Marketing Modeling course - Pennsylvania State University · MKTG 521- Spring 2016 - 2 Three...

MKTG 521- Spring 2016 - 1

Individual-Level Targeting

Individual-level targeting.

Review choice modeling framework.

Using choice models for individual-level targeting (predictive modeling).

Demonstration of software for BBBC case.

Arvind Rangaswamy

[email protected]

www.arvind.info

mailto:[email protected]


Three Approaches to Targeting

Macro-level targeting (e.g., segment selection as in Pacific Brands)

GE-McKinsey Portfolio Matrix

Micro-level targeting using Classification models

Discriminant function

CART – Classification and Regression Trees

Micro-level targeting using RFM, Regression, Choice Models, Neural networks, Data mining, etc.


Models for Targeting Individual Customers

Create database of customer responses (choices) based either on test mailing to a sample of prospects/customers, or historical data of past customer purchases.

Step 1

Use models such as regression, RFM, and Logit to assess the impact of independent variables (drivers) of customer response. Step 2

Score each customer/prospect based on the drivers identified in Step 2 - the higher the score, the more likely is the predicted response.

Step 3

Classify customers into deciles (or smaller groupings) based on their scores. Step 4

Based on profitability analyses, determine the top deciles to which a marketing action (e.g., mailing of brochure) will be targeted. Step 5


Database for BookBinders Book Club Case

Predict response to a mailing for the book, Art History of Florence, based on the following variables accumulated in the database and the responses to a test mailing:

Gender

Amount purchased

Months since first purchase

Months since last purchase

Frequency of purchase

Past purchases of art books

Past purchases of children’s books

Past purchases of cook books

Past purchases of DIY books

Past purchases of youth books

Step 1


Attributes in ABB’s Choice-Segmentation Model

Invoice price

Energy losses

Overall product quality

Availability of spare parts

Clarity of bid document

Knowledgeable salespeople

Maintenance requirement

Ease of installation

Warranty


Drivers of the RFM Model

Recency

Frequency

Monetary

Value

Time/purchase occasions since the last purchase

Number of purchase occasions since first purchase

Amount spent since the first purchase

R

F

M

Total RFM Score: R Score + F score + M Score

Step 2


Example RFM Model Scoring Criteria

R

Months from last purchase

13-max

10-12 7-9 3-6 0-2

Max Score = 50

F

Frequency > 30 21-30 16-20 11-15 0-10

Max Score = 30

M

Amount purchased

> 400 301-400

201-300

101- 200

100

Max Score = 20

Implement RFM model using Nested If statements in Excel

Step 2

Present

State

Behaviors

Ignore

Postpone

Engage in

Purchase Process

Desired

State

Functional

and

Economic

Needs

Perceived

and

Psychological

Needs •Search for options

•Evaluate options

•Choose product

•Purchase product

•Use product

Customer

Value

Measurement

Approaches

Objective

Measures

of Value

Perceptual

Measures

of Value

Behavioral

Measures

of Value

Customer Needs and Buying Process

Motivation

Customer Needs and Customer Value Measurement


Choice Models vs Needs Surveys

With standard survey methods . . .

preference/ importance

choice weights perceptions predict observe/ask observe/ask

But with choice models . . .

importance

choice weights perceptions observe infer observe/ask

MKTG 521- Spring 2016 - 10

Using “Choice Models” to Determine the Value of a Customer

1. Observe choice:

Buy/not buy -- direct marketers Brand bought -- packaged goods

2. Capture related characteristics data:

demographics attitudes/perceptions market conditions (price, promotion, etc.) pattern of previous choices

3. Link

1 to 2 via “choice model” – the model predicts customers’ probabilities of purchase and also reveals importance weights of characteristics.

MKTG 521- Spring 2016 - 11

Logit Model of Response to Direct Mail

Probability of function of (past response behavior,

responding to = marketing effort, characteristics

direct mail of customers)

solicitation

Step 2

MKTG 521- Spring 2016 - 12

The Multinomial Logit Choice Model (Marketing Engineering Software)

The probabilities lie between 0 and 1, and sum to 1.

