Insight Tech

Knowing what your customers like, what they don’t like, and what piques their interest in real time could be invaluable for retailers, helping inform campaigns, displays, and content. But it seems like you’d need to be a mind reader to gather this information. While loyalty programs provide some information, brick-and-mortar retailers have had to rely on intuition alone when selling to customers.

For years, online retailers have had an advantage over physical stores because they can gather analytics from visitors’ browsing activity. But AI technology levels the playing field. With the help of systems integrators, luxury brands use edge technology to gather and leverage analytics inside physical stores. These valuable insights are used to tell stories that help improve the customer experience, adapt to the changing market, and grow sales. And the timing is perfect, as shoppers return to stores after the pandemic had sent many online.

“Despite one-click shopping, 90% of sales are still transacted in stores,” says Luigi Crudele, CEO of Wonderstore, a manufacturer of AI solutions for the retail industry. “That means retailers have plenty of information to gather about visitors to their locations. Stores are the main place to create high-level relationships with the consumer based on involvement.”

Retail Data Analytics Helps Retailers

For example, one fashion brand used the insights collected by Wonderstore to pinpoint the best day and time to launch its new accessory line. By understanding its customers’ behavior, it was able to increase accessory revenue by 30% the day the merchandise was available in the store.

Another luxury fashion brand uses Wonderstore data analytics to uncover an average of 20% transaction variation between stores. It also found there was a 15% difference in conversion rates between its highest- and lowest-performing locations. Managers used this information to better understand regional customer profiles and the success rate of sales tactics.

By leveraging #data, Wonderstore helps create #smart stores that can adapt to the customer and understand their needs from the moment they enter the store—not just at checkout. via @insightdottech

AI Retail Technology Creates a Smart Store

To create Wonderstore’s IoT retail analytics solution, Crudele drew upon his wealth of experience in storytelling and branding. His first company created 3D computer animation for video games, and he later launched an agency for developing interactive digital brand experiences, working with Italian brands like Tiscali.

“Brands spend millions of dollars in advertising campaigns and super shop windows,” says Crudele. “We measure the effectiveness of those messages. With our solution, retailers can measure performance, understand conversion rates, and improve their investments.”

Computer Vision and Machine Learning Ease Data Collection Process

Wonderstore uses sensor technology to collect data about the in-store customer journey. Using computer vision technology, the solution can count, track, and analyze customers, collecting data that includes gender, age, and even emotions. It can measure dwell time, visitor flows, browsing patterns. The data can be very granular to offer more personalized customer service, measuring performance of every single point of interest in the store, such as shop windows, entrances, shelves, fitting rooms, mirrors, and the point of sale.

The company relies on the latest computer vision storytelling technology, with best-in-class IoT sensors from Intel^®. Meeting GDPR regulations, sensors collect anonymous data that is sent to the cloud to be analyzed and transformed into actionable information. The solution is fully developed on Microsoft Azure architecture and cloud services. Using a storytelling data visualization platform, data is immediately readable, allowing the retailer to make decisions in near-real time.

Retail Technology Partnerships Provide Scale

“Partnerships help create awareness and trust in the marketplace. Wonderstore chose Microsoft and Intel to align with their vision of the cloud and IoT services,” says Crudele.

“Through these relationships, Wonderstore was able to quickly enter the market with a prototype, show the product to the customer, and build a business with the top luxury brands,” he says.

Wonderstore also partnered with Tech Data, which provided immediate scale to its business as well as awareness and trust in the marketplace.

“We are a startup and Tech Data is an international IT distributor,” says Crudele. “Tech Data transformed our team from two people selling our product to thousands of resellers across Europe. The company is helping us change our business model from delivering technology to a solution. Our customers are no longer the retail brand but the partner. This paradigm is allowing us to scale up our business more easily and faster.”

By leveraging data, Wonderstore helps create smart stores that can adapt to the customer and understand their needs from the moment they enter the store—not just at checkout.

