nedma14: answering marketing’s top 3 questions using predictive analytics - william b. disch, ph.d

How can we boost response, reduce churn andupsell current customers?

William B. Disch, Ph.D.Senior Vice President of Analytics, Virtual DBS

May 14th, 2014

Answering Marketing’s Top 3 Questions Using Predictive Analytics

Today’s Presenter

William Disch, Ph.D.Senior Vice President of Analytics

• Heads the analytics division at Virtual DBS

• Primary focus is on client collaboration and employing ROI-oriented multivariate predictive modeling and algorithm creation, product sequencing, segmentation, and custom analytics specific to industry verticals

• Key is collaboration in operationally defining context of measurable objectives

• Presenter at major database, analytics and academic conferences nationwide including the DMA, DM Days, the AMA, and others

How can I effectively improve my campaign response rates?

A Marketer’s Top 3 Questions

My customer churn rate is too high. How can I reduce it?

My company sells many products. How can I identify customers who will buy more than one?

Issue/Question Modeling Solution

Lower than optimal conversion rate for new leads.

How do I increase new acquisitions while at the same time keeping costs down?

Customer Acquisition Model

Churn rate higher than acceptable.

Can I identify current customers at the highest risk to churn before they leave?

Churn (Attrition) Model

Missing upsell opportunities.

How can I identify customers most likely to buy their next product?

Upsell Model

Three Cases

Case 1

Customer Acquisition Model:

Specialty Foods Retailer

Customer Acquisition Model Specialty Foods Retailer Business Case

Specialty Foods Retailer Current State600,000 prospect mail pieces sent annually

Current Results

2% gross response rate (6,000 responders)

25% conversion rate (1,500 customers purchased)

Specialty Foods Retailer Desired StateNo change in mail volume

Desired Results

2.6% gross response rate (9,000 responders, 30% improvement)

25% conversion rate (2,250 customers purchased)

Customer Acquisition Model –The Process Simplified

Campaign Responders

Campaign Non-Responders

Virtual DBS Appended

Demographics

Purchasers(subset of Campaign

Responders)

Predictive Analytics

Processing

Predictive Algorithm Reveals Top Response/Acquisition Drivers and

their Predictive Weight

Virtual DBS Compiled B2B and B2C Data Includes Hundreds of Demographics and Related Elements

Demographic

Income Wealth Age Ethnicity Occupation Household Type Marital Status Length of Residence Home Ownership Home Value Mortgage Info Home Size (Sq Ft) Lender Codes Age of Home Dwelling Type Small Office/Home Office Presence of Children Ages of Children

Interests

Fitness Outdoors Athletic Cultural Charitable Events Community Involvement Gardening Financial Travel Donor Do It Yourselves Etc.

Buying Behavior

Product Types Travel Upscale Retail Finance Etc.

Life Stage Clusters

Mutually ExclusiveClusters Life Stages:

- Springs: 18 - 24- Summers: 25 - 44- Autumns: 45 - 64- Winters: 65+

Income Range:- Low: 40k- Mid: 40k – 75k- High: 75k+

Family Type:- Single- Couples- Families

Community Type:- Rural- Suburban- Urban

B2B Firmagraphics

SIC Division SIC/NAICS Codes No. Employees Annual Sales Ownership Type Location Type Years In BusinessBusiness Verticals Technology Use SOHO Etc.

0 0.05 0.1 0.15 0.2 0.25 0.3

Gender

Household Type

Dwelling Type

Marital Status

Household Income

Gifts

Sports/Leisures

Mail Order: Food Products

Health

Buyer Orders: Home Care

Garden

Number of Children

Assessed Median Home Value

Political Donor

Hobby: Knitting/Needlework

Likes to Read

Hobby: Cooking

Reading: Cooking/Culinary

Health/Institutional Donor

Predictive Attributes Driving Response/Acquisition

A specialty foods retailer wants to increase response rates of new customers buying holiday food products.

We operationally defined the event group of those who had purchased during the past season at a dollar value of X or higher.

The drivers in the algorithm show that the best prospects tend to be females in single family households with children, with moderate to high income, and who have a propensity to use discretionary income for a variety of personal and social needs and behaviors.