The model is consistent with the proposition that customers pick the choice alternative that offers them the highest utility (value) on a purchase occasion, but the utility has a random component that varies from one purchase occasion to the next.

The model has the proportional draw property -- each choice alternative draws from other choice alternatives in proportion to their utility.

The primary objective of the model is to predict

the probabilities that an individual will choose

each of several choice alternatives. The model

has the following properties:

Step 2

MKTG 521- Spring 2016 - 13

Conceptual Background for Choice Model

Uij = Vij + eij

where:

Uij = Utility (or hidden value) that customer i has for choice alternative j. In Bookbinder’s case, choice alternatives are “Buy” and “Don’t Buy.” We can set j = 1 for Buy and j = 0 for “Don’t Buy.” We do not observe Uij.

For “identification,” we will also set 𝑼𝒊𝒋 = 𝟎 for “Don’t Buy”

Vij = Deterministic (or observable) component of utility that is a function of several attributes associated with choice alternatives.

eij = An error term that reflects the non-deterministic component of utility.

Step 2

Value for

Gender Value of Buying

Art History of Florence = +

Value for previous

purchase of art books + + …..

Value of

unknown factors

MKTG 521- Spring 2016 - 14

Specification of the Deterministic Component of Utility

where:

i = an index to represent customers.

k = an index to represent an independent variable.

𝑿𝒊𝒋𝒌 = The values of the K independent variables for customer i

for choice alternative j.

wk = estimated coefficient to represent the impact of 𝑿𝒊𝒋𝒌 on

the value (utility) realized by customer i.

𝑽𝒊𝒋 = 𝒘𝒌𝑋𝑖𝑗𝑘

𝑲

𝒌=𝟏

Step 2

MKTG 521- Spring 2016 - 15

The Special Case of Binary (Two Alternatives) Logit Model

If customers maximize the value associated with their choices and the non-deterministic component error (𝒆𝒊) has a specific form called double exponential, then:

𝒑 𝒊𝟏 =𝒆𝑽 𝒊𝟏

𝒆𝟎 + 𝒆𝑽 𝒊𝟏=

𝒆𝑽 𝒊𝟏

𝟏 + 𝒆𝑽 𝒊𝟏

where:

𝒑 𝒊𝟏 = predicted probability that customer i will respond (j = 1) to the direct mail.

𝑽 𝒊𝟏 = estimated value of the offering to customer i (i.e., based on estimates of wk) obtained from maximum likelihood estimation. It varies from - (minus infinity) to .

The value to the customer of not having the offering (j = 0) is set to 0 (base value).

Step 2

MKTG 521- Spring 2016 - 16

More General Case

(Multinomial Logit Model)

𝒑 𝒊𝒋 =𝒆𝑽 𝒊𝒋

𝒆𝑽 𝒊𝒋𝑱

𝒋=𝟏

If there are three choice alternatives, we will get three

probability predictions:

𝒑 𝒊𝟏 =𝒆𝑽 𝒊𝟏

𝒆𝑽 𝒊𝒋𝟑

𝒋=𝟏

; 𝒑 𝒊𝟐 =𝒆𝑽 𝒊𝟐


𝒋=𝟏

; 𝒑 𝒊𝟑 =𝒆𝑽 𝒊𝟑


𝒋=𝟏

MKTG 521- Spring 2016 - 17

An Important Property of the Logit Model

Probability of Individual i Choosing the product 𝒑𝒊

0.0 0.5 1.0

Low

High

The marginal impact is highest when the customer is “sitting on

the fence,” i.e., when 𝒑𝒊 = 0.5.

Question: Is this a good property to have?

