Not all personal shoppers are the same. Some have psychic-like abilities to predict the tailored suit their client will cherish, while others show up with an oversized suit jacket for a client whose Don Johnson days are long gone. Likewise, not all retail site search engines are the same. Some (most) will fail to understand what their customer means when searching for “lovely evening dress for the mother of the bride,” while others (highly intelligent AI search engines) will deliver results for sleek evening gowns that are sure to please.
However, the personal shopper pushing the latest fashion trend on their client will be fired long before the next red-carpet event. So, why don’t retailers fire their old site search engines when there is an AI alternative that will understand which products their customers really want?
The Fault in Today’s Search
Most retailers have kept the same search experiences they’ve used since the early dot com days — even if their pages have consistently grown to be more colorful, trendy, and eye-catching. Underneath these smooth user interfaces are vast product catalogs, which consumers must sift through via keyword search engines.
For shoppers, keyword search means they must work through large catalogs of products, fiddling with filters, clicking through categories, and guessing the right keywords. Yet, even with their hard work, many consumers still come face-to-face with the ever-dreaded “no results” page if they make a small typo or search “satin button-up blouse” instead of "silk shirt.” For retail website developers working with a keyword search engine, this means they are constantly working around the clock to add synonyms and rules to override typos or guess at the eight different search terms users may leverage to track down the same product. The problem? These developers can never write enough synonyms and rules to eradicate null results.
This analysis of online shopping experiences in 2023 is all to say: Shoppers must still do all the work to find the perfect product to make their day (or outfit). Luckily, their frustrations will come to an end soon. AI search is taking over e-commerce site experiences, and it’s so intuitive and human-like that it is almost as if every shopper will have a personal stylist in their search bar.
AI Search Devours e-Commerce
Keyword search cannot process complex queries like “Brown or black shoe with no laces and a little pocket on the side.” Luckily, true end-to-end, vector-based AI search knows this person is looking for a Chelsea boot, even though the searcher does not. This complex, multi-word query is an example of a long-tail search query. Though some retailers may dismiss these queries as silly and uncommon, sophisticated retailers know long-term search queries comprise up to 55% of searches. Now, there's an AI search revolution to help these savvy retailers process them.
Recent generative AI breakthroughs catapulted consumer expectations of the experiences that technology can provide. While GenAI solutions may be better suited for kicking off work emails than sifting through massive retail catalogs, now that forward-thinking AI search vendors have eradicated cost, hallucination, and scaling hurdles, there’s no reason retailers cannot explore vector search solutions powered by large language models (LLMs), the same technology underpinning GenAI. In fact, retailers that do will provide consumers with ChatGPT-like intelligence in their search bars.
Retailers fail consumers when they do not provide technology that understands the intent behind their searches, which can translate into billions of dollars lost. Organizations that take advantage of these tools and create shopping experiences that make consumers feel like they have a personal shopper with them will come out on top, whether they know exactly what they want, have some idea or need a conversational flow to help them narrow down the exact item that matches their desire.
— Piyush Patel, Chief Strategic Business Officer, Algolia
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