This document discusses predictive modeling and its use in targeting customers for marketing campaigns. Specifically: - Predictive modeling can be used to target only 25% of a customer list but still generate great value by improving efficiency and effectiveness. - Uplift modeling, a type of predictive modeling, is used to identify which customer segments are most likely to be positively influenced by a marketing contact rather than just predicting behavior. - Case studies show how uplift modeling improved campaign ROI and reduced costs for a bank's cross-sell efforts, demonstrating the value of this approach.

1. 0

2. 1

3. 2

4. 3

5. 4

6. 5

7. 6

8. 7

9. 8
A crummy predictive model delivers big value. It’s like a skunk with bling.
Simple arithmetic shows the bottom line profit of direct mail, both in general and
then improved by predictively targeting (and only contacting 25% of the list). The
less simple part is how the predictive scores are generated for each individual in
order to determine exactly who belongs in that 25%. For details on how this
works, see Chapter 1 of the book "Predictive Analytics: The Power to Predict
Who Will Click, Buy, Lie, or Die" (http://www.thepredictionbook.com).

10. Put another way, predicting better than guessing is often sufficient to generate
great value by rendering operations more efficient and effective. For details on
how this works, see the Introduction and Chapter 1 of the book "Predictive
Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die" (http://
www.thepredictionbook.com).
9

11. 10
A crummy predictive model delivers big value. It’s like a skunk with bling.

12. 11

13. 12

14. Does contacting the customer make them more likely to respond?
MEDICAL:
Will the patient survive if treated?
"My headache went away!“ Proof of causality by example.
Driving medical decisions with personalized medicine: selecting treatment, e.g.,
treating heart failure with betablockers
Personalized medicine. Naturally, healthcare is where the term treatment
originates. While one medical treatment may deliver better results on average
than another, personalized medicine aims to decide which treatment is best suited
for each patient, since a treatment that helps one patient could hurt another. For
example, to drive beta-blocker treatment decisions for heart failure, researchers
"use two independent data sets to construct a systematic, subject-specific
treatment selection procedure." (Claggett et al 2011) Certain HIV treatment is
shown more effective for younger children. (McKinney et al 1998) Cancer
13

15. 14

16. 15

17. A more general, encompassing definition of uplift modeling.
16

18. Persuasion; influence.
17

19. 18

20. 19

21. Graph image reproduced with permission, courtesy of Kane et al (2011), as
depicted in their Predictive Analytics World presentation
20

22. 21
Slide courtesy Pitney Bowes Software.

23. 22

25. US BANK EXAMPLE
… to existing customers
Resulting improvements over prior conventional analytical approach:
Campaign ROI increased over 5 times previous campaigns (75% to 400%)
Cut campaign costs by 40%
Increase incremental cross-sell revenue by over 300%
Decrease mailings to customers who would purchase whether
contacted or not, and customers who would purchase only if not contacted.
Sources: Radcliffe & Surry (2011), Tsai (2010), Patrick Surry (Pitney Bowes
Business Insight), Michael Grundhoefer (US Bank)
24

26. 25

27. Reproduced with permission.
Response rate is not the business objective!
26

28. 27

29. 28

30. 29

31. 30

32. 31

33. The US's Democratic National Committee keeps a database of registered voters,
including how they responded to prior interaction with campaign volunteers.
This project varied from the norm since it uses persuasion modeling (aka, uplift
modeling), a less common and more advanced form of predictive modeling. Most
predictive modeling endeavors predict something recording in the past (did the
individual buy, for example), so the organization need not collect additional data
for the project - the data already collected simply in the course of doing business
provides enough material to work with. But, for persuasion modeling, you need a
control set of individuals *not* exposed to the marketing treatment (in this case,
no volunteer knocking on the door or calling). Also, since it is about voting
behavior rather than buying behavior, no organization actually has each voter
choice/outcome merged in with the voter's identity. Therefore, polling is the only
approximate way to get that. To collect data for this project, over several weeks in
2012 the Obama campaign conducted special polls, which were coordinated with
applying (and not applying) the marketing treatment (campaign volunteer
interaction) on samples of voters.
Two articles I wrote provide more details on the Obama campaign's use of
predictive analytics (one a reprint of the pertinent section in my book):
32

34. From Predictive Analytics, by Eric Siegel (http://www.thepredictionbook.com):
Unsurprisingly, 2016 presidential campaigns are gearing up to apply persuasion
modeling. The specifics are well-guarded secrets, but the trend is undeniable.
Even as early as July 2015, Hillary Clinton’s “analytics team is looking for data
nerds,” said her campaign website. Shown as one of 11 campaign job categories,
analytics included five types of open roles. Analytics job postings for the
campaign on relevant industry portals enlisted staff for “helping the campaign
determine which voters to target for persuasion.” Bernie Sanders’ campaign
website included “Director of Data and Analytics” as one of only five posted job
listings.
Years after the 2012 election, Daniel Porter’s perspective hasn’t changed. “It
remains clear that persuasion modeling is extraordinarily valuable for political
campaigns. In fact, after the experience accrued last time around, it’s sure to be
done by 2016 campaigns even more effectively than in 2012.” There’s also going
to be better data for this work, at least on the Democratic side. “The DNC is
building out further its data infrastructure about voters in battleground states.”
33

36. 35

37. 36

39. 38

41. 40

42. Improvements are relative to their existing best-practice retention models.
Case study presented at Predictive Analytics World, February 2009, San
Francisco.
Case study and graph courtesy of Pitney Bowes Business Insight.

43. 42

44. Arizona's Petrified Forest National Park
Psychology professor Robert Cialdini
43

45. 44

46. 45

47. 46

48. Thanks to Patrick Surry at PBBI for this example segment.
Contacting entire list produces a slight downlift, but the segment above produces
an uplift.
This example is simplified for this illustration.
Both training sets A and B have the same variables.
Instead of identifying a “hot” segment with more purchasers/respondents than
average (i.e., predicting behavior), identify segments like this one within which
customers are more likely to be positively influenced by marketing contact, i.e.,
for which there is a higher purchase rate in training set A (the active treatment –
contact) than in training set B (the passive treatment – no marketing contact) for
the same segment.
47

49. 48

50. 49

51. Free white paper: www.predictiveanalyticsworld.com/signup-uplift-whitepaper.php
More updated version thereof is Chapter 7 of “Predictive Analytics” (www.thepredictionbook.com)
Or see the (much more) technical papers that chapter cites - see under Chapter 7 of the book's Notes PDF, available online at http://www.PredictiveNotes.com.
See also: http://www.predictiveanalyticsworld.com/patimes/personalization-is-back-how-to-drive-influence-by-crunching-numbers/
50

52. 51

53. With events 10 times a year globally, Predictive Analytics World delivers vendor-
neutral sessions across verticals such as banking, financial services, e-commerce,
entertainment, government, healthcare, manufacturing, high technology,
insurance, non-profits, publishing, and retail.
Predictive Analytics World industry events include PAW Business, PAW
Government, PAW Healthcare, PAW Workforce, and PAW Manufacturing.
Why bring together such a wide range of endeavors? No matter how you use
predictive analytics, the story is the same: Predictively scoring customers,
employees, students, voters, patients, equipment, and other organizational
elements optimizes performance. Predictive analytics initiatives across industries
leverage the same core predictive modeling technology, share similar project
overhead and data requirements, and face common process challenges and
analytical hurdles.
52

55. 54

56. 55