Company

Customer Churn & Management

Customer Churn & Churn Rate

Unhappy customers increasingly change products and service providers. What causes and consequences does this have for companies? How can customer churn and satisfaction be measured? What is the significance of customer churn in this context and what strategies can help to counteract it?

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

The reasons for customer churn are often a poor on-boarding process (introducing the customer to the company and product, or its benefits and capabilities), poor product experience, inadequate customer communication, lack of brand loyalty. However, poor customer service is cited as the leading cause (Accenture Customer Satisfaction Report).

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

Excessive customer churn damages every company, prevents growth and causes unnecessary increased costs in acquisition, marketing and customer management. But there are strategies that can counteract churn. First and foremost, of course, is the improvement of customer service, personalized services and communication. But increasingly, companies have the opportunity to get to know and understand their customers better through analytical methods. This not only includes, for example, using surveys afterwards to find out what the possible motivation for a changing customer was.

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

In order to detect churn signs early on, one should consider interaction with customers on all channels: This includes the use of online offers, store visits and contact with customer service. The number, scope and type of transactions should also be considered. The development or absence of regular activities, social media contacts and other information paint a comprehensive picture. The quality of contacts - directly recorded by customer ratings or automated using text mining and sentiment analysis - has an equal influence on further development.

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

All information from central log files, call center records, emails, tweets or click-stream data flows through Data Engineering into a coherent timeline. From this timeline, events and meaningful features can be derived. This allows a prediction of the churn probability. Simple examples are: Total number of contacts in the last 3 months, current contract terms, previous churn events, etc.

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.

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