• Artificial Intelligence
  • Sumant Subrahmanya
  • FEB 09, 2018

Change how you solve Churn at your Company with Artificial Intelligence

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Have you watched the movie, Minority report, where Tom Cruise saves lives by detecting crimes beforehand?

In this post, you will know how to proactively save your customers from churning by detecting it beforehand.

This is part of the Solving Churn blog series. We have spoken about how Churn is hard to solve with current methods in our previous post.

Solving Churn Part 1: What is Churn and why should Consumer Internet companies worry about it?

Sections:

  1. Introduction
  2. Value of Predictions
  3. The Return on Investment
  4. Conclusion

With more and more money pouring into internet companies, companies have been collectively spending billions of dollars in growing the user base on their platform and fight off the cutthroat competition to survive in their respective domain or vertical.

But the sad reality is that companies on an average lose 80 percent of their customers in the first 30 days of acquisition.

So, for example, if you had a marketing budget of $1000

Money Spent= $1000

Money wasted = $800

Future LTV wasted= 💸💸💸

Not many companies can survive at scale if the Churn problem is not mitigated as soon as possible.

2. Behaviour Predictions can save the day 🔮

I bike to work every day in Bangalore which is known for its chaotic traffic. The likelihood of one reaching from point A to point B is determined by how good one is at anticipating the movement of vehicles and pedestrians so that one can avert a potential crash.

This sort of skill comes from experience riding on these roads for a long period of time.

Let’s break down how one is able to do this,

  • Input Data: Video of how the environment around me is like, how the vehicles and pedestrians are behaving etc.
  • Prediction Engine: Brain(Experience from previous rides)
  • Output: Preventive Proactive measures to reach my destination safely

This is exactly the same principle we can apply to our problem and proactively save users from Churning. The ‘experience’ comes from having gone through these roads safely, having a couple of crashes/falls in the past, the weather and million other signals which are subconscious to us.

If we can somehow experience or ‘learn’ how users have behaved in the past(those who churned and those who stayed) we can predict how your current users will behave in the future.

Growing your company with Predictions 🚀

Predicting Churn behavior in users can be equally life-saving for companies.

Input Data: The Data Universe

Almost all companies broadly collect the following data,

  • Product Data: Data around the products being offered by companies to the Customers. For example, Movie Genre, Actors etc. for Netflix, Food data for food delivery companies etc.
  • Marketing Data: All interactions between the company and the customer. This can be marketing campaigns through Push notifications, email or SMS
  • Feedback data: All support related queries, ratings etc.
  • App events data: All the events which a user performs on the platform to achieve the desired outcome.
  • Transactions data: Order Value, number of orders, Transaction history etc.

This helps you build an almost complete picture of the user for your Prediction engine to learn from.

Proprietary  External Signals(like locality scores etc.) relevant to your company can drastically help increase the accuracy of predictions

Prediction Engine: Making a machine learn your business 🤖

Humans suck at analyzing large amount of signals. With the recent spurt in behavior data, manually building rules for 100s of possible signals of Churn is neither feasible nor accurate.

Just like how you learn to avert danger with every different obstacle you face as a commuter, you can make a machine learn how different paths, activities and 100 other signals from the user’s Data Universe led them to either Churn or stay on the platform.

Training

Since Churn is measured in time windows(For example, Users who don’t purchase in 7 Days), we give examples to our Prediction Engine to learn how behavior over time windows has led to a resultant behavior (Churned or Not Churned) in the specific window. In the image below, each dot represents a window and the red dot being the resultant behavior.

Predicting

Now once the model has experienced how hundreds of thousands of users behave(Churned or active) based on all the signals from the Data Universe, you can Predict how your current and future users will behave in the next window.

Output:

With Predictions, your life becomes very simple,

Step 1: Focus

Find the Churn Risk %age of users with the help of the Prediction Engine and segment out the high-risk users.

Step 2: Understand

  

Understand the potential reasons for their high Churn risk to proactively improve your product and marketing efforts.

Step 3: Take Action

The best part is that once you start taking actions, the Prediction Engine will start recommending actions for different segments of users based on the performance of previous actions.

Putting it all together

3. The Return on Investment 💵

Focus on users who are actually at risk and hence save up on  retention costs

Save up on  acquisition cost to replace the Churning users

Protect the  Future Revenue from Churning users

So your war chest for Marketing just got much bigger with Predictions and the saved money can be invested back into acquiring more users and proactively retaining Churning users to compound your growth week on week.

Conclusion

A lot of legacy tech is being disrupted with the advancement of Artificial Intelligence and it is high time we had a Zero to One solution in reducing Churn.

So now the question to you is, what would you rather do?

Spend more and save less users

OR

Spend less and save more users?


 

We are offering Free early access to our AI-powered Churn Management Platform for a limited period. Sign Up now to transform how you retain your users.

Also, stay tuned for the next part of the series where we will talk more in detail about the existing prediction methods to forecast churn.

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