“The store of the future will be a place where customer will have personalized services with creative brand experiences that entice them to buy,” says Crudele. “And it’s important for retailers to move from a sales model to service model. Recognizing and understanding their customers with the same precision as Google Analytics helps create an experience that’s more than just a mere transaction.”

 

This article was originally published on June 16, 2022.

This article was edited by Christina Cardoza, Editorial Director for insight.tech.

It’s an exciting time to be an end user. The customer experience—of retail, hospitality, banking, dining, entertainment—is evolving before our very eyes. And the past couple of years have only accelerated changes already in the works. But what about businesses that provide those customer experiences? Many of them have had to make changes on the fly to stay competitive and been forced into digital transformations they weren’t always prepared for.

Fortunately, the goal of AI technology company meldCX is to help customers blend legacy systems with the latest technology to create an omnichannel experience, and get the most out of their data.

We talk with Co-Founder and CEO of meldCX Stephen Borg, and Chris O’Malley, Director of Marketing for the Internet of Things Group at Intel^®. They discuss the challenges of digital transformation, the demand for frictionless interactions, and how the power of computer vision can serve both the omnichannel experience and human engagement.

How are customer experiences evolving across different industries?

Stephen Borg: The whole COVID situation has really accelerated the curve of change. Our customers need to increase the level of service for their customers, without having as many resources—either budgetary or staff—to do it with. But when their customers venture out, they expect a higher degree of service; they expect a demonstration of cleanliness; they expect a higher degree of engagement.

So how do you do that while either reducing costs or using fewer resources? Businesses are starting to turn to technology for the elements that are not customer facing and redirecting those resources towards creating great experiences. They’re asking: “What are the opportunities to reduce friction, create automation, but increase engagement?”

How well are businesses adapting to these changes?

Chris O’Malley: All the trends and challenges that retail was facing prior to COVID still exist. Three years ago people were talking about frictionless; millennials and other digital natives are used to engaging with technology as opposed to humans. There were also inventory and supply challenges before COVID. But the level of growth towards frictionless was slow; it was nice to have this technology, but it wasn’t absolutely necessary.

What we’re finding now is that the companies that started to invest in this type of technology prior to COVID, now that COVID has hit us, are the ones that are doing really well. But many companies went in to COVID with almost no one-to-one digital contact with their customers. Now they have this massive digital relationship with them, but if they don’t know how to deal with that data, if they don’t know how to personalize with that data, they’re really struggling.

How are tools and technologies helping customers deal with challenges?

Stephen Borg: When we started out with meldCX, we wanted to create a solution out of the box, one where you could simply plug and play. No need for data scientists, no need for a massive team to stand up the most common aspects of computer vision—analytics, tracking, inventory.

Secondly, we found a lot of customers that were, say, three-quarters through a project where they had existing investment in some models. Now they can pump those models through meldCX, using OpenVINO^™, and mix them with other, complementary models that we have. Or, thirdly, we can create a model for a customer that has a specific use case or a specific problem to solve.

“Businesses are starting to turn to #technology for the elements that are not customer facing and redirecting those resources towards creating great experiences.” – Stephen Borg, meldCX via @insightdottech

And now we’re finding that the level of engagement is cross-business. Unlike the past, where we might have had an IT stakeholder or a marketing stakeholder, now we have everyone at the table. We advise customers to start with computer vision, to start with out-of-the-box modules, and then go from there. We want them to understand that it’s really an amplification of their existing capability. Then our team works with them to really drill down into that problem-solving phase for future growth.

Chris O’Malley: Another big thing is right in the name, meldCX—melding the old technology with the new. Say a customer doesn’t want to do the full investment into a new camera setup right now—they might have security cameras already in place. So, meldCX can take those feeds right away, load some models on them, and get basic data right from the get-go. That way, the customer doesn’t have to do a massive investment to start getting data. And we find that once customers start to see what computer vision can provide, then they’re interested in investing further. They see the power of it.

And we’re right at the beginning at this point. The corollary to Moore’s law is that technology also becomes cheaper every single year. But when you start adding compute to everything, there’s going to be an immense amount of data. So you need technology like this to get started making those valuable, actionable insights for your company. 

