Blog post

Customer Churn Controlling Using Machine Learning: Should Retailers Analyze Their Customers?

Learn how machine learning can reinforce customer churn analysis in retail

July 31, 2019

6 mins read

Do you know why customers stop buying from you and choose competitors instead? We bet that you focus on customer acquisition and business development, usually diminishing the importance of retaining existing customers. Just like most companies do. But no customer should ever feel forgotten. That’s the rule no retailer should ever compromise.

Research by Bain & Company shows that reducing customer churn by 5% may increase company profit by up to 25-95%. Moreover, nurturing loyal customers is five times cheaper than acquiring a new audience. Simply put, keeping existing customers helps you increase brand loyalty and improve company reputation.

But how can companies control customer retention? 


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Improving customer retention with machine learning 

Accumulating a massive volume of data, only a few companies analyze it. But in today’s data-driven world, retailers shouldn’t underestimate the impact of even a tiny percentage of information. Even a small but actionable insight may increase customer satisfaction or help optimize ROI. Here comes machine learning (ML) powered churn analysis in retail.

Customer Churn Controlling Using Machine Learning: Should Retailers Analyze Their Customers?

No matter what size or operational model is your business, you have to keep customers satisfied all the time, knowing their needs and foreseeing wants. You may know customers who already left your business, but it’s a tough challenge to identify customers planning to leave soon. The best way to tackle this problem is to analyze clients that don’t buy from you anymore. 

Machine learning algorithms and applied statistics methods can help build a solution that allows revealing clients at churn risk. Using the ML power to historical data, you’ll make it work to predict future churn as accurately as possible. The deployment model will show you valuable figures daily. 

Given that customer churn analysis is an essential part of complex customer relation management, you need to integrate it with your overall marketing plan. When you know churn probability for each client, you can apply an actionable strategy for their retention and restructure marketing activities accordingly: 

  • focus on valuable customers and let go jumpers;
  • develop customer loyalty campaigns;
  • use more flexible marketing and sales campaigns;
  • change products or consider new assortment to cover customer needs.

The most common reasons for customer attrition may even surprise you. They include such factors as the company’s service level, pricing terms, delivery policy, competitors’ strategies, economic climate, seasonality, and general industry trends. But do retailers need to keep all the customers at any cost? 

Read more: Learn how we’ve helped a global automotive player ranked among the 100 Best Global Brands by Financial Times enhance their customer journey with the all-in-one retail service platform

Keeping only valuable clients in focus

Customer Churn Controlling Using Machine Learning: Should Retailers Analyze Their Customers?

According to the CodeBroker Loyalty Program Customer Survey, an average customer reports being a member of more than six loyalty programs, while 65% actively engage with fewer than half of them. While there are always clients who switch between you and your competitors, retail companies should offer a differentiated client experience and tailor their loyalty rewards individually. 

There are always migrating clients, and a certain level of customer churn is quite reasonable. One of your main tasks is to differentiate customers who belong to this group. Defining figures that describe this client outflow and observing it is the key to successful customer relationship management.

Even if there were no jumper-customers, the natural attrition would always exist. For example, children’s products cease to be relevant when the child grows up. The client can switch segments — from an economy to a premium sector — or move to another area, region, or even country. 

Global trends also affect churn. When electronic books substituted paper ones, bookstores experienced terrible losses of customers. 

We suggest creating the criteria for a clear understanding of which customer is considered to be lost. Plus, you should understand the customer value: always define the ratio between the price of customer acquisition and the profitability they bring or will bring to the company. And if talking about churn, you should always distinguish between the customer turnover and the cash one. 

Customer churn prediction model and machine learning in retail analytics 

Customer Churn Controlling Using Machine Learning: Should Retailers Analyze Their Customers?

During the churn analysis, it’s vital to conduct an assessment of the acceptable churn level. It will allow adjusting the churn model according to the company’s current conditions. Different customer segments vary in behavior, so one or several specific customer groups form the most significant turnover. 

Now, let’s define critical factors for a successful churn analysis. 


The prerequisite to building an actionable machine learning model is that you collect and analyze customer data. Here are the characteristics of the data relevant to create an ML algorithm:

  • Quality

Your data should correspond to reality. For example, if your sales assistants don’t validate customer questionnaires, the data collected is most likely useless. 

  • Variety

You need every piece of information: social data, financial data, data on transactions, purchases, and preferences.

  • Amount

The more, the better. 

  • Relevance 

Only up-to-date insights are valuable. 

  • Origin and format

The format of data is also precious — text, photos, videos, and so on. The higher the variety of relevant data used to build the model, the more accurate is its prediction.

You may find inconsistencies and anomalies during the initial data review — it’s possible to eliminate them with each iteration, validating and selecting the most relevant pieces. 

Prediction window

The forecast period is called the prediction window. You should adjust this parameter along during the process of model validation. One of the most compelling cases is to create a stepped scheme that allows making predictions for several periods simultaneously. 


Every model becomes outdated over some time — internal and external conditions change, company goals grow. You should update and calibrate the model frequently upon the results you get. 

Results interpretation

Customer Churn Controlling Using Machine Learning: Should Retailers Analyze Their Customers?

The ML-powered model aims to tell you what is the probability of this or that client leaving your business within a specified period. Considering clients based on the profitability they bring will allow managing churn risk with the help of targeted marketing strategies — either loyalty campaigns or assortment changes. The acceptable churn rate is defined by industry peculiarities in general and company specifications in particular. Thus, for example, the 75% churn rate of a client is quite risky for one company, while another will accept the level of 85%. The same company may also have different numbers in various locations. 

Read more: Learn how we’ve helped one of the largest online stores in Eastern Europe create the roadmap for their retail marketplace platform migration

Stop customer churn before it even starts

Knowing the probability of customer churn lets you create targeted loyalty campaigns aimed at the segment with the highest chances of attrition. Churn analysis provides valuable insights on the risk and level of outflow (both client and money) as well as the ability to manage these factors. 

At Intellias, we help provide churn assessment in a tight cooperation with you. We provide a detailed report on key factors that make customers leave. We conduct the assessment based on both industry and company peculiarities, defining segments at the highest churn risk. Our experts validate data, eliminate anomalies, and help customers remove errors in data collection and storage processes. 

If you fail to nurture existing customers, don’t know how to analyze retail data, or want to perform customer churn prediction with machine learning, contact Intellias. Our retail software development experts will make your business profit with ML-based models that will help you engage customers and optimize ROI.

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