The Importance of Search RelevanceMost companies are familiar with search relevance based on keywords. Whether you’ve seen it while searching for something online or have seen it from the back end for marketing and creating content for users to find, you know how important keywords are for finding and creating relevant content. Optimizing for search relevance is critical for online retailers and organizations with large amounts of content or data on their website. A report from Forrester found that over 40 percent of customers go directly to the search bar after navigating to a website. Customers want to find the information they’re looking for and want to do it quickly. Your internal search engine is one of the first impressions customers get of your website. And, if your search bar can’t return relevant results, customers are likely to bounce from your website and leave frustrated, looking for a new solution. Currently, many companies rely on keywords to direct their search relevance. And, so far, keywords work. They make it so that we can find the content we need. Most of the time. But, can we do it even better?
AI-Powered Search RelevanceThis is where AI-powered search relevance comes in. AI and machine learning techniques build more accurate, cutting-edge models that improve search relevance and make it even smarter. AI-powered search relevance uses statistical analysis to drive search results. As results get more complex and nuanced, so too must the technology powering the results. AI-powered search includes:
- Category markers
- Searchable metadata
- Business priorities
- Geolocation of the user
- Searcher’s past behavior
- Ability to differentiate between low- and high-quality content
You Need the Right AI Training DataTeaching a search engine how to understand and interpret queries, especially those that include variations of natural language and spelling errors or typos requires massive amounts of training data. The more and better training data you have, the better your algorithm and search relevance will be. Getting the right, high-quality training data can be a frustrating hurdle, especially for small- or medium-sized companies. There’s no need to be discouraged. Appen works with companies of all sizes to develop high-quality training data sets and to build a search bar that returns the right results for customers. Shotzr worked with Appen to hone their training data by identifying 17,000 images that didn’t need labeling. This allowed them to focus on the images that need a label, which will improve their search relevance for stock photos.
You Must Continuously Update Your Training DataOnce your AI-powered algorithm is built and trained, it’s done, right? Not quite. Training data must be continuously updated and checked to make sure it’s continuing to return the most relevant results to customers. AI-powered search engines use natural language processing or NLP to understand and analyze search queries. To do this, it must be trained on millions of data points, use cases, and edge cases that run from vague to precise. A good algorithm will be able to return accurate search results, even when the query isn’t clear. To harness the full power of NLP and AI-powered search relevance, training data must be continuously updated and optimized so your customers continue to get the best results.
What AI-Powered Search Relevance Can do For Your CompanyUser experience or UX is a common topic of conversation around any website design or redesign. But what about searching? Your search bar and search relevance need just as much care from the UX team. The stats prove it.
- 30 percent of website visitors want to use the search function. When they do use search, they’re two times as likely to convert.
- 79 percent of customers who search and don’t find what they’re looking for will leave the site and look for a different one.
- In a study by Econsultancy, they found that visitors using search contributed 13.8 percent of revenue.