Predictive Modeling for E-Mail Marketing

1. Predictive Modeling for E-Mail Marketing Arthur Middleton Hughes – Senior Strategist Anna Lu - Director of Research and Analytics Predictive Analytics World Feb 18, 2009

2. What Does E-mail Marketing Do? • Produces online sales – in many cases • Produces retail sales – in many more cases • Produces customer retention and loyalty • Helps to acquire new customers • Announces new products • Creates cross-sales and upgrades • Can be the most powerful and cost effective marketing method that marketers have available today -- particularly in an economic downturn

3. E-mail’s Role Not Understood • In many companies, e-mail is not recognized as the marketing powerhouse that it is • It is somewhere off on the side, producing Web sales which are about 3% or less of total sales • That may be the perception, but companies that think that way are missing the boat • Here is the reality…

4. E-mail Produces Four Times as Much Offline as Online

5. The value of multi-channel customers • E-mail marketing budgets are often based only on online sales • This is a mistake, because e-mail produces four times as many sales offline as they do online • Calculate the true effect of e-mail so that the marketing budget can reflect the true worth of e-mail marketing

6. E-mail Influences all channels

7. Predictive models seldom used • Most e-mail marketers today do not use predictive modeling. Why not? • Predictive modeling is used in Direct Mail where the CPM is $600 or more. In e-mail marketing the CPM is $8 or less. Many marketers feel that the savings from a model would not pay for the model. • Many e-mail marketers are young people who have never heard of predictive modeling • The philosophy is: “Mail ‘em all. Someone is going to buy…” • This attitude is beginning to change. Here’s why….

8. Email open rates are falling

9. People are unsubscribing • It costs between $10 and $40 to acquire a permission based subscriber e-mail address. • Inboxes today are so crowded with e-mails that millions unsubscribe or delete e-mails en masse without reading them. • A relevant email to a good customer gets lost in the spam. • Many marketers are mailing too often • The annual loss from unsubscribers from large mailers comes to millions of dollars

10. Predicting the unsubscribers • Unsubscribe rates are often 3% or more per month. • If a mailer has 4 million subscribers, and the value of each subscriber is $15, he could be losing $21 million per year. • If the unsubscribe rate could be reduced by 10% he would save $2.1 million per year. • You could pay for several predictive models with that kind of saving.

11. Finding Likely Unsubs with CHAID Case Study: Loyalty program for a major US low cost airline

12. Program Background • Frequent flyer program for a major low cost airline in US • Semi-weekly e-mail program offered to members who wish to accumulate "points" they can put towards flights, SkyMall products and more • E-mail drives a significant percentage of the total revenue

13. Business Problem 18.5% have opted out of the program e-mail communications but they represent 30% of the total revenue generated by all members In addition, 88% of the opt-outs happened within the past 12 months

14. Objective • Understand key characteristics of previous opt-outs • Identify likely unsubs • Initiate save programs to prevent unsubs from happening

15. Analysis Background • Random sample of 5% of member base • Approx 50 predictor variables • Program attributes such as enrollment date, mile accumulation, usage, recency of mile redemption, total reward points, Lifetime revenue, etc. • E-mail behaviors such as opens, clicks and purchases (from e-mails sent) • Response variable – Unsubscribed versus still mailable (binary level variable) • CHAID (Chi-square Automatic Interaction Detector) algorithm • Cross validation method

16. About CHAID • A type of decision tree technique • Use of the chi-square test for contingency tables to decide which variables are of maximal importance for classification • Advantages are that its output is highly visual and easy to interpret • Often used as an exploratory technique and is an alternative to multiple regression

17. Output (Partial) % Unsub Overall % Unsub among people with # of opens in last 60 days=1

18. Predictors Selected • # of e-mails opened last 60 days • Days since loyalty club enrollment • # of e-mails opened last 30 days • # of Bonus (partner) credits earned YTD • Days since last travel • Days since most recent e-mail opened or clicked • Date of Last earn/ or redemption of flight/ or Bonus (partner) credit • # of e-mails opened last 365 days • # of vouchers redeemed in lifetime

19. Node Gain • Gain Chart on model development sample

20. Revenue • Top 10% of the members contributed to 67% of total revenue

21. X-Tab: Node vs. Revenue • Each of the top nodes have high revenue producing members

22. Identifying most profitable flyers • 4% (or 120K) of frequent flyers contributed 15% (~$3.1 million) of program revenue

23. A risk-revenue matrix

24. Using the output of the model • Now that you know those most likely to unsubscribe • And know who are the most valuable • You can single out these folks and make them an offer that they cannot refuse. • Analytics helps the airline target the right people.

