Businesses Need to Consider AI Algorithms and Spatial Intelligence to Increase Success

A look into how Spatial Intelligence can drive gains for businesses across their physical locations
4 min readMay 24, 2022

This post was originally published on AiThority.Com on April 27, 2022

By George Shaw, Founder and CEO at

Artificial Intelligence (AI) is transforming how businesses analyze processes both digitally and physically, putting behavioral science at the forefront of many technology advancements today. The latest solutions can evaluate any series of interactions that take place within a company’s designated core destinations or locations — with the goal of enhancing customer experience, increasing profits and maximizing logistical efficiencies. These locations might include a diverse number of environments, such as a retail store, grocery store, shopping mall, commercial real estate office — or even a shared municipal space or public area, where spatial management is essential to streamlined operations and smooth traffic flow.

Fueled by big data, the progress in AI is transforming the economy, culture, society, and lives of individuals. It is also transforming behavioral science. Behavioral science has been around a long time and is a classic discipline charting and analyzing actions between people to predict patterns. But what if a business 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? This is where the difference between basic analytics and spatial analytics comes into play.

Basic “counting” analytics can be used for minor, more obvious changes, but other changes 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 — or spatial analytics. This takes data results to a different level by incorporating advanced predictive analytics and Machine Learning (ML) tools and strategies.

Using spatial analytics to access this information in real-time can help businesses become not only more successful, but more effective in helping their customers.

For example, in a retail environment, having access to real-time, location-based data, managers can make changes to store floor operations to maximize sales, at any time. They can amend a display formation, better position staff to interact with customers, or adjust the flow of traffic from the entrance to the cash register. Adjustments can be made at any time during the day to respond to customer needs as they occur. This helps retailers become better equipped to support customer needs and more highly in-tuned with shopper marketing dynamics.

With analysis in hand, companies can make any changes immediately or wait until downtime hours. Either way, changes made based on spatial intelligence and analysis can be timed to incidents and fluctuating movements in floor traffic as they occur, an application that has never before been available to the industry. In addition to traditional brick-and-mortar retail applications, the same premise would apply, for example, to understanding the behavior of individuals as they move inside a grocery store, a commercial real estate office lobby, or inside a shopping mall. The insights and deeper knowledge into these physical movements and interactions have the potential to render game-changing financial impact.

The key element of this AI-driven intelligence revolves around the detection of ‘movement’, where an individual is recognized only as a digital dot on a visual floor plan, thus completely protecting anonymity. These movements may represent a customer in a retail establishment or a shopper in a grocery store. 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 (staff) moving about within the same space.

Imagine how retail management could benefit from this type of enhanced behavioral intelligence to better understand customer patterns and traffic flow — knowing these:

  1. which departments attract the most customers,
  2. in what direction they often travel in-store,
  3. how often they wait in checkout lines,
  4. in what group sizes do they enter the store.

Other critical information can be gathered such as why customers might choose to end the shopping journey altogether and leave the store. With this added knowledge, store operations can improve staffing management in checkout stands or retail merchandising teams can boost product placements according to highly trafficked areas inside a store.

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. 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. Spatial Intelligence might also be used to pick out complex behaviors such as a sales associates restocking shelves or to measure various aspects of those behaviors, allowing business owners to optimize guidance, training, and real-time feedback for their associates.

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 their in-store growth and improve competitive performance.

Gaining knowledge about key constituent movements provides the totality of knowledge that business managers need to make an in-location experience as satisfying as possible. In addition, the ability to translate these movements into actionable data brings behavioral science into the digital age with a new level of knowledge acquisition. Insights and deeper knowledge into these physical interactions have the potential to render game-changing financial impact.

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