Real-Time Analytics Can Make the “Last Checkout Line” a Reality—and Drive Bottom-Line Gains for Retailers
As retailers compete for consumers’ attention—and purchases—they are always looking for ways to deliver a better shopping experience that will make customers want to come back to the store, again and again. A good starting point is to eliminate some of the common frustrations of the shopping experience, such as long checkout lines.
Cisco’s new campaign on the “Museum of Lasts” shows how the Internet of Everything (IoE) will bring about the “last checkout line.” But that’s just the beginning. When retailers apply real-time analytics to the networked connection of people, process, data, and things, they not only improve store operations and customer service, but also provide the “hyper-relevant” experiences customers crave.
“Hyper-relevance” simply means delivering the right experience to the right customer at the right time and in the right way. But without deep insight into the needs and context of each customer, it’s a bit like trying to hit a target while blindfolded. Today, the power of real-time analytics is helping retailers take the blindfolds off.
A study of 1240 U.S. and U.K. consumers conducted by Cisco Consulting Services identified several new digital retail concepts that can not only enable hyper-relevance but also contribute 15.6 percent to a retailer’s bottom line. It showed that with robust data analytics capabilities, retailers can assess their customers’ current context—where they are, and what they are looking to accomplish—and dynamically provide the most suitable experience at that moment.
Up until now, analytics capabilities have been used by retailers primarily to improve operational efficiency and provide a certain level of personalization. Now retailers need to look at analytics in a new way — combining, for example, mobile search information with such inputs as location-based data, in-store Wi-Fi data, and shelf sensors to understand what, exactly, your need is at that moment. If a retailer knows that a customer has just been to three stores trying to find the right valve to fix a leaking faucet, it can serve up an in-store map directing the customer to that product. That’s hyper-relevance.
In regard to the checkout process I mentioned earlier in this blog, my team in Cisco Consulting
is strongly focused on bringing the “last checkout line” closer to reality. We’re helping retailers use predictive analytics to measure traffic, wait times, and queue lengths to automatically deploy staff before the checkout lines start to back up. In addition, dynamic digital signs can display the wait times for each line and indicate the shortest current wait. Our research shows that in many cases, the most relevant element you can offer a shopper is, simply, efficiency.
Many retailers with whom we collaborate understand that this increased use of analytics will have a dramatic effect on their networks and business models. In our study of the Internet of Things (IoT) last year, 88 percent of retailers predicted significant growth of data captured by their networks, and 47 percent said that within three years, most of this IoT data will be processed at the “edge” of the network. The ability to capture and analyze data locally, at the edge, is the lynchpin of hyper-relevance for retailers. Here’s why:
- Need for speed: To meet the shopper’s needs in the moment, data must be gathered, analyzed, and turned into actionable insights locally, in real time, or opportunities will be lost.
- Focus on what matters:Processing data at the edge enables the network to separate the information that can be used to capture immediate opportunities from other relevant data that can be sent back to the central data repository for later processing—dramatically reducing the load on the network.
Combining in-store data streams from the edge of the network with clickstream data from the web and historical data from the data center can offer very powerful capabilities, giving the retailer visibility into all the touchpoints along the shopping journey. The resulting insights will be highly relevant to the customer’s needs—whether that means shorter checkout lines or a better route through the store to find that faucet.