What is AI in Retail?
AI in retail refers to the use of artificial intelligence technologies to analyze data and optimize how retailers operate, merchandise, and engage customers across physical and digital channels. By identifying patterns at scale, AI enables retailers to improve efficiency, accuracy, and customer experience across their operations.
In practice, AI in retail is applied across several core areas:
- Computer vision powers smart shelves, automated checkout, planogram compliance, and loss prevention by interpreting video and image data from stores.
- Demand forecasting and inventory optimization use machine learning models to predict sales, reduce stockouts, and minimize overstock by accounting for seasonality, local demand, and external factors.
- Personalization and recommendation engines tailor product suggestions, promotions, and messaging based on customer preferences and purchase history.
- Sales-floor agents embedded in staff-held devices that give advice for assisting customers and product recommendations based on inventory, minimizing training time and empowering employees.
- Dynamic pricing adjusts prices in near real time based on demand, competition, inventory levels, and market conditions.
While AI in retail spans many technologies, most in-store applications focus on execution within individual locations – improving efficiency, transactions, and merchandising at the store level.
To understand who customers are, where they come from, and how stores perform within broader markets, retailers rely on AI-powered location analytics.
How Physical Retailers Use AI Today
To fully understand consumer behavior, target audiences, store foot traffic, and market trends, AI-powered location analytics is critical. Here are just some of the ways in which the scalability, pattern recognition, and predictive abilities of AI drive actionable insights for retailers:
1. Improving In-Store Operations and Efficiency
AI helps retailers run stores more efficiently by optimizing day-to-day operations. By analyzing historical performance, from hourly activity to multi-year trends, AI supports smarter labor planning, reduces checkout bottlenecks, and helps store teams respond quickly to operational issues. The result is lower costs, better service levels, and more consistent execution across locations.
2. Understanding Audience and Trade Area
AI delivers audience visibility at scale, linking visitor behavior with demographic and psychographic insights such as lifestyle preferences, brand affinities, and household size. Retailers can identify the audiences each location attracts, track market differences, and focus on customer segments that drive the most value for their brand. This allows retailers to move from broad assumptions to location-specific audience strategies grounded in real-world behavior.
3. Benchmarking Store-Level Performance
AI enables retailers to track store performance at a granular level. Teams can quickly benchmark stores against one another or within a chosen market, and demystify their positioning relative to the competition. This visibility helps retailers move from reactive decision-making to proactive management.
4. Informing Site Selection and Expansion Strategy
AI informs decision-making and expansion strategy by synthesizing foot traffic, audience demographics, and co-tenancy patterns. Using AI, retailers can manage cannibalization risk and predict trade area overlap when evaluating potential sites and vacancies. AI aggregates large amounts of consumer, brand, and shopping center data that would be nearly impossible to sort through otherwise – not to mention time consuming.
As physical retail becomes more data-driven, success depends on understanding how customers move and choose between brands across markets. AI-powered location analytics provides this critical outside-in perspective.
AI-Powered Location Analytics in Practice
AI-powered location analytics helps retailers answer critical business questions across performance, marketing, and expansion. Below are several concrete examples of AI-powered location analytics in action.
Retail Foot Traffic Analytics and Visitor Behavior Analysis
Visitor behavior directly impacts location and brand performance.
AI-powered location analytics helps retailers understand:
- Foot traffic trends over time, including YoY growth and seasonality
- Visit timing, frequency, and dwell time
- Visitor origin and destination patterns
- Cross-shopping behavior with other brands
Example: An apparel retailer analyzes year-over-year cross-shopping behavior within a specific DMA to identify its closest competitive set and uncover potential sources of traffic leakage.
Identifying Regional Demand and Measuring Promotional Impact
True visibility into audience demographics, preferences, and lifestyles helps retailers understand:
- Who their core and high-value audiences are across different markets
- Which consumer segments are over- or under-indexing by location
- Where to prioritize advertising and promotional spend
- Whether digital and offline campaigns are driving incremental in-store visits
Example: A grocery chain compares two locations to determine which market is more likely to adopt wellness-oriented products, guiding both assortment strategy and localized advertising ahead of a new private-label launch.
Efficient Trade Area Analysis and Retail Site Selection
AI-powered location analytics enables retailers to evaluate real estate opportunities by analyzing:
- Underserved markets and gaps in retail supply
- Demand and foot traffic patterns around potential sites
- Co-tenancy, competitive presence, and cross-visitation behavior
- Planned development and changing market dynamics
- Cannibalization risk and trade area overlap with existing locations
Example: A coffee chain uses AI-powered site selection tools to evaluate a potential location’s market fit, forecast demand, and assess the likelihood of cannibalizing visits from nearby stores – before committing to expansion.
AI-powered location analytics enables retailers to turn real-world consumer behavior into actionable insights, revealing how customers behave, how locations perform, and where growth opportunities exist.
AI-Powered Location Analytics Software - The Placer.ai Solution
Placer.ai leverages a panel of tens of millions of devices and utilizes machine learning to make accurate estimations for foot traffic across the country – from specific points-of-interest (POIs) to large markets. Visitation data is enhanced with Placer Marketplace 3rd party datasets that further describe businesses, audiences, and markets.
Using an AI-based location intelligence platform like Placer.ai enables retail professionals to analyze stores – theirs and their competitors’ – with unprecedented visibility and greater precision.
Whether you need AI-tools to optimize your business, understand market conditions, or benchmark properties, Placer.ai provides the data and insights to strengthen your strategy.
Key Takeaways:
- AI in retail extends beyond in-store optimization to market-level intelligence.
While many AI applications focus on improving execution within individual stores, AI-powered location analytics enables retailers to understand who their customers are, where they come from, and how locations perform within broader competitive and geographic markets. - Location intelligence turns real-world behavior into actionable insights.
By analyzing foot traffic, visitor behavior, audience demographics, and cross-shopping patterns, AI helps retailers segment audiences, benchmark performance, measure marketing impact, and identify growth opportunities based on actual consumer behavior. - AI-powered location analytics reduces risk and drives smarter growth decisions.
With dedicated tools for site selection and trade area analysis, retailers minimize cannibalization risk and optimize expansion planning. AI allows retailers to make data-driven decisions that minimize uncertainty, improve network performance, and support sustainable physical retail growth.
