Forecast of terminations in retail
Churn is an artificial word made up of the words change and turn. It refers to customers turning away from the company. Churn can take the form, for example, of customers terminating existing contracts or, more simply for the customer, no longer purchasing the company's products or services and switching to another provider.
For many companies, acquiring new customers is one of the biggest cost items in customer management. According to a well-known rule of thumb, it is around five times more expensive to acquire a new customer than to retain an existing one. Customer retention and customer loyalty are therefore key objectives of a sound business strategy. Avoiding customer churn through preventative measures should be standard practice for every company.
But how can you recognize at an early stage which of your existing customers want to leave? What are the various reasons for customers to leave? Only when these questions have been answered can suitable measures be derived to retain customers in the company.
Forecast of terminations in industry comparison
Especially in industries such as telecommunications, banking and insurance with contract-based business models, proactive churn management and churn forecasting have long been a high priority. The use of the term "churn prediction" indicates that in these industries the churn of a customer means that they are actively terminating a contract. The point in time at which a customer leaves can therefore be clearly identified.
However, retail companies with transaction-based business models generally do not actively cancel their contracts. Instead, customers simply stop purchasing the products on offer at some point. Or they only buy very rarely and to a lesser extent. Consequently, the challenge for retailers is to first consider how customer churn is defined and how it can be determined that churn has occurred or is likely to occur.
Retailers can also find indications of a customer's impending migration in the data collected on customer behavior. A drop in purchase frequency and other deviations from regular purchasing behavior or even searches for certain terms on the website can provide clues, provided the various data can be collected, extracted and linked together. Retailers who also have an online store or a customer loyalty card have an extremely valuable database for creating a model to predict customer churn.
The company can analyze customers who are considered to have already churned according to the defined criteria with regard to their characteristics and behavior before churning. It can then compare them with customers who have not churned.
Use of data science for churn prediction
Churn prediction is a forecasting method from the field of predictive analytics. It enables predictions to be made about the likelihood of each individual customer churning. These forecasts can be used to take measures to retain customers before they churn.
When creating a churn prediction model, the focus is initially on technical issues. For example, the usage context, relevant products and customer groups as well as the target variables to be modeled. This is because the definition of the churned customer is rarely directly clear. Based on the answers to these questions, a decision can then be made as to whether a single model is sufficient for churn forecasting or whether different models need to be created for different customer groups and products. Our experience shows that the best churn prediction models are developed in a multi-stage and iterative process. A process that incorporates information from an existing customer segmentation and the position of the respective customer in the customer lifecycle into the prediction of the probability of churn.
In addition to the development of the model for predicting the probability of churn (churn prediction), the most important step in the technical implementation is the process of developing and preparing the data. New variables, known as features, are created from the raw data by means of feature engineering. Consequently, they form an essential basis for the churn prediction model.
Deep learning in churn prediction
Classic forecasting models from the field of data science can be used to predict customer churn and assign a probability. These forecasting models include, for example, logistic regressions, decision trees or modern deep learning methods (e.g. neural networks).
Deep learning methods are characterized by the fact that they can map a very high level of complexity within the models. However, they are difficult to interpret, for example which characteristics (features) have an influence on the probability of migration. For this reason, classic forecasting models are often preferred in practice. Their advantage lies in the simpler interpretability and robustness of the model. This makes it possible to identify behavioral patterns of churners in order to transfer them to the current data. For example, to identify potential churners among customers with a high level of predictive accuracy.
As soon as the churn prediction model has been created and implemented, an individual churn probability is calculated for each customer. This is regularly recalculated and updated. This means that companies from the retail or e-commerce sectors have up-to-date churn predictions at all times, which they can use directly in marketing and sales campaigns.
The result: Valuable information for marketing and sales
As a result of implementing the churn prediction model, the marketing and sales departments in retail receive important information to prevent customers from churning in good time. If the reasons for the churn of past customers are also recorded and the possible reasons for churn are predicted within the model, marketing has crucial information for optimizing campaign management. Ineffective or counterproductive measures can thus be avoided. This allows the company to use valuable budgets efficiently for individually tailored campaigns.
Specific catalogs of measures for proactive customer loyalty can also be created for employees in the customer service center. Ideally, this information is stored directly within the customer profile, for example in the CRM system. In this way, employees can select the measures individually for each customer and adapt them to the respective context.
