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Hi All ,
I've recently build a predictive data model for a campaign to target group of customer who can switch from payment method "A" to Payment method "B".
The idea was to come up with predictive model with payment/usage variables as independent variable and the event of switch in past is taken as dependent variable.
i) I used logistic regression model for probability estimation.
ii) The data is sorted in order of descending probability and customer's were solicited.
iii) The results analysed after campaign suggests , the customer have low probability have better switch rate and high probability have lower switch rate.
iv) Below are the results:
Decile | Control | Test | Total | ||
No | Yes | No | Yes | ||
1 | 2784 | 5 | 2450 | 7 | 5246 |
2 | 2871 | 9 | 2191 | 6 | 5077 |
3 | 2861 | 5 | 2145 | 6 | 5017 |
4 | 2795 | 11 | 2851 | 12 | 5669 |
5 | 2924 | 11 | 1060 | 5 | 4000 |
6 | 3018 | 17 | 3153 | 14 | 6202 |
7 | 3200 | 27 | 2781 | 11 | 6019 |
8 | 3142 | 22 | 2145 | 4 | 5313 |
9 | 3079 | 28 | 3142 | 16 | 6265 |
10 | 3161 | 31 | 3078 | 13 | 6283 |
Grand Total | 29835 | 166 | 24996 | 94 | 55091 |
I assumed the customer with high propensity should have high switch rate , so the switch rate in top decile should have been higher.
Could anyone please suggest
i ) what mistakes I could have done.
ii) Any similar experience and remedy .
Regards,
Himanshu
Tags: analytics, campaign, model, propensity, scoring
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