Bringing It All Together. Adding Hyper-Personalization Into Conversational Shopping.

August 21, 2024

Ok, let’s wrap it up. We started the series with the Impact of Conversational Shopping on e-commerce. We continued to discuss The Benefits of Adding Multimodal Search. Today let’s take a look at how hyper-personalizing the e-commerce experience will turn the process of search and discovery upside down.

So, 1st, what is hyper-personalization and how is it different from just plain old personalization? Here’s what shopperGPT‘s cousin, ChatGPT, has to say about the subject:

“In e-commerce, personalization refers to tailoring content and product recommendations based on basic customer data like purchase history or demographics. Hyper-personalization goes a step further by using advanced data analytics, AI, and real-time data to create highly individualized experiences, considering a broader range of factors such as real-time behavior, location, and even mood.”

The hot button for e-commerce over the past several years has been to personalize the experience as much as possible, using shopper history and actions to offer better recommendations to them. With the availability of AI, that opportunity is dramatically extended. Let’s take another look again at conversational shopping and compare it to traditional search and discovery.

For the past several decades, search has looked like this – add a few words of text in a search bar (“wool sweater”) or navigate through seemingly endless menus (Clothing-Women’s-Sweater-Wool-…) and maybe throw in a color choice from a side palette, like “red”. After this, the shopper would scroll through perhaps dozens of product listings to maybe find something close to their target. This process might take a few minutes and can often lead to frustration and churn.

However, with a tightly-tuned LLM in place, and some backbone AI for semantic, color and image similarity search occurring in the background, the experience can be hyper-personalized to the client. The 1st step is to allow the shopper to begin a personalized journey. Of course, the preferences can vary depending on the product catalog, but might look something like this:

Now, with the properly tuned/engineered LLM and AI search tools in place, the experience changes dramatically. The shopper receives new personalized recommendations every time they come to the site, based both on their preferences and history, filtered through an LLM that produces not just targeted recommendations, but also a highly personalized conversation that grows and learns over time. This process produces precise results in just seconds!

So that’s a wrap on this series highlighting some of the affects AI will have on the e-commerce search and discovery process. Pretty sure, much like the adoption rate that saw ChatGPT skyrocket to 180M in less than 2 years, e-commerce sites and marketplaces will show a similar adoption pace. After all, the potential of 10x accelerated discovery seems to good to ignore.

visualAI retail solutions delivers AI-based e-commerce products that fundamentally change the shopping experience. shopperGPT is the platform’s hyper-personalized, multimodal conversational shopping agent.