AE Stories

KBC is one of the largest bank-insurers in Belgium for both private individuals and medium-sized businesses. In order to strengthen their market position, and evolve towards a data-driven organisation, the bank wants to maximize the value from the available data.

With the evolution towards a data-driven organisation, the intensive use of commercial data and data analysis has developed into a strategic core-competency for KBC. Together with their business partners, they are building multiple new quality data layers (Commercial data, Risk data, etc.) which contain accurate and consistent data, are based on unambiguous definitions, comply with the new GDPR legislation and can be unlocked at the right moment, in the right form and via the right channel. These new Data Layers are developed on a Big Data platform with specific tools and technologies.The bank recruited the help of AE in order to convert the data that they are gathering into useful and practical insights for the internal services. The ultimate objectivel is to ensure a personalised approach for end-users of KBC. 

In an attempt to show one of our customers a better way to connect a legacy Postgres system to a large data set, a team of AE experts turned the discussion into an internal competition. Contenders Phoenix and Cassandra already lost the challenge, as you can read in Part I.  In Part II we'll explore whether Drill or Impala can rise to the occasion.

Together with a large organization, we’re building a platform that presents company data to its data scientists so they can use it to develop innovative applications.

This is the last post in a series of three about the added value of Analytics in Marketing. The first post, by my colleague Bram Vanschoenwinkel, gave an overview of a number of Analytics techniques tailored to a better understanding of your customers and their specific needs. Jessica Ruelens discussed Customer Segmentation & Profiling and a specific case for a company that sells professional training seminars in a second post. I will conclude this series with a discussion about Churn Prediction and a specific case of a bank.

Have you ever wondered how your customers are behaving on a grand scale? Do you know which customers are most valuable to you and which customers represent an undiscovered opportunity? You might have a general idea about the behaviour of your customers - having observed their behaviour in the past - but are you sure your observations are supported by the facts or the data?