Presentation is loading. Please wait.

Presentation is loading. Please wait.

Predictive Modeling for E-Mail Marketing Arthur Middleton Hughes – Senior Strategist Anna Lu - Director of Research and Analytics Predictive Analytics.

Similar presentations

Presentation on theme: "Predictive Modeling for E-Mail Marketing Arthur Middleton Hughes – Senior Strategist Anna Lu - Director of Research and Analytics Predictive Analytics."— Presentation transcript:

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 2

3 E-mails 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… 3

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

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 5

6 E-mail Influences all channels 6

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. Heres why…. 7

8 Email open rates are falling 8

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 9

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. 10

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

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 12

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 Status % of Program Base % of Revenue Generated (Lifetime) % of E-Mail Revenue (2 yrs.) % of E-mail Revenue (12 months) Mailable81.5%70.6%78.7%82.4% Opt-out18.5%29.4%21.3%17.6% 13

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

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 15

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 16

17 Output (Partial) 17 % 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 18

19 Node Gain Gain Chart on model development sample 19

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

21 X-Tab: Node vs. Revenue 21 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 22

23 A risk-revenue matrix 23

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. 24

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. 25

26 Identify Best-Customer Look- Alike with Logistic Regression 26 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 27

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 28

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 29

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

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 31

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, its help to anticipate the likelihood of customers responding to a direct mail, or the likelihood a person is about to churn from a subscription 32

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 33

34 Model Gain Gain Chart on model development sample 34

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… 35

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. 36

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. 37

38 To learn more…. 38 Available from or

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

Download ppt "Predictive Modeling for E-Mail Marketing Arthur Middleton Hughes – Senior Strategist Anna Lu - Director of Research and Analytics Predictive Analytics."

Similar presentations

Ads by Google