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From entry to exit, the average time a consumer spends in a grocery store is about 41 minutes for one trip. But when checkout lines are long and shoppers spend time scouring shelves for out-of-stock items, that trip quickly gets much longer. Neither consumers, who may quickly lose patience, nor retailers, who are already dealing with post-pandemic staffing shortages, supply chain disruptions and reduced foot traffic, want that.
That is where cashierless checkout and inventory management comes in, powered by artificial intelligence (AI) and computer vision. A variety of companies, both big tech and startups, have taken different approaches over the past few years, using cameras and sensors to identify items and ringing them up – allowing the customer to quickly grab items off the shelf and leave without standing in line.
These days, even as the economy slows, investors show no signs of pulling back on investments in this sector. Big funding rounds are still making news, including the Tel Aviv-based Trigo, which last week announced a $100 million series C investment, bringing its total funding to around $199 million, according to Crunchbase.
Amazon Go spawned many competitors
Six years ago, everyone thought Amazon, with its novel Amazon Go, had the cashierless grocery store model in the bag. But while many predicted the technology would scale massively, there are still only 28 Amazon Go stores worldwide — and that includes its recent expansion in India.
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In general, scaling is the biggest challenge, said Brad Jashinsky, marketing analyst at Gartner. “The main technical challenge behind check-out computer vision is scaling up the systems to handle more customers and larger store formats,” he said. “There is also a need to more easily integrate them into existing store footprints.”
As Amazon has wavered, several startups have stepped in with their own computer vision-powered technology — aimed at solving for scale and ease of integration. Some startups, like Caper AI – which was acquired by Instacart in 2021 – and Mashgin have focused on AI-powered plug-and-play smart carts or kiosks. Others, like AiFi and Trigo, are focused on ceiling cameras, shelf sensors and digital twin technology. Even though the economy is taking a downturn, it hasn’t seemed to phase-out opportunities in this niche sector.
Bringing computer vision to the physical store
According to Michael Gabay, CEO of Trigo, the draw of computer vision for grocery retailers is gaining the capabilities currently available to them in an ecommerce space and bringing that intelligence into their physical store spaces.
“That, for grocery retailers, is by far a bigger market than the online market,” Gabay said. ”Their confidence [in technology] is much higher than it was last year or two years ago.”
Supporting that statement, a Gartner study released late last year found that 73% of retail respondents expect to increase store technology investments for 2022.
In addition, retailers have been forced to refocus on connecting stores to their entire ecosystem, Jashinsky explained in an email. “New attention and investment has been given to digitalization of store technology investments — including check-out computer vision,” he said.
Trigo was founded in 2017 by brothers Michael and Daniel Gabay, who grew up on a kibbutz in northern Israel and served in technological roles in elite Israel Defense Force units. The CEO and CTO, respectively, set out to be the antidote for the headache of waiting in long checkout lines.
Its 3D store mapping and computer vision capabilities uses artificial intelligence (AI) and machine learning (ML) to keep track of a customer’s shopping tab as they go — even updating the total if they put an item back — and charges them accordingly when they walk out, no lines necessary. It also tracks item stock and customer body language so it can alert store employees if it suspects an item has been hidden in a jacket, for example. This is all without harvesting any biometric or facial recognition data.
While that may sound like a lot for retailers to wrap their brains around, all they need to do is work with Trigo to set it up, and once the installation is complete — which the company claims is typically overnight — nothing more is needed and they have the green light to begin using it.
Despite the swath of competitors and growing interest in the market as a whole, Gabay is confident in Trigo’s capacity to stand out among the crowd. The company is currently deployed in supermarket chains worldwide including the Wakefern Cooperative in the U.S., the U.K.’s Tesco chain, Israel’s Shufersal stores, Aldi Nord in the Netherlands and REWE, a chain in Germany.
“We are the only startup and the only company that converts existing stores into autonomous stores,” Gabay told VentureBeat. “We’re also the only startup, that is not Amazon, currently focusing on supermarkets and not just convenience stores or small stores.”
Trigo’s technology can be implemented by stores that are 3,000 square feet to 5,000 square feet — and claims it is working toward use in 10,000-square-foot stores next. However, its competitor AiFi also claims it can be used in up to 10,000 square-foot spaces.
Retail-focused computer vision surge
Experts expect the computer vision market, specifically, to surge worldwide to $41 billion by 2030. Investing in technology like this is the logical “next step for the industry,” McKinsey analysts Tyler Harris, Alexandra Kuzmanovic, and Jaya Pandrangi recently wrote.
“Investments in technology used to feel optional for grocers — an opportunity to experiment or increase the ‘wow factor’ in stores rather than to support mission-critical operations,” the article reads. “Today, a wide range of affordable, field-tested technologies can help retailers reduce the cost structure of their stores while delivering a better experience for both consumers and employees.”
In contrast with the tightening of the current financial market, a survey from Battery Ventures found that 54% of C-suite executives have plans to increase their tech budgets next year — with 75% saying they have plans to at least increase it within the next five years.
With its range of use cases from labor allocation, checkout ease, merchandising, inventory management, loss prevention and maintenance — Jashinsky doesn’t expect the boom in retail-focused computer-vision innovation for retail to phase out anytime soon.
“The ability to provide ambient customer transactions is only one use case for smart check-out computer vision technology,” he said.
The real power, he explained, comes from additional real-time business insights: “Retailers using computer vision for smart check-out can leverage the real-time insights captured to improve decisions.”