This document discusses uplift modeling using concurrent decision trees to identify customers who are most likely to improve a given metric, such as traffic, sales, or revenue, when targeted with a specific treatment. Traditional approaches target high propensity customers, but uplift modeling directly models the incremental impact of a treatment. The document explains how uplift trees work by identifying splits in a tree that show the greatest difference between test and control groups, and then recursively splitting subgroups, with the goal of finding statistically significant micro-segments. Key differences between uplift and traditional segmentation models are also outlined.

1. UPLIFT MODELING USING
CONCURRENT DECISION TREES
PRESENTED BY: And Ozbay
June 22, 2015

2. INTRODUCTION
•Which customers should I target with my <treatment> in order to improve
my <metric>? (traffic / sales / revenue)
•Traditional wisdom suggests: Find people who have a high propensity to
respond and target them. Is this a sensible approach?
•Lift is measured by the difference in the average <metric> between
two groups
•Uplift Modeling is a predictive modeling technique that directly models
the incremental impact of a treatment
UPLIFT MODELING 2

3. AB TESTING
Test Control
UPLIFT MODELING 3

4. AB TESTING
USING SEGMENTS
Test Control
Red
Segment
Blue
Segment
Green
Segment
UPLIFT MODELING 4

5. AB TESTING
USING UPLIFT M.
Test Control
Micro
Segments
UPLIFT MODELING 5

6. MODELING MECHANICS – DECISION TREES
UPLIFT MODELING 6
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7. MODELING MECHANICS – UPLIFT TREES
UPLIFT MODELING 7
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8. MODELING MECHANICS – UPLIFT TREES
•Check to see where the greatest difference between the test and control
groups exist, testing all possible cuts
•Once a cut is established, keep identifying new cuts for the newly
identified sub-groups
•Run a t-test for every cut to see if the difference is statistically
significant using a Bonferroni Correction
•Stopping condition is finding no further cuts that are statistically significant
UPLIFT MODELING 8

9. QUESTIONS?
UPLIFT MODELING 9

10. DIFFERENCES BETWEEN SEGMENTATION MODELS
AND UPLIFT MODELS
UPLIFT MODELING 10
Uplift Segmentation
Specific for a stated objective – e.g.
interest in handbags, sports shoes, etc.
More generic, activity oriented – e.g.
unawares, “one and done”s, etc.
Identifies people who will purchase
conditional upon an action (e.g.
sending an email)
Identifies people who are alike, does
not recommend any course of action
Self correcting – changes happen both
when the user does something and
when marketer does something
Static – changes happen when the
user does something
Suitable for trade off decisions (e.g. get
more total iGMB as long as iGMB / e-
mail is greater than $0.14)
Trade off decisions harder to
implement