Benefits of Churn Prediction
Cancellation forecast in retail
Churn is an artificial word that is composed of the English words change and turn (turn away). This means the departure of customers from the company. Churn can take the form, for example, that customers cancel existing contracts or in the form that is easier for the customer, that the customer no longer purchases the company's offer or services and migrates 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 customer. Customer loyalty and customer loyalty are therefore key objectives of a solid business strategy. Avoiding customer churn through preventive measures should be part of the standard of every company.
But how is it recognized at an early stage which of the existing customers have the will to migrate? What are the various reasons for customers to emigrate? Only when these questions have been answered can appropriate measures be derived to continue to retain customers with the company.
Industry comparison of cancelers forecast
Proactive churn management and cancellation forecasting have long been of great importance, particularly in sectors such as telecommunications, banking and insurance with contract-based business models. The use of the term “cancellation forecast” indicates that in these industries, the emigration of a customer means that he or she actively terminates a contract. The point in time at which a customer emigrated can therefore be clearly identified.
However, trading companies with transaction-based business models generally do not actively cancel. Instead, at some point, customers simply stop purchasing the products on offer. Or they only buy very rarely and to a lesser extent. As a result, the challenge in retail is to first consider how the departure of a customer is defined and how it can be determined that an emigration has taken place or is likely to be imminent.
Indications of an imminent departure of a customer can also be found in retail in the collected data on customer behavior. A falling purchase frequency and other deviations from regular buying behavior or even the search for certain terms on the website can provide clues, provided that the various data can be collected, extracted and linked together. Retailers who also have an online shop or a customer card have an extremely valuable database for creating a model to predict customer migration.
Customers who are considered to have already migrated according to the specified criteria can be analysed by the company with regard to their characteristics and behavior before emigration. It can then compare them with customers who have not migrated.
Using data science to predict churn
Churn Prediction is a forecasting method from the area of predictive analytics. It makes it possible to predict the likelihood of each individual customer canceling. With these forecasts, measures can be taken to retain customers even before customers migrate.
When creating a churn prediction model, the focus is initially on technical issues. For example, the context of use, relevant products and customer groups, and the target variables to be modelled. This is because the definition of the emigrated customer is rarely directly clear. On the basis of answering these questions, it is then possible to decide whether a single model is sufficient to predict migration, or whether different models must 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 includes information from an existing customer segmentation and the position of the respective customer in the customer lifecycle to predict the probability of termination.
As part of the technical implementation, in addition to developing the model for predicting the probability of migration (churn prediction), the process of indexing and processing the data is the most important step. Using feature engineering from the area Data Engineering New variables, the so-called features (characteristics), are formed from the raw data. As a result, they represent an essential basis for the churn prediction model.
Deep learning in churn forecasting
Classic forecasting models from the area can be used to predict customer churn and assign a probability Data Science & AI be applied. These forecasting models include logistical regressions, decision trees or modern deep learning methods (such as neural networks).
A characteristic of deep learning processes is that they can represent 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 easier interpretability and robustness of the model. This makes it possible to identify behavior patterns of emigrants in order to transfer them to the current data. For example, to identify potential emigrants among customers with a high level of predictive accuracy.
As soon as the churn prediction model is created and implemented, there is an individual churn probability for each customer. It is recalculated and updated regularly. As a result, retail or e-commerce companies always have up-to-date migration forecasts, 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, marketing and sales departments in retail receive important information to prevent customers from emigrating in good time. If the reasons for the migration of past customers are also recorded and the possible reasons for termination are predicted within the model, marketing has crucial information to optimize campaign management. Ineffective or counterproductive measures can thus be avoided. As a result, the company can efficiently use valuable budgets for individually tailored campaigns.
For employees in the customer service center, specific catalogues of measures can also be created for proactive customer loyalty. 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.
