How Computer Vision Will Revolutionize Retail
What is Customer Analytics?
Customer analytics refers to organizations’ processes to capture and analyze customer insights to make critical business decisions. Without customer analytics, businesses have difficulties delivering relevant, anticipated, and timely offers or experiences.
Customer analytics is a type of big data and serves as the backbone for enhancing the customer experience. Data aggregation and analytics techniques may include data visualization, information management and segmentation, and predictive modeling. Customer analytics is crucial for showing and explaining customer behavioral insights to stakeholders, managers, and salespeople. These insights inform new business decisions that aim to improve revenue generation.
To level up your customer analytics, you can use customer analytics (CV) applications, which employ deep-learning artificial intelligence (AI) to gather real-time data from vision systems within your business. This will help you learn important information about your customers, including what brands they like and which part of your store they want to explore the most. You can then use this information to create better products, campaigns, and services for existing and potential customers.
Read on to learn more about what customer analytics is and how computer vision will revolutionize customer analytics.
The Benefits of Customer Analytics
Research has shown that businesses that use customer analytics are much more likely to outperform their competitors. In a survey by McKinsey & Company, companies that used customer analytics extensively had:
Further, more than 85% of companies that report using customer analytics extensively claim their company has experienced a significant value contribution from customer analytics. These include customer loyalty, reduced marketing costs, better in-store experience, and deeper customer understanding.
Boosting Customer Loyalty
With customer analytics, you can boost customer loyalty, response rates, and ROI by contacting the right customers at the right time with tailored messages and offers.
You can also use analytics to decrease customer attrition by predicting when customers will probably leave and develop appropriate campaigns to retain them.
Reducing Marketing Costs
Customer analytics can also help you reduce marketing costs and boost ROI.
By telling you which types of customers are more likely to respond to your campaigns, customer analytics can help you better understand who to target in your marketing campaigns. You’ll have a better understanding of your niche’s persona and how to appeal to them, so you won’t have to spend a lot of money and time marketing your products and services to people who probably aren’t interested in the first place.
Providing Better In-Store Experiences
Customer analytics will tell you which areas of the store may need improvement.
- Which areas of the store have the lowest dwell time
- How often do customers pick up items and put them back
- Are your customers waiting in line too long
- Are your conversion rates low because customers aren’t getting the assistance they need
Once every corner of your store has been optimized for customer in-store experiences, you’ll be able to boost your ROI.
Deeper Customer Understanding
As previously discussed, customer analytics can tell you what your customers want and what types of customers are more likely to respond to your campaigns. As such, you’ll be able to deliver more effective messages and boost your ROI due to your improved understanding of target populations.
Using Computer Vision is like relying on your own experience with customers to develop your own model of what customers want. However, the predictions created through Computer Vision are faster, more accurate, and more insightful in ways that only computer programs can be.
How to Collect Customer Analytics
There are many ways to gather customer analytics, including:
- Email marketing software
- Point-of-Sale (POS) (Read about our new partnership with POS Upgrades)
- In-store analytics
Out of these, Computer Vision is the most similar to in-store analytics in that it shows the ground truth and doesn’t require the active participation of customers.
This objective information removes the possibility of bias. Computer Vision customer analytics only show what can objectively be seen. For instance, a customer is looking at a product, picks it up, and decides to put it back after five seconds. This example also illustrates the depth of data you can glean from a simple object detection model like this. Secret shoppers and other manual means of analyzing customer behavior cannot feasibly provide data on exactly how long a shopper held onto a particular item before putting it back on the shelf.
Customer Analytics vs. Predictive Analytics
Customer analytics are not the same thing as predictive analytics, although they are related. While customer analytics involves examining datasets to draw conclusions about the information, predictive analytics can predict the future by analyzing patterns in historical data.
Since they are different, businesses should distinguish between customer analytics and predictive analytics and use them to achieve different goals. While companies should use customer analytics to make real-time, pragmatic business decisions, particularly in business-to-consumer (B2C) applications, they should use predictive analytics to predict, avoid, and plan around risk.
