Retail Analytics: 5 Applications of Computer Vision

By Liz Oz • Aug 23, 2021

Happy customers in a mall

Computer vision is revolutionizing retail analytics. See how CV provides data on dwell time, time-in-store, hot spots, and more.

Retail Analytics: 5 Big Data Points to Track with Computer Vision

If you’re not leveraging advanced data in your retail business, you’ll have a challenging time seeing the results you hope for.

Data is the make-or-break element that can set up a business for success. It can help you understand your customers - who they are, how they act, and how to offer them products and services in a way they’ll most likely respond to.

Gathering this data is accomplished with customer analytics, and retailers should take a closer look at how they are collecting this information and what they are doing with it, especially if they're not already leveraging computer vision.

How Does Computer Vision Work in the Context of Retail?

Computer vision (CV) is an AI-enabled technology that helps digital systems recognize and understand the content of images, similar to how people use their eyes to make sense of the world around them. When you add a photo to your Facebook and the network recognizes one of your friends and asks you if you want to tag them, that's computer vision. But, there's more to CV than just tagging people on social media. The AI-driven technology can translate an image’s content into actionable data that identifies significant trends, outliers, and patterns that can impact a business. 

With CV, you can utilize your pre-existing cameras and sensors to gather real-time information. Retailers can gain deeper insights into customer behavior and act on that information to boost revenue faster than ever before.

Retail analytics are essential for understanding how to improve your operations. Without data, all the measures you employ are essentially educated guesses.

Computer vision is the most comprehensive and accurate method for gathering data. It is a potentially 24/7, real-time approach to data aggregation that is not subject to human error or biases. The technology allows you to analyze a broader range of details than your POS system.

What Are the 5 Most Important Data Points to Begin Collecting with Computer-Vision-Powered Retail Analytics?

Here are five powerful ways retailers are leveraging CV in their stores as we speak. 

1. Shop Times

With computer vision, your store can gather essential customer behavior analytics regarding when they like to shop:

  • Average shop times, even across a particular time of year or time of day
  • The shop time for an individual customer

CV can also help you understand macro-shopping moments and the level of service your shoppers are receiving. For example, decreasing time-in-store could be attributed to a corresponding decrease in the level of service and attentiveness from employees.

Tracking shop times with computer vision can help you increase the number of people buying something, the number of products they buy, and how much they spend in your store. It can also help you discover patterns that might not be working in your favor. For instance, if you notice you have a lower average shop time at a particular time of day because your customers seem to be in a rush, it might not be the right time to come out with your best offers.

2. Where Your Customers Go (Or Don’t Go)

Consider the potential of these retail metrics:

  • What sections are visited most or least often
  • Various hot spots and dead zones

You can then use this data to learn how your customers' shop. Let’s assume you have 100 people who look at a particular shelf, but nobody picked up any products from it. Maybe the actual products aren’t what the customer expected, or their placement needs a bit of work to allow shoppers to see the offers better.

3. Dwell Time

Dwell time refers to the length of time a potential shopper spends looking at your display. It's an essential metric, as the longer a person looks at your display, the higher the chances they will buy something. 

But do you have data on this behavior? 

Customers pass by your store, but they don’t come in. How many come in? How many stop to look at featured in-store displays? Do some of them leave but then come back?

These questions are impossible to answer accurately without the use of computer vision. Suppose you have a camera that captures the front of a store looking at those passing by. When you spot potential customers dwelling, you can use the data to determine specific changes you could make to convince dwellers to cross the threshold. 

4. Cut Down Time-in-Line

No one likes waiting in line, so people greatly appreciate any effort retailers make to smooth out the waiting process as much as possible.

With CV, you can see the average time your customers spend waiting in line in real-time. You can get notified when the time frame increases and send more staff to the checkout point to resolve the issue.

CV can also reveal personnel issues by comparing average checkout times between cashiers. The data could reveal that between two cashiers, one had a 10 minute longer average waiting time. If you look at the register, you can also see there was less revenue as a result. This cashier might require extra support or training to perform their tasks efficiently. 

5. Pick Up and Put Backs

Sometimes, a customer picks up an item, and instead of putting it into their cart, they put it back on the shelf. By itself, it’s not a big deal, but with CV, you could see it is not an isolated event.

Instead, it’s a pattern. Multiple of your consumers end up acting the same way. It could be an issue with the actual product or an offer that’s not enticing enough to make them spend money. 

By identifying the pattern and the problem, you can make necessary adjustments and increase your conversion rates - the number of people who buy something rather than just browse. 

Increase Your Store Revenue with alwaysAI

Computer vision and big data analytics aren’t trends that retailers can afford to skip. They have a genuine impact on operations, and with the right approach, can help you grow your business.

alwaysAI makes it easy to build, deploy, and analyze the results of advanced CV initiatives.

Best-in-class support 

You don’t need to have a team of data scientists and machine learning developers on board to truly leverage CV. alwaysAI’s team of world-class machine learning experts is here to support your model development, deployments, and application-specific needs to ensure your project meets business objectives.

Next-level edge-computing 

Choose a pre-trained computer vision model from the alwaysAI catalog that fits your goals, or upload your own model; then train it locally or in the cloud using our model training features. Finally, customize your app with alwaysAI’s powerful Python APIs. 

Simple, scalable deployment 

Deploy your application easily and quickly to your existing edge infrastructure on a wide variety of devices (ex: ARM-32,  ARM-64, or X86). 

Completely custom CV solutions  

Design, train and improve applications tailored to your business needs. Expand the scope of your applications with custom analytics solutions for use with 3rd party BI tools or internal systems. 

Learn more about retail analytics in our video series: The Business of Computer Vision: Customer Analytics.

About alwaysAI

alwaysAI® provides developers and enterprises with a comprehensive platform for building, deploying, and managing computer vision applications on IoT devices. We make computer vision come alive on the edge - where work and life happen. The alwaysAI platform offers a catalog of pre-trained models, a low-code model training toolkit, and a powerful set of APIs to help developers at all levels build and customize CV apps. alwaysAI® has an easy deployment process and a state-of-the-art run-time engine to accelerate computer vision apps into production quickly, securely, and affordably.

Make advanced AI easy for your organization. Register for free to get started today, or schedule a demo and talk to our computer vision experts about your business.

By Liz Oz • Aug 23, 2021

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