GenderFemale - 63%

Household TypeAdult Male & Female Present w Kids - 49%

Dwelling TypeSingle Family – 74%

Marital StatusMarried - 58%

Household Income$150k + - 35%$125-$150k - 10%$100-$125k - 15%

Assessed Median Home Value$750k + - 5%$700-$750k - 1%$500-$550k - 2%

Customer Acquisition Algorithm Example

An algorithm is a mathematical equation that incorporates predictive drivers and their weights.

Constant (unique to each algorithm)

+ Gender (x .43)

+ Household Type (x .38)

+ Dwelling Type (x .32)

+ Marital Status (x .31)

+ Household Income (x .27)

+ Gift Behavior (x .25)

+ Sport/Leisure Interest (x .23)

+ Mail Order Food Product (x .23)

+ Health Interest (x .22)

+ Home Care Buyer (x .21)

+ Garden Interest (x .20)

+ Number of Children (x .18)

+ … remaining predictors… (x .XY)

= Propensity to Purchase

How Do We Know the Model Works?

VALIDATION PROCESS:How we assess the power and efficacy of a model:

Acquisition model strength is tested on a validation dataset:

1. Randomly see event group/target records into the prospect universe set of records

2. Run the algorithm

3. Event group/targets should score near the top of the scored file

4. Conduct multiple iterations

Model Scoring Validation Gains Table

Probability Random Validation Acquisition Score Rank

5% 0.50 7.34Ranks

1, 2 and 3, 410% 0.50 1.50

15% 0.50 1.37

20% 0.50 1.14

25% 0.50 1.15Ranks 5,6

30% 0.50 0.93

The validation shows that the top 5% of scored prospects are 7.3 times more likely to become a customer than a random prospect

Before Acquisition Model Scoring We Are Here

FirstName LastName Address1 City State Zip Phone Email

Acquisition Score

Aimee Falcigno 9 Rocamora Road Rocky Hill CT 06067 8605718495 [email protected] ?

Ann Reynolds 94 Brookmoor Rd West Hartford CT 06109 8603130922 [email protected] ?

Amy Lubas 823 Edgemere Lane Sarasota FL 34242 9419939899 [email protected] ?

Andy Skarzynski 34 Ballard Drive West Hartford CT 06119 8602333240 [email protected] ?

Ann Schmidt 21 Onlook Rd Wethersfield CT 06109 8605294388 [email protected] ?

Anthony Paquette 88 Oak Leaf Drive Colchester CT 06415 8605378667 [email protected] ?

William Derech 132 Randy Lane Wethersfield CT 06109 8605298060 [email protected] ?

Suzanne Costanzo 9 Deerfield Trace Burlington CT 06013 8603067433 [email protected] ?

Gina Cunningham 28 Cockle Hill Road Salem CT 06420 8608893943 [email protected] ?

Caperton Beirne 7100 Quail Hill Road Charlotte NC 28210 7047808753 [email protected] ?

Carol Heminger 515 Edgebrook Lane West Palm Beach FL 33411 5617750098 [email protected] ?

Christine Peterson 60 Iroquois Road West Hartford CT 06117 8602332239 [email protected] ?

Craig Meeker 44 Garden Gate Farmington CT 06032 8602840123 [email protected] ?

Cynthia Poglitsch 98 Cobblestone Way Windsor CT 06095 8606885207 [email protected] ?

Carrie Stephens 3135 Crittenton Place Baltimore MD 21211 5618012538 [email protected] ?

David Iaia 37 Wilson Avenue Belmont MA 02478 6174894839 [email protected] ?

David Wurts 6404 S Ross Road Morrison CO 80465 3036974486 [email protected] ?

David Vining 65 Falcon Lane Glastonbury CT 06033 8604636450 [email protected] ?

We have no way of predicting a prospect’s response/purchase behavior.

