Personalization of offers

What is the challenge?

Increasing competitive pressure requires companies to offer differentiated products and services. Personalization in the offerings can already take place during the manufacturing or service provision process (hard customization, e.g. configuration of equipment features in a new car). If personalization is taken into account after the fact, it is referred to as soft customization (e.g. selection of operating software for existing hardware or service customization). The personalization concept of “mass customization” (customer-specific mass production) is a key objective in Industry 4.0. The challenge in all personalization processes is to define the modules or product elements for personalization that are relevant from the customer’s point of view and to assign a value to them. There are also special requirements for the digital information and communication systems to be used.

What data can help?

  • Customer data
  • Purchase data (order transactions, input in search masks, purchase histories and shopping carts with selected offer features)
  • Usage data (via IIoT devices, service requests)
  • Internal data on goods availability and production (inventory and production costs, characteristics of manufacturing processes)
  • “Command and prohibition rules” that companies define to pre-define certain combinations in the course of personalization
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Use case category: personalization of offers

How can companies use their data?

By using data and digital solutions to personalize offerings, companies can better serve individual customer needs while optimizing their procurement, manufacturing and inventory costs. Patterns are generated from the collected data to make predictions for new market developments. Another benefit can lie in the optimization of “open innovation” processes that already involve customers in the production process.

Where is this use of data already being applied?

The media company Netflix uses a recommendation algorithm. This generates personalized recommendations based on millions of user data (movies or series watched).

The online trading platform Taobao collects, stores and uses data from the areas of customer service, operations and security. The stored data is processed using a machine learning algorithm in order to provide customers with individual, precisely tailored suggestions.

Sporting goods manufacturer Adidas uses sensor data from athletes, together with computer simulations, to identify optimal running characteristics, enabling 3D printing to create customized shoe sole manufacturing.

Chemical company RobNor AB produces individualized painting robots. VR glasses and software applications are used to customize the programming of the painting robots based on motion data. Customers control the movement of the digital twin of their subsequent painting robot via the VR glasses and thus provide the motion data. 

How does this use of data contribute to value creation?

The processes in the company are adapted in order to be able to make individualized offers to customers. This can affect both the creation processes of the product or service and the variations of the existing product/service portfolio. With personalization, a company can respond dynamically to the needs of its customers and thus increase the attractiveness of its products or services as well as customer loyalty. Companies also obtain valuable customer data which they can use as a basis for anticipating market developments and generating new added value.

Aim of data use

Sources: Netflix (2017), Zeng, M., Harvard Business Review (2018), Adidas (2017), Kostis, A. & Ritala, P., California Management Review (2020)