How Instacart is bringing the best of online shopping to grocery stores

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Grocery tech company Instacart Monday launched their Connected Stores technology features aimed at enhancing the grocery shopping experience by letting consumers use a retailer’s app or website as well as in-store.

“We believe the future of grocery won’t be about choosing between shopping online and in-store — consumers are going to do both,” said Fidji Simo, chief executive officer at San Francisco-based Instacart, in a statement. “Ultimately, we believe that the more customers connect with grocers across both online and in-store experiences, the more retailers’ businesses will grow.”

AI sensors and scan-less technology 

Among the six new platform offerings is the next version of Instacart’s Caper Cart, an AI-powered smart cart that comes with scales, sensors, touchscreens and computer vision technology. The screens are designed to help consumers navigate the store and with the scan-less technology, items do not have to be manually scanned. 

The new Caper Cart is slimmer and lighter than the previous version and has 65% more capacity. It enables stacked charging so that grocers can charge batches of carts at one time and eliminates the need to charge carts individually or swap out batteries. 


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The new Scan & Pay feature lets users scan items as they shop and pay for them from a mobile phone so they don’t have to wait in line to check out. The items that are purchased are also linked to a user’s online account so it’s easier to buy them again, the company said.

Scan & Pay also identifies EBT SNAP-eligible products once they are scanned. 

The Lists feature is designed to let users walk into a store and sync their shopping list from the Instacart App or their grocer’s Instacart-powered app directly to a Caper Cart by scanning a QR code.

With the Carrot Tags feature, retailers can connect electronic shelf labels to the Instacart Platform to add functionality such as pick-to-light capabilities, which allow customers, associates, or Instacart shoppers to select an item on their phone and flash a light on its corresponding shelf tag, making it easier to find the products they’re looking for, Instacart said. 

Carrot Tags is also designed to help retailers display key information, such as whether a specific product is gluten-free, organic, kosher or EBT SNAP eligible.

The new Department Orders feature of Instacart’s FoodStorm order management system lets different prepared food departments inside the store collaborate to better time customers’ orders. For example, customers will be able to order a cake from the bakery and a sandwich from the deli and have them both ready at the same time. 

The Out of Stock Insights API feature aims to help retailers provide automatic, real-time alerts to associates when items are running low or out of stock. 

Omnichannel offerings create new opportunities for grocers

Instacart and Good Food Holdings will jointly open the first Instacart Platform-powered Connected Store at Bristol Farms in Irvine, Calif., “in the coming months,’’ the company said.

“With Connected Stores, retailers can pick and choose the modules that work for them,’’ David McIntosh, Instacart’s vice president of platform growth and technology, told VentureBeat. “They don’t have to wire the whole store, which is expensive. It’s making tech more accessible for retailers,’’ and they can scale their systems. 

“Technology is completely transforming the grocery industry and working with Instacart is helping us unlock and adapt innovative solutions that will shape the in-store experience for our customers,” said Cheryl Williams, CIO at Wakefern Food Corp., in a statement. 

Grocers also need to digitally transform themselves to keep up in today’s competitive environment and create a better experience for shoppers, said Jordan Speer, research director of product sourcing, fulfillment, and sustainability at IDC. “Omnichannel offerings such as Instacart’s modular Connected Stores create opportunities for grocers of all sizes to innovate in stores and offer seamless experiences across online and in-store.”

Smart grocery carts

Others getting into the hybrid grocery tech market include Shopic, a smart grocery shopping cart startup based in Tel Aviv, Israel. In August, Shopic received a $35 million series B investment round led by Qualcomm Ventures for a total of $56 million in funding. 

Shopic’s AI-powered clip-on device is designed to turn shopping carts into smart carts. With computer vision algorithms, Shopic can identify more than 50,000 items once they are placed in a cart in real time while displaying to shoppers product promotions and discounts on related products. The Shopic system acts as a self-service checkout interface as well, saving customers the time and hassle of standing in line to pay.

The system also provides real-time inventory management and customer behavioral insights for grocers through its analytics dashboard, the company said. Grocers can receive reports that include aisle heatmaps, promotion monitoring and new product adoption metrics, Shopic said.

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Hungryroot delivers AI-powered grocery experience

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There’s Netflix for movies. Stitch Fix for clothes. Hungryroot, an AI-powered delivery service, hopes to occupy a similar niche for online groceries in the United States.

