Cookie Settings

By clicking "Agree," you consent to the storage of cookies on your device to enhance site navigation, analyze site usage, and support our marketing efforts. For more information, please refer to our Privacy Policy.

Case Study

Decrease churn rate

Churn is a huge problem in companies. The churn rate, also known as customer churn rate or cancellation rate, describes how many customers have left the company as customers in relation to the total number of customers in a specific period of review.
#churn
#growth
#publishing
Firma
Location
Germany
Branche
Media
Unternehmen

Challenge

The reasons for termination can be varied. Especially when it comes to ritualized products, there are often several factors that have grown historically. This includes content, usability, technology, customer service and offer design. In the case of a magazine, not only can the content result in cancellation, but also delivery.

In principle, it is easier to make existing customers happier than to win back those who have already turned away from the company as customers. That is why it is important to start early in the customer life cycle when optimizing the churn rate.

Solution

The advantage of users who leave the company as customers is that they enable companies to learn.

This can be based on subjective and objective data. Subjective data describes what the terminators tell the company when they emigrate. The objective data is based on the observation of how the user as a customer has behaved with the product. This data must be brought together and can be very individual.

An example from publishing is when a customer emigrates because they have received too little recipe content from the company. Here you look at which category this user has moved to find out how much “too little” means in this statement.

It is easier than asking these things to measure them and combine them with the subjective feedback from the user.

The benefits of a solution from The Data Institute

At the FUNKE Media Group, The Data Institute has implemented this case in the form of a customer data platform, which automatically questions cancelers and measures behavior in order to obtain information about their motivations.

The feedback from cancelers consists of partly unstructured information, e.g. reviews, but also continuous text.

Use AI-powered models

Our solution analyses feedback using AI text tools and channels it directly to the right place in the company so that appropriate measures can be initiated.

This results in a reduced churn rate and happier existing customers — and sometimes even to winning back cancelers and then doing better.

Questions about the project?

Catherin Hiller, who implemented it on an interdisciplinary basis at FUNKE, was responsible for this project.

The contact to our client can be organized on request.

Thomas Borlik, Managing Partner
Thomas Borlik
Managing Partner
Abstrakte Form eines Pfades

Want even more data news?

Get it in our newsletter!

Data news for pros

Want to know more? Then subscribe to our newsletter! Regular news from the data world about new developments, tools, best practices and events!

Abstrakte Form eines Pfades des Data Institute

Want even more data news?

Get it in our newsletter!

Abstrakter Pfad des Data Institutes