How AI and Machine Learning Are Helping Smaller Retailers Compete Against Amazon, Walmart

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Without the tech infrastructure of Amazon or Walmart, it has been harder for small and medium-sized retailers to compete in an increasingly digital market.

For starters, knowing how to manage inventory between physical stores and online can be challenging for retailers who lack access to the data insights of their larger competitors. But new developments in AI and machine learning are democratizing the use of data.

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“Five or 10 years ago, you really had no way out,” said Kishore Rajgopal, founder and CEO at NextOrbit, an AI platform for inventory and price management. “Amazon was the king, Amazon and Walmart had all the data and the data scientists and you could never catch them — but now you can.”

Most companies already have a wealth of information at their fingertips, due to existing e-commerce technologies. Every transaction and customer interaction is tracked, creating a pool of data that can inform future strategy. But taking advantage of this, without hiring a specialist team, has been a challenge for retailers whose backgrounds are more aligned with merchandising than machine learning.

“So many people have so much data that’s just sitting somewhere, but they don’t have the ability to access it,” said RJ Talyor, CEO at Pattern89, an AI digital marketing platform. “They’re coming from a creative and marketing perspective, and they don’t have the data language skills that are required to pull the inference out of that data.”

Fortunately, they no longer need to. In response to soaring demand, a number of companies are leveraging machine learning in order to help retailers price competitively, stock strategically and use data to their advantage.

Yaguara, an operations management platform, uses AI to spot patterns in company data and make actionable recommendations. These help to keep employees on track to achieve set objectives, whether doubling sales at one location or improving customer retention online.

At NextOrbit, the platform combines a retailer’s sales data with relevant local factors to build a more granular model for product demand. A local store opening, an upcoming blizzard and an Apple promotion are mapped to help determine how much inventory a retailer should be stocking each week at each store.

The company also provides a service for price monitoring and elasticity modeling. NextOrbit found that optimized retail pricing can account for as much as 10% in additional sales revenue, according to its customers’ internal reporting. Knowing when to mark down a product and by how much can help a smaller store maximize the return on its inventory, keeping it competitive with e-commerce giants.

“It’s not just that you monitor the price,” said Rajgopal. “It’s whether you can say ‘hey, more than 10% of your SKUs in items in that category are more expensive than the market.’ If I can give them a trend alert, they can wake up and see that they need to do something about their pricing.”

Watch the video below to see how digitally-native brand Allbirds is succeeding:

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