When we talk about a retail omnichannel experience, what does that mean?

Stephen Borg: There’s been a lot of focus on mobile and a lot of focus on web, but we haven’t seen a lot of connecting mobile to web in a single, seamless experience at the in-store or physical contact point.

It could be connecting the computer vision to a locally occurring event. For example, if you go to a self-service device and it knows you’ve used it multiple times, it won’t go through all the instructions again. It’s little, subtle things done anonymously that create convenience and context.

How would you suggest that businesses get started on this omnichannel journey?

Chris O’Malley: In the last 10 to 15 years, online advertisement has really eaten up a lot of the market share—largely because behavior could be tracked that way. But when someone goes in-store, there’s none of that information. With the technology that meldCX is offering, that computer vision is offering, you now have that ability to figure out: Is my campaign working? Did it actually influence people? Were they engaged with it? And you can make changes. Before the advent of computer vision, that option didn’t really exist.

That’s pretty powerful. And now that information can start to be monetized. But the retailer or the business can also figure out how it might change a display, for example. Or how it might change associate activities. I’m in marketing, and we always say that 50% of the money we spend is useless, 50% is valuable. We just don’t know which one is which. With the technology that meldCX has, we’re starting to be able to figure that out in-store. What’s valuable? What’s not.

Stephen Borg: For example, we work with a retailer that has started monitoring the bay you see when you first walk in the store, and monetizing that based on the customer touching a product that’s on the shelf there. So instead of vendors paying to be displayed in that front-of-store bay, they pay for every touch of an individual product that’s located there. And then monetizing it like a website.

What’s the best way to analyze and process data to make more-informed decisions?

Chris O’Malley: There’s a huge amount of very valuable data out there that goes unused. One reason for that is that a lot of retailers—legacy retailers in particular—have very siloed data. The POS data is in one place, the kiosk data in-store is over here, mobile data is over here, there’s online data over here. And the data is never shared between them.

When you move to an edge-based or microservices-based architecture, you can have shared data, or a data lake—that’s when you can start to make sense of all of it. But you’ve also got to make sure that the data is standardized.

The other thing is big data analytics. You might look at pieces of data and think there’s no correlation, no relation. But if you see them over and over on big data, you might figure out that actually, yes—product A does influence product B. It all comes down to making sure that data is shared by all the different apps.

How do you team up with partners to make all this possible for businesses?

Stephen Borg: There are multiple types of data, and some of it is immediately actionable. For example, we work with a large hotel chain, and when data points to the fact that the front desk has hit a threshold and needs to be cleaned, we push that data through an intermediate alert; the hotel uses a Salesforce communication app to notify the staff. At meldCX we don’t necessarily store that data; it’s immediately actionable.

There’s also historical data or multisource data that we’re trying to get insights out of. We work with Intel from an OpenVINO perspective to make sure our models are optimized. That means less heavy infrastructure at the edge, which significantly reduces cost, and that’s great.

We also work with partners such as Microsoft, Google, and Snowflake to provide customers the data set in the way they want to consume it. We have a suite of dashboards that can be used depending on the person’s role in retail: you sign on, which pulls your persona, and we’ll give you the data that’s relevant to your role.

Chris O’Malley: Another thing is that with a lot of activities in a retail store, that information may be wanted in absolute real time, and so it needs to happen at the edge. We mentioned earlier that compute is getting so cheap that companies are adding more and more of it. But their data is growing significantly faster than the cost of connectivity to the cloud is reducing. So this type of thing has to be done at the edge. And Intel with OpenVINO is very much optimized for using edge capability to do the inference and get those real-time analytics that are needed.

Stephen Borg: We don’t send any video to the cloud. We strip out everything we need at the edge to reduce the cost, and, more importantly, for privacy reasons. That way there’s no instance of any private data going into the cloud or going through our system. It’s all stripped out by that edge device and OpenVINO.

What do the end customers think about all these changes?

