NEDMA14: Answering Marketing’s Top 3 Questions Using Predictive Analytics - William B. Disch, Ph.D.
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NEDMA14: Answering Marketing’s Top 3 Questions Using Predictive Analytics - William B. Disch, Ph.D.

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This informative presentation will teach you how predictive modeling will answer difficult marketing questions, allowing you to focus your resources where you will achieve the highest ROMI. This ...

This informative presentation will teach you how predictive modeling will answer difficult marketing questions, allowing you to focus your resources where you will achieve the highest ROMI. This presentation covers three business cases that answer the three questions: How to effectively improve response rates? How to reduce churn? How to identify customers who are most likely to become best customers?

This presentation was given by William B. Disch, SVP of Analytics at Virtual DBS, at NEDMA's Annual Conference on May 14, 2014.

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NEDMA14: Answering Marketing’s Top 3 Questions Using Predictive Analytics - William B. Disch, Ph.D. Presentation Transcript

  • 1. How can we boost response, reduce churn and upsell 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
  • 2. 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
  • 3. 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?
  • 4. 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
  • 5. Case 1 Customer Acquisition Model: Specialty Foods Retailer
  • 6. Customer Acquisition Model Specialty Foods Retailer Business Case Specialty Foods Retailer Current State 600,000 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 State No change in mail volume Desired Results 2.6% gross response rate (9,000 responders, 30% improvement) 25% conversion rate (2,250 customers purchased)
  • 7. 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
  • 8. 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 Exclusive Clusters  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 Business Business Verticals  Technology Use  SOHO  Etc.
  • 9. 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. Gender Female - 63% Household Type Adult Male & Female Present w Kids - 49% Dwelling Type Single Family – 74% Marital Status Married - 58% Household Income $150k + - 35% $125-$150k - 10% $100-$125k - 15% Assessed Median Home Value $750k + - 5% $700-$750k - 1% $500-$550k - 2%
  • 10. 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
  • 11. 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 7.34 Ranks 1, 2 and 3, 4 10% 0.50 1.50 15% 0.50 1.37 20% 0.50 1.14 25% 0.50 1.15 Ranks 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
  • 12. 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 ajfal@cox.net ? Ann Reynolds 94 Brookmoor Rd West Hartford CT 06109 8603130922 amtr007@hotmail.com ? Amy Lubas 823 Edgemere Lane Sarasota FL 34242 9419939899 Amy.lubas@gmail.com ? Andy Skarzynski 34 Ballard Drive West Hartford CT 06119 8602333240 andyskar@comcast.net ? Ann Schmidt 21 Onlook Rd Wethersfield CT 06109 8605294388 ann.schmidt@mx.ch ? Anthony Paquette 88 Oak Leaf Drive Colchester CT 06415 8605378667 apaquet@harthosp.org ? William Derech 132 Randy Lane Wethersfield CT 06109 8605298060 bderech@yahoo.com ? Suzanne Costanzo 9 Deerfield Trace Burlington CT 06013 8603067433 Beamer4332@Sbcglobal.net ? Gina Cunningham 28 Cockle Hill Road Salem CT 06420 8608893943 beanie1970@sbcglobal.net ? Caperton Beirne 7100 Quail Hill Road Charlotte NC 28210 7047808753 c.beirne@hotmail.com ? Carol Heminger 515 Edgebrook Lane West Palm Beach FL 33411 5617750098 carolhoyt3@hotmail.com ? Christine Peterson 60 Iroquois Road West Hartford CT 06117 8602332239 chris.mpeterson@sbcglobal.net ? Craig Meeker 44 Garden Gate Farmington CT 06032 8602840123 cmeeker@smithbrothersusa.com ? Cynthia Poglitsch 98 Cobblestone Way Windsor CT 06095 8606885207 crpoglitsch@comcast.net ? Carrie Stephens 3135 Crittenton Place Baltimore MD 21211 5618012538 cstephens8@yahoo.com ? David Iaia 37 Wilson Avenue Belmont MA 02478 6174894839 david.iaia@globalinsight.com ? David Wurts 6404 S Ross Road Morrison CO 80465 3036974486 david.w.wurts@lmco.com ? David Vining 65 Falcon Lane Glastonbury CT 06033 8604636450 davidavining@gmail.com ? We have no way of predicting a prospect’s response/purchase behavior.