Computer Vision can link customer analytics to predictive analytics by providing recommendations of previous transactions and statistics. As such, you’ll be able to determine what needs to be changed to boost performance. For instance, if your Computer Vision software tells you that a particular area of your store is almost always empty, it may be time for you to think about what you can do to boost its popularity. Ask yourself the following questions:
- Do you need more staffing in that particular area of the store?
- Do you need to add more products to that particular part of the store?
Putting a Microscope on Customer Experience with Customer Analytics
The main goal of customer analytics is the customer experience, mainly what makes customers happier and what you can do to provide the best experience possible for customers. Not only do more satisfied customers return more often, but they also buy more products. By providing as much value as you possibly can through your products, marketing, and service, you will make your customers happier, which means you will make more money.
However, optimizing customer experience is easier said than done. A customer can come into your store, but they may not be experiencing it the way you think they are. For instance, a customer may look at your selection of bags but get distracted by a flashy clearance rack nearby and walk away. In this situation, even if they fill out a survey, you may never know if they would have spent more time looking at the bags had there not been a clearance rack so close by. You can only infer what they think based on analytics.
This is what analytics is ultimately all about: putting a microscope on the customer experience and making inferences based on analytics data. Going back to our example, you could tell exactly how long customers, on average, lingered around your bag display before and after moving the clearance rack to a less valuable area of the store. Compare it to turning a qualitative customer experience into something quantitative so you can build a database of information that can help you monetize your business better.
Why Companies are Turning to Computer Vision for Collecting Customer Analytics
Companies are turning to Computer Vision for collecting customer analytics because Computer Vision-powered customer analytics are consistent and give you the benefits of Edge computing with real-time dashboards, data privacy, low latency, and more.
Before Computer Vision, analytic systems were pieced together from different labor-intensive techniques. This disjointed process meant you couldn’t get real-time data other than from employee observations, which aren’t always reliable or consistent.
Fortunately, Computer Vision provides consistent results that you can follow to get a thorough understanding of the experience you’re providing customers. It’s like having an employee watch your store at all times, but without biases and human errors combined with a richer depth of data.
The term “Edge” in Edge computing refers to how computers run through the aggregated data to produce analytics rather than sending all of it to a server. This process makes Edge computing good at delivering results quickly, especially important for applications requiring information in real-time.
Compared to cloud solutions, Edge computing offers better privacy because computing data on the Edge means that data doesn’t have to be sent to the cloud every second. You can also blur faces at your store to protect people’s identity.
These techniques will help you comply with legislation such as GDPR in Europe and help you aggregate personalized customer data without sending it to servers outside your network.
Edge computing is also low-cost. It only requires you to have:
- A computer
- A camera with a 32 or 63 bit ARM architecture
By choosing Edge computing, you can lower your costs significantly by not relying on the processing data on someone else’s cloud servers.
Complex Business Challenges
Edge computing also understands complex situations such as:
- How long did the customer spend at the store?
- How many products the customer looked at?
- How many products the customer took off the shelf?
- How many products did the customer put back on the shelf?
- Did the customer pick up a shopping cart?
- What products did the customer look at the longest?
BBy providing you detailed, quantitative answers to these questions, you’ll be able to tackle complex business challenges and plan.
Edge computing will also give real-time dashboards and data that you can use for:
- Decisions regarding where to move staff and/or checkers
- Occupancy counting
- Fraud prevention
Unlike surveys and other “sensors” like employee observation, computer vision gives you a holistic view of the customer experience.
You could, for instance, create a real-time dashboard based on your Point of Sale data, but that would only tell you that this customer purchased those items. You won’t find out how your customer got to a particular end experience and how many customers didn’t get the same end experience. Similarly, you also won’t see that some customers left without buying anything because the lineup for the cashier was too long.
How Are Industries Using Computer Vision-Powered Customer Analytics
There are several industries where customer analytics is in demand due to the amount and quality of information it offers: retail, quick-service restaurants, grocery, and commercial real estate.
Retail analytics is a burgeoning industry dedicated to reviving the retail sector. As online shopping becomes more ingrained in our day-to-day lives, retailers need tools to enhance the in-person shopping experience. Tools like Computer Vision provide valuable insights into the sorts of marketing, amenities, services, and products that facilitate customer engagement.
