Predicting customer recharge/topup value for prepaid customer

predictedvalue
spss
categorical
predictive_model

#1

We are trying to predict customer’s Total recharge value for the next week. We came up with 8 recharge bands. So its now a classification problem. We have tried history of last 8 weeks(Weekly aggregated attributes). We have tried all the famous techniques like feature selection, PCA, feature engineering, tried different algorithms, balancing, boosting, bagging. But the overall accuracy is always below 45%. How can we improve it ? Also can time series be of any help for this problem?


#2

if u have trnxs data, i think you can also adopt RFM approach to predict total value recharge by users in one week time period. there is a very good package in R called BTYD, it can help you predict the value of recharge a customer will go for in specific time period (day, week, month, year). you got to have user_id, trnx_date, amount variables in data & go for BTYD. you will also be able to get churn, LTV etc out of it


#3

Hi ParindDhillon,

Thanks for the help but we are using SPSS Modeler without the integration with R.


#4

please is there any update of this question?

I tried BTYD package in R but it did not perform well !!


#5

you can use ensemble method.That is combining various models to improve the accuracy.

Also validation is important for the sample you take.try various sampling techniques like kfold sampling etc.