Consumer Myths Shattered by Marketing Analytics

UP NEXT… 11:00am

Consumer Myths Shattered by

Marketing Analytics

DR. RAJKUMAR VENKATESAN

Follow the action on Twitter using #AtE2014

Wednesday, 15,October, 2014

Consumer Insights From Marketing Analytics

Agenda

•  Myths Shattered by marketing analytics. •  Implementation of Analytics. •  Darden resources on Marketing Analytics.

Myths Shattered by Marketing Analytics

I.  Marketing is a fixed cost

II.  Coupons are a short-term promotional vehicle

III.  Target Customers who are responsive

IV.  Competition’s loyalty program decreases customer retention

V.  Soft metrics are not valuable in predicting customer value VI.  Traditional media (TV) is not dead

Old World New World

Marketing is a fixed cost Marketing can be variable, test and learn

Coupons are a short term promotional vehicle

Customized coupons can build longer term brand value

Target customers who are more responsive to offers

Target customers who are more valuable even if they are less responsive

Competition’s loyalty programs decreases retention

Spatial agglomeration is amplified by mobile devices, co-opetition not competition

Soft Metrics are not valuable for predicting customer value

Harness information from all data sources, customer attitudes, online chatter etc.

TV creates brand awareness and is all-powerful

TV is still powerful, but it enables other media; email, paid search etc.

Myth I. Marketing is a Fixed Cost

Venkatesan, Rajkumar, and Paul Farris, “Transformation of Marketing in the Ohio Art Company (A+B)”,

UVA-M-0833

Betty Spaghetty TV Experiment June-July, 2007

Be#y Spaghe#y was supported with the 2M adver6sing campaign in 2007 holiday season

Sales Units Color Crazy Go Go Glam

Test Arizona 163 206

Control California 30 112

2007 Holiday Season

2007 Holiday Season

The Nanoblock Amazon Goldbox Experiment –March 2012

Nano Eiffel Tower- Amazon Goldbox Promotion

Units Sold

Sales Price

Jan.–Feb. 2012

Mar. 12

May 12

Lift on units sold

(promotion vs pre-promotion)

nanoblock Eiffel Tower 19.99 274 686 219 501% nanoblock Taj Mahal 19.99 308 163 132 106% nanoblock Castle Neuschwanstein 19.99 244 184 146 151% Classic Etch a Sketch 12.99 344 352 399 205%

Goldbox Promotions provide dividends in search results

Myth II. Coupons are a promotional vehicle

Venkatesan, Rajkumar, and Paul W. Farris. "Measuring and managing returns from retailer-customized coupon campaigns." Journal of marketing 76, no. 1 (2012): 76-94.

Venkatesan, Rajkumar, and Paul W. Farris (2012), “Unused Coupons Still Payoff“

Harvard Business Review, May.

Data – Quasi Experimental Design

Purchase History FSI Coupons Retailer Discounts Retailer Matching Feature/Display

Purchase History FSI Coupons + Targeted Coupons Retailer Discounts Retailer Matching Feature/Display

Q1 Q4 Q8

Purchase History FSI Coupons

Retailer Discounts Retailer Matching Feature/Display

Q1 Q8

N = 1,584

N = 952

37%

5% 6%

46% 38%

60%

17%

57%

34%

0%

10%

20%

30%

40%

50%

60%

70%

Number of Customers in Group

Growth In Trip Spending Per Customer

Growth in Total Group Trip Spending

Did Not Receive Received But Did not Redeem Received and Redeemed

Exploratory Results

+

Exposure and Redemption

Myth III. Target Customers Responsive to Offers

Bodily, Sam, Rajkumar Venkatesan, and Gerry Yemen, “Dunia Finance LLC”,

Darden Business Publishing Case Study, UVA-M-0842

Mubadala

•  Government of Abu Dhabi •  $53B assets

•  Industries: Aerospace, Oil & Gas, Healthcare, Information and

Telecom, Financial services

dunia is born in the midst of an unprecedented period of global macroeconomic stress, as a highly pedigreed enterprise

Temasek Holdings •  Government of Singapore •  $152B portfolio •  Industries: Financial services,

telecom, media & tech, transport, real estate, energy, lifesciences

Achieved a lot since launch…

•  2012 First Half Net Profits of AED 29.1 Million, up 61% vs. Full Year 2011

• Deposit balances up 84% vs. H1 2011 to AED 313 Million

•  Broke-even in third year of operation, ahead of plan

Customer centricity: 360° view of the customer

Call center

ATMs

Internet

Branches

•  Shows complete relationship details of the customer

•  Active lead management facilitating x-sell and relationship deepening

•  Allow customer access through diversified channels set

Making customer centricity a reality: Cross-sell

Cross_product Penetration Grid

CardsUnsecured Loans Auto Loans Investment Insurance

Revolving Credit

Banc-assurance

Cards 100%

Unsecured Loans 100%

Auto Loans 100%

Investment 100%

Insurance 100%

Revolving Credit 100%

Bancassurance 100%

Cross-sell discipline: Drive the penetration matrix every month and identify opportunities

