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Predictive Modeling for E-Mail Marketing. Arthur Middleton Hughes – Senior Strategist Anna Lu - Director of Research and Analytics Predictive Analytics World Feb 18, 2009 . What Does E-mail Marketing Do?. Produces online sales – in many cases Produces retail sales – in many more cases

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predictive modeling for e mail marketing

Predictive Modeling for E-Mail Marketing

Arthur Middleton Hughes – Senior Strategist

Anna Lu - Director of Research and Analytics

Predictive Analytics World Feb 18, 2009

what does e mail marketing do
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
e mail s role not understood
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…
the value of multi channel customers
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
predictive models seldom used
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….
people are unsubscribing
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
predicting the unsubscribers
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.

Finding Likely Unsubs with CHAID

Case Study: Loyalty program for a major US low cost airline

program background
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
business problem
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

  • Understand key characteristics of previous opt-outs
  • Identify likely unsubs
  • Initiate save programs to prevent unsubs from happening
analysis background
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
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
output partial
Output (Partial)

% Unsub Overall

% Unsub among people with # of opens in last 60 days=1

predictors selected
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
node gain
Node Gain
  • Gain Chart on model development sample
  • Top 10% of the members contributed to 67% of total revenue
x tab node vs revenue
X-Tab: Node vs. Revenue
  • Each of the top nodes have high revenue producing members
identifying most profitable flyers
Identifying most profitable flyers
  • 4% (or 120K) of frequent flyers contributed 15% (~$3.1 million) of program revenue
using the output of the model
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.
how modeling reduced churn
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.
  • 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
what can be measured
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
business problem29
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
  • Understand what variables describe best customers
  • Identify likely best customers
  • Initiate programs to nurture these subscribers, to keep them happy
analysis background31
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
about logistic regression
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
impact of predictors
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
model gain
Model Gain
  • Gain Chart on model development sample
now that we know who to target
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…
model beats random select
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.
  • 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.
to learn more
To learn more….

Available from or


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