The Impact of Artificial Intelligence in Retail

As we’ve seen unfold in recent years, artificial intelligence (AI), machine learning (ML) and data analytics are rapidly changing the speed at which the retail industry operates. As these technologies become increasingly popular among leading retail companies, it’s clear that early adopters of AI have seen a sizable financial advantage compared to retailers that haven’t yet adopted the technology. Non-adopters will need to erode their margin to stay competitive on price, while adopters with sizable financial gain will be able to weather volatility on price inputs.

AI is being used as a differentiating factor between smaller retailers as a way to get ahead and capture market share. The gap between adopters and non-adopters will continue to grow, meaning AI is no longer just a way to get ahead of competitors — it’s become a pivotal part of staying relevant in the industry and maintaining innovation.

Data is King

Pure e-commerce players have historically held an advantage over traditional retailers due to the wealth of consumer data at their fingertips and immediacy of which they can analyze this data for business decisions. Now, traditional retailers are closing this gap. New capabilities, including in-store measurement; mobile commerce; buy online, pickup in store; and delivery services are changing the way the industry operates by focusing on understanding both the physical and digital shopper and giving retailers access to data previously unavailable. For example, Walmart increased its second-quarter earnings by 2.8 percent by implementing AI and automated processes to improve the customer experience. Walmart stated in its most recent earnings call that it wants to restock its customers’ groceries before they realize they’re running low.

From e-commerce startups to retail powerhouses, AI is now a necessary investment that provides benefits for both the retailer and its customers. Companies are using ML to mine clickstream, local weather and event data, and purchase and customer data in real time to provide targeted recommendations to customers, ultimately driving increased conversions. More often, customers are turning to personalized recommendations to drive purchase behavior and, in fact, 51 percent of consumers expect companies to anticipate their needs well before they personally interact with the brand.

With rich datasets, companies have the ability to optimize pricing and promotions, both online and in-store. They can use data to serve the right promotion, at the right time, to the right person, on the right device. As retailers analyze data to create the optimal experience for each customer, it’s important they're able to perform fine-grained analysis at scale. Historically, traditional retailers would aggregate analysis, leading to imprecise and inaccurate recommendations. Fine-grained analysis and focusing on the individual customer can lead to improvements in accuracy of recommendations, further improving customer satisfaction.

The New Retail

Forty percent of companies are using AI, and that number is only expected to grow. In fact, executives in the industry expect to see at least 70 percent of retailers adopting AI in the next two years. This should come as no surprise.

Combining rich customer and causal data with the power of ML is enabling companies to optimize supply chains, pricing, and trade promotions at a level of accuracy only made possible with this technology. Retailers are able to analyze individual stock-keeping units (SKUs) and store performance in real time to ensure on-shelf availability, as well as hyperoptimized product assortments, promotions and store layouts. An average grocery store running daily, store-level SKU models will run roughly 350,000 models per week. Current tools and approaches cannot handle the increase in scale. By implementing AI, retailers are able to handle the number of models deployed in each store, resulting in a reduction in supply chain costs while helping to deliver an optimal experience for customers.

It’s no secret that adopting AI technology is key for retailers to succeed. Still, some organizations aren't yet investing. Why? Uncertainty about how to begin, a perceived skills gap, and general conservativeness in the use of technology are the primary barriers. Meanwhile, their competitors are generating large economic wins and improving the rate of product purchases. In fact, retailers that don’t meet the level of personalization customers are looking for risk losing those customers to competitors. The window for adopting technology is closing, and non-adopting companies will soon find themselves at a significant financial disadvantage.

AI in retail is delivering real-world value. By focusing on capturing consumer data, retailers are able to provide personalized recommendations and improve the shopping experience for their target customers. In doing this, retailers will improve their financial gain and accelerate their investment in the new, technology-focused retail industry. AI is accessible for all retailers to help improve operations and, ultimately, the customer experience. Retailers are currently faced with an ultimatum: innovate or risk losing out to savvy, technologically advanced competitors.

Rob Saker is global industry leader, Retail and CPG at analytics company Databricks.