Data Science Use Cases in Ecommerce – part 1

Running a web store is difficult but harder than losing customers
One of the most unexpected things are customers leaving without any warning

This is an unpleasant phenomenon and one of the factors that can not be explained is why it happens
And most importantly when it happens

Would you like to answer some answers?

Know who the customers are in the amount of departure?
Why are they leaving?

What possibilities we have and what solutions we have

The field of data science can help us find a partial solution in this field
Success Percentage Use of science and data starts from 25%

 

How to make that ?

we need to build our model base in this

  • Inaction on the customer’s part
  • A change in customer behavior
  • Overt action taken by the customer

this info we can get from database or be use system  analytics and tracking

 

Which customers are at risk of leaving my platform?

Another key concept in customer retention strategy is figuring out which customers are at risk of leaving your platform. Knowing this information is valuable because you are able to take preventive steps to win them back them.

This model is 25% more accurate than than how businesses usually look at churn, which is to consider a customer who hasn’t bought for a period of time (say 12 months) as at-risk.

what you can do with customer retention ?

You can setup email autoresponders that go out when a customer becomes at-risk or lost. These are usually the best moments to winback a disengaged customer. Experiment with different offers to win your customers back. Keep in mind that the best offer is not necessarily the one with the deepest discount. You have to test it.

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