An old marketing adage is: "New customers cost money, existing customers bring money." Customer loyalty and Customer loyalty are therefore two central goals of a sustainable business strategy. One important element of this strategy is the prevention of Customer churn - respectively from Churn (eng.: "to churn" = jmd. move). Translated into the vocabulary of the data age, this strategy is called: Churn Prediction Model.

In recent studies on the topic of customer loyalty and customer churn, two findings in particular stand out:

1. Customer loyalty tends to decrease and customers are more willing to switch manufacturers or providers.
2. No blanket judgements about customer loyalty are possible. Rather, individual decisions depend heavily on age, life situation and marital status.

For companies, this means that they must increasingly Data evaluation have to set. With data science, it is possible to segment customers according to suitable criteria, to individualise the customer approach based on this and to determine the decisive moment for the approach. A Churn Prediction Model helps to reduce the Time to determinewhen a customer is thinking about switching to another provider.

A churn prediction model detects changing moods

A Churn Prediction Model works with different analytical tools. For example, many enquiries and complaints from customers are received via email. With Text mining methods, these messages can be examined and sorted in advance. Intelligent Algorithms can distinguish between factual, friendly or angry messages. Even irony can be safely recognised today.

But also the analysis of the purchase history or the Data about workshop visits allow conclusions to be drawn about customer satisfaction. By combining the right data, the probability of switching can be determined very accurately. In one of our projects, we were able to achieve a hit rate of over 90 percent by using this method.

Reading tip: In this article we look at the Customer Journey and question when your customer is thinking about a new purchase.

Churn Prediction is interesting for these companies

A churn prediction model is not equally suitable for all companies. It depends heavily on the services or products offered whether the calculation of the churn rate is appropriate. Churn rate makes sense. Background: This rate is usually determined as a quotient of the number of total customers and the number of churning customers.

Accordingly, a calculation model like this is particularly suitable for manufacturers of luxury goods such as cars, which are bought new at regular intervals, or providers with a subscription structure such as mobile phone providers. A Churn Prediction Model can be used in different ways. Use Cases Apply:

• The impact or effectiveness of individual offers, features or services on customers can be measured precisely.
• Based on the predicted churn probabilities, actions such as individual discounts can be triggered automatically.
• Customer understanding is improved through the analysis of anonymised user data.

Gain valuable insights from data and act

However, all the insights that can be gained in this way are of little use if they are not implemented promptly. A churn prediction model only makes sense if it is combined with an intelligent Customer Relationship Management is linked. Especially when it comes to anticipating customer churn, quick action and appropriate preparation is important. This is the only way to implement effective measures to retain customers with a high risk of switching.

Customer segmentation and classification as important factors

There are several conditions under which a churn prediction model becomes a powerful tool. As already mentioned, the Data quality an important criterion. This also includes the identification of suitable, meaningful data sources.

This is all the easier the more precise the Use Case defined in advance. In our experience, the choice of unsuitable data or faulty data is the most frequent source of errors in data science projects.

Also decisive for a high hit rate with Forecast models is the combination of several factors. The exact calculation of the probability of churn alone is not decisive. After all, not all customers who are planning to switch are equally worth the effort to retain. In this respect Customer segmentation and Classification important intermediate steps.

→ Reading tip: Learn more about the topic of customer value here, respectively Customer Lifetime Value.

Solution approaches for an optimised customer journey

As a general rule positive customer experiences Prevent churn. This can be achieved, for example, through a smooth customer journey. The solutions can be divided into two categories: On the one hand short-term actions and on the other hand in long-term measures.

Short-term actions can be:

• Individualised approach by mail, push notification or telephone
• Price reductions, special conditions or up-selling opportunities
• Determining the right time to approach customers

Long-term measures:

• Optimisation of the entire sales and resale process
• Improvement of products and services to increase customer satisfaction
• Integration of new technologies (artificial intelligence, Chatbots) to individualise the customer journey

Churn Prediction Pays Off

Preventing customer churn pays off simply because the cost of acquiring new customers is very high. But that is not the only reason why a Churn Prediction Model pays off. If you know the causes of customer churn, you can not only prevent the churn of individual customers.

Even in the long term, the quality of one's own offer can be improved and precisely matched to the needs of the customers. A churn prediction model is an integral component of a Customer-centric business strategy.