Retail analytics is the system by which data on inventory levels, consumer demand, sales data, etc., are collected and used to inform critical future business decisions like marketing or procurement. For example, retailers can use customer analytics to help them design great in-store product displays that will guide customers to areas of interest in a store and persuade them to make a purchase.
Numerous analytics data sources should be collected, including from your POS, email marketing, secret shoppers, and surveys. However, none of the above provides comprehensive and objective data about in-store customer experience like computer-vision analytics.
Computer-vision customer analytics tells you what your customers can’t or won’t. Computer Vision is the only method available to gather statistically significant samples on subconscious trends like traffic hot-spots, pick-up/put-backs on a shelf, and more.
Essential Data Points to Track with Computer Vision for Retail
Computer Vision is the most comprehensive and accurate method of gathering data. It represents a potentially 24/7, real-time approach to data aggregation that is not subject to human error or bias like surveys and secret shoppers. The technology also allows you to drill down into a broader range of details than through your POS system. Here are some powerful ways retailers are leveraging CV in their stores as we speak.
1. Shop Times
Through CV, 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 more specific like time of day
- The shop time for individual customer
Tracking shop times with CV 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 a few 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 during this specific window.
2. Foot Traffic Analytics
Consider the potential of these retail metrics:
- What sections are visited most or least often
- Various hot spots and dead zones
While your store managers will be able to tell you what areas of the store are more popular than others, CV allows you to take it a step further and drill down into exactly how long, on average, customers linger in popular areas. Data like this can inform decisions to rearrange the store to add more marketing materials, checkout stands, or seating areas in those store sections.
3. Dwell Time
Dwell time refers to the length of time a potential shopper spends looking at your display. It&apo;s an essential metric as the longer a person spends looking 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, or do they just pass by? Might some of them leave but then come back?
They are all impossible to answer accurately without the use of computer vision.
4. Queue Times
Through CV, you can see the average time your customers spend waiting in line in real-time. You get notified when the time frame increases, in which case you can send more staff to the checkout point to resolve this issue.
5. Pick Up and Put Backs
A customer picks up an item, and instead of putting it in their carts, they put it back on the shelf. By itself, it’s not such a big deal, but through CV, you could see this is not an isolated event. This data point allows decision makers to focus on products that are currently underperforming their potential. Obviously, the product is interesting to the visitor for a certain reason, but not enough so to lead to a purchase.
The Market for Retail Analytics
Expected to grow at a rate of 18% annually and reach a value of $9.5B by just 2025, retail analytics is allowing stores to operate smarter and provide an enhanced customer experience to all.
As eCommerce continues to eat into the profits of stores across the globe, retailers have to be more data-centric than ever in order to make sure the next move is the right one.
Quick Service Restaurants (QSR) and Computer Vision
It's no secret that restaurants run on the slimmest of margins. Combine that with the grim reality of severe labor shortages and you can understand why many QSR chains are turning to technology to add new layers of efficiency and productivity to their operations.
Brands are leaning on AI and Computer Vision for real-time analytics to collect, analyze, and make informed decisions from key data points. The data collected provides a comprehensive view of the customer experience as well as the food prep and production processes. Computer Vision is the modern solution for digitizing storefront operations.
Through CV on Edge devices like your current surveillance system, automated models including person detection and object tracking collect data points you deem valuable in real-time without cumbersome human oversight. Autonomous real-time data aggregation analyzes complex business problems instantly and are commonly used to compare different experiments and tests while encouraging swift ideation.
Some popular use cases for CV in QSR include making sure people are:
- Wearing masks as needed
- Not waiting too long in line
- Not waiting too long for an order
- Satisfied with their order
Applications also involve monitoring staff to ensure:
- Proper promotional scripts are being delivered
- Processes are being followed correctly
- Proper breaks are being taken
- Staffing levels are appropriate for the time of day and day of the week
Edge CV is critical to navigating the new challenges facing the QSR industry. It’s affordable and straightforward to implement on existing infrastructure. Moreover, intuitive dashboards like alwaysAI offers allow decision-makers to connect business initiatives (queue maintenance for example) with CV model data output (object counting models to count cars in a drive-through line). And since the data is collected in real-time, decision-makers can more quickly make informed, powerful decisions.