List down all possible product pairs, determine the channel and generate the list

Day 1 cross sell: Each new customer should come with multiple products

On-going cross sell through CRM: For example, each auto loan customer would be contacted for a card at 3rd month and investment at 4th month (unless cross sold day 1)

Use of statistical propensity models for better targeting

Track: Products per customer and profit per customer

Cross Sell Principles:

• List 1: All auto loan customers with mid size+ new cars

• List 2: All preterminated auto loans

• List 3: All auto loans booked in last 2 months

Objective: Address customer’s additional product needs, so as to maximize our products / customer ratio.

Responsive Customers Are not Necessarily Profitable

High Profits Low Profits

High Propensity to

Respond

Very Good Targets (18%)

Reduce Marketing

Spend (34%)

Low Propensity to

Respond

Invest Until Marketing Spend < Customer

Return (30%)

No Investment (18%)

Myth IV. Competition’s loyalty program decreases customer retention

Rajkumar Venkatesan (2014), “Cardagin: Local Mobile Rewards,”

Darden Business Publishing Case Study, M-0825

Pancras, Joseph, Rajkumar Venkatesan, and Bin Li, “Returns from customizing mobile loyalty programs,”

Working Paper, Darden GSB.

The Market

(1)  Source: VSS Communications. 2009 figure. (2)  Source: BIA/Kelsey. 2011 figures.

Total Addressable Market local advertising spending (2)

$132 billion

Target Market Loyalty spending (1)

$2.19 billion

Served Available Market online & mobile spending (2)

$11.1 billion

Current Mobile Coupon Landscape

Case Study: Shenandoah Joe’s •  Three location coffee shop in Charlottesville, VA •  Launched in April 2012

“Cardagin has turned our occasional customers into regulars and compelled regulars to visit the shop more often than before.”

Shenandoah Joe’s Management

Month 1 Month 4 5.1 9.4 monthly transactions per member

$22.84 $47.22 monthly revenue per member

$4.46 $5.02 average spend per member

Case Study: Calvino Café •  A family-owned, single location coffee shop •  Empirical results:

–  More than 1,500 transactions and $10,000 recorded during first four months on Cardagin

–  Approximate ROI of 450% in first four months •  Customer Testimonial:

–  “Previously, there were numerous customers whose names we did not know. Now, we’re learning everyone’s names because their names come up on Cardagin.” - Katie, owner

Consumer Graph

•  John frequents 9 participating businesses in Charlottesville •  Information inferred from Cardagin:

–  John spends most of his time in two Charlottesville neighborhoods –  John has relatively high disposable income given his merchant visits and purchase history

John member id: 5453

Visits: 73 Spend: $371


Visits: 1 Spend: $2





Visits: 2 Spend: $8


Spatial Map of Retailers on Cardagin Network in Charlottesville

Spatial aspects of Mobile Coupons

Positive spatial agglomeration among stores in the mobile loyalty program

Value of Information From Mobile Loyalty Program Network

•  Estimated maximum net sales per store –  without competitive information = $1194.92 –  with competitive information = $443.61

•  One additional competitor on the network within a 1 mile radius reduces the –  Number of rewards provided by a retailer by 15% –  The range of rewards by 2 points

Myth V. Soft metrics are not useful for predicting customer value

Venkatesan, Rajkumar, Werner Reinartz, and Nalini Ravishankar (2013), “Role of Attitudes in CLV based Customer Management,” Marketing Science Institute (MSI) White Paper, 12-107.