Stephen Borg: If it’s all done with privacy in mind, customers respond to it quite well. They’re getting through checkout more quickly, or staff members have information that’s relevant to them, maybe even tailored to them. And we’re finding that if you’re providing a frictionless experience, staff members can focus beyond just the transaction—on a conversation, or on some type of real engagement. We’ve found that, particularly in some countries with strict lockdown conditions, in some cases shopping has become even more social because when people do get out, they want to be engaged.

Any final takeaways on the omnichannel customer-experience journey?

Chris O’Malley: Customers want a frictionless experience and a personalized experience. You can have the parts of shopping that everybody still loves, but you can also bring in the benefits of the online experience by using all of these tools. There’s also the ability to replace human resources in some instances, because the worldwide labor shortage is real; restaurants can’t fill up all of their seats because they don’t have enough staff. The same thing is happening in hospitality and entertainment venues. If you can automate some of the things that were formerly done by staff, you can keep those valuable human resources for the things people really like—the interactions.

Focus your human resources and your human talent on those interactions that really drive experiences, and all the stuff in the background—all the operational aspects, all the inventory, all the insights—set those up with computer vision and automation. That’s what it’s really good at.

Related Content

To learn more about latest innovations from meldCX, listen to our podcast on The Power of Omnichannel Experiences with meldCX and Intel^®.

 

This article was edited by Erin Noble, copy editor.

Labor shortages and ecommerce competition have created a real crisis for retailers today. Customers now expect quick and frictionless interactions. And they no longer have the patience for understaffed stores, empty shelves, or long checkout lines. Retailers need to start rethinking their operations and experiences, otherwise they risk being put out of business.

The Achilles heel of brick-and-mortar stores is the complete dependence on staffing—a weakness that has become painfully clear to so many retail businesses over the past two years.

“Staffing in retail is now a worldwide problem,” says Mark Perry, Head of Global Business Development for Cloudpick, a provider of autonomous store solutions. “In Japan and South Korea, where you have aging populations, businesses have historically struggled with it. But now in places like Hungary, companies have to offer thousands a month just to hire a cashier. Even in the United States, some physical stores are no longer able to serve their customers due to the lack of workers.”

To address this growing problem, many retailers are adopting AI capabilities with an eye toward a fully automated retail future.

“The future of retail is AI-driven,” says Ria Cheruvu, AI Software Architect at Intel. “Today, we are starting to see transformations in customer experiences via automated self-checkout and intelligent queue management, automation of inventory monitoring procedures, and more, with the long-term future of sustainable restocking procedures.”

AI Smart Store Takes Over French Campus

When the French supermarket chain Auchan became overwhelmed by the influx of foot traffic at its EDHEC Business School campus location, they knew they needed to step up their game. They transformed their pick-and-go Auchan Go convenience store into a fully automated smart store.

To do so, the supermarket chain partnered with Cloudpick. The goal: to offer busy students a faster and more convenient shopping experience, without lines or long waits.

Many retailers are adopting #AI capabilities with an eye toward a fully #automated #retail future. @CloudpickTech via @insightdottech

One of the issues Auchan wanted to solve was its inability to rapidly serve students around the clock. For instance, students would often get up early in the morning and rush to classes, which meant they might not have enough time to eat. Or they forgot something and needed to grab supplies quickly before class. But with a lack of staff, the store wasn’t always able to fulfill these needs at certain hours.

By partnering with Cloudpick, it was able to design a completely autonomous AI retail solution capable of running 24 hours a day without cashiers or customer service staff. Because of this effort, Auchan was recognized as a 2021 retail innovator by LSA.

How an AI Smart Store Operates

To access the store, customers must first download a mobile application and provide their payment details. When they arrive at Auchan Go, they’re provided with a QR code that assigns them to a virtual shopping cart. With this in place, customers can simply pick out the items they want, and walk out of the store.

Payment is automatically processed and handled in the cloud. The store also uses edge and computer vision technology to ensure customers are charged only for the items they left the store with (Video 1).