We are on the precipice of a new age in the Grocery industry in which automation and AI provide greater profits and better customer experience. However, what many grocery retailers don’t understand is that CV has the capability to empower not just their Amazon Go-style future, but also reduce waste and improve the profitability of their current operating model.
Self-checkout has revolutionized the grocery space and allowed stores to increase margins and improve customer experience by reducing lines and the number of cashiers needed at registers at one time. The data shows global shipments of self-checkout terminals increased 52% year over year in 2019 alone. So with this trend booming, how can grocery retailers enhance the effectiveness of their self-checkout process to improve customer experience and limit human oversight?
The answer lies in Computer Vision. CV has the power to rid retailers of a system founded on barcode scanning in favor of Computer Vision models that can accurately identify products placed in front of your existing machines using “object detection” models. Grocery-trained computer vision models can be trained to distinguish subtle differences between different foods like a Fiji versus a Gala apple. This enhancement to your self-checkout process eliminates troublesome barcode scanning for customers, missing labels, or mislabeled products.
The Future of Grocery
While self-checkout is the current industry standard, CV is also hard at work, powering the next significant evolution of grocery shopping. Cashierless shopping will go a step further in the future of grocery shopping than simple self-checkout.
Customers will be able to walk into a store, scan a code and begin shopping. The customer can grab a bouquet and bag of chips, drop them in their bag, and walk right out of the store without interacting with any people or machines should they decide not to. This revolution will be powered by Computer Vision models installed in Edge devices throughout the store that monitor individual shoppers’ added items to carts or bags. Payment profiles linked to an account are charged automatically, and your profit margin gets healthier by eliminating overhead.
An obvious benefit to having smart cameras monitoring every corner of your store, retailers can expect reduced shrink by adding computer vision. When cameras are capable of detecting a bottle being shoved into a jacket pocket, for example, it makes it very difficult to commit theft and get away with it.
While apps and technology like Instacart have improved the virtual shopping experience for many, grocery retailers know that to keep people coming into stores, they have to keep pushing the boundaries of what a great shopping experience actually means. Adding Computer Vision will be a seminal step in creating a seamless future shopping experience and is sure to boost customer loyalty and the bottom line.
Commercial Real Estate
Commercial real estate companies use customer analytics to improve the lifetime value of a lease and return more revenue for their retail stores.
Using Customer Analytics to Improve the Actual Lifetime Value of a Lease
Commercial real estate developers invested in and planning shopping centers and strip malls demand greater visibility into customer behavior and preferences in those spaces. Developers are no longer waiting for individual stores to apply advanced analytic approaches to increase sales and traffic. By implementing Computer Vision capabilities into the existing Edge infrastructure in the mall or shopping center’s stores, the executives can compare apples-to-apples the performance of all stores in the complex.
Customer analytics can provide commercial real estate companies the following information to determine ways to get more revenue from leasing stores in malls:
- How many people walk by a particular store every single day?
- Percentage of people who look at a store display but don’t enter
- Percentage of visitors who enter a store but exit without purchasing
- Time spent in stores
- Tracking an individual shopper’s path to determine what stores people are visiting in the same day
Armed with this information, real estate executives have infinitely more data that allows them to make key leasing decisions about:
- What types of stores are needed
- Pricing leases effectively according to the placement of the storefront
- Recommending marketing/display collateral based on past performance.
And not only does the in-store experience matter to real estate execs, so too do the amenities in and around the mall. Millions of dollars are spent planning beautiful spaces, childrens’ areas, events, and other amenities in and surrounding shopping centers. Wouldn’t it be great to know how much these amenities are worth to your bottom line? With Computer Vision, you can gain deeper insights into just how much these amenities are being interacted with, and where the shopping flow moves around them.
The Malls of the Future
Consumer habits and preferences have been changing for years. The COVID-19 pandemic has only accelerated this pace of change. Commercial real estate executives are perhaps more aware of these shifts than any other industry. There are mountains of data that show what customers prefer now:
- 45% of total respondents in a Deloitte survey stated they planned mainly for one-stop shopping before the pandemic. Now, that number is 59%.
- The percentage of consumers expected to shop in enclosed malls once a week after the pandemic dropped by half.
- More customers (35%) say a great assortment of food and dining options was the top choice among amenities that would encourage them to visit malls in the future.
