How AI and personalization are revolutionizing e-commerce search (finally)

Search has been an integral part of our lives for decades — but it’s due for an overhaul.

Let’s go back to the days of early search engines to understand why. Say you were looking for up-to-date information for a research report. Your initial instinct was to type a question into the search box. The results appeared, but they were mostly irrelevant. You had to learn how to translate your thoughts into a few searchable keywords, which even then would cast a wide net that required sifting through pages of results before you could piece together the right information.

As SEO evolved, the search process became less arduous, but conceptually, it hasn’t undergone any meaningful evolution in decades. Instead of search dynamically adapting to people as they engage, humans have adapted to the logic of machines. This lack of nuance has left users frustrated and disengaged.

Today, we’ve reached an inflection point. A growing number of users are turning to AI tools like ChatGPT — which receives over a billion queries each day — to find what they want in seconds.

Now search is finally evolving to meet consumers’ ever-growing wants and needs.

AI opened the floodgates to new ways of engaging with brands and product discovery. Shoppers now expect more sophistication, speed and intelligence in their search experience. Here’s how we got here:

The way people search conflicts with traditional search capabilities

Let’s consider how a person typically engages with search. Imagine someone looking for a dress to wear to a friend’s wedding. Even though 50% of questions are more than three words (according to data from Dynamic Yield), consumers typically use queries of 2-3 words to zero in on what they want. So, this person enters the query, “dress for wedding,” but the search engine only surfaces white dresses, failing to recognize that guests should not wear white.

Now, picture a situation in which the shopper just asks for what they want: “I need a dress for a friend’s al fresco wedding in Florida.” The additional context and specificity contained in those extra words could save consumers a lot of time — if the search function were sophisticated enough to understand them.

Consumers have adjusted because historical keyword search is not designed to handle complex queries, since product feeds are often poorly tagged and search hasn’t been able to draw from real-world knowledge. Such queries can even lead consumers to unrelated products, as the meaning behind the words matters just as much as the words themselves.

AI-driven guidance and recommendations are more personal

Traditional search relies on user input and filtering but is unable to leverage potentially valuable data about consumer preferences, leading to generic results. To get around the frustration, some 70% of people have opted for generative AI over traditional search for guidance and recommendations. Further, most trust these AI recommendations and accept them without additional research since they address their needs so specifically. For brands to avoid losing valuable opportunities like these to engage (as well as a lack of control around how their products show up across Gen AI tools), they’ll have to evolve their search experience from generic to tailored based on context and data.

Consumers have come to rely on visual information when shopping

Online shoppers that struggle to describe what they’re looking for prefer to use visual information to bridge the gap. In fact, 85% of respondents to a Pinterest survey said visual information was more important than text when searching for clothes and furniture online. Traditional keyword searches simply can’t deliver results based on visual analysis.

Today, AI-driven algorithms and personalization are elevating search to new heights.

Search isn’t just one-size-fits-all anymore; it can dynamically adjust to the various ways that people look for the products they need. In fact, there are three common ways that people discover products. Let’s unpack each one and delve into how these groundbreaking new search capabilities can better meet consumer needs.

1. Personalized navigation for browsing-driven consumers
These are shoppers interested in exploring what’s in a particular category rather than navigating to a specific product.

To streamline the search experience based on this high intent browsing behavior, brands can use personalization to identify navigational search queries (like “men’s shoes”) and direct these queries to a tailored category page rather than the site’s default search experience.

These category pages can also be sorted and optimized using sophisticated, deep learning algorithms to surface the most relevant products for each user according to their preferences and predict what they might be most interested in next. Different algorithms and merchandising rules can also be targeted to different pages and audiences to determine the best possible combination for maximum engagement.

2. AI-powered assistants for purpose-driven consumers

Purpose-driven consumers know what they want and are ultimately seeking guidance. For instance, they might know they need clothes for the gym but have yet to narrow in on specific products.

Personalization and AI can now help retailers understand the intent behind colloquial, question-based search queries. When paired with advanced semantic search, which can interpret the meaning behind a query (not just the words), the experience gets even better. A great example of this in action: generative AI-powered conversational experiences like Shopping Muse. Whether a shopper searches for “running jacket for winter” or “What should I wear jogging in cold weather?”, these AI chatbots use natural language processing and deep learning models to answer direct questions, recommending the most relevant products every time. And by analyzing contextual and behavioral data, the chatbot is better suited to anticipate what the consumer might need next.

In our experience, retailers have found that shoppers are more inclined to buy and have higher cart values than those who do not engage with tools like this. It can also be an easy way for consumers who are shopping for someone else, or simply lack product knowledge, to find the perfect gift.

3. Advanced semantic search and visual analysis for product-driven consumers
These are high-intent shoppers who are trying to navigate directly to a particular item, like “high-top basketball shoes.” With such a specific query, we might assume traditional search methods are enough. But advanced search capabilities enable consumers to filter through the noise of large product catalogs and reach those shoes in an instant. Drawing from user history, affinity and contextual clues, personalized search can surface high-top basketball shoes with the colors and features they prefer.

Personalized autocomplete can also drive shoppers to related products they wouldn’t have discovered navigating through category and product listing pages.

Through visual analysis tools, physical attributes of items in a brand’s product catalog can now be recognized, eliminating the need for third-party catalog enrichment and enabling retailers to surface the right products quicker. For instance, even if an item is not tagged “striped trousers,” AI can “see” the stripes in the image and know to include it in the relevant search results.

Further, consumers who spot something they like — for instance, a stylish pair of sneakers in a window while out running errands — can upload a photo and instantly be matched with lookalike products from the retailer’s catalog. Even when a certain item in the catalog is out of stock, visual search can return the next most relevant item.

Overall, these visual analysis tools improve accuracy and reduce manual product feed management, benefiting both brands and consumers alike.

The days when search, personalization and AI held separate roles in e-commerce are coming to pass, and as they merge in exciting new ways, consumers can expect faster, simpler and more impactful digital experiences. On top of that, brands can tap into a host of benefits, including: hyper-relevant search results that lead to higher engagement and average order value; the ability to scale search globally by enabling consumers to submit queries in almost any language; and the power to align search experiences with business objectives, ranking results based on profit margins, return rates, inventory levels and more.

And the benefits multiply as more people begin to turn to search as a truly helpful tool for product discovery, with each interaction enabling deeper personalization across other touchpoints. A frictionless search experience maximizes conversions, drawing on what retailers know about their customers — from previous purchases to loyalty memberships — to deliver an ultra-tailored, smooth experience.

With AI at the helm, search is primed to not only reclaim the fundamental role that it once held for consumers — but to revolutionize the way they discover and purchase products.