On Tuesday, July 22, George Shaw, head of Research & Development at RetailNext, facilitated our live webinar, “A Vision for the Data-Driven Store of Tomorrow,” where he led an interactive presentation on how data and technology are rapidly redefining both store design and shopper experience.
In retail, there is a shift of power occurring, and it’s swinging towards shoppers as they wield their foot traffic and spending toward stores that best cater to their needs. Collectively, their voices are being heard, and they’re asking for their “retailers of choice” to be:
- Responsive – “I want store staff and the store itself to respond to my needs.”
- Personalized – “I want the store to work for me, not just customers in general.”
- Optimized – “I want the store to be run perfectly, laid out efficiently, etc.”
Related to forming strategies for customer experience and store performance, Shaw walked through the “Technology Framework” for data-driven decisions – Sense, Process, Analyze and Present.
Sense includes not only stores generating their own data streams like traffic counts, dwells, merchandise and sales associate engagement, etc., but also incorporating third-party data sets like weather and payment transactions into the mix.
Process involves cleansing and normalizing the data to eliminate irregularities, and then discovering patterns that begin to funnel the “big data” into more discreet, identifiable segments.
Analyze is the layer where segmented statistical modeling ties the data set to the retailer’s business and business strategies – it’s the layer where data becomes actionable.
Lastly, Present is the dissemination of the data and its findings to the organization, with effective communication assisting not only in implementation and execution of corrective actions and countermeasures, but assisting in change management efforts as well.
If you missed all or part of the webinar, be sure to catch the recording here.
Additionally, there were several questions George was unable to address during the session, so we captured his answers to those questions, below:
Are you seeing this type of analysis (e.g.; shopper movement) being done by shopping malls to assess shopping patterns or is it primarily individual stores.
George Shaw: “Shopping Centers are very much concerned with traffic into the mall, as well as how traffic moves throughout the mall and what mall-controlled amenities – like seating areas, for example – are most in-demand and most popular. In the not-so-recent past, malls were generally considered too large to effectively and efficiently capture meaningful, actionable data through video analytics, as the store-specific examples in the webinar were developed.
As such, most malls looking to measure shopper traffic and movement have deployed different data acquisition and sensing technologies, usually Wi-Fi analytics, with some malls deploying new beacon technologies.
That’s changed. Savvy, cutting-edge retail malls are deploying new solutions where RetailNext artificially generates concrete movement data using a much smaller number of video cameras coupled with powerful statistical models. In the ‘technology framework’ we discussed in the webinar, what that actually means is fewer sensors and raw data generated in the Sense layer, and much more robust statistical manipulation of the data in the Process layer, effectively filling in the initial data gaps. From there, the output, Analyze and Present, stay the same.”
Has RetailNext considered tracking customers via GSM triangulation as opposed to Wi-Fi, beacons, Bluetooth, etc.?
George Shaw: “Innovation is one of our top priorities at RetailNext, and we continually look at creating and developing both new technologies and new technology applications. With respect to GSM, we have deployed it and experimented with it, and we have found it to be both interesting and complicated at the same time. We have found that, in most cases, we can collect the same data sets more effectively and with less investment from other sensing solutions.”
Regarding outbound APIs, how do you ensure protection of third-party data, like credit card information?
George Shaw: “The most important consideration is to simply not collect sensitive, private information. Third-party data like credit card information and cross-store spending are all in aggregate and anonymous. As a result, we can’t provide any sensitive data through outbound APIs.”
Can you provide an example of a success story where one of your customers made a business decision based on this information that resulted in an increase in revenue?
George Shaw: “Absolutely. One RetailNext customer, a national sporting goods merchant, looked at traffic streams and POS data to identify opportunities for improvement in the shoe department – traffic, engagement, conversion and sales were all areas that management wanted to address.
Customer movement data showed the layout of the store design, it’s actual merchandising, was limiting traffic flow to the back wall of the shoe department, where the majority of shoes were displayed, effectively ‘blocking’ it off from shoppers. A simple re-design of the merchandising displays resulted in significant improvements across the board.”