Retail Analytics for an Omnichannel World

Hello, there! My name is Kathryn Howe, and I’m a senior advisor in Cisco’s Retail Industry Practice. I am joining this blog to write regularly on my favorite topic and one of today’s hottest retail trends: Shopper behavior analytics and how they can support omnichannel selling.

Your store is probably among those that are collecting and analyzing masses of data about customers, products, and store operations to earn additional revenue and savings. The challenge of this big data, of course, is that metrics don’t mean much unless the store has access to the right data to meet your specific business needs. But when you do, such metrics become a powerful tool to create efficiencies and support your omnichannel strategies.

Most of the retailers I meet are extremely enthusiastic about the idea of utilizing shopper analytics technologies to generate deeper insights they can use to better manage their businesses – but aren’t too sure of how to do it. However, the truth is that the use cases for analytics in the store are almost infinite. As just a few examples, you can:

Predict resource requirements

Retailers can use analytics tools to measure traffic, wait times, and queue lengths, proactively anticipating resource demands across the store. For example, front-end staffing demand in grocery can be anticipated using a combination of real-time traffic counting, trip time data, and data on staff on hand. Resources are thus dynamically allocated based on real-time information, improving productivity of labor hours and improving customer satisfaction.

Drive traffic to the store

Through presence and location-based mobility analytics, retailers pinpoint the location of opt-in shoppers when they are close to a store location. With personalized reminders or discount offers sent directly to their smartphones, consumers are more motivated to visit the store if they are nearby.

Counteract showrooming

Retailers can leverage customer showrooming by providing real-time discounts and price matching on the shopper’s mobile device based on their location in the store. For example, analytics from mobile or video may detect high wait times in a department or category. In response, the store can alert staff to offer immediate assistance, or send a personalized offer to the shopper’s mobile device. This turns showrooming from a threat into a promotional opportunity, improves the shopper’s opinion of the store, and builds a strong long-term relationship.

I recently authored a white paper that addresses these and many other use cases, which you can find here. For a dynamic conversation on these and other analytics topics, please join us on June 25 for a free hour-long webcast on real-world analytics. It’s being hosted by Cisco and a group of our partners to discuss how to optimize operations and workforce efficiency, increase marketing effectiveness, and strategize for Analytics 3.0. See you there!