How retail AI is helping sellers last this holiday season (and beyond)

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Retail AI is everywhere this holiday season — even if you don’t realize it.

Suppose you are a fashion retailer. You’ve always had to try to predict trends, but now with a slowed supply chain, you have to look 12 months ahead instead of six.

Or you simply don’t have enough staff as a grocer, but you have to make sure that the perishable items piled up in your fresh produce department or in your dairy box are fresh, otherwise you will scare off customers.

[Read more about retail AI in How AI taps data to make ecommerce more dynamic from our newly released special issue on data privacy.]


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With retail sales at an all-time high, retailers are struggling to keep up with a whirlwind of (sometimes competing) factors: inventory surpluses, long lead times, economic uncertainty, macroeconomics, inflation, shifting consumer sentiment (just top of mind).

In this unprecedented environment, retailers in various sectors are taking advantage of the numerous possibilities of artificial intelligence (AI).

From predictive analytics to advanced demand forecasting and pricing, to omnichannel fulfillment management, retail AI helps move inventory, predict retail trends, and appropriately price products.

“We’ve seen AI being deployed across a range of functional retail areas and business applications as part of the business process and execution,” said Gartner senior director and analyst Sandeep Unni.

Unparalleled time for retail

According to the National Retail Federation (NRF), retail is growing at levels not seen in more than 20 yearsan increase of 7% in 2020 and more than 14% in 2021. The trade association forecasts that revenue will grow by 6% to 8% to more than $4.9 trillion by 2022.

In addition, retail holiday sales grew 14.1% to a record $886.7 billion in 2021, according to the NRF. Despite inflation and the shockwaves of the pandemic, the company expects the 2022 holiday season to be the same.

At the same time, the last two and a half years have been unparalleled in their variability. The industry has seen historically low inventory levels and historically high inventory levels, Unni said.

The NRF reports that retailers started planning for the holiday season early, as they learned from the ongoing supply chain disruptions. Many extended their shipping season for the holiday season and brought products in earlier than usual to ensure they would be in stock. Some also shifted to East Coast/Gulf Coast ports to avoid potential disruptions to the West Coast due to ongoing labor contract negotiations.

There is no doubt that supply chain problems are easing, but there is still a way to go, said Sivakumar (Siva) Lakshmanan, VP and GM of Antuit AI Bee Zebra Technologies. In some cases, for example, freight costs have increased tenfold.

Likewise, shipping turnaround times were “incredibly high,” leading to system overload, he told VentureBeat. “Everyone is talking about a surplus of stock.”

All this – coupled with inflation and macroeconomic problems – will make for an interesting holiday season, he added. And, “it feels like these disruptions will become more frequent.”

Using AI in Retail to Predict Consumer Confidence

As a result, experts say AI will play an increasing role in retail.

Gartner has seen a “rising interest” in AI in the industry since 2016, Unni said. Findings from the 2021 Gartner CIO survey identified AI as an emerging technology, with 49% of retailers having implemented or planning to implement it in the next 12 months.

Unni predicts that, while consumer spending is expected to pick up this holiday season, historic inflation levels and the current macroeconomic environment are likely to dampen real momentum and consumer confidence.

“Retailers can leverage AI and ML [machine learning] holistic to help with more seamless experiences throughout the shopping journey,” said Unni. “This, in turn, will enable them to capture the consumer’s share of the wallet.”

AI across the value chain

The most commonly implemented use cases, Unni said, include demand forecasting, optimized inventory availability and pricing, fulfillment and delivery flexibility, item assortment and variety, personalization, social media analytics, conversation trading, and fraud/threat detection.

Other use cases span the entire business value chain and vary by industry segment, he said. For example, in the grocery store, freshness algorithms reduce waste, and in fashion, the best-fit technology lowers the return rate.

Ultimately, AI in retail is being used to enrich merchandising and marketing processes through input from various big data sources, Unni said. Computer vision is used in the physical store as an important factor for automation, smart checkout, loss prevention and inventory management. ML has been used to power retail applications that enable demand forecasting, replenishment, and allocation strategies for broad supply chain optimization.

One of the easiest places to start — and one that promises the biggest ROI — is predictive modeling, Lakshmanan said. This can help clothing stores, for example, determine which products to buy and in which range: “Will this red t-shirt be in fashion in nine months or not? If so, should we take it with us?”

Retail AI can help answer questions about where and how to sell price cuts (in-store or online), and deal with concerns such as how much to keep online, Lakshmanan said. Also whether to order items from a store or a warehouse (and the geographic logistics there; a warehouse that is further away may ship in one package and take longer, while a closer warehouse may ship multiple packages that arrive quickly).

It is “super critical” to get the “right product, in the right place, at the right time and at the right price,” Lakshmanan said.

Incremental adoption of AI in retail

Most retailers will likely adopt AI for the first time through AI-enabled third-party vendor applications, Unni said. They often start with tactical deployments and move on to more strategic deployments once they have proven their benefits.

“Early-stage support is needed for assessing their AI readiness, in terms of their current infrastructure, technology and human factors,” he said.

Data governance and general clean data challenges — especially for multi-channel retailers that rely on disparate data silos — are an important consideration.

Unni warned that legacy processes and organizational resistance to change often lead to long implementation cycles.

“This change management needs to be addressed in advance as part of the technology onboarding process,” he said.

Finding the best-fit AI

Retailers cannot have rigid processes aligned with how things have worked in the past in a predictable world, agrees Lakshmanan; they have to adapt to disturbances. Importantly, they do not want to place AI on top of existing processes.

He underlined the fact that simple statistical models can suffice; retailers do not need to use deep learning or neural networks.

“In our experience, there is no evidence that you need to have the most complex models to get the most value,” he said. “You don’t need the most complex algorithms to generate value. That is a misconception in the market.”

In any case, retailers can’t afford to ignore AI this holiday season — and beyond. Simply put, Unni said, “retailers that are too slow to implement critical AI-led initiatives to support business transformation for customer centricity will not survive.”

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