The recommender system uses a collaborative filtering, supervised learning model to match consumer preferences to foods. Customers answer questions about their dietary habits, the kinds of foods they (and family members) like, the family size, budget, and more. On a weekly basis, the Hungryroot algorithm predicts the groceries the customer might like. Once the customer approves the list, a box ships from one of three Hungryroot locations. Customers also receive a set of recipes, also predicted by the algorithm, that use the week’s ingredients.

Neil Saunders, the managing director of GlobalData’s retail division, has seen grocery retailers of all stripes lean into AI as a way of better forecasting demand. “With the disruption from the pandemic and more people buying groceries online, demand forecasting has become increasingly difficult for retailers and AI can help them make sense of the data and make more accurate decisions about what to stock,” Saunders says.

The AI-powered grocery challenge

Hungryroot works on a collaborative filtering model much like Netflix, learning from customer likes over time and pooling their preferences with others’. But AI-based recommendations for groceries are challenging, says CTO Dave Kong. For one thing, Netflix can recommend movies from a near-infinite queue. There are no additional constraints. Food, on the other hand, is not a consumable entity like movies. Food is perishable. Your choices depend upon inventory and on how much you can fit in the box.

While consumers who like horror movies can feed on films in that genre for a while, the same need not apply to food. Feed consumers pasta three weeks in a row and they might complain. “The first step is to dissect the problem better for each customer. For example, what does variety mean to each customer? Is it different items (i.e. types of pasta like spaghetti vs. penne), or different dish types (i.e. pasta vs. salad vs. stir-fry, etc.),” Kong says.

Hungryroot is also trying to figure out what repetition means to the customer. “Are they looking for similar recipes and items to their last order, even if it’s two weeks ago, or does the skipped week matter to them? We can then focus on the right AI approaches depending on what we learn,” he says. “Understanding repetition and variety is the key to success in the food model that is not a factor anywhere else.”

The other challenge is that the number of customers who might like the exact same recipes using the exact same ingredients is not as large as movie buffs liking a genre. Consumer food preferences need to be digested at a much more granular level: salty, different types of protein, texture, and more.

The Hungryroot factorization machine crunches 60 different parameters (that number continues to increase) into its model. And data sources aren’t limited to only what customers say or do — Hungryroot also relies upon additional sources, like nutritional data.

A pleasant side dish: reducing waste

The Hungryroot algorithm optimizes recommendations not just for an individual user, but across the board for all its customers. Tweaking what’s in the box just a bit — if a customer likes one kind of white fish, they might like a similar one in large supply at Hungryroot — can help optimize food distribution across all boxes, cutting down waste, Kong says.

In addition, the AI-powered grocery suggestion algorithm itself is smart and helps Hungryroot to predict how much of each kind of food to buy. Since customer preferences are known, it’s easier to forecast demand and manage inventory. Saunders agrees. “The main advantage for brands is that they get better at providing customers what they want and have enough stock to satisfy demand. With regular grocery delivery, one of the most frustrating things is bad substitutions or unwanted products. If AI helps brands to understand what customers want they have a greater chance of building loyalty and repeat business,” Saunders says.

Hungryroot also makes sure to keep customers’ pantry purchases in mind: While every recipe might need salt, customers don’t need to buy salt every week.

Growing appetite for AI grocery delivery

Customers have responded well to Hungryroot: The startup is up 133% year-on-year for active customers. In June 2021, Hungryroot raised $40M in a series C funding round.

The algorithm has a high success rate. Consumers buy 72% of the AI-powered grocery deliveries. Kong expects including more unsupervised learning in addition to the supervised learning model. “We believe a neural-network model that is great at factoring in temporal information and excels at pattern recognition is the key to creating a successful and effective AI-enabled grocery service,” Kong says. “If we can nail the right level of predictability and variety for each and every customer, then we’ve solved the hardest problem with AI-enabled grocery shopping.”


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Tech News

Amazon brings in-garage grocery deliveries to more than 5,000 US regions

If you’re an Amazon Prime customer who orders your groceries from Whole Foods or Amazon Fresh for delivery, there’s a good chance you can now have them dropped off directly in your garage. The company has announced a big expansion of the service, one that is now available in more than 5,000 towns and cities across the United States.

