Shopper Basket Analytics: 11 Real World Examples

The introduction of the barcode scanner ushered in an era of metrics-based decision making for retailers. It was the first data analysis revolution in retail – providing a complete and reliable source of objective sales data.

Since then, the POS T-log (transaction log) has been heralded as a “goldmine” of information. It contains everything about the sales transaction – or shopper basket – to answer questions such as:

  • What time of day do my best customers shop?
  • Are stores keeping the right number of checkout lanes open?
  • Do any cashiers have an unusually high number of transaction voids?
  • Which promotions result in increased unit sales of related items at full margin?
  • What patterns emerge among the best-selling items?

However, the POS T-log has not been systematically leveraged to answer these types of questions. That is because this level of depth is not available from POS data that is typically summarized first and then stored in traditional data warehousing systems.

While this traditional approach may have been adequate in meeting the needs of SKU-by-store level analysis, successful retailing today requires a comprehensive understanding of transactions at the line item level. As the retail landscape has changed rapidly in recent years and will continue to change, a retailer’s use of their basket level data must switch gears from simply reporting what sold to providing insight into the who, when, and why behind a transaction.


Those retailers who are able to quickly, easily, and inexpensively analyze the repository of detailed, transactional data can:

  1. Effectively price and promote
  2. Conduct more intelligent merchandising and marketing
  3. Optimize store operations

Download this whitepaper
for an easy-to-follow approach to maximizing the shopper basket using basket analytics. It features 11 real world examples such as product profitability, a promotions deep dive, loss prevention, assortment, planograms and more.


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