After Acquisition Model Scoring We Are Here

FirstName LastName Address1 City State Zip Phone Email

Acquisition Score

Aimee Falcigno 9 Rocamora Road Rocky Hill CT 06067 8605718495 [email protected] 1

Ann Reynolds 94 Brookmoor Rd West Hartford CT 06109 8603130922 [email protected] 6

Amy Lubas 823 Edgemere Lane Sarasota FL 34242 9419939899 [email protected] 9

Andy Skarzynski 34 Ballard Drive West Hartford CT 06119 8602333240 [email protected] 1

Ann Schmidt 21 Onlook Rd Wethersfield CT 06109 8605294388 [email protected] 1

Anthony Paquette 88 Oak Leaf Drive Colchester CT 06415 8605378667 [email protected] 2

William Derech 132 Randy Lane Wethersfield CT 06109 8605298060 [email protected] 10

Suzanne Costanzo 9 Deerfield Trace Burlington CT 06013 8603067433 [email protected] 1

Gina Cunningham 28 Cockle Hill Road Salem CT 06420 8608893943 [email protected] 4

Caperton Beirne 7100 Quail Hill Road Charlotte NC 28210 7047808753 [email protected] 7

Carol Heminger 515 Edgebrook Lane West Palm Beach FL 33411 5617750098 [email protected] 8

Christine Peterson 60 Iroquois Road West Hartford CT 06117 8602332239 [email protected] 2

Craig Meeker 44 Garden Gate Farmington CT 06032 8602840123 [email protected] 9

Cynthia Poglitsch 98 Cobblestone Way Windsor CT 06095 8606885207 [email protected] 10

Carrie Stephens 3135 Crittenton Place Baltimore MD 21211 5618012538 [email protected] 2

David Iaia 37 Wilson Avenue Belmont MA 02478 6174894839 [email protected] 1

David Wurts 6404 S Ross Road Morrison CO 80465 3036974486 [email protected] 10

David Vining 65 Falcon Lane Glastonbury CT 06033 8604636450 [email protected] 4

We know exactly which prospects are likely to respond and buy.

Case 2

Churn Model:

Telecommunications Business Case

Churn Model Telecommunications Business Case

Telecom Company Current State (2013 results)Subscriber count on January 1 : 1,000,000Net Subscriber count on December 31 : 1,010,000

Current ResultsCustomers churned : 200,000Net subscriber gain : 10,000Churn rate : 20%

Telecom Company Desired State (2014 plan)Subscriber count on January 1 : 1,010,000Net Subscriber count on December 31 : 1,119,500

Desired ResultsCustomers churned : 100,500Net subscriber gain : 109,500Churn rate : 10%

How Difficult Is It to Sell Something?The Economics of Marketing

It is 3x to 7x HARDER to sell to new customer than an existing one.

Existing Customer New Customer

Existing Product 1X 3X

New Product 2X 7X

Retention makes a hard job many times easier

Churn makes an already difficult job many times harder

If we can acquire new customers at the lowest possible cost, extra resources can be applied to retention efforts

Churn Model The Process Simplified

Current Customers

Lapsed CustomersVirtual DBS Appended

Demographics

Payment history, price/promo,

products purchased, CS

calls, etc.


Processing

Predictive Algorithm Reveals Top Churn Drivers and their

Predictive Weights

Predictive Attributes Driving Churn

The top predictive churn drivers show us why customers left:

7. Aggressive Competitive Offer

6. Promotion Period Expiring

5. SOHO*

4. Technical Issues

3. GeoVector*

2. Price

1. Service

0% 5% 10% 15% 20% 25% 30%

Care Call: Service - Last 30 Days

Care Call: Price - Last 30 Days

Care Call: Tech Probs - Last 30 Days

Duration to Promo Roll-Off

Care Call: Service - Last 90 Days

Competitor Aggressive Promotion

Product Grade (single to bundles)

Care Call: Service - Last 60 days

Active or Inactive Promo Flag

Last Package (single, double, triple)

Promo Duration

Significant Churn Predictors (Customer Variables)

* Virtual DBS appends

Telecom Churn Algorithm Example

A churn algorithm is a mathematical equation that incorporates predictive drivers and their weights.

Constant + Aggressive Competitive Promotion (x .16) + Time to Promo Expiration (x .21) + SOHO (small office, home office), (x .23) + Number of Tech Support Calls (x .27) + GeoVector (Age, Income, Geo, Family Type), (x .32) + Price (x .36) + Number of Service Issues (x .43)

= Propensity to Churn

Before Churn Model Scoring We Are Here

We have no way of predicting future churn behavior.

FirstName LastName Address1 City State Zip Phone Email Churn Score











Carol Heminger 515 Edgebrook Lane West Palm BeachFL 33411 5617750098 [email protected] ?