Reinartz, Werner, and Rajkumar Venkatesan (2014), “Track Customer Attitudes to Predict Their Behavior”,

Harvard Business Review Blog, September. http://blogs.hbr.org/2014/09/track-customer-attitudes-to-predict-their-behaviors/

Firms Do Collect Attitudes

Conceptual Framework

Estimation period

(months 11 – 45)

Calibration period

(months 6 – 10)

Relative Customer Attitudes

Competitive Sales Calls

Share of Wallet

Specialty

Sales Calls

Lagged Sales

Time Trend

Retention

Sales

Sales Calls

Recency

Time Trend

Value of A?tudes in Customer TargeCng

Value of A?tudes in Customer Level Resource AllocaCon

•  Average Customer Profits = $2,368 (in 2 months)

•  Incremental lift of 18% equals $426 in annual profits per customer

Percentage Improvement in Maximized Customer Profits compared to Predicted Customer Profits

All Customers (n=1161)

Observed Attitudes (n=553)

Imputed Attitudes (n=608)

Including Attitudes

25.0% (22.7%, 28.8%)

26.2% (23.9%, 29.5%)

23.9% (21.2%, 26.4%)

Excluding Attitudes

7.0% (5.4%, 8.3%)

8.4% (5.7%, 9.8%)

5.8% (3.8%, 7.2%)

Myth VI. Traditional Media (TV) is not dead

Venkatesan, Rajkumar, and Joseph Pancras (2014), “Estimating the Consumer Purchase Funnel From Aggregate Media Metrics,” Working

Paper, Darden GSB.

Context drives device choice

The goal we want to accomplish

The time and day of the week

Our location and “velocity”

The device capabilities

The device we choose to use at a particular time is often driven

by our context:

Assigning value to all mobile actions: an attribution model

Google’s Attribution Setup

Last Interaction

Last non direct Interaction

Last AdWords Click

First Interaction

Linear

Time Decay

Position Based

A Media Mix System of Metrics

Units Sold

Email Impressions

Price

Web Visits

Emails

Paid Search Clicks

TV

Facebook, Mobile

Paid Search Spend

Facebook Clicks

TV

Paid Search, Mobile reach

Facebook Spend

Mobile Clicks

TV

Facebook, Paid Search reach

Mobile Spend TV Impressions TV Spend

-‐ sales

-‐ First level media effects

-‐ Second level media effects

-‐ Media Spend

AJribuCon Model Findings

•  Sales = f(lagged sales, web visits from search….)

•  Webvisits from search = f(lagged webvisits from search, paid search clicks, mobile search clicks)

•  Paid search clicks = f(lagged paid search clicks, TV spend, paid search impressions, display impressions)

Myths Shattered by Marketing Analytics

I.  Marketing is a fixed cost

II.  Coupons are a short-term promotional vehicle

III.  Target Customers who are responsive

IV.  Competition’s loyalty program decreases customer retention

V.  Soft metrics are not valuable in predicting customer value VI.  Traditional media (TV) is not dead

Old World New World

Marketing is a fixed cost Marketing can be variable, test and learn

Coupons are a short term promotional vehicle

Customized coupons can build longer term brand value

Target customers who are more responsive to offers

Target customers who are more valuable even if they are less responsive

Competition’s loyalty programs decreases retention

Spatial agglomeration is amplified by mobile devices, co-opetition not competition

Soft Metrics are not valuable for predicting customer value

Harness information from all data sources, customer attitudes, online chatter etc.

TV creates brand awareness and is all-powerful

TV is still powerful, but it enables other media; email, paid search etc.

Implementation of Marketing Analytics

Organizational Structure

1.  What is the function and process of marketing analytics?

2.  What are the organizational metrics for resource allocation?

3.  Does the business cycle match the marketing analytics cycle?

4.  How to foster sales and marketing collaboration?

Analytics Process

5.  How to combine data and heuristics?

6.  Does the language of marketing analytics match the language of the business?

Organizational Change

7.  How to develop effective feedback loops?

Resources on Marketing Analytics

46

Resource Videos and Datasets @ http://dmanalytics.org

Strategic Marketing Analytics: Leveraging Big Data

Monday, November 10, 2014

Tuesday, November 11, 2014

Wednesday, November 12, 2014

Thursday, November 13, 2014

7:00 -‐ 8:00 am 7:00 -‐ 8:00 am Con6nental Breakfast Con6nental Breakfast

8:00 -‐ Noon 8:00 -‐ noon Resource AllocaCon

Framework II Pricing AnalyCcs ImplemenCng AnalyCcs

System of Metrics Conjoint, Willingness to Pay, Tradeoffs

Apply the alloca>on framework, telling a story

Allocator SimulaCon Regression Workshop

12:00 -‐ 1:00 pm 12:00 -‐ 1:00 pm 12:00 -‐ 1:00 pm Boxed Lunch Lunch Lunch Lunch

1:00 -‐ 5:00 pm 1:00 -‐ 4:00 pm 1:00 -‐ 4:00 pm Resource AllocaCon

Framework I Digital AnalyCcs Sales Force AnalyCcs

System of Metrics Experiments, Paid Search Customer Life>me Value, Sales Pipeline

November 10-13, 2014, Charlottesville, VA