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Spoiler Alert, It's not magic it’s machine learning

An Absurd Challenge

Today I will show you how to obtain churn predictions before your coffee is ready. Put some coffee on the machine or french press so that when you get all those churn predictions you can enjoy going through them with that hot coffee you just brewed in the meantime.

A Friendly Introduction :)

Let me introduce myself. I am M Ahmed Tayib working as a Data-Scientist in Gauss Statistical Solutions. I am your friendly neighborhood data-scientist guy who loves coffee and loves an irrelevant challenge like making coffee vs conducting churn prediction.

Firstly, A definition

Churn is a term/label that is given to the customers who discontinue the services/subscription a company provides. For instance; if a user has not renewed Spotify subscription for 4 months then Spotify may consider that user a Churn.

Customer Churn

Similarly, this can be said for any business in this modern era. Every business has churn customers, every business has a few segments of customers, well to be precise ex-customers, that discontinued the services.

Why Churn Prediction is Important?

A wise guy once said;

| “Retaining a customer is always less expensive than acquiring a new one.”

I guess the quote speaks for itself. Of course, you need to obtain new customers to grow but that does not mean you have to lose some of them and do nothing to retain them.

Solution; Once you know which customers are likely to churn and why you can take appropriate action to retain them. However, the problem is in real life it is much much and much hard to know which customers are about to pull a stun of churn and let alone why.

How to Predict Churn?

Now that I have established what churn is and why churn prediction is important, lemme wrap it up with how to actually do it and do it really fast like never had been done before.

All you need is to have those sales and customer/user data. Please follow the steps below;

  1. Go to Enhencer.com and log in or signup
  2. Upload your Sales data and Customer data
  3. In about 2–3 minutes Enhencer will provide you with Churn Predictions like the picture below;

Customer Churn Customer Churn

Voila! That’s it. Literally all you need to do is just upload the data and everything rest is taken cared of.

Go grab your coffee it should be ready by now and then we can see what these predictions are and what good they will do.

A Much Needed Explanation

Well, you must be asking what about feature engineering, model training, model testing, and all those tasks. Lemme clear that up for you. Once you upload your data to Enhencer platform what it does is;

  1. First, it does all the necessary Feature Engineering automatically for you. We all know how pain in the a** this feature engineering is. This should save hours of coding in SQL or similar platform if not days of your time.
  2. Enhencer uses Machine Learning Algorithms behind the curtain to train the best model for your data automatically. That should also save hours of your time, again.

What you see in the first picture is the summary of the whole process. It gives the historical churn rate over time. Then it provides the likelihood of churn for all your customers so that you know which are customers are going to churn in the near future. Lastly most important of all in the second picture it provides the segments of customers. This shows why customers in these segments are going to churn.

Pretty neat hah… All you need you to do is look at which customers are highly likely to churn and see what is the reason behind that and take immediate appropriate action. This should be more than enough to help anyone to retain their customers and reduce the churn rate significantly.

An Unnecessary Conclusion

Definitely you can change the models and tune them later if something is not to you liking and what’s more, they have tons of algorithms as options for you, but that’s for the advanced enthusiast users.

You can't get easier than that and from my experience in the data science field, all these would have taken days, if not weeks, in the traditional manner using R, Python, SQL, etc.

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