The document discusses how predictive models can be used to gain customer insights from existing customer data. It describes building a predictive model by collecting customer data from various sources, selecting significant variables, grouping variables through analysis, and building a predictive model to sort customers. A case study example is provided of a company that used predictive modeling to successfully target customers for a cross-selling insurance campaign.

1. Alessandro Leona – http://www.linkedin.com/in/alessandroleona
IMPROVING CUSTOMER INSIGHT
THROUGH PREDICTION MODELS

2. Alessandro Leona – http://www.linkedin.com/in/alessandroleona
1
EXECUTIVE SUMMARY
How to tell if one of your clients will be leaving you in the very next future or on the contrary if he will be
upgrading his offer ? Which client segments are more prone to churning and which are the best targets for
outbound calls promoting a new product / service ?
Companies are striving to get insights from market researches, focus groups and the likes, forgetting that
most of the answers already resides in their hands and in the tons of information contained in their
databases, which is now fashionable to call “Big Data”
This presentation shows how a prediction model can be used to:
- identify patterns within your customer databases
- express these patterns in the form of an equation to be applied across the whole database
- sort your database in order to group all the similar clients in clusters
- take actions targeted at relevant segments
…without being a statistics guru or an IT expert
…without investing millions in expensive software
…in a very short time

3. Alessandro Leona – http://www.linkedin.com/in/alessandroleona
2
The objective of prediction models is to spot similar
behaviours within your customer base
Client problem:
"I have a customer base of million clients and I lost 2% of them in
the last month, how can I spot who could be the next churner and
take preventive action ?”
”I just launched a new product trial in a region and it was a
success, how can I select the roll out strategy ?”
“I need to revamp sales on a product I sold last year, who should
the new campaign target ?
ID Age Gender Tenure Usage/(min) CC/calls Churner
1 26M 5,6 811 0
2 36M 9,6 124 0
3 41F 0,4 137 0
4 48F 1,1 635 1
5 55M 5,0 655 0
6 34F 4,9 500 0
7 22M 9,4 63 0 1
8 28M 5,2 849 0
9 54M 7,7 577 0
10 23F 3,8 13 0
11 28M 8,6 286 0
12 33F 1,7 407 0
13 52M 6,6 353 0
14 30F 2,7 859 0
15 33M 5,5 544 0
16 36F 8,9 211 1
17 20M 8,7 243 1
18 39M 6,2 520 0
19 27F 0,8 663 1
20 35F 0,5 937 0
145 25F 1,6 679 0
146 48F 1,4 329 0 1
147 29F 9,3 918 0
148 50M 9,2 270 1
149 52M 7,3 741 0
150 23M 3,8 442 0
151 26F 6,4 263 0
152 60F 7,2 14 0
153 23M 0,7 20 0
566 65F 8,8 797 2
567 33M 8,0 798 0 1
568 65F 9,8 412 0
569 67F 5,7 561 0
1343 48M 8,2 52 0
1344 26M 6,0 834 1 1
1345 49F 9,2 664 2
1346 63F 1,7 197 2
1347 35M 3,3 100 0
A predictive model helps to understand whether there is any
correlation between a certain behaviour (flagged in yellow) and a
set of variables related to the client:
- Anagraphics: Age, Gender, Occupation, Address, Family
composition, …
- History as a client: Past purchases, Revenue, Product portfolio, ...
- Channel interations: Visits per store, Complaints, Time spent on my
website, …
- …

