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5 Ways to Use Customer Analytics to Improve Customer Experience

By Liz Oz Aug 23, 2021

Retail Analytics_ 5 Applications of CV (3)

Computer vision AI is revolutionizing customer analytics. Learn how CV provides retailers with real-time data on foot traffic, brand preferences, and more.

There’s no getting around the need to improve customer experience. It’s directly tied to a company’s success, whether talking about an eCommerce business or brick-and-mortar store. Brands and businesses know this, and for the most part, they dedicate a lot of their resources to ensure their customers have a genuinely good experience.

But, the way they do it isn’t always so efficient, especially if they’re not tapping into an essential resource: customer analytics. The data you have on your customers can help you significantly improve their experience, optimize operations, and boost your revenue. 

So, why is it that when companies try to enhance their customer experience, the conversation about customer analytics is almost always nowhere to be seen? Below, you’ll find the answer and five creative ways to finally start leveraging customer experience analytics.

Why is Improving In-Store Customer Experience so Important to Business Leaders Today?

The experience customers have in your store is directly tied to many different goals your business needs to address.

Happy and satisfied customers translate to:

  • Selling more products
  • Increased revenue
  • More loyalty
  • Better reputation
  • Competitive advantages

These can all occur when a brand puts in the work to make sure their customers don’t just access a high-quality product but also have a good time doing it.

And you might be thinking, "sure, but where does customer analytics factor in?"

The answer is relatively straightforward, though far from simple. Customer analytics involves aggregating data from various sources that allow you to understand your customers to a higher degree, so much so that you can address certain expectations they have proactively instead of reactively.

Now, many retailers do gather data in various ways, but by only relying on your POS, surveys, and secret shoppers to tell you your customers’ story, you’re missing a huge chunk of the insight pie. 

Utilizing Computer Vision (CV) to round out your customer analytics strategy empowers retailers to gather real-time information from cameras already inside the store and better understand how their customers behave. This data is robust, able to collect information to inform complex business challenges 24/7 with supreme accuracy. If you really want to explore how customers interact with your storefront, they need to show you, not tell you.

5 Ways Businesses Can Utilize CV-Driven Data to Improve Customer Experience in Stores
1. Optimize In-Store Foot Traffic

Gathering footfall data with CV will help determine the types of business measures that will enhance customer experience. 

CV can help you identify the hot spot regions in your store that drive the most interest. You can use this data to make decisions like moving your highest-margin items to that area of the store, decluttering the area to provide more space to roam, or designating increased employee support to that area.  

Customer data can show you the flow of your customers inside the store - what path they follow from the moment they enter to the moment they make a purchase. 

2. Cut Down on Checkout Times

CV improves your queue management efforts as well. The technology goes so far as to calculate the number of customers at the checkout queue and the average time they have to spend there until they can buy the products they want. These metrics are essential for any retailer who wants to make sure customers don’t get frustrated because of long lines.

For example, you get notified in real-time of a longer than acceptable queue or when store capacity is filling, which allows you to send additional staff to the checkout counters before customer experience starts to deteriorate. 

The same can be said about self-checkout machines, which often come with a learning curve on for the customer. CV technology can make your self-checkout smarter by relying less on barcodes and more on object identification, rendering self-checkout faster and easier for everyone involved. 

3. Understand Customer Marketing Preferences

With customer data, marketing doesn’t have to be such a trial-and-error process anymore. Learning more about your shoppers can reveal the types of posters, displays, ads, or messages that work best at catching their attention.

Retail marketers can get a complete picture and weigh the costs and benefits of their in-store marketing. With CV, you can get specific demographic information and provide targeted advertising to your shoppers. 

CV can also reveal if you’re placing your marketing materials are in the right location. If your poster is in an area with the least amount of traffic, most of your shoppers are unaware of your offers and promotions.

4. Monitor and Improve In-Store Safety

A safety issue in your store is a sure-fire way to deter customers from coming back. Data can help you identify parts of the store where:

  • Products are consistently falling off the shelf;
  • Consumers keep bumping into each other quite literally;
  • There are many accidents or spills, etc.

When such an event occurs, real-time CV can help retailers quickly see and address the issue.

5. Make Shopping Enjoyable Again

When customers genuinely enjoy shopping, they are likely to spend more and return to the store more often. CV can empower brands by helping them analyze their customer’s behaviors and emotions to get a better sense of whether they are enjoying the experience you set out. 

Pose estimation models allow an analysis of body language, attentiveness, and other physical signs of enjoyment. This data can inform in what situations customers are visibly frustrated or content.

Let alwaysAI Propel Your Customer Analytics

For any retailer to fully reach their goals and grow their revenues, they must ensure the customers genuinely enjoy shopping from their stores. Computer vision should be a business-critical tool that delivers rich insights into all elements of the customer experience, where it excels, and where it can improve. 

alwaysAI gives any retailer the ability to deploy powerful CV applications cost-effectively and with minimal technical oversight required. 

Best-in-class support 

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

Start the process by creating a free account at alwaysAI.co, or schedule a demo to speak to our Computer Vision experts.

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


About alwaysAI

alwaysAI provides developers and enterprises 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 happens. 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.

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By Liz Oz Aug 23, 2021