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AI in retail
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How do you know when a customer has experienced that magic moment of falling in love with a product at your store? This is a huge question for retailers of all types, from high-consideration fashion and luxury boutiques to chain stores.
People exploring the aisles in a grocery store, browsing through blouses in a clothing store, or even making a beeline across the store and bypassing all other departments drop clues about what they want along the buying journey—long before they make a purchase.
They might dwell at an endcap display for several minutes. They might handle a product and put it back only to pick it back up again. They might try on three different shirts that are similar in color or linger in an aisle full of similar products.
These clues aren’t always easy for store associates or management to notice, especially in stores where employees are tasked with a variety of other jobs, such as stocking shelves and managing inventory.
But what if you could observe every customer’s browsing habits on the retail floor all at once? What if you could then alert store associates via hand-held devices and direct them to those customers? Imagine the impact insights can have on marketing, merchandising, and product availability.
You would capture more revenue and larger baskets, prevent lost sales due to customers not getting timely service, and operate more efficiently.
Point solutions can help you, but they’re not the complete answer
Fortunately, AI technology is making some big inroads.
Most retailers capture large amounts of video data every day. But they also tend to store the video data on hard drives or in the cloud and only access it only when there’s a problem—if at all. Today, advances in AI and GenAI have now made the “art of the possible” change to the “art of now.”
You can now use a combination of technologies to dive deeper into what’s happening at the store. Computer vision, multi-camera and multi-object tracking, video search and summarization, and IoT sensors can analyze customer behavior, detect trends, and optimize operations.
A great advantage for physical store retailers is the use of multi-modal AI that combines data from various sources, such as text, images, audio, and sensor inputs. AI-powered applications such as vision language models (VLM)s, large language models (LLMs), and small language models (SLMs) can process video data from in-store cameras to track customer or product movements, interactions with products, and purchasing patterns.
These technologies are essential, but they alone aren’t the answer. For example, computer vision can help observe; but, to understand your customers’ journeys in your store, you need a frontier AI data foundry platform that integrates key data elements into a sustainable AI strategy.
A frontier AI data foundry platform applies technology to help you understand your customers’ journeys
By pulling together multiple technologies, a frontier AI data foundry platform underpins the entire process of observing and assisting customers. Such a platform should:
Deliver actionable insights derived from various data sources—such as computer vision—needed to observe customer journeys in the store.
Provide notifications and analytics, which are critical for your store to act on AI-generated insights in a timely manner.
Enhance customer service and satisfaction through insight-driven associate actions.
These capabilities can help you solve a variety of core business challenges, from minimizing shrinkage to preventing violent escalations.
A frontier AI data foundry platform simplifies tracking the entire path to purchase
With a frontier AI data foundry platform, you could identify repeat customers and their paths to purchase. Similar insights could include:
How customers find the products they want.
Whether in-store marketing or promotions are effective.
The ease and speed with which shoppers find products.
Availability and accessibility of “go-with” items.
Buying groups versus solo shoppers.
Dwell time spent near certain products or sections.
Customer interactions with specific products.
Let’s say two customers enter a sporting goods store. One walks directly to the shoe departments, picks up different shoes, and examines them closely. The other slowly meanders from department to department and stares at their mobile phone frequently before lingering in fitness equipment, engaging in a text exchange, and showing no interest in fitness products. With only one associate available to restock shelves and help customers on that side of the store, AI can help prioritize the associates’ tasks.
AI could determine that the customer in the shoes section is a priority due to the directness of their path and possible intent to purchase versus the customer slowly moving around the store and not paying attention to merchandise.
While the associate is helping the customer in the shoe department, the AI notices the fitness customer puts their phone away and begins to handling similar products, increasing their intent to purchase. So, the AI sends a notification to the associate’s handheld device, requesting that they stop restocking and flex to fitness to help.
The AI-enabled shopping system could also enable automatic adjustments to in-store displays, such as changing the video content to correlate with the products being evaluated. This real-time adaptation could more effectively capture customer attention and provide more information to support the sale.
Be mindful of challenges and privacy considerations
It’s important that you be mindful of how a frontier AI data platform manages data. For instance, as these systems collect and analyze detailed video and other data, you must take extra measures to anonymize information and comply with privacy regulations like GDPR or CCPA.
Clear communication about how data is collected, stored, and used is essential to maintaining transparency and developing a sense of security among customers.
To address these risks, you should adopt a balanced approach, combining technology with human oversight to manage issues such as privacy.
Succeed with a frontier AI data foundry platform
A frontier AI data foundry platform can help you succeed while managing the risks and privacy issues associated with computer vision.
Centific’s frontier AI data foundry platform helps retailers apply computer vision by transforming standard CCTV systems into intelligent tools for real-time insights, addressing challenges like shrinkage, stockouts, and customer behavior analysis.