2. How to identify potential repeat customers?
Type of Customer ( since 2005)* No. of Customers
Repeat Customer 125,629
Customer who purchased 1 car with ALJF 425,745
How many are Repeat customer? A “subset” of 425,745 ( about 20 %)
Need to identify a time window beyond which second purchase chances are very low.
First purchase Customer older than this can be safely assumed to be non-repeat customer.
3. Is there a pattern in the time between
subsequent purchase for repeat customers?
5%
10%
12%
10%
9%
8%
7%
6% 6%
5%
4%
4%
3%
2%
2% 1% 1% 1% 1% 1% 1% 0% 0% 0% 0% 0% 0%
%ofsubsequentpurchase
Years between purchase of subsequent vehicles
Distribution of Time interval between purchase for all Repeat Customers
Need to identify a time window beyond which second purchase chances are very low.
First purchase Customer older than this can be safely assumed to be non-repeat customer.
Beyond 8 years only 5 % of the repeat customers bought their subsequent car.
Hence considering 8 years as a time window to observe repeat customers.
4. Training data for predictive modeling.
Subsequent Car
Buying preference generally depends upon multifactor ( Additional Data we have of our Customers)
Marital Status Nationality Work Place Job Car Model
Guest Age Range Guest Income Range Guest Gender Government/
Non Government
No. of Dependents
Customers profile Labelling as Repeat Customers
All repeat customers from 2005 till now 1
Single purchase customers 8 years from now, since 2005 0
Independent variables which determines
repeat/non repeat behavior:-
Data Partitioning:-
Training ( to train the model) Validation ( to test the model)
55% 45%
5. Probabilistic Binary Classification using Decision Tree
Decision Tree Model: Non-linear Prediction Model for binary
classification through hierarchical segmentation of the data, by
partitioning recursively.
A tree structure of rules over the input variables are used to
classify or predict according to the target variable ( Repeat).
Outcome assigns each case a probability, upon which one can
apply a threshold ( 0.5). Higher probability than threshold is
predicted as repeat customers and lower probability than
threshold is predicted as non-repeat customers.
7. Compare Models
Each model gives an improved classification than a naïve (randomly selecting
customers for solicitation) model. Decision Tree out performs other models.
8. Decision Tree Performance:-
The validation leaves are almost likely populated
as the training leaves indicating no over fitting.
The misclassification rate stabilizes beyond 50
leaves, indicating model converges. The depth
of the tree is good enough.
9. 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 100 200 300 400 500 600 700 800 900 1000
Probability
Customer Cases
Observing Probability Levels and Actual Repeat Customer Cases
More “crowded” plots at higher probabity.
10. Probability Zone for Maximum Prediction
Probability above 0.8 will have most repeat customer cases!
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Probability rating on actual Cases
Repeat Customer Non-repeat Customer
11. But who is “ready” to buy?
Most of subsequent purchase occur in this time window.
Superimpose on probability model to get Priority rating.
0
0.01
0.02
0.03
0.04
0.05
0.06
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0.08
0.09
0.1
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10
Percentage of Customer making 2nd purchase after 1st
Purchase wrt. time interval
12. Strategic Solicitation(Ranking and Targeting) :-
1. Score all single purchase customers within 8 years using different
predictive model(logistic regression/ neural network/decision tree).
2. Identify region of maximum purity derived from actual case frequency
on different probability levels.
3. Adjust the weight using “Distribution of time between purchase for
purchase customers” as a factor depending upon the time since
purchase. Assign peak(frequency) as weight of 100% and weights of
other period based on relative frequency to peak and modify
probability of being a repeat customer with the above factor.
4. Remove delinquent customers.
5. Sort them in descending order of repeat customer probability score
from the model.
15. Advantages of dashboard
Integrated Data availability for all stake holders ( Operation, Telemarketing).
Summary area provides a snapshot of campaign performance.
Automated process with minimal work to launch campaign.
Top management will observe telemarketing feed and actual buying behavior to
generate insight to improve sales.
Telemarketing can view the progress of their previous call, and pursue customers
who planned to buy but did not buy yet.
16. Future plans
1. Extend this campaign to customers acquisition
through external data.
2. Automate Telemarking update through web pages.
3. Create SMS broadcast service.
4. Direct campaign to auto-dialer.
