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Data-based online marketing at OTTO

OTTO is Germany’s biggest online retailer for fashion and lifestyle. Founded in 1949, the company was one of Germany’s first catalogue retailers. Since then it has successfully transformed itself into an e-commerce company. More than 90 percent of its products are sold online today. Big data plays a significant role for the company. More than 900 different data points can be analyzed per customer. Kerstin Pape, OTTO’s senior manager in performance marketing, provides insights into how the company uses big data and an outlook for its future use.


Key facts

  • Goal: Online data should be linked with back end data within 30 to 60 minutes to steer communication.

  • Requirements: Big data platforms and smart algorithms are important in achieving this goal. OTTO has developed this infrastructure inhouse.

  • Data security: The main reason for inhouse development is to ensure maximum data security and privacy as every aspect of the data management is controlled by OTTO. The main advantage of this is that OTTO has the full control over the data and can use it to steer communication.

  • Customer journey: OTTO aims to show customers relevant/target customers with the relevant advertising materials at the right time and in the right place and they want to steer this process with the collected data from all touchpoints.

» As a family business data security is very important for OTTO. That’s why we measure everything ourselves. «

Kerstin Pape, Head of Online Marketing


The significance of data

Big data offers a big potential for OTTO in the following areas:

  • Online platforms such as
  • Real time advertising
  • Personalized newsletters
  • Push messages in the app
  • Chatbots

The company tries to manage and optimize their full range of channels and also hopes to include their offline channels in the future. In addition, data analyses can be used to support strategy as they help to identify bigger trends.

Collection and analysis of data

OTTO collects a lot of detailed success indicators from different sources inhouse in order to evaluate the success of its marketing activities. Indicators include:

  • Budget
  • Sales and results
  • Impressions
  • Click rates
  • Conversion rates
  • Returns

Increases in the number of new customers etc.

The concrete indicators used for evaluation depends on the specific application.


» In addition, we evaluate our own business intelligence process for the attribution model once a week. In order to do this, we select a quality factor which we then use to rate the accuracy of our projection model. If it is too low, we readjust or add some new indicators. This allows us to react dynamically to developments in the company’s environment and to continually improve the accuracy of our big data applications. «

Kerstin Pape, Head of Online Marketing


  • Central unit: At OTTO there is a central business intelligence unit where data scientists are employed. They work closely with the online marketing team. There have not been any reports of communication problems with the business intelligence unit despite their distinct functions.

  • Team composition: On the one hand, specialists are needed because OTTO is increasingly working more collaboratively. On the other hand, generalists need to be able to think across channels and to act independently.

  • Expertise: Real data scientists are not needed on the online marketing team – they are placed in a separate unit.

  • Need: OTTO needs employees who have a high affinity for analytical topics, a growing knowledge of technologies as well as technological skills.

  • Interfaces: In the area of software development it is useful to have someone working at the interface of IT and marketing who is able to communicate between the different ‘languages’. In general, it is important that employees are team players and that they are also committed to working together in interdisciplinary teams.

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