Professor Michaela Draganska

Developing Customers: NPTB Model

Applications of predictive techniques

Modeling and predicting response rates

ResponderNew

Customer

VoluntaryChurn

ForcedChurn?

FormerCustomer

HighPotential

HighValue

LowValue

Retained orrepeat customer

Prospect

- Modeling and predicting the success of cross/up-sell attempts

- Modeling and predicting customer attrition

- Modeling and predicting reactivation success

Acquisition Development Retention

So far we have been interested in the response to a given product offering

- Given a certain product we want to offer, which customer is the most likely to positively respond to the offer? Prospecting Simple add-on/cross-selling Up-selling

- Given that we would like to target a certain customer, which product is most likely to lead to a positive response (or maximize profits)? Add-on/cross-selling

COMPARISON OF QUESTIONS

Next Product To Buy Model (NPTB)

NPTB models rely on a different economic trade-off than response models

COST-REWARD TRADE-OFF COMPARISON

Response model

- Profit from sale of product 1

NPTBModel

- Cost of marketing

- Contribution from sale of the product

- Opportunity cost of selling product 1, i.e. the profit from selling product 2,3, etc. instead

NPTB models have many applications

- Banks: Which financial product to offer next?

- eRetailers: Which product to promote in a weekly e-mail

- B2B: Which product to push in the next sales call

- Call centers: Which additional product to offer during an inbound call?

- ...

APPLICATIONS

BANKING CROSS-SELL EXAMPLE

A NPTB model predicts which product(s) a customer is most likely to buy next

- Compiling data

- Selecting a statistical model (technique)

- Estimating and evaluating the model

BUILDING A NPTB MODEL

- Scoring and targeting customers

- Equation that can be used to predict which product a particular customer is most likely to buy

NPTB models relate characteristics and past purchase behavior to recent product choices

DATA REQUIREMENTS FOR NPTB MODEL

We can use a variety of approaches to estimate the NPTB model

- Logistic Regression Prob(Customer i's next purchase is product j) With J products we estimate J separate logistic regressions For each product dependent variable is 0-1

- Multinomial Logit (and Nested Logit) Like logistic regression but dependent variable is the chosen product Dependent variable has J values (number of products) More information?

- Neural networks

- Decision trees

MODEL ALTERNATIVES FOR NPTB MODEL

Inbound telephone calls present a very good opportunity to apply cross-selling techniques

- Customers are overexposed to unsolicited offers

- Mail has very low response rates

- Telemarketing is intrusive and becomes more and more restrictive (FTC rule)

- E-mail suffers from the same overload problem as direct mail

PROBLEMS IN CONTACTING CONSUMERS FOR CROSS-SELLING

- Caller feels in control of interaction

- Call is solicited

- Good service experience can create good starting point to deepen customer relationship

- Two-way real-time conversation allows for fine-tuning of cross-selling opportunity

Inbound calls do not suffer from these problems

Lets look at an example of a cross/upselling model

- Large German mail-order seller for close-out books

- Sell books in many different categories:

RHENANIA EXAMPLE

The catalog is sent to consumers at varying intervals

RHENANIA CATALOG

How would we determine which product to cross-sell to a customer who calls in?

- Many orders placed through call centers

==> cross/up-sell opportunities

We have a special offer on book X, would you like me to add it to your

order?

RHENANIA EXAMPLE...

What book should we offer to each customer who calls?

How would we determine which product to cross-sell to a customer who calls in?

- Many orders placed through call centers

==> cross/up-sell opportunities

We have a special offer on book X, would you like me to add it to your

order?

RHENANIA EXAMPLE...

What book should we offer to each customer who calls?

- Company knows which book is the most attractive by product category

- Predict the category that a customer is most likely to buy in

A multinomial logit estimates the response probability for each of 10 different book/CD/DVD categories

- Predict which category a customer was mostly likely to purchase from given their category specific behavior in the last 12 months.

For each category:- Recency- Frequency- Average order size

in last 12 months

In last quarter:-Category in which customer placed most orders

Customer 1:Customer 2:Customer 3:Customer 4:Customer 5:

DemographicsDemographicsDemographicsDemographicsDemographics

t-4 t-3 t-2 t-1 t

A

AC

D

B

A

C

B

D

A

BD

Independent Variables Dep. Variables

Time

We obtain for every customer a suggested category for cross-selling

Suggested category can be fed back into call-center system

NPTB Summary

- Consideration of tradeoff among products to offer

- Intelligent way of selecting right product for the right customer

- Most powerful predictor for future purchase is past behavior

- Predictive accuracy is similar across statistical models and they all outperform significantly simple prospecting

ADVANTAGES

CAVEATS

- No distinction between products that a customer is likely to buy on her own and ones that need to be offered

- No consideration of timing of offers (can be extended)