Subscribe to DSC Newsletter

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

Views: 161

Reply to This

Replies to This Discussion

Dear Himanshu,
Good afternoon. I'm not expert but still, had you seen the distribution of your sample? Is it normal? I mean to say, if few categories having large probability then there is the possibility of such situation occur.
Try to change weighted which tool will generate for predictive model. I hope it will resolve you issue.
Regards,
Sagar.

RSS

On Data Science Central

© 2019   AnalyticBridge.com is a subsidiary and dedicated channel of Data Science Central LLC   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service