  • 13. 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 ajfal@cox.net 1 Ann Reynolds 94 Brookmoor Rd West Hartford CT 06109 8603130922 amtr007@hotmail.com 6 Amy Lubas 823 Edgemere Lane Sarasota FL 34242 9419939899 Amy.lubas@gmail.com 9 Andy Skarzynski 34 Ballard Drive West Hartford CT 06119 8602333240 andyskar@comcast.net 1 Ann Schmidt 21 Onlook Rd Wethersfield CT 06109 8605294388 ann.schmidt@mx.ch 1 Anthony Paquette 88 Oak Leaf Drive Colchester CT 06415 8605378667 apaquet@harthosp.org 2 William Derech 132 Randy Lane Wethersfield CT 06109 8605298060 bderech@yahoo.com 10 Suzanne Costanzo 9 Deerfield Trace Burlington CT 06013 8603067433 Beamer4332@Sbcglobal.net 1 Gina Cunningham 28 Cockle Hill Road Salem CT 06420 8608893943 beanie1970@sbcglobal.net 4 Caperton Beirne 7100 Quail Hill Road Charlotte NC 28210 7047808753 c.beirne@hotmail.com 7 Carol Heminger 515 Edgebrook Lane West Palm Beach FL 33411 5617750098 carolhoyt3@hotmail.com 8 Christine Peterson 60 Iroquois Road West Hartford CT 06117 8602332239 chris.mpeterson@sbcglobal.net 2 Craig Meeker 44 Garden Gate Farmington CT 06032 8602840123 cmeeker@smithbrothersusa.com 9 Cynthia Poglitsch 98 Cobblestone Way Windsor CT 06095 8606885207 crpoglitsch@comcast.net 10 Carrie Stephens 3135 Crittenton Place Baltimore MD 21211 5618012538 cstephens8@yahoo.com 2 David Iaia 37 Wilson Avenue Belmont MA 02478 6174894839 david.iaia@globalinsight.com 1 David Wurts 6404 S Ross Road Morrison CO 80465 3036974486 david.w.wurts@lmco.com 10 David Vining 65 Falcon Lane Glastonbury CT 06033 8604636450 davidavining@gmail.com 4 We know exactly which prospects are likely to respond and buy.
  • 14. Case 2 Churn Model: Telecommunications Business Case
  • 15. Churn Model Telecommunications Business Case Telecom Company Current State (2013 results) Subscriber count on January 1 : 1,000,000 Net Subscriber count on December 31 : 1,010,000 Current Results Customers churned : 200,000 Net subscriber gain : 10,000 Churn rate : 20% Telecom Company Desired State (2014 plan) Subscriber count on January 1 : 1,010,000 Net Subscriber count on December 31 : 1,119,500 Desired Results Customers churned : 100,500 Net subscriber gain : 109,500 Churn rate : 10%
  • 16. 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
  • 17. Churn Model The Process Simplified Current Customers Lapsed Customers Virtual DBS Appended Demographics Payment history, price/promo, products purchased, CS calls, etc. Predictive Analytics Processing Predictive Algorithm Reveals Top Churn Drivers and their Predictive Weights
  • 18. 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
  • 19. 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
  • 20. 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 Aimee Falcigno 9 Rocamora Road Rocky Hill CT 06067 8605718495 ajfal@cox.net ? Ann Reynolds 94 Brookmoor Rd West Hartford CT 06109 8603130922 amtr007@hotmail.com ? Amy Lubas 823 Edgemere Lane Sarasota FL 34242 9419939899 Amy.lubas@gmail.com ? Andy Skarzynski 34 Ballard Drive West Hartford CT 06119 8602333240 andyskar@comcast.net ? Ann Schmidt 21 Onlook Rd Wethersfield CT 06109 8605294388 ann.schmidt@mx.ch ? Anthony Paquette 88 Oak Leaf Drive Colchester CT 06415 8605378667 apaquet@harthosp.org ? William Derech 132 Randy Lane Wethersfield CT 06109 8605298060 bderech@yahoo.com ? Suzanne Costanzo 9 Deerfield Trace Burlington CT 06013 8603067433 Beamer4332@Sbcglobal.net ? Gina Cunningham 28 Cockle Hill Road Salem CT 06420 8608893943 beanie1970@sbcglobal.net ? Caperton Beirne 7100 Quail Hill Road Charlotte NC 28210 7047808753 c.beirne@hotmail.com ? Carol Heminger 515 Edgebrook Lane West Palm BeachFL 33411 5617750098 carolhoyt3@hotmail.com ? Christine Peterson 60 Iroquois Road West Hartford CT 06117 8602332239 chris.mpeterson@sbcglobal.net ? Craig Meeker 44 Garden Gate Farmington CT 06032 8602840123 cmeeker@smithbrothersusa.com ? Cynthia Poglitsch 98 Cobblestone Way Windsor CT 06095 8606885207 crpoglitsch@comcast.net ? Carrie Stephens 3135 Crittenton Place Baltimore MD 21211 5618012538 cstephens8@yahoo.com ? David Iaia 37 Wilson Avenue Belmont MA 02478 6174894839 david.iaia@globalinsight.com ? David Wurts 6404 S Ross Road Morrison CO 80465 3036974486 david.w.wurts@lmco.com ? David Vining 65 Falcon Lane Glastonbury CT 06033 8604636450 davidavining@gmail.com ?
