Creating an Omnichannel Banking Experience with Machine Learning on Azure Databricks - Databricks

Creating an Omnichannel Banking Experience with Machine Learning on Azure Databricks

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Ceska sporitelna is one of the largest banks in Central Europe and one it’s main goals is to improve the customer experience by weaving together the digital and traditional banking approach. The talk will focus on the story of how in order to reach this goal Ceska Sporitelna created a new team focused on building use cases on top of a combined digital and offline customer engagement 360 powered by a Spark and Databricks-centric agile advanced analytics platform in the Azure cloud combined with a on-prem data lake. This talk will cover:

  • The customer engagement 360 vision powered by machine learning and the cloud
  • Deep dive into the use case of optimizing and personalizing programmatic ad buying on the individual user and ad placement level thanks to Spark MLLib and NLP on top of hundreds of millions of ad interaction data
  • Deep dive into the use case of supporting the seamless transition of the customer journey from digital to traditional offline channels
  • The approach to building the agile analytics platform and experience of adopting the cloud in a EU-regulated financial institution

 

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About Petr Pluhacek

Ceska sporitelna

Petr is a digital marketing manager at Ceska sporitelna, one of the largest banks in Central Europe and part of the Erste Group. He is responsible for digital engagement and acquisition via owned and paid digital channels. But all these are not possible without alignment of offline and online data, clients and non-clients profiling and advanced data management for constantly improving customer experience no matter where the customer is.

About Jakub Stech

DataSentics

Jakub is a senior data scientist at DataSentics, a European machine learning and cloud data engineering boutique. His key area of expertise is using digital customer interaction data such as ad impressions to understand customer needs and personalise his experience using machine learning approaches.