Keep your most valuable customers who are considering leaving you
It is difficult and expensive to acquire new customers. In the majority of mature industries, when the market is not growing as dynamically, you have to drag customers away from your competitors, which often involves offering large discounts and other types of costly benefits, and spending on advertising. In this situation, it is therefore crucial to keep your own customers and prevent them from going to the competition. By using predictive models, it is possible to predict which customers are most at risk of churn at a given time (e.g. during the next quarter). With this knowledge, appropriate retention campaigns can be directed to endangered customers.
Later, with the analytics, it is possible to measure the effectiveness of such campaigns and estimate their ROI. In combination with models that estimate customer value, it is possible to prioritise the retention of the most valuable customers, whose loss would be the most significant burden on the company’s budget. It is also possible to allocate the budget for retention measures accordingly. For example, the most valuable customers can be targeted with a telephone campaign supported by an attractive discount offer, while less valuable customers can receive the emails. The key to the success of such a strategy is to forecast the risk of churn as precisely as possible. This is possible thanks to predictive models, as customers usually send signals (sometimes quite subtle) that indicate an increased probability of churn. The more data sources can be used by the models, the more accurate the forecasts (e.g. data on complaints and contacts with the customer service are of great value), but often even the transaction data themselves are a good basis for building models.
The analysis of customer churn makes it possible not only to identify those most at risk but also to understand the factors that increase the probability of churn (e.g. purchase of a specific product or service, visit to a specific point of sale, too long waiting time for a complaint to be processed). This allows you to identify areas within the company where changes are necessary to increase consumer satisfaction and retention.
The phenomenon of churn usually refers to businesses based on subscriptions or contracts (for example, telephone subscriptions), but it can also be defined and analysed for relationships based on irregular transactions. Advanced analytical methods can, in such cases, answer the question of what is the probability that the consumer without a transaction in the last 9 months will return to the store. The answer depends, of course, on the industry, but also on the history of a particular consumer and the moment in their life cycle (the identification of which is possible thanks to customer life cycle models). It is worthwhile to start with an analysis of basic data sources and gradually expand the tool, increasing the accuracy of forecasts and deepening understanding of the phenomenon.
