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Improve Campaign Targeting and Data Management Using Big Data Analytics

Read how GSPANN helped a US-based Fortune 500 departmental store chain with over $20B annual sales and more than 1000 stores in running personalized campaigns by segregating unique IDs from the customer database using big data analytics. We chose the most frequently used source to store the customer details, fetch information from it and mapped it with all available customer IDs.  

Key highlights:

  • On average, the solution matches more than 100K customer IDs every day, which resulted in a 12% decrease in the total number of duplicate customer IDs.
  • De-tokenization (de-linking) and re-tokenization of customer ID with profile data were performed before mapping the information of different customer IDs.
  • Customer data was passed through a standardization process before matching the customer IDs. We parsed the data with a sample template provided by the client to eliminate any discrepancies in the data extracted from the profiles.

This case study can help you in utilizing your promotional budget better by ensuring that the promotional codes meant for the new customers are not used by the returning customers. The correct identification of a new customer and returning customer is critical for any e-commerce business to run relevant campaigns and drive sales.

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