  • 21. 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 Aimee Falcigno 9 Rocamora Road Rocky Hill CT 06067 8605718495 ajfal@cox.net 1 Ann Reynolds 94 Brookmoor Rd West Hartford CT 06109 8603130922 amtr007@hotmail.com 6 Amy Lubas 823 Edgemere Lane Sarasota FL 34242 9419939899 Amy.lubas@gmail.com 9 Andy Skarzynski 34 Ballard Drive West Hartford CT 06119 8602333240 andyskar@comcast.net 3 Ann Schmidt 21 Onlook Rd Wethersfield CT 06109 8605294388 ann.schmidt@mx.ch 1 Anthony Paquette 88 Oak Leaf Drive Colchester CT 06415 8605378667 apaquet@harthosp.org 2 William Derech 132 Randy Lane Wethersfield CT 06109 8605298060 bderech@yahoo.com 10 Suzanne Costanzo 9 Deerfield Trace Burlington CT 06013 8603067433 Beamer4332@Sbcglobal.net 1 Gina Cunningham 28 Cockle Hill Road Salem CT 06420 8608893943 beanie1970@sbcglobal.net 4 Caperton Beirne 7100 Quail Hill Road Charlotte NC 28210 7047808753 c.beirne@hotmail.com 7 Carol Heminger 515 Edgebrook Lane West Palm BeachFL 33411 5617750098 carolhoyt3@hotmail.com 8 Christine Peterson 60 Iroquois Road West Hartford CT 06117 8602332239 chris.mpeterson@sbcglobal.net 2 Craig Meeker 44 Garden Gate Farmington CT 06032 8602840123 cmeeker@smithbrothersusa.com 9 Cynthia Poglitsch 98 Cobblestone Way Windsor CT 06095 8606885207 crpoglitsch@comcast.net 10 Carrie Stephens 3135 Crittenton Place Baltimore MD 21211 5618012538 cstephens8@yahoo.com 8 David Iaia 37 Wilson Avenue Belmont MA 02478 6174894839 david.iaia@globalinsight.com 1 David Wurts 6404 S Ross Road Morrison CO 80465 3036974486 david.w.wurts@lmco.com 10 David Vining 65 Falcon Lane Glastonbury CT 06033 8604636450 davidavining@gmail.com 4
  • 22. Case 3 Upsell Model: Utility Company
  • 23. 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)
  • 24. Upsell Model – The Process Simplified Customers without Insurance Product Customers with Insurance Product Virtual DBS Appended Demographics Predictive Analytics Processing Predictive Algorithm Reveals Top Upsell Purchase Drivers and their Predictive Weights
  • 25. Predictive Attributes Driving Upsell Purchase GeoVector 3323: 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 Type Single Family-~100% Homeowner Status Owner- ~97% Renter- ~1% Pro-Environmental Status Yes- ~3% Mail Responder Multiple- ~78% Single- ~1% Length of Residence 15+ Years- ~39% 11-14 Years- ~17% 8-10 Years- ~14% Socio-Demographic Clusters 3-Corporate Clout- ~5.68% 9-Platinum Oldies- ~5.58% 5 Sitting Pretty- ~5.45% Interests Garden- ~14% Investments- ~38% Travel- ~56 Donor Religious Donor- ~20% Health Institutional Donor- ~22%
  • 26. Before Upsell Model Scoring We Are Here FirstName LastName Address1 City State Zip Phone Email Upsell Score Aimee Falcigno 9 Rocamora Road Rocky Hill CT 06067 8605718495 ajfal@cox.net ? Ann Reynolds 94 Brookmoor Rd West Hartford CT 06109 8603130922 amtr007@hotmail.com ? Amy Lubas 823 Edgemere Lane Sarasota FL 34242 9419939899 Amy.lubas@gmail.com ? Andy Skarzynski 34 Ballard Drive West Hartford CT 06119 8602333240 andyskar@comcast.net ? Ann Schmidt 21 Onlook Rd Wethersfield CT 06109 8605294388 ann.schmidt@mx.ch ? Anthony Paquette 88 Oak Leaf Drive Colchester CT 06415 8605378667 apaquet@harthosp.org ? William Derech 132 Randy Lane Wethersfield CT 06109 8605298060 bderech@yahoo.com ? Suzanne Costanzo 9 Deerfield Trace Burlington CT 06013 8603067433 Beamer4332@Sbcglobal.net ? Gina Cunningham 28 Cockle Hill Road Salem CT 06420 8608893943 beanie1970@sbcglobal.net ? Caperton Beirne 7100 Quail Hill Road Charlotte NC 28210 7047808753 c.beirne@hotmail.com ? Carol Heminger 515 Edgebrook Lane West Palm Beach FL 33411 5617750098 carolhoyt3@hotmail.com ? Christine Peterson 60 Iroquois Road West Hartford CT 06117 8602332239 chris.mpeterson@sbcglobal.net ? Craig Meeker 44 Garden Gate Farmington CT 06032 8602840123 cmeeker@smithbrothersusa.com ? Cynthia Poglitsch 98 Cobblestone Way Windsor CT 06095 8606885207 crpoglitsch@comcast.net ? Carrie Stephens 3135 Crittenton Place Baltimore MD 21211 5618012538 cstephens8@yahoo.com ? David Iaia 37 Wilson Avenue Belmont MA 02478 6174894839 david.iaia@globalinsight.com ? David Wurts 6404 S Ross Road Morrison CO 80465 3036974486 david.w.wurts@lmco.com ? David Vining 65 Falcon Lane Glastonbury CT 06033 8604636450 davidavining@gmail.com ? We have no way of knowing which customers will make a next purchase.
