Download Data Mining for Profitable CRM

Post-processing Decision Trees
to Extract
Actionable Knowledge
Qiang Yang and Jie Yin
HKUST, Hong Kong
China
and
Charles X. Ling and Tielin Chen
Department of Computer Science
University of Western Ontario, Canada
CRM
Customer Relationship Management:
focus on customer satisfaction to improve profit
Two kinds of CRM
 Enabling CRM: Infrastructure, multiple touch point
management, data integration and management, …


Oracle, IBM, PeopleSoft, Siebel Systems, …
Intelligent CRM: data mining and analysis, database
marketing, customization

Vendors/products (see later)
From Data Mining to Actions

What to do to help Sammy to get loan
approval?
Applicants
Customer Database
Sammy
Beatrice
Dylan
Mathew
Larry
Basil
Income
50K
50K
80K
30K
40K
80K
Married
n
y
n
n
n
n
Cars
1
1
2
1
0
1
Approved?
?
Yes
Yes
No
No
Yes
•Action 1 (Dylan): get higher income to 80K
• Action 2 (Beatrice): get Married!!

Actionable vs. Passive Data
Mining
Improve customer relationship


What actions to take to change customers from an undesired status
to a desired one







Actions (promotion, communication)  changes
From churn to loyal
From inactive to active
From low spending to high spending
From non-buyers to buyers
…
and still make a profit (the ultimate goal)
Approach: Post-processing Decision Trees

Mining actions from decision trees



Bounded action problem
Bounded segment problem
Our solutions
Post-processing Decision Trees
1.
2.
3.
4.
Get Customer Data (marketing DB)
Build Customer Profiles
Search Actions for Maximal Profit
Action Delivery
Step 1: Get Customer Data
Marketing DB: Segmentation, data preparation, pre-processing…
Define a “target”: undesired status and desired status
ID
Name
Age
Sex
Service Rate Prof …
Retained
(Target)
1001 John
50
M
H
L
A
…
Yes
3010 Sue
25
F
M
H
D
…
No
…
…
…
…
…
…
…
…
40
M
M
L
B
…
???
…
1112 Jack
Step 2: Build Customer Profile on target
Automatically by Proactive Solution with probabilities on the target
Service
M
H
L
Sex
F
Rate
M
L
H
Prob=0.1
Prob=0.9
Prob = 0.2
Prob=0.8
Prob=0.5
Step 3: Search Actions for Maximal
Profit
Proactive Solution searches more desired nodes in the profile…
ID
Name
Age
Sex
Service Rate Prof …
Retained
…
…
…
…
…
…
…
…
…
40
M
M
L
B
…
???
1112 Jack
Jack: …, Service = M, Sex = M, … Profit =$4000
Service
M
H
L
Sex
F
Rate
M
L
Prob=0.1
H
Prob gain
= -0.1
Serv:
MH
E.Profit= -400
Rate:
?L
Cost= $500
E.Net Profit= -900
Prob=0.9
Prob gain = 0.7
E Profit= $2800
Cost = 
E Net Profit= - 
Prob = 0.2
Prob=0.8
Prob gain = 0.6
E
Profit=$2400
E.Profit=$2400
Cost=$800
E
NetProfit=$1600
E.NetProfit=$1600
Prob=0.5
Prob gain = 0.3
E Profit=$1200
Cost=$400
E NetProfit=$800
Step 4: Action Deployment
ID
Name
Prob Actions
diff
1112 Jack
… 0.6
3010 Sue
0.5
3421 Bill
…
Action
costs
Service: M  H $800
Rate: L  M
SigAcc: 0  1
$500
Service: L  M
N/A
NetProfit
… $1600
… $700
$0
• Selective deployment: human intelligence, …
• Customer segmentation by actions
Practical Issue: Resource is
Bounded

Limited number of account managers


Thus, the number of customer segments is bounded
Research: how to generate no more than K customer
segments, such that



We call this the bounded segmentation problem (BSP)
Limited number of marketing actions



for each segment, find a set of common actions to apply
Thus, types of actions are limited
We call this the Bounded Attribute Set Problem (BASP)
Both problems are NP-hard.
The Bounded Segmentation
Problem



Resources are bounded!
Group (potential) negative-class customers into prespecified k customer segments.
Recommend “near optimal” actions to help each of the k
customer segments switch to a more profitable positive
class.



Each segment is applied by the same actions (same manager)
The expected net profit is to be maximized
 Each action may have a different cost and bring different
profits
The Bounded Segmentation Problem is NPComplete

Equivalent to maximum coverage problem.


NP-hard problem!
We seek approximate solutions!
The Bounded Segmentation
Problem: Greedy Algorithm
1.
Discover who are negative-class customers.

2.
build decision tree as the classifier
Group negative-class leaf nodes into k customer
segments using greedy algorithm.



Each customer segment  one action set
The total profit gain by applying such k action sets
can be maximized.
Algorithm is based on finding the current largest
coverage in linear time
An Example: K=2
Service
L
H
Status
A

Rate
B
C
D
L1
L2
L3
L4
0.9
0.2
0.8
0.5
If we want to find two customer segments (k=2)


It is more profitable to transform L2L1 and L4L3 than others
Profit gain = (0.9-0.2)*1-0.2 + (0.8-0.5)*1-0.1=0.7.
cost
Experiment on Mutual Fund Data


GreedyBSP can find k customer segments with maximal
profit. Result is very close to those found by OptimalBSP.
GreedyBSP is more scalable than OptimalBSP.
Summary
From decision-tree model building to extracting
actions for profit
 Goal: maximal net profit
 Resource is bounded

Design optimization solutions for action extraction
 BASP and BSP


Future: explore more efficient solutions