Walk into any library or bookstore and you’ll find bookshelves organized by the Dewey Decimal System, a card catalog, or some other taxonomy that helps you find what you’re looking for. Searching through an online product catalog or knowledge base can be less straightforward, often generating tangential results that bring confusion instead of clarity. That’s where search relevance comes in. By measuring the number of relevant search results provided to a user, based upon a  particular search string, your company can establish a baseline for how to improve the search experience and provide search results that most accurately reflect the user’s search intent. 

Unlike SEO, which is about ensuring your content ranks at the top of a user’s search results, search relevance takes a slightly different tack, focusing instead on how to serve up exactly the results a user is looking for in their first query. Why is this important? Because people are faced with a glut of information, and simplifying the search experience can bolster loyalty, increase conversion rates, and make users more productive by surfacing the information they need more quickly. Perhaps more to the point, nearly  40 percent of customers surveyed by Forrester said that the search bar is the first place they go whenever they visit a company’s website. That’s why it’s critical that you provide visitors to your website with a search experience that saves them time and helps them find what they’re looking for. 

The evolution of search relevance

Search engines used to focus on the frequency of key terms contained within a particular website or document. That shifted as they became more adept at crawling through page elements such as headlines, tags and other metadata. With the emergence of recommendation engines, data scientists have developed even better ways to measure the relevance and ranking of search results using metrics such as MRR (mean reciprocal rank), MAP (mean average precision), and NDCG (normalized discounted cumulative gain).

Improving your search relevance begins with cleaning and organizing your data, rooting out anything that might generate false positives, and creating additional data fields through which search and recommendation engines can scour to generate more precise results. It also includes identifying missing results, determining the cause and fixing it. 

Capitalizing on AI for greater impact

Organizing and cleansing your data is a manual, time-consuming process, and ensuring that customers and end users can easily find what they’re looking for requires that you continually monitor the accuracy and effectiveness of your search algorithms.  In addition, you must ascertain the end user’s intent, which is rarely clear given the opaque nature of search queries and the dynamic nature of cultural context. 

With the help of machine learning and natural language processing, you can empower your search engine to more intuitively ascertain user intent, and learn about their preferences over time. The result is a faster search experience that leads to more personalized results. 

Reaching this goal requires large volumes of training data, with a particular focus on training your algorithm how to recognize relevant entities, and how to handle typos, grammatical errors, and other data anomalies. We also recommend adopting a human-in-the-loop-reinforcement training approach to ensure accurate data and a better search experience for the end user. 

How leading organizations improve search relevance

When it comes to improving the performance of their onsite search, leading companies are constantly training their machine learning algorithms through a variety of human-in-the-loop solutions including:

  • Relevance ranking: determine the relevance of query results based on user intent
  • Whole page measurement: determine the performance of web pages based on user feedback
  • Side-by-side evaluation: establish the most relevant result from a pair of search query results
  • Entity tagging: label named entities such as businesses and points of interest to improve search results
  • Categorization: review and categorize content into specific categories  of products, news and more depending on the focus of your site

At LXT we specialize in developing a custom search relevance program to help you meet your goals.To learn more about improving your onsite search experience , contact us at info@lxt.ai.