But what if technology could provide the data to react to changing customer preferences as they happen? Computer Vision is capable of delivering rich data on each visitor every day of the week. Armed with that information, you can stay ahead of the next shift before the research is even published.
Preparing Your Business for Computer Vision on the Edge
Now that you have a clear idea of what customer analytics is and what it can do for different businesses, let’s look at our guide to setting up and measuring customer analytics with Computer Vision.
Establishing the culture
First of all, you need to establish a work culture that values analytics and fact-based decision-making. As a business decision-maker, it’s up to you to foster an environment that encourages team members to adopt Computer Vision-powered customer analytics enthusiastically.
Your business’ work culture should not focus purely on analytics and IT but on approaching customer analytics holistically. Investing in IT and analytics is essential, but leaders who expect fact-based decisions and an organization that can quickly translate such decisions into action are more likely to succeed.
Why is this the case? Research has suggested that organizational and execution aspects of customer analytics (such as analytics being valued by the front line, management expectations, attitudes, and having a culture of fact-based decision-making) will boost customer analytics. To establish such a culture, it’s vital to secure senior management involvement in customer analytics. You should aim to hire executives who understand and are deeply involved in customer analytics. This is illustrated by the fact that only 28 percent of executives with executives who are not involved report a significant value contribution of customer analytics. In comparison, 69 percent of companies with executives extensively involved in analytics state that customer analytics drives value.
You need to also communicate to your staff how Computer Vision and data analytics will help improve their job. Most staff members are more concerned about improving their job performance and making more money. Hence, you need to prove that adopting fact-based decision-making and valuing analytics will help them do their job better or more efficiently.
From an IT perspective, all that needs to be done is to leverage the existing camera infrastructure.
The IT department would be the most concerned about the tech stack and the equipment needed to implement Computer Vision-powered customer analytics. Talk to the IT department about their main concerns and what needs to be done. Ask them the following questions:
- What kind of issues do you foresee if we choose to use this kind of camera?
- What hardware do we need to buy?
- Does our existing hardware work, or do we need an upgrade?
- What existing cameras do we have?
- What happens if the Edge device breaks?
Skills and Human Resources
Modern CV platforms like alwaysAI are engineered to limit the amount of data science and development expertise required from customers to leverage the technology.
For example, alwaysAI’s platform allows administrators to use pre-trained models to plug in and gather data on various business initiatives. So the central challenge is understanding your business problems and what your goals are. From there, it’s important to understand the capabilities of the different CV models available and how you can match up the capabilities of the technology with your objectives.
After you’ve developed a game plan, The last important skill required is your analytics team’s ability to read and understand graphical reports and dashboards.
alwaysAI for Computer Vision-Enabled Customer Analytics
Computer Vision is a revolutionary way of understanding and using customer analytics. Not only will it help you gain a better understanding of what your customers need and like, but it will also help you grasp what needs doing to reach your business goals. With customer analytics, you’ll be able to analyze and predict customer engagement patterns like never before.
However, developing and deploying Computer Vision applications is sometimes easier said than done. Frequently, it can be too time-consuming for IT departments to take on since these applications require specialized knowledge that not every developer has.
Fortunately, alwaysAI can remove these barriers and create Computer Vision apps effectively, fast, and quickly. A Computer Vision development platform for deploying and building machine learning applications on Edge devices, alwaysAI aims to revolutionize customer analytics for your business.
Your developers will get access to more than 100 pre-trained AI models . With the ability to train models at the touch of a button, your developers will find creating Computer Vision apps easier than ever. They will also have access to world-class machine learning experts who will answer any questions or concerns about the platform and machine learning in general.
Equipped with a catalog that provides deep learning models, alwaysAI gives your developers the ability to quickly build and customize CV applications. This means they have the total freedom to discover what works best for your specific use case. They will also be able to run these apps on a wide variety of devices and join the alwaysAI developer community.
Additionally, alwaysAI comes with the cutting Edge platform, edgeIQ, which enables developers to use Python APIs to create Edge applications for object detection, counting, tracking, facial detection, human pose estimation, classification, and semantic segmentation. This helpful feature will help your developers prototype and iterate quicker, saving you money and time.Get started with alwaysAI today with a free account.