Data Analytics in Retail2020-05-29T14:11:31+02:00

Data Analytics in Retail

Data Analytics transforms retail data into key decision drivers

Which part is bought in which quantity at which location? How big was the profit effect of a marketing campaign and can one predict what success the next campaign will have? If a customer has bought a part, which part might he still need?

Since these and similar questions are omnipresent in the retail industry and are crucial for business success, many retail groups have already started to build up huge data analytics and data science departments. Data-driven decisions are more important in retail than in almost any other industry. However, there is not always a need for a separate department if you can get the expertise of experienced data science experts who then provide the right tools.


84% Time saving
94% more current data
100% Compliance

Case study: Big Data becomes a real added value in online fashion retailing

The rapid growth of our customer – an online fashion retailer – has quickly led to standard and self-developed software solutions in the field of data analytics reaching their limits. Therefore, the biggest challenge of the project for our data analytics experts was to merge legacy systems with modern systems into a central solution. Two terabytes of raw data such as key figures of the online shop, customer sales and services were generated weekly by these systems. As a result, the data was only available with a delay of at least two days – an eternity in online marketing – and if errors occurred during the data acquisition process, no analyses were available for the current week.

The complete data management process of corporate and online marketing key figures was reduced from over 48 hours to less than two hours thanks to the new data analytics solution. The fashion retailer can now not only provide updated data and analyses on a daily basis throughout the company in a short time but can also detect errors in the process promptly.

0days time-to-value
0Terabyte data volume

Data Cleaning: Pitfalls and Solutions

As the interest in machine learning and artificial intelligence grows, companies regularly find themselves confronted with the dissatisfying quality of their data. This discovery is either made early-on with a structured approach, or a lot later, when poor data quality is identified as the root-cause of poorly performing models. In either case, the next step should be a methodical exploration of the available data, followed by a series of steps to remedy the identified issues. In this article, I will [...]

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