Maps, Money, & Mobile: Defining Geospatial Commerce

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In today’s internet-driven, data-rich world, innumerable brick-and-mortar operations have failed as stand-alone entities because they lacked an understanding of consumer’s habits outside their store. Reports of the death of retail have proven greatly exaggerated; McKinsey estimates in-store sales will make up 75-85% of retail sales by 2025, compared to 90% today. Rather than disappearing, retail is evolving, and will continue serve a pivotal role by becoming friendlier with its one-time nemesis – e-commerce. Powering this shift in strategy, both in retail and online domains, is location-based data.

For e-commerce, retail, and omnichannel-focused businesses, location-based data can help optimize operations in areas like fulfillments, brick and mortar operations, and marketing campaigns for targeted consumer audiences. Powered and constantly improved through machine learning programs, geographic information system (GIS) platforms are offering hyperlocalized strategies for businesses to intelligently identify and sell to consumers. Though privacy concerns admittedly linger on the fringes of the industry, geospatial data will be an essential ingredient in the digitization of commerce, and with promising technologies on the horizon, 2020 could be a breakthrough year for G-commerce.

Breaking The Fourth Wall

Bygone retailers largely relied on what’s been called “four-wall economics” — evaluating a store by sales and profits to the exclusion of its role in other channels. Data-driven retail does away with that, recognizing that a shopper who spends $100 online typically spends an additional $171 in store. Location-based data is how businesses can understand both halves of a consumer’s behavior — online and offline —to the benefit of both channels. Whether it’s serving as a fulfillment center, a showroom for potential online purchases, or a space for consumers to tag in social media posts, every physical store has potential to bolster a vendor’s e-commerce efficacy. Some estimates even say that a retail location can have a positive, or halo effect on ecommerce operations, accounting for 20-40% of total economic value. This has precipitated so-called “halo forecasting,”, in which AI-driven prediction engines calculate the revenue any retailer can expect from a given market across different channels. Tools like these allow business to better strategize retail expansion and reduction, tailor branch services for local needs, and better synchronize online and offline operations to optimize the symbiotic effects of both channels. A brick-and-mortar presence may boost online sales in that given area, for instance, and the physical retail can complement the online shopping experience via tools like click and collect.

Today, commercial businesses have at their disposal an unprecedented amount of consumer-behavior data derived from sources like opt-in e-receipt programs and mobile-device location tracking. Location data collection begins when a user grants consent for apps or browsers to collect their location data. The data is then anonymized and aggregated among previously collected data in the given location to track movement patterns and build consumer segment profiles of the area. Geospatial data can also be combined with traditional sociodemographic information and consumer relationship management profiles to create a multilayer model, which can inform business decisions (specifically in the areas of product management and marketing).

These innovations in identity-based consumer strategy reflect the new era of consumer brands we have entered — the so-called “mecosystem.” A mecosystem is “a select set of brands that create customized experiences around a single individual, where every brand in consideration slots in seamlessly, and where the most valuable micro moments are curated, connected, and choreographed.”

Better Than You Know Yourself

What does it say about a consumer that they’re a member of a given gym, or that they’re a homeowner? Such consumer attributes apprise businesses of not only what products a person might be interested in, but of their lifestyle as a whole. ESRI, a leading supplier of GIS software for a variety of industries, utilizes such user data to classify neighborhoods in the United States into 67 unique identity segments, with each neighborhood exhibiting a certain proportion of different Tapestry Segments, whether they’re “Soccer Moms,” “Heartland Communities, ” or some other communal identity.

“It’s not just about finding anymore a zip code or city and looking at the basic demographics. That doesn’t show you anything anymore. [Baby] boomers in the 1970s had a distinct identity they were able to be marketed to. But with millennials, we are seeing there could be 30 or 40 personas.”
Alex Martonik, financial and commercial industry specialist, ESRI

For ESRI’s “Emerald City” consumer, for instance, the consumer can be expected to be well-educated, live in lower-density neighborhoods, and shop at Trader Joe’s and Whole Foods, among a host of other characteristics. With all these predicted consumer behaviors mapped out geospatially, businesses know where their potential customers live, where they go, and how they behave both online and offline.

