Customer engagement has its roots in the space-time continuum. When you’re a business trying to build bonds of loyalty, experience and influence, you must collapse the distances that make these outcomes difficult to achieve.
Distances between people, places and things matter. People’s proximity—measured in geospatial, social and temporal coordinates—is the matrix within which we measure the situational complexity of real-world engagements. How do you target the right people at the right locations at the right time with the right offers? That’s why data scientists develop statistics-based graph models of “proximity” patterns among people, considered in their capacities as customers, influencers, followers, innovators, imitators, first-movers and late adopters.
Geospatial analytics is a fundamental infrastructure in the Smarter Planet. In real-world applications, geospatial analytics are applied predominantly to use cases that span the “great outdoors”: neighborhood, city, region, country, continent and world. For example, here’s a recent article on how retailers use geospatial analytics to identify underperforming locations. This is one of the best-established “wide-area” uses of location intelligence in business.
Many people don’t realize that geospatial analytics has also proven itself quite valuable when applied to in-building applications. For example, retailers are starting to realize that in-store geospatial analytics can be one of their key tools for defending themselves from customer smartphone-based “showrooming.” Not only that, in-store location intelligence can also help them gain the upper hand in the relationship, providing retailers with a powerful tool for deepening mobile-centric contextual engagements with customers as they roam the aisles. The potential of real-time, in-store, mobile couponing depends on it.
For modern retailers, the core strategic question is: What unique situational factors encourage various mobile-equipped customers to take specific actions (e.g, redeem a gift card, read a promo, put something in their shopping cart, sample a new product) at specific places and times? Likewise, what social factors (e.g., physical proximity of specific influencers) can also sway your actions in the retail environment?
Another recent article employs ample visualization to illustrate how in-store location analytics can significantly bolster retail customer loyalty. As author Prem Couture notes, by correlating 360-degree customer data with in-store location data (especially individual customer smartphone MACs and IP addresses), retailers can measure and optimize foot traffic, visit durations, repeat shopping trips, product merchandising and display, and employee staffing. They can also more effectively personalize in-store interactions with mobile-toting customers in order to decrease customer churn, spur upsell and cross-sell buying, and target promotions and offers.
Before long, the distinction between brick-and-mortar and online shopping will have completely disappeared. Through in-store geospatial analytics, brick-and-mortar can be transformed into a type of “digital channel” in its own right. Couture spells out how a digitized in-store experience can be aligned with the customer engagement infrastructure that spans all of the retailer’s channels: “Once a customer has logged into the store network via a loyalty member number or other form of ID (e,g, Facebook) that is linked back to the customer, then a ‘push-intelligence’ engine kicks in to match the ID to the customer interest graph and send out information via a host of available options (SMS, email, member page, via store employee...).”
That describes continuous “next best action” in all its glory (my further thoughts on this topic in a retailing context are in this blog from over a year ago). By combining next best action technology with in-store geospatial analytics and smartphones, retailers can power continuous customer experience management within in-store environments. Just as powerfully, retailers who leverage both wide-area and in-store geospatial technologies can ensure seamless experience handoff as customers roam from home to store and from store to store.
As consumers, we may continue to visit physical retailing outlets for various reasons. But, clearly, the traditional notion of a retail store is dissolving as geospatial analytics, mobile gadgets, and kindred technologies take hold. No matter where we choose to shop, every consumer will receive a steady stream of real-time, contextual, personalized recommendations on which locations to visit, which aisles to roam and what to buy.
Interested in this topic?
- Listen to this podcast on how retailers are using big data and analytics
- Download the report "Real-world use of Big Data in Retail: How Innovative Retailers Extract Value from Uncertain Data"
- Watch this short animated demo