Can AI spark growth and joy for offline retail?
Summary
AI-powered location management tools can now update each store’s data across multiple search engines and maps, helping customers find and navigate to their nearest store without any friction.The hype around 10-minute delivery apps and online retail in general often blinds us to the still-relevant truth that even today, more than 85% of global retail sales still happen through physical stores. This is classic availability bias—we’re surrounded by people who rave about ordering everything from groceries to the latest smartphones online, even as the number of people buying the exact same goods from physical stores is actually a significant multiple of online shoppers.
This is not to say that the Internet or smartphones have not changed how we shop online and even offline. Increasingly, consumers use online searches and smartphone apps to find or decide which store to visit. A significant majority of offline transactions now start online with a search, a quick reading of reviews or a quick check of store or branch open hours on Maps.
This behaviour is so pervasive today that the disconnect between discovery (online) and fulfilment (offline) has actually become offline retail’s biggest barrier to growth. However, AI and Gen AI are now emerging as powerful forces capable of finally bridging this gap by bringing data-driven intelligence, personalization and precision to what are otherwise highly complex digital-analogue interactions.
Also read: Amazon, king of online retail, can’t seem to make its physical stores work
Retail’s digital divide
Retailers and brands with physical storefronts have spent years trying to replicate digital experiences offline: adding kiosks, mobile checkouts and even augmented reality mirrors. But the problem wasn’t a lack of in-store tech but the absence of smart, outside-the-store tech: which makes a store easier to find or discover, navigate to and trust before a customer even steps in.
To be fair, mapping and managing an offline customer’s journey from search discovery to fulfilment is far more complicated and has until recently required a lot more human intervention than its digital counterpart.
This explains how online retail has been quick to deeply personalize every individual’s buying journey, even taking advantage of AI and machine learning algorithms to improve it on a continuing basis. They also have access to cutting-edge digital marketing techniques, including real-time targeting, hyperpersonalized recommendations and dynamic pricing.
In contrast, physical retail often lacks visibility in online search, constrained by outdated listings or map data and poor hyperlocal search engine optimization. Customers even struggle to find their local store-specific information like product availability or timings unless brands have specialized presence management platforms or location-specific microsites.
This digital divide gets progressively worse at later stages of a customer’s journey. Many online retailers have long used data generated from every single prospect’s online behaviour at each step of the sales funnel to constantly make small but meaningful improvements in their shopping experience. While physical stores have the time-tested advantage of human interactions with prospects, very few have the capability to map, digitize and analyze this data on an ongoing basis to improve their selling.
The bridge is AI
But all this was an unsolvable problem until recently. Now, AI has for the first time started breaking the silos between digital and physical (or in-store) behaviour of a customer by adding the missing layers of intelligence and automation, starting with search or discovery.
AI-powered location management tools can now update each store’s data across multiple search engines and maps, helping customers find and navigate to their nearest store without any friction. Getting location information correct on Google or Apple Maps might appear as basic marketing hygiene to digital marketers, but it was near impossible to accomplish at scale for brands running hundreds or thousands of storefronts across cities, states and even countries.
Further along a customer’s journey, their digital and offline signals such as reviews, transcripts of calls made to the nearby store, in-store queries and visits can now be unified and analyzed using advanced AI tools to discover all points of friction that prevent sales. Advanced tools are capable of connecting phone conversations with customer relationship management data to identify high-intent customers and enable a hyperpersonalized shopping experience for them both before and during a store visit.
AI is even enabling hyperlocal yet hyperpersonalized marketing for the first time. Advanced AI-native web tools can rapidly create and constantly update search-optimized microsites for each individual store, complete with real-time inventory updates, embedded chat and actionable call-to-actions.
Ultimately, AI is now finally levelling the playing field between online and offline retail for the first time, bringing the same data-driven segmenting, marketing, and engagement capabilities to physical stores that have so far powered the rocket growth of online retail in recent years. More importantly, it is critical for offline retail brands to recognize that embracing AI is not an incremental advantage, it’s a game-changing opportunity to unlock growth—mirroring the kind of scale and precision digital-first brands have already mastered.
Sandeep Singh is chief operating officer at SingleInterface, which is one of Asia’s largest retail tech platform for multi-location brands.
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