4. Alessandro Leona – http://www.linkedin.com/in/alessandroleona
A prediction model helps identifying clusters in order
to take targeted actions
3
ID Age Gender Tenure Usage (min) CC calls Churner Prospect churner
1 39 M 0,1 222 0 1
2 35 F 2,4 581 0 1
3 30 F 1,8 399 0 1
4 33 M 8,1 536 1 1
5 21 F 5,4 423 0
6 47 M 1,1 187 0
7 29 F 4,8 172 0 1
8 33 F 3,7 946 0
9 55 M 7,9 692 0 1
10 49 M 2,7 309 0
11 44 M 9,2 931 0 1
12 28 F 5,4 334 0
13 43 M 7,4 838 0
14 44 M 4,4 485 0 1
15 29 F 5,0 850 0 1
16 32 F 2,4 640 1
17 21 F 9,3 285 1 1
18 26 F 6,3 336 0 1
19 46 M 6,0 415 1
20 57 F 1,4 890 0 1
21 38 M 6,6 61 0
22 55 F 9,6 806 0 1
23 25 M 7,5 792 0
24 24 M 9,6 763 1 1
25 23 M 10,0 738 0
26 28 F 3,1 455 0
27 21 M 4,2 151 0 1
28 38 M 5,1 275 0
29 55 M 3,2 494 0
30 20 M 3,5 577 2
31 44 F 3,6 343 0 1
32 49 M 6,2 808 0
33 50 F 7,4 532 0
34 24 F 0,5 922 0
35 44 M 9,5 8 1 1
36 56 M 9,1 478 2
37 56 F 4,3 998 2
38 38 F 3,1 840 0 1
39 41 F 7,4 936 0
40 51 M 3,5 440 0 1
41 55 F 7,6 640 0
42 31 M 1,2 702 0 1
43 23 F 9,6 341 0
44 49 M 8,0 719 0
45 44 F 9,4 707 0
46 34 F 8,4 243 0 1
47 26 F 4,9 718 0
48 40 M 8,1 423 0
49 53 F 5,4 664 0
50 41 F 1,5 63 0 1
51 39 F 9,9 787 0
52 49 F 8,4 316 0
53 29 F 9,2 190 0
54 55 F 2,5 113 0
55 43 F 1,5 690 0 1
56 34 M 4,7 643 0
57 56 F 2,7 946 0
58 33 F 7,1 628 0
59 46 M 0,6 551 0
60 59 M 1,8 775 0
61 35 M 7,6 990 0
62 58 F 6,7 115 0
63 21 F 5,0 192 0
64 43 F 5,4 791 0
65 41 F 4,0 158 0
66 57 M 4,6 2 0
67 34 F 2,3 691 0
68 49 F 8,3 642 0
69 32 F 9,2 920 0 1
70 24 F 9,0 475 0
71 50 M 2,7 164 0
72 24 F 7,2 6 0
73 58 F 6,8 114 0
74 24 F 6,8 69 0
75 55 M 7,1 909 0
76 29 M 4,5 306 0
77 35 M 7,3 368 0
78 26 M 5,4 224 0
79 46 M 9,0 547 0
80 22 M 6,7 163 0
81 51 F 2,9 432 0
82 53 M 7,1 464 0
83 36 F 1,8 375 0
84 58 M 8,4 289 0
85 24 F 4,5 796 0
86 34 M 4,4 130 0
Build the DB
Select significant
variables
Group variables
Build the
predictive model
Sort your DB
according to
the prediction
Gather relevant information from your sources
(accounting, marketing, sales channels, loyalty, CRM,
business intelligence, …)
Through uni-variate analysis the relevant variables are
selected and the others are discarded (based on
confidence, R2, etc.)
Bi-variate analysis allows to find cross correlations
(e.g. age and occupation, gender and tenure, etc.) and
to group variables by significance
Log-linear regression allows to define a predictive
function F(client)=a1*var1*a2*var2*… to be applied to
the entire DB
Based on the predictive function, the database is
sorted in order to spot the flagged clients (churners,
previous buyers of a product, etc.) and their look alikes
Real
churners
Potential
churners
STEPS

5. Alessandro Leona – http://www.linkedin.com/in/alessandroleona
4
The first step is to collect information from different
sources and create an offline database
Work
DB
Channels
Surveys
Campaigns
Past
churners
External
Mkt info
CRM
(complaints?)
Usage
…
1) Collect information from your departments
2) Define the target of your research, for example:
- Who will leave in the next future ?
- Who will buy more of this product ?
- Who will be a good target of the new campaign ?
- …
3) Don’t be afraid to add variables, the prediction model doesn’t
know what is the meaning of a field. For the same reason do
not exclude variables “a priori” based on your feelings
4) Clearly flag the clients whose behaviour is well known, e.g.
previous churners, previous buyers of a certain product

6. Alessandro Leona – http://www.linkedin.com/in/alessandroleona
5
The second step is to select relevant variables
Number of customers
Voluntary deactivation
rate
17%
17,6%
20,4%
22,0%
25,5%
15,0%
No calls Other calls 1 2 >2
No complaint calls
Average
voluntary
deact. rate
Number of
calls/year to
call center
Number of complaint calls
Usage of the bundle
Average
voluntary
deact. rate
Percentage
of bundle
usage
28,0%
20,2% 16,4% 16,0% 14,5%
56,6%
0% 0%-20% 21%-50% 51%-80% 81%-120% >120%
Very low consumption
of the bundle
17%
High/full exploitation
of the bundle
Customers with high n. of calls
to call center show a high
churn
Customers using bundle offers
have a lower churn
Through univariate
analysis some trends are
spotted
Variables with low
correlation are
discarded from the
model
EXAMPLES

7. Alessandro Leona – http://www.linkedin.com/in/alessandroleona
6
The further step is to group variables using
bi-variate analsys
0,00%
0,20%
0,40%
0,60%
0,80%
1,00%
1,20%
1,40%
1,60%
<21 21-30 30-50 50-60 60-80 >80
< 1 year
1 year
2-4 years
5-10 years
> 10 years
0,550%
Buyers of new product vs. age class and tenure of contract
Higher frequency of prospects
within middle aged customer base
acquired 2-4 years ago
Average of
buyers within
customer base
Every couple of relevant variables
is tested and sorted according to
significance
EXAMPLES

