics are such that they could be computed, if not aggregated, at multiple levels (e.g., product, category, market basket)—a suggestion for future research. Also, the dashed lines from “product” to “market basket” are future research suggestions (that require a different method).
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Figure 2. Retailers’ GMROII Response Surface Map
Read GMROII is the Gross Margin Return on Inventory Investment. GM is the product’s gross margin percentage. INV is the product’s inventory turnover rate (annualized). The arrow in the graph represents the path of highest accent, or where the most gain in GMROII would occur given a unit gain in either GM or INV. The quadratic response surface indicates that while there is a steep accent as GM and INV rates are changed, there is a point at which additional change results in a declining return.
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Figure 3. Retailers’ GMROII Response Surface Map with Promotion Data Overlay
Read: The response surface construction is based on Figure 2. GMROII is the Gross Margin Return on Inventory Investment. GM is the product’s gross margin percentage. INV is the product’s inventory turnover rate (annualized). The Circle Points = Sample A, Square Points = Sample B, Triangle Points = Sample C, Diamond Points = Sample D, for the 4 week aggregates for the prior year, same period baseline and the current year, current period promotional period. In all four samples, the grid locations accended the grid to the right and rear. The price promotions of Samples B, C, and D resulted in a decrease in GM and an increase in INV, which when combined resulted in a positive effect on product GMROII. Product A had a negative GMROII after the promotion, which is logical given that it was a loss leader (i.e., price promoted at a retail below cost).
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F
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igure 4a. Response Surface Map of Sample A Market Basket Profitability
Figure 4b. Response Surface Map of Sample A Market Basket Profitability
Texas Tech University, Jared M. Hansen, December 2007
Figure 4c. Response Surface Map of Sample C Market Basket Profitability
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Figure 4d. Response Surface Map of Sample D Market Basket Profitability
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Appendix 1: Profitability and Inventory Turnover in GMROII and GMROSI
Turnover 3 Payable 60 days Cost $ 2.50 Sales $ 4.25
Internal
Cost IRR alt
use Shareholder
Cost Retail Sales
Gross Margin
Adj GM
Inventory Turnover GMROII GMROSI
T (0,1) 0 0.17 T (1,2) 0 0.17 T (2,3) 1 0 T (3,4) 1 0 1 2 Average 0.5 0.085 41% 415% 2.10 1.47 8.719941 Turnover 4 Payable 60 days Cost $ 2.50 Sales $ 4.00
Internal
Cost IRR alt
use Shareholder
Cost Retail Sales
Gross Margin
Adj GM
Inventory Turnover GMROII GMROSI
T (0,1) 0 0.17 T (1,2) 0 0.17 T (2,3) 1 1 2 T (3,4)
0.33 0.17 38% 396% 2.40 1.44 9.501 Turnover 6 Payable 60 days Cost $ 2.50 Sales $ 3.75
Internal
Cost IRR alt
use Shareholder
Cost Retail Sales
Gross Margin
Adj GM
Inventory Turnover GMROII GMROSI
T (0,1) 0 0.17 T (1,2) 0 0.17 1 2 T (2,3) T (3,4) 0 0.17 33% 377% 3.00 1.5 11.318 Turnover 6 Payable 60 days Cost $ 2.50 Sales $ 3.00
Internal
Cost IRR alt
use Shareholder
Cost Retail Sales
Gross Margin
Adj GM
Inventory Turnover GMROII GMROSI
T (0,1) 0 0.17 T (1,2) 0 0.17 1 2 T (2,3) T (3,4) 0 0.17 17% 303% 1.20 0.24 3.634
Read: The table here shows four of the many potential points on the response surface that will be constructed. While the first three scenarios show that price promotions can, indeed, potentially provide incremental returns to the retailer and its shareholders (due to inventory turnover acceleration), the fourth scenario show how GMROII and GMROSI differ. In this type of scenario, GMROII indicates that less than one dollar is being generated for each dollar invested in inventory. However, as shown by GMROSI, this type of scenario can still provide a positive return to the investment of the shareholders of the retailer.