25. How modeling reduced churn • In one year, analytics was used for a wireless phone company –Cingular - to reduce monthly churn by 26% -- Millions of dollars.

26. Identify Best-Customer Look-Alike with Logistic Regression Case Study: US off-price e-tailer

27. Background • Off-price e-tailer of name-brand apparel and other goods in US • e-Mail is their single largest marketing channel, and their most important retention tool • e-Mail communication delivers 40% of the total revenue

28. What can be measured • Attrition and retention • Migration upward and downward • Incremental sales per program and per season • Frequency of seasonal purchases • Dollars spent per trip and per season • Number of departments shopped per trip and per season. • Number of items shopped per trip and per season– • Share of customers’ wallet

29. Business Problem • About 50% of revenue are actually driven by their loyalty club members • An annual membership fee is required • Size of loyalty club is small – just 1.8% of e-mail base • Client asked: • Who should we focus as the next tier of subscribers amongst the other ~98% of the e-mail list • Who look like the best customers I have • How can we find people who might become best customers if nurtured

30. Objective • Understand what variables describe best customers • Identify likely best customers • Initiate programs to nurture these subscribers, to keep them happy

31. Analysis Background • Random sample of 10% of e-mail subscriber base • Approx 10 predictor variables • Attributes such as # of lifetime purchases, first/most recent order, e-mail address acquisition source, etc. • E-mail behaviors such as e-mail tenure, opens, clicks and purchases (from e-mails sent) • Response variable – Loyalty program member vs. non-Loyalty program member (binary level variable) • Logistic Regression • Cross validation method

32. About Logistic Regression • Prediction of the probability of occurrence of an event by fitting data to a logistic curve • Very useful techniques when one wants to understand or to predict the effect of a series of variables on a binary response variable (a variable which can take only two values, 0/1 or Yes/no, for example) • For example, it’s help to anticipate the likelihood of customers responding to a direct mail, or the likelihood a person is about to churn from a subscription

33. Impact of Predictors • Some variables used included: • Total # of purchases • The more the better • Time on file • The younger the better • Months since first purchase • The more the better • Months since last purchase • The less (or more recent) the better • Total e-mails clicked on over the past year • The more the better • Total e-mails opened over the past year • The more the better… though not always predictive

34. Model Gain • Gain Chart on model development sample

35. Now that we know who to target… • The model enables us to focus on those most likely to be interested in the loyalty club. • We can target only those folks with messages and rewards that will get them to join. • We make them offers that we could not afford to offer to everyone. • How the model boosts profits and reduces churn…

36. Model beats random select • A model predicts those subscribers who would be interested in a particular product. • Mailing these 273,334 produces 842 sales and only 273 unsubscribers. • If the model had not been used, there would have been only 41 sales and 3,553 unsubscribers. • Replacing each unsubscriber costs $14. • Without the model, the mailing would have been a disaster.

37. Conclusions • Predictive modeling is just getting started in e-mail marketing. • Reason: e-mails are so inexpensive that the attitude was: “Blast ‘em all!” • We now realize that subscribers are very valuable. We can lose them by random blasting. • Models help us by reducing unsubscribes and also by identifying those subscribers who are most interested in what we have to say. • Predictive modeling works with e-mail marketing.

38. To learn more…. Available from Amazon.com or BarnesandNoble.com

39. Thank you for viewing. For more information, please contact:Arthur Middleton Hughes, Senior Strategist | 954-767-4558Anna Lu,Director of Research and Analytics|781-372-1961