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Enabled a fashion retailer to interpret customer behavior and improve the performance of reports by 60%

 

Client Background

The client is an online subscription fashion retailer that carries selections of shoes, handbags, jewelry, and denim.

Challenges

  • The client wanted to migrate from a legacy reporting system to a new-age system which helps in making predictive analysis using past data.
  • They wanted to understand the combination of items that a customer is most likely to purchase based on past trends.

Solutions

  • Data models were developed in Tableau and SSRS and exposed to end-users for supporting on-demand custom reports.
  • Data is rectified using Microsoft Data Quality Service and is factored in for future data loads.
  • ABC (Audit Balance Control) layer processes ensure that source data is not corrupted during the extraction process and the integrity of data between source and target is maintained.
  • Integration services resolved different load patterns like truncate loads, delta loads, slowly changing dimensions and aggregate loads.

Tools & Technologies

Microsoft SQL Server, Tableau, R

Key benefits

  • The performance of reports was improved by 60%.
  • Precise and clear reporting on various aspects like membership – activations and deactivations, chargebacks, sales, etc.
  • Standardized warehousing system and reporting environment.
  • Interactive dashboards that show KPIs and their performance over time with drill-down features.
Key Benefits - AI in Retail

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