Predictive Modeling for E-Mail MarketingArthur 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

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

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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…

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E-mail Produces Four Times as Much Offline as Online

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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

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E-mail Influences all channels

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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….

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Email open rates are falling

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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

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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.

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Finding Likely Unsubs with CHAID

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

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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

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Business Problem

Status% of

Program Base

% of Revenue

Generated (Lifetime)

% of E-Mail Revenue (2

yrs.)

% of E-mail Revenue (12

months)

Mailable 81.5% 70.6% 78.7% 82.4%

Opt-out 18.5% 29.4% 21.3% 17.6%

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Objective

Understand key characteristics of previous opt-outs

Identify likely unsubs

Initiate save programs to prevent unsubs from happening

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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

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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

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Output (Partial)

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% Unsub

Overall

% Unsub

among

people

with # of

opens in

last 60

days=1

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

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Node Gain

Gain Chart on model development sample

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Node (%) Gain (%) Unsub (%) Index Node (%) Gain (%) Unsub (%) Index6 2.07 5.56 2.14 269 2.1 5.6 2.1 269

13 2.66 6.43 1.93 242 4.7 12.0 2.0 25364 4.46 8.48 1.52 190 9.2 20.5 1.8 22314 3.46 4.39 1.01 127 12.7 24.9 1.6 19657 28.11 33.04 0.94 118 40.8 57.9 1.1 14265 6.51 6.73 0.82 103 47.3 64.6 1.1 1375 8.49 8.19 0.77 96 55.8 72.8 1.0 131

58 12.89 10.82 0.67 84 68.6 83.6 1.0 12260 9.5 7.6 0.64 80 78.1 91.2 0.9 11712 4.23 2.92 0.55 69 82.4 94.2 0.9 11462 4.06 2.05 0.4 50 86.4 96.2 0.9 11159 7.77 2.92 0.3 38 94.2 99.1 0.8 10561 5.8 0.88 0.12 15 100 100 0.8 100

Total 100 100 0.8 100

Node by NodeNodes

Cumulative

Revenue

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

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Revenue Rank % of Base % of Revenue

Decile 1 10.8% 67.2%

Decile 2 10.4% 17.0%

Decile 3 10.4% 9.6%

Decile 4 10.6% 5.9%

Decile 5 - 10 57.9% 0.3%

Total 100.0% 100.00%

X-Tab: Node vs. Revenue

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Each of the top nodes have high revenue producing members

Node Decile 1 Decile 2 Decile 3 Decile 4 Decile 5 - 10 Total6 8.8% 19.2% 21.1% 23.5% 27.4% 100.0%

13 11.8% 12.4% 12.3% 13.4% 50.1% 100.0%64 23.4% 15.0% 6.4% 4.6% 50.6% 100.0%14 15.1% 13.9% 12.2% 13.2% 45.5% 100.0%57 3.8% 2.4% 2.6% 2.8% 88.5% 100.0%65 16.6% 12.0% 5.9% 4.2% 61.3% 100.0%5 24.2% 14.6% 9.8% 10.0% 41.4% 100.0%

58 27.5% 18.9% 16.0% 16.8% 20.8% 100.0%60 7.6% 17.2% 23.7% 25.1% 26.5% 100.0%12 9.3% 10.7% 10.5% 11.7% 57.9% 100.0%62 1.6% 13.7% 32.0% 35.0% 17.7% 100.0%59 11.9% 14.2% 13.4% 12.9% 47.6% 100.0%61 0.0% 0.2% 0.2% 0.4% 99.2% 100.0%

Overall/Total 10.8% 10.4% 10.4% 10.6% 57.9% 100.0%

Revenue

Identifying most profitable flyers

4% (or 120K) of frequent flyers contributed 15% (~$3.1 million) of program revenue

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Node Decile 1 Decile 2 Decile 3 Decile 4 Decile 5 - 10 Total6 0.2% 0.4% 0.4% 0.5% 0.5% 1.9%

