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Group 3: Simon Tier Jack Cindy Lily Hector
Predicting Mail-Order Repeat Buying: Which Variables Matter?
Predicting Mail-Order Repeat Buying: Which Variables Matter?
Abstract Introduction Research Questions RFM Variables Non-RFM Variables Methodology Data Empirical Findings Conclusion
Outline
Abstract
Major proposeBy customer-oriented conceptual model of
segmentation variables for mail-order repeat buying behavior.
Traditionally- Three variables
Which variables can additional?
Introduction(1/3)
The success of a database-driven (mail-order) marketing campaign mainly depends on the customer list to which it is targeted.
Response modeling for database marketing is concerned with the task of modeling the customers’ purchasing behavior.
Introduction(2/3)
A Conceptual Model of Segmentation Variables
Independent variableIndependent variable
Dependent variableDependent variable
Introduction(3/3)
Behavioral Non-Behavioral
Company
specific
Recency
Frequency
Monetary value
Length of relationship
Type/category of product
Source of customer
Customer/company
interaction
Customer satisfaction
Non-company specific
General mail-order buying behavior
Benefit segmentation
Socio-demographics
Research Questions
3 questions3 questions
What is the total performance of the combined use of the three RFM variables in predicting repurchase behavior?
What is the total performance of the combined use of the three RFM variables in predicting repurchase behavior?
RQ1a
What is the relative importance of recency, frequency and monetary value predicting repurchase behavior ?
What is the relative importance of recency, frequency and monetary value predicting repurchase behavior ?
RQ1b
RQ2
How much predictive power do additional, i.e non-RFM,Variables offer in modeling mail-order repeat purchasing?
How much predictive power do additional, i.e non-RFM,Variables offer in modeling mail-order repeat purchasing?
RFM Variables Recency
Recency has been found to be inversely related
to the probability of the next purchase (Cullinan, 1977;Shepard, 1995)
Frequency
Frequency is that heavier buyers show greater loyalty as measured by their repurchase probabilities
(Morrison, 1966; Lawrence, 1980)
Monetary
The volume of purchases a consumer makes with a particular
mail-order company is a measure of usage which has been an
important behavioral segmentation variable in several studies (Kotler, 1994)
Non-RFM Variables(1/7)
Company & Behavioral
Length of the relationship
Type/category of product
Source of the customer
Customer/company interaction
Non-RFM Variables(2/7)
1. Social psychology
2. Economics investigate
3. Organizational behavioral
Company & Behavioral
Length of the relationship
Simpson (1987) states that ‘Relationship duration also ought to prognosticate relationship stability
Non-RFM Variables(3/7)
Company & Behavioral
Type/category of product
Source of the customer
1. Member introduces member2. Child from a member parent3. Spontaneous requests4. Rented mailing lists5. Internal mailing lists
Kestnbaum (1992) suggests to replace RFM bythe new acronym FRAC (category of product)
Non-RFM Variables(4/7)
Company & Behavioral
Customer/company interaction
Contact-information consists of several differenttypes: (1) Information inquiries (2) Orders (purchasing) (3) Complaints (post-purchase).
Non-RFM Variables(5/7)
Company & Non-Behavioral
Customer Satisfaction
When applied to direct marketing, we can state that the probability of repeat behavior will increase if the total buying experience meets or exceeds the expectations of the consumer with respect to the performance.Purchasing behavior was positively reinforced by tracking customer satisfaction.
Non-RFM Variables(6/7)
Non-Company & Behavioral
General Mail- Order buying behavioral
when the person only recently became a customer at a particular mail-order company, knowledge about the customer’s general mail-order buying behavior may be valuable in predicting future purchasing behavior.
Non-RFM Variables(7/7)
Non-Company & Non-Behavioral
Benefit segmentation
Socio-Demographic
Background ex. age education occupation salary
Convenience is often cited as one of the major driving forces for direct marketing patronage behavior (Gehrt et al., 1996).
Credit line (provided by the company or by credit cards) does facilitate spending and also increases the amounts being spent. (Feinberg 1986)
Methodology(1/4)
The binary logit model is used to approximate a probability
Whereby: Pi represents the a posteriori probability of a repeat purchase for customer i;Xij represents independent variable j for customer i;bj represent the parameters (to be estimated);n represents the number of independent variables.
This section introduces and justifies the choice of
two performance criteria:
Percentage correctly classified (accuracy) at the ‘economically optimal’ cutoff purchase
probability (PCC) Area under the receiver operating characteristic
curve (AUC).
Evaluation Criteria
Methodology(2/4)
Methodology(3/4)
When the objective is to maximize total profits, we know from microeconomics that the optimal decision rule is to mail up until the point where the incremental revenue derived from the mailing equals the incremental cost incurred by sending this additional mailing.Disadvantage
Methodology(4/4)
Cutoff value = Minimal probability of purchase
Data
Internal data from mail-order company
Internal data from mail-order company
Questionnaire datafrom households
Questionnaire datafrom households
Database marketing data warehouse
for response modeling
Database marketing data warehouse
for response modeling
Figure 2: Summary of data sources
1.Benefit segment variable
2.Customer satisfaction3.General mail-order
purchasing
1. Past purchase 2. behavior
%5858.0)5.01(
)5.0754.0(
%5.25255.0)529.01(
)529.0649.0(
AUC
PCC
AUC performance
PCC performance
Empirical Findings(1/4)
Empirical Findings(1/4)
AUC PCC
Recency 0.625 0.417
Frequency 0.743 0.678
Monetary value 0.708 0.592
Num. of var. R, F, or M AUC
1 F 0.743
2 F & M 0.753
3 R, F ,& M 0.754
Relative important of RFM value in predicting
Num. of var.
List of var. AUC
4 Best RFM & Credit 0.743
5Best RFM, Credit, &
Length.0.753
6Best RFM, Credit,
Length. & Gen.0.754
Multiple predictors
Empirical Findings(2/4)
0.73
0.735
0.74
0.745
0.75
0.755
0.76
0.765
0.77
0.775
1 2 3 4 5 6
Number of Variables in Response Model
AU
C o
n T
est
Sam
ple
Frequency
Monetary value
Recency
CreditLength.
Gen.
Cumulative AUC performance of predictor models
Empirical Findings(3/4)
The importance of Frequency
More variables = efficiency
Cutoff value is important
Different industry may choose different variables
Conclusion