More and more retailers are rethinking how their platforms work as they strive to increase revenue and brand awareness. In this regard, the search function is the main tool to quickly and efficiently connect the consumer to the desired product.

Online shoppers are not computer technicians, but they know what they want. They prefer personalized and relevant results, returned the very first time they enter a query. An efficient search engine is known to help a business increase monetization and make consumers twice as likely to convert. Unfortunately, as many have discovered, developing and maintaining a strong site search feature is not as simple as it may seem, as the most well-known still have difficulty interpreting ambiguous terms, correcting misspellings, and/or correctly interpreting user mistakes.

A 2021 study shows that within six months, 94% of consumers worldwide received irrelevant results when searching a specific retailer's website, and 85% said they had developed a bad impression of the brand after the experience, which has encouraged more and more companies incorporating Artificial Intelligence into their practices. Machine learning can leverage data such as clicks, “add to cart”, signups, conversions and purchases to further improve results ranking and relevance. For example, if the search term “iPhone” leads to 20 different results, but the only ones being viewed are the sixth and eighth, then the AI ​​will know how to place these two products first in the search return.

Even as consumer trends change over time, AI will continually adjust itself based on past sales as well as user profile. This is similar to how Google improves search results – more relevant results are pushed up.

Another interesting component of AI-powered research is "vector" based results.

Vectors have been around for some time, but they are slower than the use of "keywords" and therefore never took off. However, as new technologies emerge, this scenario tends to change, mainly because the vectors eliminate the need for companies to create/manage the so-called “synonyms” and, thus, greatly help the operation of “long-tail” research, etc.

For a better understanding, let's take a simple term like "jacket" - which is sometimes called a coat, parka or pullover. Traditionally, online retailers have had to create synonyms, add tags or other metadata so that visitors can find what they want, no matter what keywords they are using. With vector-based technology, this becomes a thing of the past.

In conclusion, without AI, companies are required to put in an extraordinary amount of effort to ensure their search engines are of a high standard (most operate based on handwritten rules and algorithms). Rules need to be constantly updated for a search engine to function at its full potential - not to mention that this approach often leads to inaccurate results. Undoubtedly, AI search offers retailers a stronger platform to compete with global markets by optimizing the entire process that takes the customer from a simple query to conversion.