Using AI Chatbots to Close the Gap Between In-Person and Online Shopping Experiences

Order the tombstone. Print the obituary. The death of brick-and-mortar stores is upon us. That’s what many headlines would lead us to believe, but the numbers tell a different story.

According to Forrester, offline retail sales in the U.S. will reach $4.2 trillion by 2028. Comparatively speaking, in-store conversion rates hover at 30 percent while the online average is around 3 percent. Why is there such a big gap?

Start by thinking of the stark difference between in-person and online shopping. In brick-and-mortar stores, customers can talk to salespeople who know the products, recommend the right one, and answer questions about each item. These salespeople create great customer experiences by connecting their knowledge of the products with the shopper’s needs, explaining features and benefits, and articulating why a product is the right match for the buyer.

Consumers, whether conscious of it or not, expect the same type of experience when shopping online. But let’s be honest, when you go online, the experience isn't always great. Consumers are left to fend for themselves in an overwhelming sea of choices, search results, complex filters, and reviews.

For retailers to see double- and triple-digit returns online, they need to strip away the complexity and increase personalization. Artificial intelligence can help, but is it ready for prime time?

The Promise and Reality of AI Chatbots to Drive Online Sales

In the ideal online buying environment, AI serves up what customers need when they need it. It also delivers an even better online shopping experience than the best salesperson on a retail store floor. For many retailers, this vision is not yet a reality.

One recent breakthrough in delivering high-touch, personalized online shopping experiences is the rise of generative AI-driven chatbots. However, they've yet to bridge the gap between the in-store and online shopping experience.

While they do offer promise, many generative AI-driven chatbots are nascent and require stronger underlying data to deliver great customer experiences. Whether the latest AI chatbots were released too soon, or consumer expectations are too high, bad experiences turn off consumers and squander the bigger opportunity to drive more conversations and create better online customer experiences.

Here are three ways to fix the issue:

1. Address underlying data issues. An over-reliance on manual data input, inconsistent product information, and an inability to apply zero-party data are the biggest stumbling blocks for being able to properly train large language models (LLMs) to engage buyers. For AI to deliver personalized shopping experiences, it requires a solid foundation of product data. Solve the data issues first — error free, standardized, and properly mapped to categories — to deliver personalized experiences for online shoppers.
2. Only offer an online chat experience if it’s going to be helpful. Just like the best salespeople learn from every interaction with a customer, harness that intel, combine it with clean data, and use it to train your AI chatbot.
3. Prioritize discovery. Consumers use search to discover the products that align with their needs, budget, lifestyle, etc. Teach your AI chatbot to create expert, personalized and guided experiences in the right place at the right time and online conversions will deliver results that exceed industry averages.

Generative AI chatbots hold a lot of promise for e-commerce companies to move from single-digit to double- and triple-digit returns. However, there’s still a long way to go for brands to maximize their potential. Retailers that take the time to clean their data, automate processes and properly train their LLMs will realize increased conversions and average order value, fewer returns, and more repeat customers.

Ken Yanhs is the chief marketing officer at Zoovu, a product search and discovery platform powered by AI.