The competitive threat faced by brick and mortar stores from online retail is huge - and it is growing every day. The key to physical retail’s survival rests in its ability to generate repeatability.
Online businesses spend big on analytics in order to offer highly personalized experiences. But physical retail does have an advantage as once a customer is inside the store, the physical movement of the customer presents a mine of information to base decisions upon. From the stores visited, the amount of time spent in each store and in each aisle, the attention given to each rack and product offers a treasure trove of clues as to what any given customer likes and dislikes.
Pattern recognition, trend analysis, predictive modelling and event probability analyses help shape in-store movement profiles - and these profiles are engineered to empower the retailer to gain deep insights into customer behavior.
Improving in person
Malls and integrated retail zones are prime examples of establishments with a tempting opportunity to leverage customer in-zone physical movements to offer incomparable personalization.
For example, I was recently at an integrated shopping plaza in New York. I cannot imagine a single option that it did not boast, be it fashion stores, stylish furniture shops, bookstalls, restaurants, bars and whatnot.
But I did sense that something was missing - a connection between the store and myself. It got me thinking, how would it be if there was an app which asked for my consent upon entering the plaza, and thereafter generated an anonymous movement profile? Such a profile would learn as I pay more visits to the plaza. Over time, the brains behind the tech would be able to start making recommendations for the right fashion store or bookstall based on my location profile, my likes and my preferences.
This is more than just a nice idea, but something that is coming to a plaza near you.
Profiles and possibilities
Retail analytics unlock various data points to encourage regular and repeat visitors. For example, it can help shop owners to measure the number of people who walk past their store versus the number who enter – with this information helping to demonstrate whether or not the potential shopper was convinced by the shopfront. Similarly, it can let shop owners determine the most popular aisles, racks, and brands.
But how is this done? The accurate attribution of visitor location typically uses WiFi fingerprinting, a pre-developed database of signal strengths from multiple access points to match against in real-time. This system is capable of pinpointing shopper location to within centimeters.
Location-aware apps have the ability to provide an in-person shopping equivalent of the “other items you might be interested in” or “frequently bought together” sections of e-commerce sites. Recommendations like this have the opportunity to generate some serious extra turnover, with 35% of Amazon’s revenue generated by its recommendation engine.
An extra third in sales would make a major difference to brick and mortar as it continues in its struggle against online sales.
The in-person answer
Brick and mortar must stop wasting physical location data. For too long, physical retailers have relied on outdated tactics to attract shoppers. The real and continued threat of online shopping means that physical players must unlock all data points to improve their conversion rates - and consent-based rich location profiling of shoppers is a sure-fire way to do this.
Of course, there are some barriers to consider. Older adults may not have smartphones and some users may not approve real-time location tracking due to privacy concerns.
There is no denying that shopping online has its advantages. However, even if an online store offers 50% discount, a large percentage of customers would still prefer to buy after touching and feeling the product. This rings true from furniture and apparel to electronics. And this preference is not going away anytime soon, at least until the world is all robots.
Until that time it is incumbent upon physical retailers to rethink their use of physical customer information.
-Mandeep Singh, the founder of ModFx Labs, a location analytics company which unlocks movement patterns to generate accurate customer behavior profiles.