How to design a great customer churn prediction software / algorithm!

How to design a great customer churn prediction software / algorithm!

Companies are building a customer churn prediction software for customer retention. This software predicts how likely the customer is to leave the company.

customer churn prediction software
customer churn prediction software

With so many SaaS companies offering similar products in different niches, customers have become central to their business. Companies have understood that to run a successful business, customer retention is as important as acquiring a new customer, if not more. Customer churn has become such an important issue that they are investing in either buying or building a customer churn prediction software to deal with this. These software are capable enough to predict how likely the customer is going to stay or leave the company.

In a subscription based business model, customers get to choose at the end of every tenure whether to continue using the service or not. Hence they are more open for new offers and deals. When the customers are faced with several bad experiences they decide to leave. 

Now, instead of spending costs on acquiring more new customers, companies are allocating budget to retain the existing customers. They are exploring new approaches and best practices to reduce churn. Churn rate is the health indicator for any business. While a small number of churns is inevitable in any business. If it is high then it shows that there is something essentially wrong in the way you are offering your service to the customers.

In order to predict customer churn machine learning algorithms are turning out to be most useful. They specially work best when you are dealing with a large amount of data. It is capable of building systems that help identify patterns in the data and simultaneously learn from it without a need of explicit programming.

Customer churn prediction model

To predict the customer churn with a right model, data scientists need access to a wide variety of data. It all starts with the company’s goals. Based on the goals, the data scientists decide what data they must collect to work with. Then the data is prepared, preprocessed and transformed into the suitable form for building the right model of machine learning. 

Designing the training modules for the machines, fine-tuning the models and selecting the one that works best is a part of building the algorithm. The project managers then choose the model with the highest accuracy in prediction to deploy that into production. Below are the steps the project managers take to build the right customer churn prediction model.

Analyze the problem and find a goal

The kind of insights you want to glean from your analysis would decide what kind of problem you are going to solve. You have to drill down the problem area of the customer churn into the right questions so that the response to them would give right predictions. To predict customer churn machine learning problems can be either of two types: Classification or Regression.


This type would need the data scientists to determine to which class or category the customer belongs. It is referred to as a data point.To train the algorithm, they make use of the historical data of the customers and use the predefined target variables. These variables are the labels we give to the subjects of the problems which are ‘churners’ in our case. Classification helps businesses to answer following questions:

  1. Will the customer churn or not?
  2. Would they repurchase the subscription or not?
  3. Will the customer downgrade the subscription plan?
  4. Is there any sign of erratic behavior in customers?

The last question specifically targets a common problem in classification which is called anomaly detection. It identifies the data points that significantly deviate from the usual behaviour.


Regression analysis is one of the widely used methods in customer churn prediction software. It is a value that is used in statistical analysis to define the relationship between the customer churn and the data values that influence it. This analysis helps in finding exact values for the business prediction. E.g. it can give you the exact time within which a customer is predicted to churn.

Collection of Data

After deciding the kind of insights you are going to use, you will need to identify which data sources would give you the best data. You will have to consider all the sources from where you can gather data to create a predictive analysis of your customers. The customer data you have on different portals will give you different data values. And the more you gather from different resources, the more detailed and precise your algorithm would be.

There are multiple sources for gathering the data. They are your CRM software, customer analytics product e.g. Google analytics, CrazyEgg, review comments on social media or any other platform where your customers have their footprint.

Preparing data 

To predict customer churn machine learning algorithms should be able to understand the data you gathered in the previous steps. Hence, you need to convert the data into the required format. For the algorithm to run without errors you have to make sure that all data points you collected have the same logic. There should be no inconsistencies in the datasets. 

Modeling and testing

This is the stage where the churn prediction model is prepared by the specialists. They prepare different models, test them, fine-tune them, and finally settle down with the one that is able to predict the customer churn in the most accurate manner.

The most commonly used models for predicting customer churn is one from the classic machine learning models. The list is long but the few worth mentioning here are logistic regression, decision trees and random forest. It totally depends on each company and their specific business which predictive model they would use. 

Deployment and monitoring

Finally, after the selected model has gone through enough testing they have to be deployed into production. The data scientist can either incorporate that into an existing software or can deploy it into the core of a program. 

After a successful deployment, the data scientists need to constantly monitor its performance in production. The manual verification of the predictions made by the software would help realize the need of further improvements. For the companies where the data becomes outdated too soon, they need frequent testing on model performance.

Wrapping up

Churn prediction is one of the most sought after features for subscription based businesses. Gone are the days when you could depend only on CRM to improve customer retention. With easy access to so much customer data these days, the benefits of customer churn prediction software can be leveraged like anything.18

Companies with large customer bases cannot simply rely on traditional CRM or servicing software anymore. A comprehensive customer success platform may fulfil the need of arresting customer churn of large as well as small enterprises. With Machine Learning spanning across wide areas of data analysis in almost all industries, the SaaS businesses are among the top ones who can make the most out of what ML software have to offer.

You might also like:

  • The SaaS Churn Handbook – Everything you need to know for understanding and preventing customer churn.
  • To understand how SmartKarrot helps top SaaS companies predict and prevent churn, Request a Demo.

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