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Data science analyses: new services and informed decisions
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, we worked with the client to develop and define various questions that would be answered in a data-driven manner through our data science analyses.
In general, these questions included:
- 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 using 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 on the platform, for example, to utilize the computing power of the cluster for model building. We used the following applications of modern data science methods within 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 result
The data-driven answers to the questions provided by data science analyses yielded 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 directly used for the future business optimization of our customer. This successful collaboration formed the basis for the follow-up project. Data science was used to gain new, relevant insights in order to achieve business objectives such as risk minimization, cost reduction, and revenue maximization. To implement this, our team used modern data science methods such as customer analysis, pattern recognition to predict potential customers, and shopping basket analysis to optimize the product range.
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