After Churn Model Scoring We Are Here

We know exactly which customers are most likely to churn.

FirstName LastName Address1 City State Zip Phone Email Churn Score











Carol Heminger 515 Edgebrook Lane West Palm BeachFL 33411 5617750098 [email protected] 8








Case 3

Upsell Model:

Utility Company

Upsell Model Utility Company Business Case

Utility Company Current State

Customers can buy an add-on insurance product to protect their furnace

Quarterly direct mail campaign with insurance offer sent to all 100,000 customers

Current Results

Gross response rate 3% (3,000 responses)

Conversion rate 10% (300 sales)

Utility Company Desired State

Quarterly direct mail campaign sent to 20,000 current customers most likely to buy insurance product

Desired Results

Mail 80,000 fewer records

Achieve 18% gross response rate (3,600 responses)

Conversion rate 10% (360 sales)

Upsell Model –The Process Simplified

Customers without Insurance

Product

Customers with Insurance Product

Virtual DBS Appended

Demographics


Processing

Predictive Algorithm Reveals Top Upsell Purchase Drivers and their

Predictive Weights

Predictive Attributes Driving Upsell Purchase

GeoVector3323: 45-64, $75k+, Suburban, Families-~18%3313: 45-64, $75k+, Urban, Families-~8%2323: 25-44, $75k+, Suburban, Families-~8%

Household Income$50,000-$74,999-~19%$150,000+-~19%$75,000-$99,999-~18%

Dwelling TypeSingle Family-~100%

Homeowner StatusOwner- ~97%Renter- ~1%

Pro-Environmental StatusYes- ~3%

Mail ResponderMultiple- ~78%Single- ~1%

Length of Residence15+ Years- ~39%11-14 Years- ~17%8-10 Years- ~14%

Socio-Demographic Clusters3-Corporate Clout- ~5.68%9-Platinum Oldies- ~5.58%5 Sitting Pretty- ~5.45%

InterestsGarden- ~14%Investments- ~38%Travel- ~56

DonorReligious Donor- ~20%Health Institutional Donor- ~22%

Before Upsell Model Scoring We Are Here

FirstName LastName Address1 City State Zip Phone Email Upsell Score











Carol Heminger 515 Edgebrook Lane West Palm Beach FL 33411 5617750098 [email protected] ?








We have no way of knowing which customers will make a next purchase.

After Upsell Model Scoring We Are Here

FirstName LastName Address1 City State Zip Phone Email Upsell Score











Carol Heminger 515 Edgebrook Lane West Palm Beach FL 33411 5617750098 [email protected] 1








We know exactly which customers are most likely to make a next purchase.

Summary

• Predictive modeling effectively answers difficult marketing questions

• Predictive modeling allows you to maximize your ROI by concentrating your resources on those customers or prospects most likely to buy or churn

• Scored data resulting from a predictive model is immediately actionable

• Predictive algorithms are portable and can be used to score a variety of internal and external lists

• Client collaboration and the operationally defined metrics specific to the current state business state are key – model performance is highly correlated with the quality of the metrics used to build the model

About Virtual DBS

What we do

Virtual DBS offers technology, data, and analytics allowing corporate decision makers to gain strategic business insights they can use to make profitable business decisions.

Best in class tools for CDI, predictive analytics, and campaign management to organize, extract, and monetize customer and prospect databases and generate positive ROI.

Highly effective and affordable products and services for B2C and B2B marketers.

Founded and managed by industry veterans with a focus on mathematical precision, customer service, and client collaboration.

For questions or further information, please contact

John Dodd, EVP

[email protected]

Direct 401.667.7595

www.virtualdbs.com

Q and A

mailto:[email protected]

http://www.virtualdbs.com/

Appendix

After successful deployment of hundreds of modeling initiatives for a

multitude of clients in widely varying marketing scenarios, we have often

seen response performance improvements of 20% to 40% over

established baseline.

• For example, where a particular package-list-offer combination has historically

generated a 2% response rate, we often see our clients enjoying response

rates ranging from 2.4% to 2.8% (i.e. 20% to 40% above established baseline)

by utilizing Virtual DBS predictive modeling in their customer development

targeted marketing campaigns.

