Have you ever wondered why your customers stop buying your products or stop using your services? Nowadays most businesses are focused on growth, business development and customer acquisition, but what about your existing customers? Have you tried to analyze why they have left your online store (e-commerce) or stopped using you services (maintain your car in your particular service center)? The answers for all those questions could be discovered using advanced technologies such as Machine Learning. Customer Churn Prediction using machine learning will help you to identify risky customers and understand why your customers are willing to leave.

Today every business utilizes a lot of data on their customers. The real value is to use it in a way beneficial to the company. Amongst the vast amount of data, find the most important factors and correlations: transform data, create algorithms and automate flow, which will give you favorable results. Those techniques could be used in up-sell system, CRM or marketing automation software.

“Increasing customer retention rates by 5% increases profits by 25% to 95%” [1]

How to start with a customer churn prediction

No matter what size is your business or what is your operational model, if you are selling products or services, virtually for every industry the problem is the same – customer retention. Keeping your existing customers with the company as long as possible could be very challenging. To keep your customers satisfied throughout the time, you need to know their needs, have a good customer service and know why would they leave your business. When you possess this knowledge, you can work on your retention rate and improve overall performance significantly.

You already know which customers are not buying from you anymore or not using your services. The real challenge is to identify which customers will leave in the future. The best way to deal with that problem is to analyze customers who already left your business.

Unfortunately, it is very hard for humans to spot correlations between thousands of data points. Using the computational power and Machine Learning algorithms (ML), your historical customer data will be put to work to accurately predict future churn.

Proven approach for Customer Churn Prediction

We are using our authorized Customer Churn modeling approach to help companies retain their customer. Using historical data you could target at-risk clients by assigning them churn ratio and find any opportunities to bring back client who will left your business.

cutomer churn prediction
  • The first thing is to start from your business definition of “churn”. Usually, customer “churn” is defined by as inactivity for some period of time. Additionally, you should setup success criteria which is very important to this step and ROI (return on investment) calculation.

  • After we collected needed historical data for prediction and extracted it from source systems, we can start modelling phase. During that stage predictive machine learning model will be created. Also this stage includes model validation, performance monitoring and parameters tuning steps – to get the most accurate customer churn prediction from your historical data points.

  • At the last step, we should define an agree on machine learning model implementation and integration inside your organization. Firstly from data architecture point, integration with internal systems such as CRM and reporting or training standpoints.

Benefits of Machine Learning for Customer retention (Customer Churn Prediction)

When all black boxed work is done and as a result you get insights on customers that are likely to churn, it will have a significant ROI for your company. Machine learning can help you to segment your customers into different groups of churn risk. Particular churn ratio will be automatically predicted for every client. Those features will help you to understand which customers are likely to leave and why, so you will be able to take a actions on them and try to retain more customers.

Benfits and Facts

Below are some key Benefits and Facts:

  • Increase Revenue: Up sell to existing customers is easier and more cost effective rather than selling to new ones. Keep your revenue stream on stable level because acquisition of new customer are 10x time more expensive than retain existing customers

  • Win Business Back: An ability to analyze customers with 360 view and understand which factors impacted a particular customer to churn can help you to get them back Increase retention KPI

  • Retain More Customers: Launch new loyalty campaigns and strategies to increase loyalty to your product or service Protect future retention by eliminating factors which led to churn

  • Avoid Losses: Retaining your existing customers can help you to prevent revenue decreases or opportunity for competition.

Those are only examples of benefits your business could obtain. Every industry has some specifics in customer service, relationship management and retention. For this reason, benefits can be much more in you particular business (Fin-tech, Healthcare, Insure-tech, Fitness or mobile gaming).

Conclusion

To sum up, you will be able to increase your customer retention rate with predictive modeling, machine learning and AI. All based on correlations in your client’s datasets. With help of machine learning you can:

  • 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

In this way, if your churn model works correctly, you will be able to invest saved money into new customer acquisition and expand your business much faster than the competition. If you have any questions or need an explanation on how to use Machine Learning for predicting customer churn ping us a message.

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