Key by Amazon is a service that allows customers to get items delivered in their garage, eliminating the risks of theft that come with leaving a box on one’s front porch. In November, Amazon announced its Key In-Garage Grocery Delivery service, but it was only available to Prime customers in five US cities. That has changed with this expansion into 5,000+ locations.

Though grocery deliveries were a ‘thing’ before the pandemic, the COVID-19 crisis resulted in a huge demand spike, forcing companies to quickly roll out their delivery services. Amazon points out that it’s likely many people will continue to utilize grocery delivery options after the pandemic is over. Being able to have those groceries dropped off in a garage away from opportunistic squirrels and nosy neighbors is a bonus.

Key by Amazon is available to customers in supported markets who have a myQ Smart Garage Hub or compatible garage door opener. Grocery orders can be placed on the Whole Foods and Amazon Fresh sections on; the customer has the option to choose ‘Key Delivery’ during the checkout process.

It doesn’t cost extra to get the in-garage delivery, but you do need a Prime membership to be eligible. Amazon is offering customers a $20 credit when they use the in-garage delivery option for the first time. You’ll need to head over to the Amazon Key website to view the full details and enter your zip code to see if you’re eligible.

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AI-powered grocery inventory startup Shelf Engine nabs $41M

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Prior to the pandemic, average grocery profits hovered around 2%, mostly due to transportation and logistical inefficiencies. Shifting product demand and sales compounded these issues, with stores now responsible for 10% of U.S. food waste. This over-ordering not only costs profitability, but forces retailers to increase prices to make up for the losses. In April 2020, grocery prices showed their steepest monthly increase in nearly 50 years, led by rising prices for perishables like meat and eggs.

Shelf Engine, a Seattle-based startup cofounded in 2015, aims to make a change by tapping AI to help stores increase profits while reducing product waste. The company today announced that it raised $41 million in series B funding, bringing its total raised to $58 million as its platform expands beyond 85 customers and over 2,000 stores nationwide.

According to cofounder and CEO Stefan Kalb, grocery retailers that fail to innovate today could put their companies at risk. Traditionally, inventory managers use computer-assisted ordering and software-as-a-service solutions that require upfront hardware and software investments. These solutions frequently fail to account for on-hand inventory data and high volatility in sales patterns, such as with pandemic or weather-related buying. An analysis by Retail Insight found that 56% of records of perpetual inventory (PI), which determines how much stock a store receives on each delivery, are incomplete and that 15% of lost sales are a result of inaccurate PI.

“[During the pandemic], grocers were impacted by new demand patterns they weren’t ready for. This created a spike in stock-outs as well as waste. This has accelerated the number of grocers who now want AI to take over their ordering and reduce their risk profile,” Kalb told VentureBeat via email. “We’re about to see a massive transformation in the grocery space, but it’s not what you’re expecting. Yes, online grocery is growing, but it will be nothing compared to store operations, procurement, and merchandising. Grocery will become a very efficient business and generate way more profit than ever before.”

Shelf Engine

Using machine learning, Shelf Engine forecasts demand for highly perishable foods, reducing waste and out-of-stocks. Drawing on point-of-sales data along with considerations like school schedules, local events, holidays, and weather, the platform generates probabilistic models for each unique product for every store, every day. These models are translated into profit maximization models to create orders, which are then fed by time-series models along with machine learning-based-dependent models. As a last step, Shelf Engine submits a store’s orders to vendors.

Shelf Engine doesn’t charge for items that don’t sell — retailers only pay for the items that do. According to Kalb, one national grocer increased its profit margin 63.7% by using Shelf Engine to eliminate the cost of shrink from spoilage, breakage, and theft. Shelf Engine’s customers on average minimize stockouts by 90% while reducing food waste by as much as 32%, Kalb claims.

“We’re reducing waste, while simultaneously increasing sales, and that goes straight to the grocer’s bottom line,” Kalb added. “We’re helping grocers to make much more money, better positioning them to gain market share and offer competitive prices, especially important as tech giants and other disruptors are entering the market. This latest round will enable us to further meet the demand from our customers to launch into thousands of new stores in the next 18 months.”

General Catalyst led the funding round announced today with follow-on investments by GGV Capital, Foundation Capital, 1984 Ventures, Correlation Ventures, Founders’ Co-op, Soma Capital, Firebolt Ventures, and Initialized Capital. In addition to continued expansion, 145-employee Shelf Engine says the round will enable it to further invest in its team of engineers, data scientists, and supply chain automation specialists.


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