Hello Community,
Hope everyone is well in these tough times!
I have a data set that contains information on several telecommunication firm customers who, at some time, purchased a mobile phone. Attaching screenshot. I am trying to find if they exist, similar groups of customers to which promotion can be applied, in order to reduce churnrate.
Solved! Go to Solution.
The question is not very clear. What's the hypothesis question that you try to prove/disprove? At the minimum you need to specify the input vs. output.
Dawn.
Hi @Digvijay_khasa ,
This looks like you're trying to create a predictive churn model.
As @DawnDuong points out, we're going to need more information along with your data (remove names etc.) to help with this.
However, I would look into the Predictive section of the Academy to get yourself up and running with Churn prediction.
M.
@mceleavey @DawnDuong Thanks for your response.
Please find the data attached. The goal here is to find segments of users with similar spending habits/KPIs so that we may create a predictive churn model which would lead to recommendations to reduce the churn rate.
Meanwhile, I am also checking the Academy.
Best,
Digvijay
Hi @Digvijay_khasa - Your dataset is story telling. You may want to go to our Academy learn how to use Data Investigation, Predictive and Machine Learning tools.
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