Data Analytics

localsearch: Increasing customer retention

localsearch wants to cater for customer needs by using targeted marketing. Zühlke therefore develops a data platform that uses machine learning to predict customer behaviour.

Customer benefits

  • Zühlke helps localsearch to define its data analytics strategy.
  • localsearch receives a decision basis for the optimal choice of cloud provider.
  • With the help of the new data platform, localsearch can predict potential customer churn.

 

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The Prediction Model sets new standards in the data-based prediction of events. The structured and business-oriented approach enabled us to achieve a maximum return on investment over time.

– Marcel Jäggi, Senior Marketing Manager, localsearch

Identifying customer needs at an early stage

As a marketing and advertising partner, localsearch helps Swiss SMEs with their positioning on the web. The company wants to analyse the large volume of customer data in order to identify potential customer churn as early as possible and initiate appropriate countermeasures. For the purposes of strategic consulting and technical development, localsearch brings Zühlke›s experts on board.

 

Consulting and technical implementation from a single source

Together with localsearch, Zühlke›s specialists develop data-driven use cases. Zühlke advises localsearch about the strategic orientation and evaluates suitable cloud providers for a data platform. An interdisciplinary data analytics team at Zühlke develops the localsearch data platform to create a sustainable and automated solution within the selected cloud, and implements application examples in close cooperation with the marketing managers to verify the effectiveness of the solution.

 

Data-based forecasts of customer behaviour

Google is selected as the cloud provider for the development of the data platform. In the cloud, Zühlke develops a platform that aggregates customer data from various sources. Based on this linkage, a machine-learning model is implemented that analyses the behaviour of customers and predicts whether they are likely to exit in the near future. This allows dissatisfied customers to be identified at an early stage and thereby reduces the number of resulting departures.

 

Your contacts

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Photo: Mr Philipp Morf

Philipp Morf

Senior Business Solution Manager
Industrialization

Photo: Mr Lukas Urech

Lukas Urech

Account Responsible
Insurance