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Predicting Buying Decisions

We helped one of our clients in the retail industry leverage the ability to interpret customer behavior from past purchases

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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