Cequity has built a Tree Model to reach high propensity customers after identifying the variables which makes the difference through rigorous statistical modeling and analysis. To find out about Cequity's services visit this link http://www.cequitysolutions.com/analytical-marketing.php

1. Case Study
Cross-Sell Opportunity
Formulation for a reputed
bank
Building a Decision Tree Model to
help identify customers
susceptible to cross change
initiatives
Client: A new bank moving to
target its liability customers for
asset products

2. Summary
• Our client was trying to find out ways to gain wallet-share of it
Business customers (savings & current a/c holder)
Objective • It was looking for a Decision Tree model to give likely list of leads
so as to focus the marketing campaigns towards them.
• Cequity identified the variables which makes the difference
Solution through rigorous statistical modeling and analysis
• Once the variables were identified , the best possible path to reach
high propensity customers through decision tree modeling
• We built a quantifiable model for client to reach the best leads
through decile treatment
• Based on the behavior pattern, we could predict the right offerings
Results for each segments.
• There was a huge lift in conversion rate for our client using the
Cequity model. Marketing & campaigning spends were also
optimized.

3. Business Objective
Our client was facing low conversion rate in cross-selling the Assets
products to its Liability customers. Although the Liability and Asset
products have been on the market for quite some years, the
overlaps for its customer into these Venns were very low.
But it would have been imprudent to expend marketing resources
on entire liability customer base with for cross-selling them asset
product. It was desperately looking for a model to focus its
resources better.
We built a Cross-Sell model taking into consideration all factors like
Demographics, Transactions, Psychographics and Response from
previous campaigns.
The result was evolution of a non-linear model for predicting the
chances of buying its asset products within its liability customer
base.

4. Solution – Finding out micro segments
Uni-Variate Analysis Multi-Variate Analysis
Response Response
Criteria Criteria
Rate Rate
Quantum leap
in targeting
Marital
the right Marital Status =
Status = Y1 % X1%
XXX
customers
XXX
Marital Status =
Ledger Y2 % XXX & Ledger balance X2%
balance < XXX < XXX
Marital Status =
XXX & Ledger
Number of balance < XXX X3%
Fixed Y3 % &Number of Fixed
deposits < X deposits < X
Marital Status =
XXX & Ledger
Amount balance < XXX &
X4%
Number of Fixed
credited in
Y4 % deposits < X & Amount
last x months
credited in last x
> xxx months > xxx

5. Solution – Analysis
Uni-Variate Analysis Multi-Variate Analysis
Response Response
Criteria Criteria
Rate Rate
Marital
StatusEmpower with Y1 % Power of Multi-Variate
The Marital Status =
= X1%
XXX
Analysis
XXX
Marital Status =
Ledger Y2 % XXX & Ledger balance X2%
balance < XXX < XXX
Marital Status =
XXX & Ledger
Number of balance < XXX X3%
Fixed Y3 % &Number of Fixed
deposits < X deposits < X
X4 is much
much higher Marital Status =
than Y4 XXX & Ledger
Amount balance < XXX &
X4%
Number of Fixed
credited in
Y4 % deposits < X & Amount
last x months
credited in last x
> xxx months > xxx

6. Solution – Building the Decision Tree
Supe rvised
C lassification
Criterion # 1
Increasing Gain – “Good” customer characteristics
Gain
Gain on 6,00,000
18%
X 1
%
Criterion # 2 INR xxx – INR xxx INR xxx – INR xxx
< INR x x x INR x x x – xxx > INR x x x
(montly avg
balance)
Gain
Gain
X
28% 2
%
Criterion # 3 < x m onths x – y m onths y-z m onths z+ m onths
(MOB) Gain
Gain
X3
35%
%
0-a de bits a-b de bits b-c de bits c-d de bits d+ de bits
Criterion # 4
(# of debits) Gain
Gain
X4
39%
%
Se lf e mployed Em ployed with Em ployed with Sm all scale
Criterion # 5 PSU C orporate business
Gain
(occupation) se tup pe rson
Gain
X5
45%
%
p-q yrs q-r yrs r-s yrs > s yrs
Criterion # 6
(age group)
Gain
Gain
49%
X6
%
6

7. Solution – Building the Decision Tree
Supe rvised
C lassification
Criterion # 1
Increasing Gain – “Good” customer characteristics
Gain
Gain on 6,00,000
18%
X 1
%
Criterion # 2 INR xxx – INR xxx INR xxx – INR xxx
< INR x x x INR x x x – xxx > INR x x x
(montly avg
balance)
Gain
Gain
X
28% 2
%
Criterion # 3 < x m onths x – y m onths y-z m onths z+ m onths
(MOB) Gain
Gain
X3
35%
%
0-a de bits a-b de bits b-c de bits c-d de bits d+ de bits
Criterion # 4
(# of debits) Gain
Gain
X4
39%
%
Se lf e mployed Em ployed with Em ployed with Sm all scale
Criterion # 5 PSU C orporate business
Gain
(occupation) se tup pe rson
Gain
X5
45%
%
p-q yrs q-r yrs r-s yrs > s yrs
Criterion # 6
(age group)
Gain
Gain
49%
X6
%
Monthly A vg Bal INR XXX The customers belonging to the adjacent
The Ideal Months on books x-y months segment would be the preferred target for our
Profile # of debits b-c debits cross sell exercise (a given asset product)
Occupation XXX
A ge group q - ryrs
7

8. Results
Optimized
marketing
efforts and
Increased
spend
Response rates
and
conversions
Identify
customers in
Top Deciles
who have
propensity of
Target right buying an
customer Asset product
with right
product

9. Thank you
Customer Equity Solutions Pvt. Ltd.
Worldwide Offices
INDIA USA
Mumbai Office: 105-106, 1st Floor, Chicago Office: 626,
Anand Estate, 189-A, Grove Street, Evantson, IL
Sane Guruji Marg, Mahalaxmi, 60201
Mumbai-400 011
Phone: +91 22 4345 3800
Fax: +91 22 4345 3840
www.CequitySolutions.com