Data driven decisions are meant to maximize impact - right? Well, the only way to optimize influence is to predict it. The analytical method to do this is called uplift modeling (aka, persuasion modeling). This is a completely different animal from standard predictive models, which predict customer behavior. Instead, uplift models predict the influence on an individual's behavior gained by choosing one treatment over another. In this session, PAW founder Eric Siegel provides an introduction to this growing area.

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

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

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13. 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
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16. A more general, encompassing definition of uplift modeling.
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18. Persuasion; influence.
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19. Heisenberg’s uncertainty principle.
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23. Graph image reproduced with permission, courtesy of Kane et al (2011), as
depicted in their Predictive Analytics World presentation
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Slide courtesy Pitney Bowes Software.

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27. 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)
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41. 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.

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43. Arizona's Petrified Forest National Park
Psychology professor Robert Cialdini
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48. Net
weight
of
evidence
(a
measure
of
upli7)
varies
by
a
customer's
number
of
open
revolving
accounts.
Graph
courtesy
of
Larsen
(2011).
Example variables that may generate uplift:
Engagement: Upside-down U such as the graph
above is common. Those customers towards the
right are "tapped out".
Other variables with similar upside-down U
phenomena:
Recency: Purchased their last car between 4 and
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49. 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.
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52. (This paper in turn references all the core technical papers on this topic.)
Free white paper:
www.predictiveanalyticsworld.com/signup-uplift-whitepaper.php
See also: http://www.predictiveanalyticsworld.com/patimes/personalization-is-
back-how-to-drive-influence-by-crunching-numbers/
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54. Reproduced with permission.
Response rate is not the business objective!
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55. 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.
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