GenAI offers retailers a variety of operational, customer service, and other benefits, described in more detail below.
Boost Operational and Cost Efficiency
Retailers tend to operate with very narrow margins, so any improvements in operational efficiency can go a long way toward increasing profitability. For example, retailers can use GenAI to replace or augment customer service agents, both online and over the phone, reducing the time their personnel need to spend helping customers with rote tasks such as returns or exchanges. One retailer has lowered its procurement costs by 3% by using chatbots powered by GenAI and informed by conventional analytics and third-party market data to conduct contract negotiations with equipment suppliers via their online portals. Retailers also claim that they are increasing employees’ productivity and reducing costly turnover by developing their people using training videos with built-in GenAI to walk trainees through a multitude of interactive scenarios.
Increase Customer Loyalty
Retail brand loyalty has been on the wane for decades. Retailers need to work harder than ever to retain their customers.
GenAI can be a valuable tool that helps retailers harness data on each individual customer, allowing them to put out highly targeted emails and other marketing materials at scale—to an extent that wouldn’t be possible with human labor alone. The way it works is that GenAI sorts through aggregated shopping histories, social media posts, and other third-party data to determine which specific marketing messages may appeal to a given shopper. The fact that these messages are personalized rather than mass-produced can help reduce brand fatigue, improves the relevance of content, and increases customer loyalty.
Improve Customer Experiences
Most retailers not only suffer from high employee churn rates, but they also have to bring on seasonal help, which means they’re always managing lots of new employees with little institutional knowledge. GenAI can help by generating summaries of product features and walking directions for these employees to help them direct customers around stores. GenAI can also surface customer histories and product information for call center agents, in addition to powering interactive customer service chatbots.
Retailers can also use GenAI to respond to customer questions or complaints, either directly through an online chatbot or indirectly by providing scripts to store associates. Both take into account the full context of a shopper’s experience as well as relevant product information.
For example, if a customer asks about a store’s returns policy, a response that includes, “Well, that grill you bought last month is still under warranty, and I can make an appointment for someone to pick it up for you,” would be a lot more helpful than a response such as, “it depends” or “usually 30 days.” It would also go a longer way toward establishing a long-term relationship with that customer.
Additionally, retailers can use GenAI to respond to queries about order status, and even suggest language and images for customized goods such as T-shirts and coffee mugs.
Foster Product Development and Innovation
Product lifecycle management has always been a goal for retailers, but it has been honored more often in the breach than in the observance. Until the advent of GenAI, it has simply been too time- and labor-intensive for retailers to sift through reams of customer and end-user feedback, find common complaints about a given product, then communicate those complaints to the product development teams of their suppliers or their own private label manufacturers. Such regular feedback could lead to beneficial product changes—or even entirely new products.
Using GenAI, however, retailers can comb through call center transcripts and audio records, social media posts, and customer reviews on retail and aggregator websites (such as Yelp and Google), synthesize that data, and even distinguish irrational rants from cogent suggestions. And then they can use GenAI to summarize that data in a timely and succinct manner. GenAI models can then make suggestions based on their interpretation of broad generalizations by, for example, translating comments such as “I keep dropping it and it breaks all the time!” into “make it more ergonomic by narrowing the handle by a few centimeters.”
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