Customer Churn & Management

Customer Churn & Churn Rate

Customer churn refers to the migration of a company’s customers. Gaining new customers is significantly more expensive and time-consuming than retaining existing customers. This makes churn a crucial metric for customer satisfaction and in customer relationship management. For companies it is therefore worthwhile to observe and analyze customer churn carefully. As a result, they are better able to evaluate successes in customer retention and develop strategies to improve them.

The churn rate describes the percentage of lost customers. It can be viewed in various ways, for example as the number of lost customers, as a percentage of the total customer base, as the value of lost customers or again as a percentage of the value of the customer base.

The churn rate is calculated for a specific period, such as quarterly or for a fiscal year. Typically, Customer Churn is determined by dividing the number of customers lost in a period by the number of customers at the beginning of the period.

Reasons for Customer Churn

In fact, customers are four times more likely to switch to a competitor company because of service problems than because of lower prices or better product offerings. But an even more serious problem is that very few dissatisfied customers seek contact — 96% of lost customers leave without even once complaining directly (1st Financial Training).

In addition, the lost customers have a reinforcing effect, since they — especially the younger ones — increasingly share their experiences via social media. The effort to compensate for this negative publicity exceeds many times the effort to intervene from the outset and prevent the negative customer experience.

Strategies against customer churn

The data available for each customer provides information about their behavior, intentions and expectations. Advanced analytics and Big Data technologies allow companies to anticipate these intentions and meet their expectations individually. This includes identifying customers who are at risk of leaving the company and finding ways to improve their satisfaction, for example, by improving their offerings or simply by indicating the benefits that an offering offers the customer.

Churn — Prediction and prevention

All this information can be used to develop models that predict the churn risk of a customer. They are calibrated and evaluated with historical data in order to integrate them into the existing customer management. The latest customer data is continuously incorporated to control targeted measures.

Here, there is a risk of discovering risk customers only after the actual cause can no longer be eliminated. The knowledge gained must therefore also be fed back into the process. In the best case, this enables the right signal and the right offer to be sent out almost in real time, precisely and at the right time. Churn can thus be avoided.

Ultimately, the goal of Advanced Analytics is to avoid churn and increase customer satisfaction and loyalty. This is achieved by combining machine learning, real-time data and targeted customer management. In this way, it is possible to create an advantage over competitors. Smart companies understand how important it is to look after customers beyond the first contact. They appreciate the value of a loyal customer and consistently pursue appropriate measures.

Technical Insight

Also the definition of churn must be chosen appropriately: For example, is a customer considered lost if he cancels his contract or account? Or after 12 months of inactivity or even after a few weeks without activity? It can also be useful to first segment customers according to their long-term behavior. More specific prediction models are then used for each segment, which can evaluate the most recent activity. Which time periods are relevant for long-term and short-term behavior again depends on the segmentation, but also on the business area.

Originally published at https://www.steadforce.com.

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