{"id":22725,"date":"2024-04-24T09:31:41","date_gmt":"2023-09-07T20:33:00","guid":{"rendered":"https:\/\/www.bloomreach.com\/library\/predictive-search-exploring-the-autocomplete-technology-and-beyond"},"modified":"2025-01-30T15:38:47","modified_gmt":"2025-01-30T15:38:47","slug":"predictive-search-exploring-the-autocomplete-technology-and-beyond","status":"publish","type":"library","link":"https:\/\/www.bloomreach.com\/en\/blog\/predictive-search-exploring-the-autocomplete-technology-and-beyond","title":{"rendered":"Predictive Search: Exploring the Autocomplete Technology and Beyond"},"content":{"rendered":"

Sure, we\u2019ve all experienced instances where technology can be creepy. More often, though, we\u2019ve experienced moments where technology\u2019s ability to read our minds has been very convenient. Take the search bar, for example. If you\u2019ve ever relied on the search bar\u2019s autocomplete to fill in missing letters or correct misspellings, then you know first-hand how technology can lead to accurate and relevant results. Think of these search suggestions as a means of guiding your site visitors and a way to maximize the relevance of items in search results<\/u><\/a> to increase revenue.<\/p>\n

Autocomplete, often called autosuggest<\/u>, surfaced at Google in 2004<\/u><\/a> \u2014 thanks to a junior software developer named Kevin Gibbs, who saw the technology that taps into the human collective consciousness as inevitable. But Google’s predictive search feature was just the beginning. Nowadays, autocomplete can be found across different devices (e.g., desktops, tablets, and mobile devices) and is incorporated in technologies beyond the search engine, like word processors, email programs, and source-code editors.<\/p>\n

When talking about autocomplete suggestions in any type of search \u2014 whether it be content search or\u00a0product search<\/u><\/a> \u2014 it\u2019s often referred to as \u201cpredictive search.\u201d Predictive search has become a part of a website’s user experience (UX) over the past decade. It\u2019s intended to give helpful suggestions to searchers, using natural language processing (NLP)<\/a> and machine learning (ML). This artificial intelligence (AI) gets smarter with its suggestions over time as it learns from popular queries.<\/p>\n

\"Artificial<\/p>\n

Unfortunately, just because predictive search has become more utilized in recent years, does not mean that everyone does it well. We\u2019ll explain how predictive search works and why it can help your customers.<\/p>\n

What Is Predictive Search?<\/h2>\n

Since predictive search no longer only applies to Google Search, it’s time to consider how exactly it’s different for the ecommerce industry. To start, it\u2019s higher stakes. Because ecommerce site search is focused on products, not content, surfacing the right search result can lead to more revenue and higher ROI. As people have come to expect autosuggest in many aspects of their user experience, more brands and marketplaces have begun to see the value in predictive search and integrate it into their digital experiences: helping customers avoid typos, providing contextual suggestions, or simply displaying popular search queries in the most effective ways possible.<\/p>\n

Search technologies using AI<\/u><\/a> pick up on the terms your customers use when searching your site (i.e., search history, trending searches, and user behavior), regularly refreshing it to provide the most relevant results and improve your predictive search capabilities. Not only will predictive search help you capitalize on the traffic of certain queries by showing the right products to the right customer at the right time, but it will also suggest these products in a particular order, ranked based on engagement to establish trust with your customers<\/u><\/a> and encourage them to convert.<\/p>\n

How Does Predictive Search Work?<\/h2>\n

In a nutshell, predictive search can gauge the intent of your customers based on the queries they (and other shoppers on your website) type into the search bar. A good ecommerce search solution<\/u><\/a> will account for every search, detect certain patterns, and adjust the search experience based on consumer behavior.<\/p>\n

While accounting for popular searches across all sessions, your product search solution of choice should also analyze the audience segment, the geolocation of a particular search, and even the products you carry or have in stock. Once ML understands all the different data components, it will make intelligent suggestions that improve and personalize discoverability<\/u><\/a> and decrease bounce rates.<\/p>\n

\"Site<\/p>\n

Next, certain search solutions on the market will generate \u201cstructured\u201d search queries using semantic understanding<\/u><\/a>. This combination of NLP and ML can determine a search query’s contextual meaning and intent by separating products from their attributes. With it, brands can introduce new product lines into their catalog and help those offerings get discovered in a way they wouldn\u2019t have before. When you implement predictive search, for instance, a search for \u201cdinner set\u201d could have a corresponding structured search query of “red metallic 12-piece dinner set.”<\/p>\n

Last but not least, some rules determine how suggested queries are ranked. These go beyond semantic relevance, and brands can expect them to function based on:<\/p>\n