- Understanding the customer


Benefits from Data and Analytics

In the retail sector, data and analytics play a crucial role and offer numerous benefits for companies. They provide a better understanding of customers' behavior and preferences by providing comprehensive insights into their buying habits and interactions with the company. Based on this, personalized offers and marketing strategies can be developed to attract and retain customers. Data analytics can optimize supply chains and manage inventory efficiently. By analyzing data such as inventory levels, sales figures and delivery times, better forecasts can be made and processes automated to avoid supply bottlenecks and overstocks.

In addition, analytics can increase operational efficiency and reduce costs; by analysing data on operating costs, human resources and supply chains, companies can identify potential areas for improvement and take appropriate action.

Overall, data and analytics in retail offer valuable insights and opportunities to enhance business processes, leading to an important competitive advantage over your rivals.


Well prepared

Expert knowledge in Retail

D&A Use Cases

Our wide range of use cases includes over 100 successful projects, covering a broad spectrum of requirements.

Some selected use cases are:


  • Standard use cases like sales forecasts, receipt analysis, customer analysis, logistics optimisation, web traffic analysis, transaction analysis, fraud detection, supplier analysis, accounts receivable, etc.
  • Loyalty programmes that offer users relevant coupons based on their location within the store.
  • Using geolocation technologies to offer promotions to customers both inside and outside supermarkets.
  • Connection to new e-commerce applications.
  • Generating reports on planned vs. actual product air time on home shopping channels.
  • Clickstream analysis and e-business evaluation.
  • Monitoring seasonal trends in machinery usage.
  • Consolidating financial and non-financial key figures from SAP, ERP, and planning systems, into a centralised data warehouse.
  • Real-time sales reporting for broadcast channels.
  • Online store analytics, such as traffic, visitors and content.
  • Building complex advanced analytics models, leveraging the latest ML and AI technologies for applications like reassortment and inventory planning.

Preparation and Tools

We made use of these tools and techniques, amongst others:


  • App-based geolocation data, Spark, Casandra, R, Java, CD/CI with Jenkins.
  • Informatica, MicroStrategy, DWautomatic, MS SQL Server, IBM DB2, SAP, TIBCO, Web Services, Webtrack, iET, Blue Yonder.
  • synvert standard templates for tool evaluation purposes.
  • Teradata, MicroStrategy, Ab Initio.
  • Oracle, Informatica.
  • Data integration from the Hadoop system with the existing Teradata DWH (Hadoop, Sqoop, Hive, Spark, Oozie, Zeppelin, Teradata Hadoop Connector).
  • Oracle, IBM DB2, IBM Cognos.
  • Teradata, SAS, MicroStrategy.
  • Oracle, MicroStrategy.
  • SQL Server, Informatica, IBM Cognos.

Standards and Regulation

In retail, you have to comply with a lot of standards and regulations that include, for example, data protection regulations, regulatory requirements regarding the handling of customer data, and specific requirements for the marketing and sale of products. Retail companies have to be thoroughly aware of these standards and regulations in order to ensure that their business practices comply with them at all times, thus avoiding legal consequences and potential damage to the company image.


Our customers in Retail


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