In addition to enabling category management (as discussed in Part One of this series), analyzing point of sale data amassed by such solutions as pcAmerica’s Cash Register Express paves the way for merchandising products according to “common purchasing affinities”.  Here, data mining tools are used to determine which specific items are most often bought with other specific items. Planograms based on these findings are then created, in turn boosting incremental sales.

Here’s a real-life example. While re-configuring one of its stores, an independent grocer used point of sale data to determine where garlic should be showcased within its produce department.  An analysis of the data revealed that the item was most commonly purchased with green peppers, so the products were arranged in adjacent displays. Utilizing the same type of analysis, the retailer determined that floral bouquets should be displayed next to the greeting cards, as both items frequently ended up together in shoppers’ carts. Sales of garlic, green peppers, greeting cards, and floral bouquets increased as a result. You can really apply this technique to almost any kind of merchandise. For example, if you sell apparel and accessories, you may discover that customers often buy scarves at the same time as a collared blouse and then set your displays accordingly. If you own a toy store, you may, through an analysis of point of sale data, figure out that puzzles are best merchandised with board games rather than in their own section in another part of the shop.

Data analysis that reveals common purchasing affinities can also be used to put together more creative, effective promotions.  For example, if you notice that two particular items are frequently bought together, you can structure a “buy two of each, get one of each free” offer. The possibilities are endless. For more information, visit www.pcamerica.com.

Next up in this series, find out how to leverage point of sale data to create individualized marketing campaigns.