8. Alessandro Leona – http://www.linkedin.com/in/alessandroleona
7
A real case example in cross-selling
Automatic toll collection player
• +40% sales of insurances after campaign launch
• +30% of opening emails on the profiled cluster vs.
random cluster
• 65% of people called by outbound were
interested about the product for the profiled
client (vs. less than 30% of random)
• Competence, skills and instruments were
transferred to the client through the client team
member who worked with us full time
• ... with an IT investment of 2.000 EUR for 2 SAS
licenses (now with software as a service it is no
longer required)
Context
•Telepass: Automatic toll collection client (5
milion clients)
•Premium program launched together with
insurances, travel agencies and fuel retailers
(cross fidelity program)
•Willingness to relaunch the insurance product
•Generic DB with info on age, address, km of
highway per year, rate of opening of emailing
campaigns, ...
Activities performed
•DB preparation correlating internal and external
sources (demographics)
•Scoring model using clients who had already
purchased an insurance
•Launch of a commercial multi channel campaign
on two client clusters, one random and one
profiled (to compare results)

9. Alessandro Leona – http://www.linkedin.com/in/alessandroleona
8
Log-linear regression allows to determine the
predictive function
EXAMPLES
1 indifferent
<1 inversely correlated
>1 proportionally correlated
Insurance
buying
interest
probability
= 0.00013413 x
1
0.78
1
1
1
1
1
1
1
Km Twin Tenure Premium x
#cars x
x x x x
1
0.58
1
1
1
1
1
1
1
1
14.9
1
1
1
1
1
1
1
1
1
0.77
1
1.29
1.26
1
1
1
1
1
1.5
1
1
1
1
1
1
1
1.64
2.08
1
1
1
1
1
2.2
Age
#apparatus Urban Camp1x x x
1
0.62
1
1
1
1
1
1
1
1
3.2
1
1
1
1
1
1
1
1
0.71
1
1
1
1
1
1
1
1
0.89
1
1
1
1
1
1
1
Camp2
After grouping the variables and transforming cluster
variables into vectors, the log linear regression helps
expressing the correlations found:
The prospect buyer is (probably):
- Somebody who does very few km in the motorway
- Somebody acquired 3 years ago
- Somebody who has a premium account
- Somebody within an age range of 50-70
- Not living in a urban area
- ….
0
66,4%
82,5%
89,3%
92,5% 94,3% 95,0% 96,5% 97,6% 98,9% 100,0%
0 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
LIFT CURVE PROSPECTS VS. RANDOM FUNCTION
To verify the model accuracy, a lift curve tells us how
many buyers and prospect buyers do we progressively
find in the database sorted according to the prediction
function.
A parallel database is sorted according to a random
uniform function. The same database is used to extract
control samples and verify the effectiveness of the
campaign
Top of DB Bottom of DB
Predicted
Random

10. Alessandro Leona – http://www.linkedin.com/in/alessandroleona
9
There are many tools available to build
a prediction model
The choice very much depends on:
- how familiar you are with programming
- whether you want to work on the cloud vs. on local
databases, with all the privacy/security issues
related
- how frequently you want to update your model
- how much you are willing to spend and whether you
want to integrate the prediction suit in your existing
IT systems
- how fast vs. how accurate the model needs to be

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10
The prediction factor can be combined with a proxy of
client value to derive priorities
“Client value @ risk” valuation... … helps preventing value loss
Churn
probability
Low High ARPU
LTV
Low
High
High client
value @ risk
Value of client loss in
case of churn
(MEUR/month)
Number of clients
38%
56%
76%
83%
88%
91%
94% 96%
0%
20%
40%
60%
80%
100%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
68%
10% of clients represent more than 1/3
of client value at risk
First segment
to address

12. Alessandro Leona – http://www.linkedin.com/in/alessandroleona
11
Lessons learnt
• Consider all possible variables, do not exclude “a priori”
• Group variables in bigger categories to obtain better reliabitlity of samples
• Measure the validity of your model comparing actions versus a random list
Statistical
validity of
prediction
model
• Low number of segments (keep it simple)
• Always verify with external interviews (outbound) the level of interest of
retention actions or new proposals
• Build the statistical analysis within the company, do not fully rely on the
external consultant or the IT integrator, when they will leave you have to be
able to replicate the prediction
• Update periodically the predicting model
Other elements
Database
preparation
• It is wise to launch a survey on a sample of churning clients to identify causes (some
keywords can be isolated and later utilised in the call center to give warning signals
of possible churn)
• Churners must be isolated from wanted (grasshoppers switching at the end of
contract period) or internally driven churners (bad debt)
• The analysis must be done in a “quiet” period, far from campaigns / offer changes