8 Computer Vision Applications in the Retail Segment

Hazel Raoult
Published 02/11/2022
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7 ways computer vision is used in retailFrom football to e-commerce, artificial intelligence (AI) has been at the forefront of technological innovation. By 2025, the global AI market is expected to be valued at a whopping 60 billion dollars.

Out of all the fields AI encompasses, there’s one field being closely watched by techies and industry tycoons alike – computer vision. Computer vision is a segment of Machine Learning that enables computers and digital systems to understand and derive useful information from different types of visual data such as pictures and videos.

As futuristic and technical as it may sound, the applications of such a technology are far-reaching. Let’s take a look at what computer vision is, and its diverse applications in the retail segment for the coming year.



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Now, for the basics.

Computer vision can be labeled as the “eye” of AI and Machine Learning. Just as AI and Machine Learning algorithms enable computer systems to analyze and learn from data patterns and instructions, computer vision enables these systems to actively “see” visual inputs and make suggestions and recommendations based on their understanding.

The global computer vision market is expected to hit almost 41 billion dollars by the next decade. A world with computer systems that can “see” and “think” just like we do will be an efficient, productive, and highly advanced one.

When it comes to its applications, computer vision is already being tested out in industries as diverse as travel, sports, healthcare, security, logistics, and, especially, retail.

This blog post is a quick guide to eight cutting-edge applications of computer vision in the retail sector and will cover how this segment of AI will radically change the industry in the coming years.


Creating Effective Ads

The best way to capture your audience’s attention? Creating eye-catching ads. Businesses across niches and business models are vying to create engaging, relevant, and eye-grabbing advertisements for product campaigns and brand promotion.

A powerful ingredient of an effective ad is visual material. Visual content and image-based marketing will be used by 50-80% of all brands in the coming year, and for good reason. Studies show that people are much likelier to engage with an advertisement that has a strong visual base.

For businesses, this usually means having to use complex software like Adobe Photoshop or Illustrator for creating and editing graphics. But with modern AI-powered background remover and image editing tools that use computer vision, businesses can readily edit images and resize graphics for different platforms on autopilot.

Furthermore, enter visual listening. AI can constantly analyze visual content and mentions all across the web and attempt to understand customer preferences and attitudes through their response to a brand’s visual content, and by extension, the brand itself.

Here, computer vision helps brands and marketers focus on effective social listening, helping them create better ads and products that are suited to their target audience’s preferences and needs.

Customer tracking

Any retail giant will swear by the importance of tracking customer statistics. Be it tracking the most popular spots in a store, or average buy-time, tracking audience analytics has far-reaching advantages. According to reports, effectively tracking consumer analytics through ML and DL-based algorithms leads to an increase in ROI by 115%, and a 93% increase in overall profit.

Computer vision can help stores and retail sites to track insights into consumer behavior:

  •  “Which region of the store has the densest footfall?”
  •  “What’s the average time a customer takes to make a purchase?”
  •  “Which product is most heavily bought at X time of the day?”
  •  And, “which products lead to the highest put-backs?” are all questions computer vision can help answer.

This data is collected using both 2D and 3D-based CV, and using data segregation and appropriate statistical methods, useful insights into consumer behavior can be gathered.


Cashierless Stores

As revolutionary as it may sound, cashier-less stores are paving the way for a more streamlined, AI-assisted shopping experience in stores across the world. Computer Vision is being tested out in various retail stores for the following functions, to completely replace the need for human staff:

  •  Tracking items a user picks and automatically adding the amount for their bill
  •  Tracking important customer analytics, such as put-backs and average buy-time
  •  Visually identifying any pain points a customer may run into at the store
  •  Identifying purchase patterns and product preferences according to demographics to later help improve overall CX
  •  Enabling customer self-checkout at designated intervals of time


People Counting and Crowd Analysis

COVID-19 has greatly impacted customer retail experience, especially when it comes to footfall in stores. Due to pandemic guidelines in many countries, only a certain number of people are allowed in a store at a time.

Enforcing these guidelines can be a tedious and potentially risky process, as store staff is constantly at risk due to interactions with hundreds of people each day.

Computer vision is being used to identify and track the number of users in the store at a particular time and alert the staff to help enforce guidelines.

Using CCTV cameras based on deep-learning, a store can easily identify the heaviest footfall in a day, the number of people currently in the store, masked/unmasked individuals, as well as basic user profiles based on demographics or purchase interests.


Shopper Measurement

To add to this data, Computer Visions is also used to track other aspects of customer behavior in a store. Here are a few further metrics store owners can gather using ML-based models, to improve CX, track store performance, and work on creating a more functional retail space.

  •  Footfall – Once again, identifying the busiest store times can help stores work on staff availability, stock and inventory management, and help adhere to COVID-19 guidelines.
  •  Interactions – Tracking and identifying customer interactions, complaints, and queries can help stores identify gaps in their purchase process, set up more support stations, as well as gather insights on product performance and overall customer satisfaction.
  •  Passer-by/traffic – While this may sound strange, tracking window-shoppers, passer-by, general inquiries from non-buyers all help retail stores calculate their conversion rate. The visitor-to-customer conversion rate can help create more powerful ad campaigns, opportunities to attract more consumers, and discounting strategies based on passerby demographics.


Theft detection

A teen on the lookout for some retail adventure steps into a high-end store, looking for empty aisles and expensive products to shoplift. While he may be successful in-store with an all-human staff, he’s got no chance in a store with CV-based surveillance.

CV helps stores thwart potential shoplifting incidents through cutting-edge face recognition and tracking shifty/suspicious customer behavior. Many stores have also used “seeing” technology to track whether a user has paid for an item in his/her shopping cart, or whether they’re simply shoplifting.


Waiting time analytics

Reduce the number of frustrated, seriously bored shoppers and super long queues at the cashiers with Computer Vision. As mentioned earlier, tracking customer analytics (such as footfall and waiting time) can help stores improve facilities such as available staff, counters, inventory, and ease of access for various products available.

Waiting-time analytics also help stores answer these CX-based questions:

  •  At what stage of the purchase process do customers take the most time?
  •  Is this because of reduced staff, low availability of inventory/products?
  •  Are customers taking longer at cashier queues due to less active counters?


Inventory Management

Here’s a statistic. Over 64% of retailers are looking to use computer vision-based solutions to help manage their inventory better.

Automating inventory orders and self-checkouts can help retail stores keep a close eye on their product cycles and real-time product stock to help create a powerful and smooth retail experience that’s quite literally, never out of stock.


Wrapping Up

Retail experiences have been greatly impacted by the pandemic, and AI-powered tools such as Computer Vision can help stores create a more optimized and customer-centric retail experience. From theft prevention and inventory management to insights on marketing strategies, the applications of Computer Vision for the retail industry are limitless.


About The Author

Hazel Raoult is a freelance marketing writer and works with PRmention. She has 6+ years of experience in writing about the latest technology, entrepreneurship, marketing, and all things SaaS. Hazel loves to split her time between writing, editing, and hanging out with her family.