Search Personalization is a search engine process that uses a user’s search history, location, and listed preferences to customize their results. The primary reason to incorporate search personalization onto retail web pages is to improve customer experience. 

Why does Search Personalization matter?

Search is an essential part of any eCommerce retailer’s strategy. Shoppers who use site search are 2-3 times more likely to convert. Providing site search tools is vital in fostering a positive shopper experience and boosting firm profits. 

According to a 2018 Internet Retailer report, customers’ main challenge with website searches was getting irrelevant search results or search results organized in the wrong order. People cited personalized results as their #1 need. Without personalized search results based on customer demographics and behaviors, websites may display irrelevant products or return no results. According to research, 80% of shoppers abandon a site after a bad search experience.  

The shopper may want to narrow down possible search results by using specific tags or categories. Ideally, it should take only one click to exclude all items other than those in a particular category (e.g., all tops other than crop tops). Shoppers’ interests should be as accessible and detailed as possible.

Search personalization also presents different options for the shopper to engage easily with. By presenting shoppers with more appealing options, personalization can also encourage them to stay on a website (or in an app) for more extended periods.

Incorporating search personalization will reduce bounce rates while simultaneously increasing conversion rates. 

How does Search Personalization work?

Search personalization is performed through machine learning or AI-powered solutions. Based on the strategy of the retailer or enterprise, the search engine algorithm observes and takes note of a shopper’s behavior over time. 

To implement personalization, the process begins with collecting shopper data. This happens in two stages. One is determining which behavior to track. Two, capturing that behavior by sending patterns to a software program. Then the company develops a personalization relevance strategy. This is simulated and tested before being deployed to production.