1. Do you like to receive mail and email messages from businesses?
Consumers
Marketers
Relevant Message
Junk Mail
Spam

2. Junk Mail Spam $ 1900 1950 2020 Productivity Drivers
Competition: $ $ (Falling Prices) $
$ $ $
Productivity
Drivers
Mass Marketing Customize, target low incidence, high value
1900
1950
2020
High Buyer Incidence
Moderate Buyer Incidence
Low Buyer Incidence
Low Cost to Marketer
High Cost to Marketer
Spam
Junk Mail

3. Predictive Modeling
Predictive models identify people likely and unlikely to buy.
SEG: Will Buy
SEG: Won’t Buy
Good models increase response rates because marketers contact fewer people
who are unlikely to buy.

4. Predictive Modeling
Predictive models identify people likely and unlikely to buy.
SEG: Will Buy
+Error:
-Error:
SEG: Won’t Buy
Good models increase response rates because marketers contact fewer people
who are unlikely to buy.

5. $ Productivity Drivers Creation of Value Through Exchange
Competition: $ $ (Falling Prices) $
$ $ $
Productivity
Drivers
Mass Marketing Customize, target low incidence, high value customers
Mass Customization
DATABASE
1 to to 1
Marketer Pushes Product (Queries, Mail Merge) Customer Pulls Product (Web App)
Segmentation
& Targeting
Measuring Market Segment Value:
Predictive Models:
Gain Scores
Non-Statistical

6. Segment Predictive Model
Steps
Identify Segments
Test Segment Response
Market to Responsive Segments

7. Three Step Process ● Identify segments among current or
prospective customers.
● Test market to the segments to identify
cheaply those most likely to respond.
● Do a full rollout to the segments that are
most responsive to the test. Target all
cells for which the response rate in the
test is equal to or greater than the
breakeven response rate.

8. Breakeven Response Rate Calculation
Breakeven Definition
BE is the point at which: Marginal Cost = Marginal Revenue
Breakeven Response Rate Calculation
The response rate is the proportion of customers
who respond to a proffer by making a purchase.
The breakeven response rate is the rate at which
the revenues from sales equal the cost of making
the proffer. The specific response rate formula is:
(Cost per proffer) / (net profit from single sale) = BE Response
● As costs get higher, the BE Response Rate gets higher.
● As profit per sale gets higher, the BE response rate gets lower.
-- $1.00 / $10.00 = .10 or 10%
-- $ .50 / $10.00 = .05 or 5%
-- $1.00 / $50.00 = .02 or 2%

9. Four Controllable Factors
● Cost per marketing communication.
● Net revenue per sale.
● Number of segments to test and target.
● Sample size when test marketing. (Number
of people in each segment who buy during
the test if you meet the break even
response rate.)

10. Breakeven and Test Sample Size
Cost Per Piece / Revenue Per Sale = Break Even
$1.00 / $10.00 = .10 or 10%
$ .50 / $10.00 = .05 or 5%
$1.00 / $50.00 = .02 or 2%
How many people should be contacted within
each segment to make one sale per segement
if we reach each of the three break even
response rates listed above?

11. Sample Size and Test Reliability
Cost Per Piece / Revenue Per Sale = Break Even
$1.00 / $10.00 = .10 or 10%
Number of Purchases Per Cell Rules of Thumb
Very Reliable: 4
Reliable: 3
Somewhat Reliable: 2
Minimally Reliable: 1
How many people must be contacted given a 10%
Break Even Response Rate to attain each level of
reliability?

12. Practice Exercise Data in the practice exercise are the
results of test marketing to see which
segments can be profitably targeted.
We will assess the adequacy of the test
and develop a marketing strategy based
on these test results. We identify
segments that can be expected to
purchase the product in the full rollout.

13. TAPPS: Talking Aloud Paired Problem Solving
Segment Response Rate
Break Even Response Rate
Segment Mean
Overall Mean
( ) = ROI
15.63%
5.10%
( ) = 2.06
= Gain Score
15.63
5.10
= 3.06
Compare the Return on Investment and Gain Scores.
How are they alike, how different? Which would you
rather have if forced to choose between them?

14. Reasons to Conduct Market Tests--

15. Value and Incidence of Relevant Product Messages
without and with Predictive Modeling
Value of Relevant
Product Messages
High
Low
Value
Low High
Product Assortment

16. Value and Incidence of Relevant Product Messages
without and with Predictive Modeling
High
Low
Value
Low High
Product Assortment

17. Value and Incidence of Relevant Product Messages
without and with Predictive Modeling
High
Low
Incidence of Relevant
Product Messages Given
Predictive Modeling
Value
Waste Eliminated
by Predictive Modeling
Low High
Product Assortment

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