Tips to Improve Site Search Performance

A myriad of factors can contribute to an online shopper receiving a poor site search result. This can be true even if the retailer’s product catalog includes what the shopper wants. The most common issue leading to poor site search results — and subsequently poor customer experiences — is limited product attributes (e.g., size, color, materials used in manufacturing, design features, etc.). Due to limited product attributes, site search might yield irrelevant results, no results, too many results or another undesirable outcome.

To help retailers address this challenge and optimize their site search functionality, Total Retail, in conjunction with Lily AI, a customer intent platform, recently released a new report, Searching for Higher On-Site Conversion Rates? Why AI and Predictive Intelligence is the Answer Retailers Are Looking For.

What's at stake? Up to 43 percent of visitors to retail websites immediately go to the search bar, and those consumers are two times to three times more likely to make a purchase than site visitors who don’t use the search bar, according to research from Lily AI. Additionally, shoppers who use site search spend 2.6 times more than shoppers who don’t. Lastly, 68 percent of shoppers say they won’t return to a site that provides a poor search experience.

With that in mind, the report includes several actionable steps that retailers can take to improve their site search performance. Here are two of them; download the full report to see the rest:

1. Understand one’s current performance level for site search in order to then be able to improve upon it. Retailers should be consistently monitoring on-site metrics such as bounce rate, conversion rate, and average order value. Also, retailers can run qualitative tests for products via their own on-site search, perhaps related to a current trend or occasion, to see if their product attribution is expansive enough to capture the trend or occasion and incorporate it into the search results. Identify areas for improvement based on this test. Remember, build a robust product taxonomy that takes into account how real people search in the real world. That’s how your company can capitalize on shoppers’ search diversity.
2. Augment supplier and manufacturer product descriptions with consumer-driven attributes that use the language of the consumer. For example, a brand may call its item a “jumpsuit,” while a consumer may search for the same product using the term “romper.” AI will recognize that even though the user searched for a romper and the product is called a jumpsuit, it still fits the desire of the searcher. Additionally, a user may search the brand name of a home good or beauty product, but would be satisfied with the same product produced by a different brand thus under a different title.

By incorporating the tips above, retailers can boost conversion rate, average order value, and repeat purchase rate (i.e., customer loyalty). Find out more valuable information on this topic by checking out the full report, Searching for Higher On-Site Conversion Rates? Why AI and Predictive Intelligence is the Answer Retailers Are Looking For.