13 0.3% 0.3% 0.3% 0.3% 1.3% 2.6%64 1.0% 0.7% 0.3% 0.2% 2.3% 4.5%14 0.5% 0.5% 0.4% 0.5% 1.6% 3.5%57 1.1% 0.7% 0.7% 0.8% 25.0% 28.2%65 1.1% 0.8% 0.4% 0.3% 3.9% 6.4%5 2.1% 1.2% 0.8% 0.9% 3.5% 8.6%

58 1.6% 1.1% 0.9% 1.0% 1.2% 5.8%60 0.7% 1.7% 2.3% 2.4% 2.6% 9.7%12 0.4% 0.5% 0.4% 0.5% 2.5% 4.3%62 0.1% 0.6% 1.3% 1.4% 0.7% 4.1%59 1.7% 2.1% 2.0% 1.9% 6.9% 14.6%61 0.0% 0.0% 0.0% 0.0% 5.8% 5.9%

Total 10.8% 10.4% 10.4% 10.6% 57.9% 100.0%

Revenue

Node Decile 1 Decile 2 Decile 3 Decile 4 Decile 5 - 10 Total6 0.7% 0.6% 0.4% 0.3% 0.0% 2.0%

13 2.0% 0.5% 0.3% 0.2% 0.0% 3.0%64 6.3% 1.1% 0.3% 0.1% 0.0% 7.8%14 3.0% 0.8% 0.4% 0.3% 0.0% 4.5%57 9.4% 1.1% 0.7% 0.4% 0.0% 11.6%65 6.9% 1.3% 0.4% 0.1% 0.0% 8.6%5 13.9% 2.1% 0.8% 0.5% 0.0% 17.3%

58 10.3% 1.8% 0.8% 0.5% 0.0% 13.5%60 2.8% 2.7% 2.1% 1.4% 0.1% 9.0%12 2.6% 0.7% 0.4% 0.3% 0.0% 4.1%62 0.2% 0.9% 1.2% 0.8% 0.0% 3.1%59 9.1% 3.4% 1.8% 1.0% 0.0% 15.4%61 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Total 67.2% 17.0% 9.6% 5.9% 0.3% 100.0%

Revenue

A risk-revenue matrix

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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.

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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.

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Identify Best-Customer Look-Alike with Logistic Regression

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Case Study: US off-price e-tailer

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

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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

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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

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Objective

Understand what variables describe best customers

Identify likely best customers

Initiate programs to nurture these subscribers, to keep them happy

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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

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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

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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

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Parameter BetaLIFETIME_ORDERS 0.105TENURE_MON -0.052MON_SINCE_FIRSTPUR 0.047MON_SINCE_LASTORDER -0.047CLKS 0.01OPNS 0.004

Model Gain

Gain Chart on model development sample

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Node (%) Gain (%) Best (%) Index Node (%) Gain (%) Best (%) Index

1 10.1 92.5 14.1 783 10.1 92.5 14.1 783

2 10.3 6.4 1.1 61 20.4 98.9 8.7 484

3 9.6 0.5 0.1 6 30.0 99 6.0 331

4 11 0.4 0.1 6 41.0 100 4.4 243

5 10.2 0.2 0 0 51.2 100 3.5 195

6 8.8 0.2 0 0 60.0 100 3.0 167

7 14.3 0 0 0 74.3 100 2.4 135

8 5.9 0 0 0 80.2 100 2.2 125

9 5.4 0 0 0 85.6 100 2.1 117

10 14.4 0 0 0 100 100 1.8 100

Total 100 100 1.8 100

DecileDecile by Decile Cumulative

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…

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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.

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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.

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To learn more….

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For more information, please contact:

Arthur Middleton Hughes, Senior Strategist | 954-767-4558

Anna Lu, Director of Research and Analytics | 781-372-1961