The Potential for Omnichannel Analytics is Real

The retail industry has recently seen a great deal of focus on omnichannel strategies — i.e., an integrated approach for each retailer to take for online, mobile and, of course, brick-and-mortar. With all the discussion on this topic, however, I've yet to see any dialog about omnichannel analytics, which I define as the following:

"the integration and intelligent use of the various pieces of information available for each different retailing channel in order to more holistically understand the buyer and path to purchase or to create opportunities that otherwise wouldn't exist."

From a systems perspective, analytics for these three channels are quite separate from each other. A web team is collecting a whole raft of metrics like visits, average time per page, bounce rates and the like; a mobile app is owned by the IT department, which is collecting a similar set of usage statistics; and store operations is measuring traffic, movement throughout the store, transaction count, average transaction value and other data.

Different metrics in different systems being viewed by different people. They're all looking at the same set of customers, but they're doing it through different lenses and taking different actions based on what they see. This approach flies in the face of the basic idea of an omnichannel strategy, where the customer is a single human being who apprehends your brand through different channels. So why would you treat them like different people based on whether they choose to use their phone, PC or feet?

Now imagine what happens when you start to pull all these separate analytics together into a single data cube. Important questions about customers’ holistic shopping behavior begin to answer themselves. Here are just a few examples:

• When customers start using your mobile app, how does that affect their behavior in other channels? Are mobile purchases net-new or cannibalized from older channels? Or does mobile app usage actually increase use of these other channels? All are possible, but for each retailer only one is the case. Which is it for your brand?
• How much mobile app usage takes place in-store? Are shopper behavior patterns different when a mobile app is in use as opposed to when one isn't?
• How frequently does the same customer look at your products in the store, then purchase from you via another channel? Many retailers are geared up to combat "showrooming," but if the showrooming shopper makes a purchase through your website or mobile app, then your store becomes an asset to the electronic channels. Or does your online store serve as the starting point, enabling consumers to narrow down their choices before they actually purchase in-store — maybe to get the items immediately or see them in person before making a final purchase decision?

There's tremendous value in analyzing customers holistically across channels as a critical part of a successful omnichannel strategy. The technology exists today to gather insights like these. For a comprehensive picture of the customer environment, retailers need these key pieces in place:

• Full-featured website analytics: These tools are considered to be a best practice among e-commerce professionals. It's likely that any serious omnichannel retailer has met this requirement.
• Comprehensive in-store analytics: Systems like RetailNext can measure and communicate in-store customer behavior at the same level of detail provided by their counterpart e-commerce systems.
• A robust, location-based mobile app: By giving shoppers motivation to download and opt in to your location-based mobile app, you can begin gathering insights into when they come to your stores, which stores they go to and what they do when there. Compelling location-based functionality includes providing directions to your stores (where's the nearest location?), directions inside the stores (where's the product I want?) and real-time offers (what products in this aisle are on sale right now?).
• Real-time in-store messaging systems: This component is necessary to turn your store into an intelligence-driven, optimized selling environment. It enables your store associates to take actions based on real-time intelligence, helping them become more effective sellers.
• Good reason to attach accounts: Offer your customers motivation to connect their mobile app usage to their online accounts and attach those in turn to their in-store loyalty cards. Then, give them a good reason to link their Facebook and other social accounts. This gives you the ability to correlate behavior across these channels and build a full picture of their effect on the market.

Once you have these pieces in place, you can become empowered to offer the best shopping experience across channels in a way that never was possible before. There are some useful insights you can uncover and actions you can take when you see all the data for a customer, which wouldn't have been possible without complete omnichannel usage data and these systems in place. Consider the following examples:

Scenario No. 1: A customer leaves a watch abandoned in his shopping cart on your site. Two days later, he walks into the men's watches department of your store. The sales rep in that department receives a real-time alert on her phone that the shopper in her section is interested in this specific watch. Now she can direct his attention to the item and close a sale.

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.

Tim Callan is the chief marketing officer at RetailNext, a provider of in-store analytics. Tim can be reached at tim@bviretailnext.com.