It’s difficult to overstate just how broad the potential use cases are for such kinds of geospatial data-driven platforms. One of ESRI’s clients, Indonesia’s Bank Muamalat, used the company’s platform to understand where pockets of the bank’s potential customers were to optimize their physical branch locations and the services each provided, going so far as to retrofit an RV to do customer service and account management on a personal level in underserved communities. Domestic US banks are following suit to better find and target banking deserts, subsequently providing services to consumers on a hyperlocalized basis.

On the commercial side, the tenets of this omnichannel-driven system are applicable for any businesses utilizing location for strategizing sales, delivery, or marketing. Nike opened a store in Los Angeles, for example, which was designed based on data obtained specifically from the local market. Using a combination of online shopping data from the area and data from the brand’s workout apps, the store customized store inventory and layout to suit local needs.

Waiting For The Big Guys

As ecommerce, retail, and omnichannel businesses increasingly leverage geospatial data, the question becomes if (and more likely when) the companies richest in location-based data will transition to processing ecommerce transactions themselves on a massive scale. Google, who manage the world’s greatest repository of geospatial user data, is an obvious candidate to take a shot at uniting geospatial and consumer data. Already, Google Maps offers tools for local business through its Smart Shopping Campaigns, like “local inventory ads” (which acts as a virtual storefront). Google’s e-commerce forays have accelerated recently, and the tech giant is beginning to construct what PYMNTS calls “a contextual ecommerce ecosystem of its own” — with heaps of geospatial data at hand to offer features even Amazon can’t match. For the 2019 holiday season, Google unveiled new local ad tools to boost their e-commerce infrastructure, showcasing merchant products with a mini-catalogue and detailing which products in store are in stock and which are be available for pickup later.

While geospatial apps like Google Maps are a core part of the shopping process for many consumers, they have yet to be fully integrated with other features. On Google Maps, a user can find the telephone number of a restaurant to make a reservation, but the actual call is made on a separate application; likewise, users can compare hotel rates on Google Maps, but reservations are again made via third-party vendors like Hotels.com. Google did integrate complete ordering and payment features for Uber beginning in January 2017, but 18 months later, the feature was mysteriously removed; now (as before), Google Maps users can simply find prices and times for different ride-sharing options before being redirected to the third-party app.

Although payment transaction features have yet to be truly unleashed on geospatial apps, deepening integration will continue to improve convenience for users. Perhaps more importantly, it will make data-rich entities like Google Maps even richer in their understanding of consumer behavior in a hyperlocalized context. As this immature space evolves, companies possessing detailed, user-driven location-based data like Waze (owned by Google) may seize opportunities to build on Google’s early efforts to integrate commercial features within geospatial apps. Instead of deriving consumer data from aggregated and anonymized processes, user contributions could also be leveraged to produce valuable insights — though some privacy pitfalls then come into play. The rollout of 5G will only generate greater sources of highly accurate location data for such companies, with billions of sensors deployed through the Internet of Things creating amazingly detailed consumer profiles.

Destination Unknown

These potential next steps come on the footsteps of the EU and California recently implementing data privacy and security laws amidst growing consumer concerns. In particular, the EU General Data Protection Regulation (GDPR) serves as the most significant escalation in data privacy regulation in 20 years, forcing companies to use transparent data collection methods and require opt-ins from users to share such data, all in accordance with a matrix of rules and guidelines prescribed by the law. GDPR and other regulations have pressured companies who lag in data privacy and protection – particularly in the US, where one survey said two-thirds of US companies believed the GDPR will require them to rethink strategy in Europe and 85% see the EU law as putting them at a competitive disadvantage compared with their European competitors.

As always, obstacles persist. Many regions around the world still lack requisite levels of network infrastructure for sophisticated geospatial data to even be collected in the first place. The lack of address systems in parts of the Middle East and Sub Saharan Africa makes it difficult to segment populations in hyperlocalized contexts, though companies like What3Words offer potential solutions via geocoding. The imbalance in network infrastructure may currently favor developed markets like North America and Europe, but that will change as geospatial data continues to be harvested, analyzed, and utilized by companies around the world.

With such improvements just around the corner, geospatial data and applied analytics could have a huge year in 2020. Soon, consumer profile-inclusive, data-driven platforms may be able to place a person’s favorite store not on every street corner, but wherever they seemingly go. Businesses – large and small, retail and online, multination and domestic – who take the geospatial commerce proposition seriously may soon enjoy an advantage as the technology catches up to its potential.