Prevent Customer Churn with Smartech’s Automated Predictive Churn Model

Automated Predictive Churn

“Am I carrying a leaky bucket?” is a question that pops up in the mind of every modern marketer like you.

Customer Churn is a bitter reality that every business has to deal with. The biggest and most successful brands also deal with varying customer churn rates, let alone the start-ups and small businesses. Acquiring new customers costs 5 times more than retaining the existing ones. It is well understood now that the key to sustainable growth lies in retaining the existing customers. And hence, the objective of business owners has now shifted from just acquiring new customers to acquiring a customer who helps acquire more customers.

This is why proactively identifying potential churners and designing strategies to retain them is imperative in today’s highly competitive market.

Customer Churn Rate in Apps

There is an evident market trend as most of the new age B2C companies are going app first nowadays for rapid market penetration, ease of usage and enhanced personal touch. Marketers leverage the fact that compared to websites, apps are a better medium to sell as well as communicate with their customer base.

Data from BuildFire shows that the average smartphone user spends 2 hours and 15 minutes each day using apps. Research shows that there are between 60 and 90 apps installed on the average smartphone, with top 3 apps receiving 77% attention.

However, this also points to the insanely high competition in the B2C app market, where there are tens and hundreds of replacement apps for different needs of customers. High churn rates are grave concern in the mobile app world. Users may abandon your app because of bad user-experience, some may just forget your app, or some just uninstall the app for reasons difficult to fathom.

“Average app loses 77% of its DAUs within the first 3 days after the install. Within 30 days, it’s lost 90% of DAUs. Within 90 days, it’s over 95%. The average app mostly loses its entire userbase within a few months, which is why of the >1.5 million apps in the Google Play store, only a few thousand sustain meaningful traffic.” – as per Andrew Chen.

We know the problem. Solution?

So, what happens to the apps that are generally not used by a customer? With smart phones getting smarter with each passing day, the phones now shows messages to its user about how many days an app has not been used and if the user would want to uninstall it.

Once someone uninstalls your app, it becomes extremely difficult to win him/her back, more so because you have also lost your channel of communication.

“We all know how hard it is to win back our ex”  

So how do we go about tackling this problem!

An easier (and almost magical) implementation could be to pre-empt possible uninstalls within a period of time, and take actions to prevent your customer from removing your app from his/her phone. In other words, predict the uninstalls, and design strategies to save those customers.

Doesn’t the good old proverb say- “Prevention is better than cure”.

Easier said than done, though.

Two major challenges that need to be dealt with are:

  1. Designing right retention strategies take some time after predictions are out. What happens to the customers who churn out early in the prediction time period?
  2. The predictive model learns from historical data. Not all customers who have stopped using the app uninstall immediately. As every predictive model has some good and bad (accurate & inaccurate) predictions, aren’t we relying too much on the accuracy of the model? What if the model mis-predicts and an actual uninstall has not been predicted without any fall back strategy!

The key is: “Everything is created twice, first in the mind, then in reality”.

Often, churn take place in the mind of the user much before the actual churn.

Predictive Churn Model

Smartech’s Automated Churn Prediction Model

We at Netcore Smartech, we did some research and have come with a prediction model. We focus on the period of non-usage over actual uninstalls. Based on Machine Learning & AI-powered data analysis, you can decide what period of app non-usage is a cause of concern for your business. Once it is fixed, the self-tuning ML/AI engine builds the best possible predictive models with all the relevant and important variables. These models are capable of highlighting the variables which drive app non-usage for efficient strategy definition as well. The major benefits that the model offers are:

  1. The model learns to predict non-usage of app which may or may not lead to uninstall. But what good does it do the business if your app is just lying unused in your customer’s phone! So, you get to know if your customers are engaged with your app or not.
  2. Even if the model misses out in predicting non-usage for a few customers, there is still a chance that the model can capture those customers in the next set of predictions before they actually uninstall.

How does the Model Work?  

The Churn Prediction model works at a client level. It goes through the available client data, finds out which data points are consumable and useful for predicting churn and then starts the model building process. We at Smartech understand that every business is unique and has different characteristics and thus have made the models completely “self-tunable”.

The models are robust enough to treat each client in a different way according to their needs, and finds out what works best to predict churn for your business. You can follow the diagram below for a better understanding.  

Data Driven Retention Strategy

So, implementation of this model can help you prevent build stronger and more impactful retention strategies. And you can just stop worrying about the leaky bucket!

Netcore Smartech’s Automated Churn Prediction Model is here to do the bulk work and help you solve your challenges. To know more, get in touch with us today!

Join 20,000 Marketers who get our Blog Emails Every Week!

Kickstart YOUR growth journey with our expert tips on marketing automation, personalization, email, mobile marketing and more. Only 2 emails per week.

Debapriya Das

Debapriya Das

I am currently working as Lead - Machine Learning at Netcore, and have a rich experience into analytics, deep learning, DWBI, solutioning, consulting, and project management. I have spent a significant amount of time in my career, building solutions and strategies for pricing, loss mitigation, risk scoring etc in the Insurance industry. Most of the other problems that I have worked on have been pretty spread out and abstract, ranging from problems which can be solved by traditional statistical/machine learning models to practical image recognition and video intelligence.

Join 20,000 Marketers who get our Blog Emails Every Week!

Most Popular Blogs