Video 1. Cloudpick smart stores automatically detect and charge for items customers leave the store with. (Source: Cloudpick)

Cloudpick’s solution can process up to more than 800 shoppers per hour, so there’s little chance customers will ever have to wait in lines. And Auchan store managers are relieved that they no longer have to be on-site all the time. If they’re needed to handle a problem or help restock shelves, they’re notified via the smartphone app.

The overall results included improved operational efficiency, reduced labor costs, and faster automated checkout.

“Cloudpick’s integration of AI demonstrates the exciting combination of the benefits of AI in this space,” says Cheruvu.

Powering an AI Smart Store

To achieve this level of autonomy and accuracy, Cloudpick combines several different types of AI technology.

The solution uses a computer vision and deep learning system built on the OpenVINO^™ AI toolkit to create a virtual inventory of all the products. This is how the smart store differentiates one item from another.

With OpenVINO’s CNN-based deep learning inference, the company can optimize AI model performance and compress model sizes to run adequately on low-power devices, according to Perry. Additionally, Cloudpick can minimize its time to market with OpenVINO’s library of computer vision functions and pre-optimized kernels.

Inside the store, Cloudpick uses AIoT motion and weight sensors to keep track of what’s going on with products, and Intel^® RealSense^™ 3D cameras track shopper behavior in real time by performing gesture recognition at the edge. The computational heavy lifting is done with on-premises Intel^® edge servers.

“It is incredibly exciting to me to see the breadth of AI capabilities that Cloudpick demonstrates leveraging Intel and OpenVINO,” says Cheruvu. “The combination of AI pose estimation, object detection, personalized ads, and more. Working with computer vision and sensor fusion algorithms to create a comprehensive shopping experience for consumers that also enables convenient inventory management is to me, the future of retail.”

Retail’s Missing Link

While the retail space is no stranger to digital transformation, Perry believes AI has been the missing link in the evolution of retail and gathering better retail data.

“When you add AI to the equation, suddenly everything changes, because every process can be automated: from checkout and payment to inventory and restocking,” he explains.

AI not only enables retailers to collect data points they could never access before, it helps stakeholders understand exactly what the data means to make more informed decisions. From understanding customer behaviors better to providing relevant personalized offers in real time, the opportunities are endless.

“Retailers may consider experimenting with different types of models and capabilities to find the optimal scheme that fits their use case,” Cheruvu explains. “To scale AI integration, retailers may also consider upgrading the quality, and types of sensors and capture devices placed throughout the store to capture additional data.”

A Smarter, More Sustainable Future

In the years to come, Perry expects to see in-store inventory systems capable of integrating directly with manufacturers and distributors upstream. This will help goods flow through the supply chain more smoothly, eliminating delays and stock shortages.

Longer term, he sees AI completely automating retail supply chain with self-driving vehicles and stocking robots to be commonplace. The smart stores of the future will know when they need more product, order it automatically from the supplier, and have it delivered and stocked by machines.

“If you automate restocking,” says Perry, “you can optimize the loading of the delivery vehicles, and the routes that they drive. It’s a very effective way to reduce carbon emissions, because you’re not wasting fuel by making excessive deliveries or sending stock where it isn’t needed.”

 

This article was originally published on June 8, 2022.

This article was edited by Christina Cardoza, Editorial Director for insight.tech.

Whether your customers are eating in or taking out, there are so many little details that go into creating a great dining experience. Systems like self-serve kiosks and Point of Sale (PoS) must be connected and work reliably to ensure all orders and payments go through. It’s essential that you have enough restaurant staff to fulfill orders—an extra challenge with today’s worker shortages. The right inventory must be stocked at the right time to properly serve customers.

On top of all this, there are the security, energy control, sanitation, and safety measures required to operate a successful business in the hospitality industry. But it can be difficult to manage a business when multiple systems don’t communicate with one another. This can make it feel like an impossible task to carry out all the different functions of the business, from ordering to payments to staffing.

Ultimately, dine-in and quick-serve restaurants (QSR) are under pressure to speed up operations and keep costs down to remain competitive.

That’s why Shenzhen GIFA Industrial Control Co., Ltd, a smart manufacturing, retail, and healthcare solution provider, created its Retail Integrated POS Solution.

