Insights

Where Does Digital Reality End and Physical Reality Begin? With AI Algorithms, It Doesn’t Matter

January 11, 2020

George Shaw
Founder / CEO at Pathr™.

Behavioral science is a classic discipline charting and analyzing actions between people to predict patterns. But what if a business (of any vertical) could get enough information about the behavior of its customers—in any number of physical locations pivotal to that organization’s success—without having to wait for a lengthy analysis? That’s the powerful promise of emerging Artificial Intelligence (AI) technology.

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Today’s newest solutions can evaluate any series of interactions that take place within a company’s designated core destinations—with the aim of enhancing the customer experience, increasing profits and maximizing logistical efficiencies. These locations might include a diverse number of commerce or non-commerce oriented environments, such as a retail store, bank, casino, airport or sports or entertainment venue—or even a shared municipal space or public area, where spatial management is essential to streamlined operations and smooth traffic flow.

Being able to access this information in real time can help entities doing business across a spectrum of industries to become more effective in helping their customers. The same also applies to better supporting the role of employees who work to make the in-venue experience more positive and goal-oriented. For example, in a retail environment, with access to “live” location based data analysis, managers can make changes to store floor operations—amending a display formation, better positioning staff to address customer needs, or adjusting the flow of traffic from the entrance to the cash register ad hoc. These adjustments can even be made throughout the course of a day to respond to customer needs as they occur—in an effort to be more highly in-tuned with shopper marketing dynamics.

Some of these changes can be immediate and others can be achieved at appropriate downtime hours, but they can be timed to incidents and fluctuating movements in floor traffic as they occur—something unprecedented to date in the industry.

There are some changes and insights that can be derived using basic “counting” analytics, but there are many more that require a sophisticated, holistic view of the movement of people in order to produce actionable, relevant insights with little effort on the part of the end user. This is spatial analytics taken to the next level—evolving into a wholly new form of “Spatial Intelligence” available from companies like Pathr.ai and made possible through advanced predictive analytics and Machine Learning (ML) tools and strategies.

In addition to traditional brick and mortar retail applications, the same premise would apply, for example, to guiding and more intuitively directing traffic and responding to patrons in a bank, or at the check-in window of an airport terminal, or at a sports stadium where crowds of attendees quickly move about to seek refreshments in the interim moments during commercial breaks.

"The quality and effective inter-connectivity of these movements—and the way that an organization’s constituents migrate within essential crowd patterns—really do matter. And, the insights and deeper knowledge into these physical interactions have the potential to render game-changing financial impact."

In this scenario, stadium management could benefit from the intelligence to better understand crowd patterns and traffic flow—knowing 1) when and where people first go when the game play pauses, 2) in what direction they travel, 3) whether they seek out refreshments individually or in small groups of friends, 4) what causes them to bypass a particular refreshment stand line in search of another, and so on. It also will offer up invaluable real time education about why they might choose to abort their journey altogether and return prematurely to their seat, in lieu of spending pre-disposed dollars on drinks and food that carry premium pricing. With this knowledge in hand, venue sales strategists might have been able to intervene with an offer, or staff assistance, to make the attendee’s experience more positive, and ultimately, to close a concession sale.

The key element of this AI driven intelligence revolves around detection of ‘movement.’ The actual customer or patron is only recognized as a digital dot on a software program’s visual map, thus completely protecting anonymity. These movements may represent a customer in a retail establishment or a tourist patronizing an iconic public space. They might also represent the customer’s spatial interactions—not just within the physical space itself (i.e. how and where they move about a store floor)—but also with other entities (people and products) moving about within the same space.

Using advanced pattern-matching capabilities that learn from data and operate in real-time, the movements of these “digital dots” are quantified both individually and in aggregate to find the hidden patterns and metrics that business owners care about. Pathr’s Spatial Intelligence turns simple, common location data (x, y coordinates of people over time) into the insights and events that are most valuable to business operators.

For example, we might map a customer’s exchange with a particular sales associate or venue representative using an “interaction classifier” that detects these person-to-person interactions automatically. Or we might represent a patron’s interaction with a non-human entity, such as a robot, or the “interaction” between robots and other inanimate objects essential to an organization’s functionality and fiscal success. Pathr’s Spatial Intelligence might also be used to pick out complex behaviors such as a sales associate restocking shelves, or to measure various aspects of those behaviors, allowing business owners to optimize guidance, training, and real-time feedback for their associates.

The quality and effective inter-connectivity of these movements—and the way that an organization’s constituents migrate within essential crowd patterns—really do matter. And, the insights and deeper knowledge into these physical interactions have the potential to render game-changing financial impact. For one, gaining knowledge about key constituent movement provides the totality of knowledge that business managers need to make an in-location experience as satisfying as possible. Second, the ability to translate these movements into actionable data brings behavioral science into the digital age with a new level of knowledge acquisition.

This level of data-based intelligence is, at present, only possible with advanced ML protocols, Artificial Intelligence, and cutting edge predictive analytics. In the retail context, this offers a good example of how brick and mortar stores can employ advanced technology to not only enhance the customer experience and better compete with online commerce, but also to directly increase cash register results and improved competitive performance.

So, where does digital reality end and physical reality begin? For today’s businesses that become adept at evolving with the assistance of AI-powered technology, the answer may be that it doesn’t really matter. It’s the knowledge acquired through the blending of these realities that counts. And, when it comes to opening up new ways to interpret and understand any number of facets about a business’ key operations, economic opportunity and revenue upside, we’ve only touched the surface of what data analytics—and Spatial Intelligence, in particular—is able to provide.

George Shaw is Founder and CEO of Pathr™ (www.pathr.ai), the first AI-powered Spatial Intelligence platform that uses anonymous location data to derive actionable business insights in real time— fueling enhanced sales, operations and marketing results.

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