Marginal impact of

variable j (e.g., price)

on 𝒑𝒊

Step 2

MKTG 521- Spring 2016 - 18

Computing Scores Based on Regression

Run regression model to predict probability of purchase:

Choice (0 or 1) = Function of variables (e.g., Gender) + error

i.e., the estimated regression equation is given by:

𝑷 𝒊𝒋 = 𝒘 𝟎 + 𝒘 𝒌𝒌 𝑿𝒊𝒋𝒌

w's are estimated regression coefficients. Pij is the probability

that individual i will choose alternative j. Note that predicted

choice probabilities from the regression model need not

necessarily lie between 0 and 1, although most of the

probabilities will fall in that range.

Step 3

MKTG 521- Spring 2016 - 19

Compute Prediction Scores from Different Models

RFM Score: Use computed score as an index of the probability of purchase (i.e., higher the RFM score, the greater the probability of purchase).

Regression:

Logit:

𝒘 's are weights estimated by the Regression or Logit models.

RFM and Regression models can be implemented in Excel.

Also, all three scoring procedures for “probability of purchase” can be implemented in Excel.

k

ijkk XwwicustomerforScore ˆˆ)( 0

ijkk

ijkk

Xww

Xww

e

ety)(probabiliscore si' Customer

ˆˆ

ˆˆ

0

0

1

Step 3

MKTG 521- Spring 2016 - 20

Score Customers for their Potential Profitability (Example)

A B C D Score Average Customer (Purchase Purchase Expected $ Customer Probability) Volume Margin = A B C

1 30% $31.00 0.70 6.51

2 2% $143.00 0.60 1.72

3 10% $54.00 0.67 3.62

4 5% $88.00 0.62 2.73

5 60% $20.00 0.58 6.96

6 22% $60.00 0.47 6.20

7 11% $77.00 0.38 3.22

8 13% $39.00 0.66 3.35

9 1% $184.00 0.56 1.03

10 4% $72.00 0.65 1.87

Average expected purchase per customer = $3.72

Step 3

MKTG 521- Spring 2016 - 21

Decile Classification Example

Decile Customer(s) $

1 5 6.96

2 1 6.51

3 6 6.20

4 3 3.62

5 8 3.35

6 7 3.22

7 4 2.73

8 10 1.87

9 2 1.72

10 9 1.03

If the marketing cost to reach a customer is $3, at what decile will you stop

your targeting effort? How is this targeting plan different from one based

on average purchases of customers ($3.72)?

Step 4

MKTG 521- Spring 2016 - 22

Decile Classification

Standard Assessment Method

Apply the results of approach and calculate the “score” of each individual (calibration versus test sample).

Order the customers based on “score” from the highest to the lowest.

Divide into deciles.

Calculate/graph hit rate and profit.

Customer 1 Score 1.00


….



Decile1

Decile10

…..

…..

Step 4

MKTG 521- Spring 2016 - 23

Determine Targeting Plan (Example shows potential profitability of top 6 deciles)

Model

Number of hits (favorable responses at 60th percentile of

ordered scores)

Expected response rate by mailing the

top 60% of customers in the ordered list

% of likely responders recovered at

60th percentile

RFM

Regression

MNL

Compute profit/ROI for the models based on the number of mailings recommended

by each model and compare that to mailing to the entire list (equivalently to a

randomly selected list of the same size).

Step 5

MKTG 521- Spring 2016 - 24

Develop Lift Charts and Choose Model for Implementation

0%

20%

40%

60%

80%

100%

1 2 3 4 5 6 7 8 9 10

Decile

Cu

mu

lati

ve

Hit

Ra

te (

%)

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

55,000

Cu

mu

lati

ve

Pro

fit

($)

Hit Rate Random Hit

Profit Random Prof

Step 5

MKTG 521- Spring 2016 - 25

Database Approach to Implementing Individual Targeting

Sales, customer support,

shipments, payments, etc.

Appended

Company Data

Third-Party

Data (e.g.,

Credit Scores)

Model-based

Scores

Offers and

Responses

Customer

Database

Operational

Database

Transactions General

Ledger

Marketing Modeling course - Pennsylvania State University · MKTG 521- Spring 2016 - 2 Three...

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