Data science analyses

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Data science analyses on an analytics platform

The Challenge

Our experts used the Microsoft Azure Cloud to set up a modern, scalable and future-proof analytics platform for Messe Düsseldorf and ensure its smooth operation. The project started with a greenfield setup in the cloud. The project team's agile approach created a minimum viable product in the shortest possible time.

This customer project builds on the previous project, the development of a data analytics platform in the cloud to enable the analysis of data from numerous specialist databases. Our customer, Messe Düsseldorf, is one of the largest trade fair providers in Germany and wants to develop new business models using data science methods.

The aim of the project was to use data science to gain new, relevant insights that would make a significant contribution to achieving business objectives such as minimizing risk, reducing costs and maximizing sales.

First of all, we worked with the client to develop and define various questions that were to be answered by our data-driven data science analyses.
In general, this involved questions such as:

How can the range be expanded in a targeted manner?
How can potential new customers be identified?
How can the offering be optimized for customers?

These questions should now be answered with the help of the linked, processed and filtered data from the analytics platform.

Our solution

To implement the data science analysis, we relied on the big data technologies available in the platform, for example to be able to use the computing power of the cluster for modeling.

We have used the following applications of modern data science methods as part of the analytics platform:

Customer analysis to expand the company's offering
Pattern recognition for predicting potential (new) customers
Analysis of products sold and correlations as part of a shopping basket analysis to optimize the product range

The findings from the analysis were then processed in order to provide data-based answers to the questions.

The result

The data-driven answers to the questions through data science analyses provided positive findings that were not expected at the start of the project. The insights gained in the areas of product range expansion, optimization and new customer acquisition could be used directly for the future business optimization of our client.

The successful collaboration was the basis for the follow-up project. Data science was to be used to gain new, relevant insights in order to achieve business objectives such as risk minimization, cost reduction and sales maximization. For the implementation, our team used modern data science methods such as customer analysis, pattern recognition to predict potential customers and a shopping basket analysis to optimize the product range.

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