What is search relevance?
A search engine should have both high precision and high recall. When you search for a product on an online store, you should expect the top five or 10 results to match what you were thinking.
Search relevance is essentially about turning a search engine into the equivalent of a helpful sales rep. It should be there to anticipate and fufill your needs. Good search amplifies a customer’s user experience; bad search does quite the opposite.
Let’s say you’re looking to buy a brown leather couch online. You go to a furniture store’s website and enter ‘brown leather couch’ in the search bar. A search engine with high search relevance will show you all the products listed as ‘brown leather couch.’ One with low search relevance might show you vastly different results — brown chairs, black leather sofas or even garden chairs — something you weren’t looking for.
Why is search relevance important?
Often, search relevance is the very first impression a company leaves on a customer. That’s because search is frequently the first thing most people use. Good search relevancy keeps customers satisfied and on your site.
Think now of bad search engines — if you consistently engage with a website that makes it difficult to find a ‘brown leather couch,’ you’d eventually stop shopping there. Companies are looking for ways to improve search relevance so that customers can actually find their products in their search engines.
Search relevance is more important than ever. On Google, for example, most people only click on results on the first page. First page results on Google garner 92% of all traffic from the average search, with traffic dropping off by 95% for the second page.
If your search engine doesn’t retain users on the first try, there is little chance it will manage to engage with them later.
How does search relevance work?
Search engines are sophisticated analytical systems that rely on a variety of functions. Consider the following:
- Semantic annotation: tagging different product titles and search queries.
- Text analysis: recognizing different variations of the same word to allow for fuzzy matching. For instance, shopped, shopping, and shopper all match up to the word shop.
- Query weights: weighting the importance of different fields based on search requirements.
- Concept tags: understanding the query in terms of specific concepts (instead of just matching terms).
- Natural language processing: understanding the grammatical structure of text in the query and search result.
- Statistical processes: statistically detecting the relationship between different words that are related. For example, cutlery and dining table should be detected as related words.
- Click tracking: determine which result is statistically most likely to be the best result for a query, given past user behavior.
Search engine evaluation
How do you know if your search engine is working properly? Or in other words, how do you know if your search engine is delivering results that lead to customer retention and not abandonment?
One of the best ways to review your search is to utilize a human relevance evaluation. This works by creating a representative sample of a few thousand or more search terms that your website is expected to get, and then noting the top results for each query. From that point you have a set of humans rate the quality of the results by a simple metric of how useful they are. The actual parameters of what counts as useful is up to you (and your human evaluators), but this is one of the fastest ways to form a baseline definition of search quality.
Search relevance elevates an engine from just a simple search tool to providing a customer the best possible online service. Reach out to learn how our Data Validation and Relevance experts can help you achieve search results that match customer expectations.