The Potential for Omnichannel Analytics is Real
Scenario No. 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 company's site and is offered a today-only coupon for 10 percent off all snowboard purchases.
Scenario No. 3: A Facebook user "Likes" a specific beverage brand. The next time he goes to his local grocery store, he receives a coupon on his phone for new products from the brand (or perhaps for competitive products instead).
Scenario No. 4: A retailer measures shopper density and dwell time in the various checkout areas of their stores. When they exceed a certain threshold, employees carrying mobile point-of-sale tablets receive automatic alerts on the devices telling them where to go to assist checkout.
Scenario No. 5: For every SKU in the supermarket, the grocer can look back at the full "paths" of all shoppers who purchased the item and build a heat map of where in the store those shoppers tend to travel. This data makes it possible to sell consumer products goods’ manufacturers merchandising programs in entirely different parts of the store.
Scenario No. 6: Using the full-path analysis described above, a clothier knows that a customer who purchases a shirt in its brick-and-mortar stores typically interacts with at least four shirts in that section before making a 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 a situation that stimulates purchasing.
These are just the tip of the iceberg. Really, the possibilities are limited only by the data and our ability to imagine new ways to benefit from it.