Business Tip 5
Data Views Adapted to Customers Create More Relevant Insights

Retailers need to look at data as it reflects their understanding of how consumers shop at their stores.

Account for differences in the way customers look at the business.

Large retailers often have their own Consumer Decision Trees for how consumers make category purchases. This could be based on in-store observations, Consumer Loyalty Program data or some other custom research. As such, retailers often want to manage the category according to how their shoppers shop the category, which may be very different from the way that syndicated services organize and structure the category for manufacturers.

Manufacturers that deliver analyses based on different product structures than their retail partners make it difficult or impossible for retailers to get a complete and accurate picture of the performance of the category.

Manufacturers and retailers both need to look at data as it reflects their own business strategy. That’s why it is so important to account for differences in the way manufacturers, retailers, and markets look at the business using flexible, customizable data views.

Example: Different Manufacturer and Retailer Category Structures

In this example, the manufacturer of the Avalon brand and the retailer Supermart are looking at the year-to-year performance for a category. While the manufacturer and Supermart have a common definition of what products make up the category and report the same sales performance for the total of all products, each one breaks out the category into different sub groups with very different results.

The manufacturer defines segments within the category whereas the retailer defines sub departments. While the manufacturer shows all segments performing well, the retailer's view shows a very different story.

By establishing a common definition (segmentation) of the category, both manufacturer and retailer are able to measure the performance with comparable results.

The data, products and accounts depicted in this example are fictitious. Any resemblance to actual data, products or accounts is purely coincidental.