  • 27. After Upsell Model Scoring We Are Here FirstName LastName Address1 City State Zip Phone Email Upsell Score Aimee Falcigno 9 Rocamora Road Rocky Hill CT 06067 8605718495 ajfal@cox.net 7 Ann Reynolds 94 Brookmoor Rd West Hartford CT 06109 8603130922 amtr007@hotmail.com 1 Amy Lubas 823 Edgemere Lane Sarasota FL 34242 9419939899 Amy.lubas@gmail.com 9 Andy Skarzynski 34 Ballard Drive West Hartford CT 06119 8602333240 andyskar@comcast.net 6 Ann Schmidt 21 Onlook Rd Wethersfield CT 06109 8605294388 ann.schmidt@mx.ch 4 Anthony Paquette 88 Oak Leaf Drive Colchester CT 06415 8605378667 apaquet@harthosp.org 2 William Derech 132 Randy Lane Wethersfield CT 06109 8605298060 bderech@yahoo.com 9 Suzanne Costanzo 9 Deerfield Trace Burlington CT 06013 8603067433 Beamer4332@Sbcglobal.net 1 Gina Cunningham 28 Cockle Hill Road Salem CT 06420 8608893943 beanie1970@sbcglobal.net 2 Caperton Beirne 7100 Quail Hill Road Charlotte NC 28210 7047808753 c.beirne@hotmail.com 8 Carol Heminger 515 Edgebrook Lane West Palm Beach FL 33411 5617750098 carolhoyt3@hotmail.com 1 Christine Peterson 60 Iroquois Road West Hartford CT 06117 8602332239 chris.mpeterson@sbcglobal.net 2 Craig Meeker 44 Garden Gate Farmington CT 06032 8602840123 cmeeker@smithbrothersusa.com 10 Cynthia Poglitsch 98 Cobblestone Way Windsor CT 06095 8606885207 crpoglitsch@comcast.net 3 Carrie Stephens 3135 Crittenton Place Baltimore MD 21211 5618012538 cstephens8@yahoo.com 2 David Iaia 37 Wilson Avenue Belmont MA 02478 6174894839 david.iaia@globalinsight.com 1 David Wurts 6404 S Ross Road Morrison CO 80465 3036974486 david.w.wurts@lmco.com 3 David Vining 65 Falcon Lane Glastonbury CT 06033 8604636450 davidavining@gmail.com 1 We know exactly which customers are most likely to make a next purchase.
  • 28. 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
  • 29. 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.
  • 30. For questions or further information, please contact John Dodd, EVP jdodd@virtualdbs.com Direct 401.667.7595 www.virtualdbs.com Q and A
  • 31. Appendix
  • 32. 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). Appendix A Note on Response Performance
  • 33. Appendix Predictive 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
  • 34. Appendix Modeling 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?
  • 35. Appendix Types of Models Churn Acquisition Customer Optimization (cross and upsell) Cluster/Segmentation Best Payer Best Customer Price Elasticity Product Sequencing Others
  • 36. 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. Model Scoring Validation Gains Table 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 1.08 Decile 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 Appendix How Do We Know the Model Works?
  • 37. 0.00 0.20 0.40 0.60 0.80 1.00 Lift and Gains Performance Random Validation Model Scoring Validation Gains Table 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 1.08 Decile 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 Appendix How 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.
  • 38. For questions or further information, please contact John Dodd, EVP jdodd@virtualdbs.com Direct 401.667.7595 www.virtualdbs.com