“We want to help restaurants, restaurant management, fast-food operators, and other business-oriented retail stores unlock valuable data, operate more smoothly, and keep up with the demands of a retail smart store,” says Li Hongming, Founder and CEO of GIFA.

End-to-End Smart Restaurant Operations

One of the biggest challenges restaurants and retail stores face is how to manage all the data coming from multiple devices such as cashier systems, security cameras, online ordering, and even thermostats. Often, restaurants struggle to support these multiple workloads, causing network availability, stability, and cost issues.

With GIFA’s Retail Integrated POS System, everything from surveillance cameras to digital-signage solutions and even checkout machines are all integrated. The management system acts as a service gateway, collecting, analyzing, and requesting data from devices, and uploading that information from the edge to the cloud for deeper insights.

The platform provides real-time #analytics that can help users improve #operations and guest dining experiences. Shenzhen GIFA Industrial Control Co.,Ltd via @insightdottech

“Traditionally, each technical module with a retail store is relatively independent, and each manufacturer develops their own module. In addition, the software and hardware between enterprises and the cloud platform are often not compatible,” says Hongming. “We want to bridge these connections and help businesses connect the dots of their operations.”

GIFA teams up with Intel^® and network infrastructure operator Exands to give its solution even more power and dig deeper into the data. Together, the companies’ platforms provide real-time analytics that can help users improve operations and guest dining experiences. For chain restaurants, the management software can compare the data between different locations to make improvements in underperforming stores.

This results in timely discovery of anomalies such as abnormal objects or theft, streamlined operations and management, and in some cases, energy conservation with smart control of air conditioners, lights, and exhaust fans.

“The solution helps businesses reduce investments in cloud services and networks, and consolidate loads into an integrated edge computing device,” according to Hongming. “Cloud applications enable an enterprise to manage businesses from its multiple chains with more flexibility and agility while edge computing provides them with more cost-effective, stable, and smart options in digital transformation.”

Powering Smart Restaurants

To make it all possible, the combined intelligent retail solution is based on the Intel^® Edge Gateway Architecture and 11th Gen Intel^® Core^™ Processors. This enables the system to instantly process data, reduce latency, and enhance service stability.

“Intel processors bring dedicated improvements to rugged environments where IoT devices are running. Users can choose to equip with Intel AI accelerator cards according to configuration requirements for different scenarios,” Hongming says. “With the help of Intel processors and accelerators, plug-in boards can deliver edge computing support with high performance, endurance, and stability.”

The Intel^® Distribution of OpenVINO^™ toolkit is leveraged to accelerate deep-learning inferencing and uncover insights from operations and customer behavior. Since OpenVINO is based on convolutional neural networks, it can scale workloads and maximize performance on Intel hardware platforms.

The integrated Intel solution is also equipped with the Data Plane Development Kit (DPDK), an open-source kit designed to improve network performance.

As an infrastructure operator, Exands adds to the solution by providing a secure network connection, centralized IoT management, security authentication, and other infrastructure operation services. Since it leverages containerized architectures and open-source components based on x86 platforms, the company can simplify development, consolidate workloads, and speed up R&D.

“Intel’s innovative software and hardware products provide a solid foundation for edge-based digital-transformation food service. Cooperation between partners such as Intel and GIFA can speed up the introduction of this solution to the market and help catering chain companies deploy edge computing boxes and smart gateways in a one-stop manner,” says Lu Guoming, Founder and CEO of Exands. “The load integration of restaurants helps to solve the pain points of chain restaurants in terms of digital cost, operations and maintenance capabilities, etc., and accelerate the transformation of smart catering.”

The Future of Smart Restaurants

As the world becomes more digital, GIFA and Exands hope their technology can be a one-stop solution for the retailers and restaurants and improve the overall experience and competitiveness in the industry.

“Catering chain enterprises will become more competitive, owing to more real-time and smart management by processing and analyzing data at the edge, and integrating that data in the cloud,” Hongming explains.

 

This article was edited by Georganne Benesch, Associate Editorial Director for insight.tech.