How AI is changing online shopping

AI and machine learning are actively changing online shopping, such as with larger cart sizes, increased conversion rates and more durable seller-buyer relationships.
Elizabeth Christenson
Editor, Retail Leader
Elizabeth Christenson

With the advent of artificial intelligence (AI), leveraging the tech in retail to win shoppers and convert browsing to sales would be ideal. Retail Leader spoke with Chinedu Eleanya, Mulberry’s founder and CEO, and Adam Ferrari, Salsify’s executive vice president of engineering, about how AI and machine learning are actively changing online shopping, such as with larger cart sizes and increased conversion rates while acting as a catalyst for more durable seller-buyer relationships.

Retail Leader (RL): How is AI reshaping the way retail brands connect with their customers?

Chinedu Eleanya
Chinedu Eleanya, Mulberry’s founder and CEO

Chinedu Eleanya: While AI conjures mixed feelings, consumers are enthusiastic about the changes it's creating for online shopping, especially in customer service. Retail brands are transforming how they connect and engage with their customers online. From utilizing AI to create more personalized experiences and enhancing customer service with tools like chatbots and virtual assistants to optimizing inventory management through predictive sales forecasting, brands can deliver more tailored, timely, and relevant experiences 24/7. Using generative AI tools to optimize product protection offerings is another compelling way to earn customer loyalty and repeat business. AI is reshaping retail brands' customer satisfaction, loyalty and revenue growth.

Adam Ferrari
Adam Ferrari, Salsify’s executive vice president of engineering

Adam Ferrari: One of the challenges of digital shopping has been the speed and massive assortments that shoppers all over the world demand. AI is beginning to allow retailers and brands to automate and scale the repetitive tasks required to get a product to market. Some examples include the creation of product descriptions and lifestyle images that are personalized to a particular shopper profile or natural language chatbots that can answer questions about an entire product catalog at any time of day. AI is also having a big impact on data analyst workflows, allowing insights on data to be reached more quickly, which in turn will let retailers make smarter and faster merchandising decisions.

RL: ChatGPT has the potential to help retail brands replicate the same personalized advice a customer would expect to get in a traditional brick-and-mortar store. How is this possible?

Eleanya: Brands adopting a customer-first mindset can employ generative AI tools such as ChatGPT to help deliver personalized experiences that mirror, or even surpass, experiences in a brick-and-mortar store. ChatGPT can be customized and integrated into an online shopping flow in a way specific to a brand’s guidelines, products, services and promotions. Over time, ChatGPT also collects data on customer interactions, which can provide data-driven insights into customer preferences and behaviors, helping retailers deliver personalized product recommendations, protection, service and advice.

Ferrari: As a large language model (LLM) AI, ChatGPT is able to first process what a customer is asking for and then generate a humanlike response based on its training and the massive amounts of data it's been exposed to. With intelligent prompting, ChatGPT can generate contextually relevant and personalized shopping advice, answers or suggested products. And with the advent of plugins, these pre-trained models will gain access to real-time data from any source, allowing recommendations to reflect recent product releases, review trends, etc.  

RL: How are AI and machine learning actively changing the game in online shopping, such as with larger cart sizes and increased conversion rates?

Eleanya: Retailers integrating AI and machine learning into their online shopping experiences can deliver customers more personalized experiences, provide improved search and product discovery, collect data to enable improved product recommendations and provide customer service pre-and post-purchase. Retailers also use data-driven insights to create tailored offers and marketing outreach to higher-converting customer segments. By improving the online shopping journey, cultivating peace of mind, and better meeting customers' unique needs, retailers see larger average order values and increased conversion rates. 

Ferrari: One big benefit of AI and machine learning technology is their ability to analyze large amounts of data and generate advice and recommendations quickly in natural language that makes it easy for most people to understand and apply. Retailers are able to leverage AI’s ability to understand patterns to select and promote the right items to the right shopper and grow total order size. Imagine a world where every shopper has their own private merchandiser who creates the store just for them, and their own personal shopping concierge to help them through the buying process. LLMs are bringing this type of vision into the realm of the possible.

RL: How is AI a catalyst for more durable seller-buyer relationships?

Eleanya: AI can catalyze more durable seller-buyer relationships when the seller utilizes technology to further a customer or buyer-first mindset. By focusing the use of AI on enhancing customer service, enabling personalization in the shopping experience, anticipating buyer needs pre-and post-purchase, and investing in data-driven insights, sellers can experience increased trust, loyalty, and enhanced relationships with their customers. AI-powered automation can also support continuous engagement and feedback loops between buyers and sellers, helping sellers understand customers' needs and helping customers feel more secure and valued long term.

Ferrari: The retailer-consumer relationship is fundamentally based on the retailer providing the consumer with a great experience. But a big part of that great experience comes from the product content sourced through the connections between a brand manufacturer and their retailers and distributor. This is sometimes a behind-the-scenes relationship but an incredible amount of coordination, negotiation and data exchange needs to happen before a branded item can get listed on a retailers site and the shopper sees it. Just as AI predictive engines and automation capabilities are helping end consumers see new product personalization faster, so too can AI be applied to aligning the exact specifications that each retailer or distributor has with the product information a brand has.  

RL: What other possibilities does AI hold for retailers moving forward?

Eleanya: As AI technologies continue to advance, so do the possibilities for retailers to reshape customer and industry experiences. The key to driving the adoption and usage of generative AI-powered shopping tools is to ensure they deliver true customer value and fit seamlessly into the shopping experience. Tools like automated customer service, personalized recommendation engines, and enhanced product protection integrations that support customer needs allow retailers to differentiate themselves, boost conversation rates, increase average order value and create more loyal, lasting customer relationships. Retailers can also use AI to advance fraud detection and security for online shopping, protecting customers and the business. Through advanced data analysis, AI can also assist in optimizing supply chains, including waste reduction and more sustainable practices aligned to a retailer's goals.

Ferrari: I believe we are just at the beginning of discovering what AI can do for retail. Now is the time to test and learn. Start with areas where you have a lot of data that could be used to tailor your strategy: pricing and promotional strategy, shopper buying personas, inventory and shelf life, online ad performance, in-store foot traffic, search rank and keyword strategy, and customer service calls.

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