• We have seen highly profitable modeling initiatives in which lesser gains were

achieved (often as low as a couple of percentage points over control) – but

have also seen campaigns come in with much higher response lift (e.g. 2x over

baseline).

AppendixA Note on Response Performance

AppendixPredictive Modeling Overview

• Modeling uses past behaviors (respond, buy, churn) to optimize those behaviors going forward

• We combine appended demographics with customer-specific fields (transaction values, dates, product details, etc.)

• Two Primary Outcomes:

1. Behavioral Profile

2. Scoring Algorithm

AppendixModeling Answers Key Strategic Questions

What do my best customers look like?

How do I find more prospects who look like them?

What is my market penetration?

Where are my new clients going to come from?

How do I stay relevant to my various customer groups?

Which of my customers are most likely to leave?

Which customers are going to spend the most?

What is the lifetime value of a customer?

What is the cost to acquire a new customer?

How do I help low-performing customers to become high-performing customers?

AppendixTypes of Models

Churn

Acquisition

Customer Optimization (cross and upsell)

Cluster/Segmentation

Best Payer

Best Customer

Price Elasticity

Product Sequencing

Others

0.00

2.00

4.00

6.00

8.00

10.00

Algorithm Performance

Random Validation

There are two primary steps for validating a predictive algorithm once the algorithm has been created.

First, the event group sample is randomly seeded into the universe sample, using multiple iterations of random samples, then the file is scored using the algorithm.

If the algorithm is successful, the event group sample should score in the “Best” deciles, and up and to the left in the above bar chart. The results mean that the randomly seeded event group sample is being successfully predicted by the algorithm.

In this case, model performance indicates that deciles 1-2 have the greatest lift. Detection of seeds suggests suppressing the top ~20% of the top scoring records yields a probability of capturing ~68% of current customers.


Probability Tier

Random Validation Prospect Selection

5% 0.50 8.04

Deciles 1 thru 2

10% 0.50 2.52

15% 0.50 1.69

20% 0.50 1.29

25% 0.50 1.08Decile 3

30% 0.50 0.89

35% 0.50 0.76

Deciles 4 thru 10

40% 0.50 0.59

45% 0.50 0.53

50% 0.50 0.50

55% 0.50 0.50

60% 0.50 0.50

65% 0.50 0.50

70% 0.50 0.50

75% 0.50 0.50

80% 0.50 0.50

85% 0.50 0.50

90% 0.50 0.50

95% 0.50 0.50

100% 0.50 0.50

AppendixHow Do We Know the Model Works?

0.00

0.20

0.40

0.60

0.80

1.00

Lift and Gains Performance

Random Validation


Probability Tier

Random Validation Prospect Selection

5% 0.50 8.04

Deciles 1 thru 2

10% 0.50 2.52

15% 0.50 1.69

20% 0.50 1.29

25% 0.50 1.08Decile 3

30% 0.50 0.89

35% 0.50 0.76

Deciles 4 thru 10

40% 0.50 0.59

45% 0.50 0.53

50% 0.50 0.50

55% 0.50 0.50

60% 0.50 0.50

65% 0.50 0.50

70% 0.50 0.50

75% 0.50 0.50

80% 0.50 0.50

85% 0.50 0.50

90% 0.50 0.50

95% 0.50 0.50

100% 0.50 0.50

AppendixHow Do We Know the Model Works? (cont.)

Second, the probability of increased responding for the modeled event group is plotted again a random sample.

Using the gains table to the right, the results also show that scored prospects in the top 5% of the prospect file are 8x more likely to look like a current Best Responder, and those from the second tier are 2.5x more likely to look like a current Best Responder.

Overall, prospects in the top 10% are approximately 5.5x more likely to look likely to look like current Best Prospects, compared to only a 50/50 probability by using change alone.

Again, in this case, model performance indicates that deciles 1-2 have the greatest lift. Detection of seeds suggests suppressing the top ~20% of the top scoring records yields a probability of capturing ~68% of current customers.

For questions or further information, please contact

John Dodd, EVP

[email protected]

Direct 401.667.7595

www.virtualdbs.com

mailto:[email protected]

http://www.virtualdbs.com/

nedma14: answering marketing’s top 3 questions using predictive analytics - william b. disch, ph.d

Marketing

household type x

dwelling type x

household income x

gender x

family type

marital status x

number of children x

home care buyer x