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Use cases for Customer Analytics and Product Analytics


In clustering, similarities or similarity structures in large amounts of data are used to map data to a group. The special feature of clustering is the goal of identifying new groups in the data, while in a classification the data is assigned to existing classes.

Customer Lifetime Value

In the case of customer lifetime value, the value of a customer is forecast, for example, as a contribution margin over a period based on various characteristics.


Existing data can be used to increase customer satisfaction. Referral systems can be used as electronic sellers to offer customers additional matching products (next best offer).

Churn Prevention

Churn Prevention is also known as Retention Management. This involves the identification of customers willing to emigrate, with the aim of keeping them as customers through targeted measures.

Other use cases

  • Optimize Campaign Management
  • Marketing Automation
  • Social Media Analytics

Customer Analytics: Digital marketing of today - the customer in 360 ° view

Customer analytics refers to the processes and technologies that allow organizations to gain deeper insights into their customers and how they behave. This information and analysis is then used by marketing, customer relationship management, design of the shop or website or other departments to optimize the product range. Most companies have the strategy of making product offerings more responsive to their customers' needs and behaviors.


In the course of this we see that more and more companies are focusing their focus and processes even more on the customer; Keyword "Face to the Customer". The goal here is to build a 360 ° view across all the company's "touchpoints" with the customer (such as a website, point-of-sale, or call center). This information can then be supplemented by additional external data such as market data, social media or weather data and demographic characteristics. For the analysis of the customer data, there are a number of different methods that are used very frequently and that can be combined with each other.

How do you start?

Although such a button would be desirable, it unfortunately does not work. In the beginning, a company needs to think about it to find out what use cases Customer Analytics are in their own operational context. It is useful to first consult your own employees. Because many ideas are born in the companies themselves. In most cases, employees who have always had ideas and have tried to implement them technically are particularly helpful.


These can be the most diverse characters that need to be identified, such as the Excel Crack in Marketing or Sales, which has built complete analysis spreadsheets. Here are often already questions that were not technically reproducible. These should be gathered together to give you a good starting position.

Then it's about scouring data and finding potential for use cases to work on in the beginning. At this stage, which likes to have the character and positive working atmosphere of an experimental laboratory, as little as possible should be invested in IT technology. Everything you need is either available free of charge or at very low cost. A larger investment only makes sense if an application has been confirmed.


Nevertheless, it is advisable to send your own personnel resources to the outlined lab for customer analytics. Ideally, these are the so-called Data Scientists, but because many companies are currently dealing with

Big Data> Big Data & Advanced Analytics, they are a rare species. <h2> It's also easier with Customer Analytics </h2> Get motivated people who have a basic understanding of advanced analytics, free space, and a little time to "roll the Euros" down. almost by itself. Because applications quickly come to light that no one at first thought of. Best practice cases show that even for large companies with several thousand employees, it is sufficient to release 0.5 to two full-time equivalents (FTE) for the test lab at the start.

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Gernot Molin
Senior Vice President - Advanced Analytics
pmOne AG
Technologiepark 21
33100 Paderborn
+49 89 4161761-0
Gernot Molin
Markus Nemeth
pmOne Analytics GmbH
Technologiepark 21
33100 Paderborn
+49 89 4161761-0
Markus Nemeth