The potential for omnichannel analytics, part 3 of 3
In the first two parts of this series, I described the high level benefits of consistently measuring and analyzing the full omnichannel experience and the technical and systems requirements necessary to pull it off. Once you have these pieces in place, you can become a truly data-empowered retailer. Here are just a few of the neat tricks you can accomplish when you see all the data for a customer.
Scenario #1. A customer leaves a watch abandoned in his shopping cart on your site. Two days later he walks into the men’s watches section of your store. The sales associate receives a real-time alert on his phone that a shopper in his section is interested in this specific watch. Now he can direct the shopper’s attention to the item and close a sale.
Scenario #2. A shopper dwells for a long time in the winter sports section of her local outdoor sporting goods store. Later that evening she logs into the website and is offered a today-only coupon for $10% off all snowboard purchases.
Scenario #3. A Facebook user Likes a specific brand of beverage. The next time he goes to his local grocery store, he receives a coupon on his phone for new products in the same line (or perhaps for competitive products instead).
Scenario #4. A retailer measures shopper density and dwell time in the various checkout areas of the store. When they exceed a certain threshold, employees carrying mobile POS tablets receive automatic alerts on the devices telling them where to go and assist checkout.
Scenario #5. For every SKU in the supermarket the grocer can look back at the full “paths” of all shoppers who purchased this item (on this or previous trips) and build a heat map of where in the store those shoppers tend to travel. This data makes it possible to sell CPG manufacturers merchandising programs in entirely different parts of the store.
Scenario #6. Using the full-path analysis described above, a clothing retailer knows that customers who purchase a shirt in its brick-and-mortar stores typically interact with at least four shirts in that section before making the purchase decision. The retailer can use this information to offer suggestive selling to shoppers via its mobile app, directing them to multiple shirts and ultimately creating the situation that it knows stimulates purchasing.
And these are just the tip of the iceberg. The possibilities are limited only by the data itself and our ability to imagine new ways to integrate and understand it.