Search Relevance: What It Is and How to Build a Relevant Site Search

The site search experience is an integral part of the user experience. When your site visitors can quickly find what they are looking for, whether it is a product, a blog article, or a customer support article, they are more likely to behave a good experience with the website and are more likely to take the next step. Depending on the user, that could mean prospects converting into customers, team members completing work assignments, or executives making mission-critical decisions.

So let's dive deep into the search relevance topic and try to understand what it is and how we can improve the outcomes for the users.

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What Is Search Relevance?

The relevancy of a website's or app's search results refers to how accurately those results match a user's search query. In other words, every time a user searches for information on your website, the results should ideally match exactly what they were looking for.

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Why search relevance is important?

It is important that the search results on your website are relevant to what the user is looking for. This will provide a good user experience.

The UX of enterprise search is crucial because if users can't find relevant results quickly, they may become frustrated and look for the information they need elsewhere, even if it takes more time. According to studies, as many as 68% of users would not return to a website that had a poor search experience.

Therefore, search relevance is a key component of a great enterprise search experience.

What makes enterprise search difficult is the multitude of content types it must be able to search through, like web pages, document files, PDFs, and videos, which are all scattered across different repositories. It also must take into account factors about the searcher, including their role, their context, and their intent in order to return the most relevant information back to the user.

While the order of answers and how well they align to the query is important, relevancy is more than that. What ultimately matters is how well the search experience helps the user accomplish a goal or task. Enterprise search systems that are considered to be relevant are ones that succeed in boosting productivity, improving customer service, and helping decision making.

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Relevance vs Ranking

Search relevance and ranking are two important concepts when it comes to understanding how search engines work. Search relevance is all about whether a search result matches a search query, while ranking refers to the order in which these results are displayed. Searchers expect to have the most relevant results at the top of the results page, so it's important for businesses to understand how these concepts work in order to ensure their website is appearing in the right search results.

There are many factors that affect search relevance and ranking, making it difficult to fine-tune. Matching results to a query is a balancing act that takes into account multiple factors. We will discuss some of them later in the article.

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How Is Search Relevance Delivered?

Retrieval and Ranking Algorithms

There are a variety of methods for retrieving and ranking information. Algorithms are the backbone of search engines and enable them to provide the most relevant and up-to-date information in their search results.

Search engines use three main types of algorithms: crawler-based algorithms, link-based algorithms, and content-based algorithms. Crawler-based algorithms are the oldest type of algorithm and rely on a web crawler to index websites and pages. Link-based algorithms use the number and quality of links between websites as a measure of their importance. Finally, content-based algorithms for information retrieval are applied to the text collection to identify important topics and keywords. These algorithms analyze the text content to determine which words are most important and then return a ranked list of these words or phrases.

One popular content-based algorithm is the TF-IDF (term frequency-inverse document frequency) algorithm. This algorithm assigns a weight to each word in a text collection based on how often it occurs in the collection and how often it occurs in different documents. The higher the weight, the more important the word is considered to be.

Good Data Quality

Data quality is one of the most important aspects of search relevancy. If you have a lot of outdated, incomplete or low-quality content, the search results will also be very poor quality.

There are a few things that you can do to improve the quality of your data:

1. Make sure that your data is up-to-date.
2. Remove duplicate data from your database.
3. Check for inaccuracies in your data.
4. Use quality data verification tools.

Semantic Understanding

Semantic understanding is the ability to identify the meaning of a word, phrase, or sentence in the context it’s used and the relationships between them. It’s what allows us to understand that the phrase “I have a big dog” doesn’t mean that someone has a large canine companion, but rather that they have a large amount of something.

Semantic understanding is important for natural language processing (NLP), which is a field of computer science that deals with the interactions between computers and human languages. NLP is used in a variety of applications, such as search engines, machine translation, and natural language processing. 

Factors that affect search relevance optimization

When you search on the internet, the results that you see are determined by a complex algorithm that takes into account a variety of factors. Below we explore some of those factors that you can do to improve the outcomes for your users.

Textual relevance

One common statistical algorithm for retrieving query terms is to use term frequency (TF) and the number of documents containing the term itself (IDF). This algorithm tries to optimize the ranking with help from indicators like TF and IDF.

Term weighting and boosting

The process of determining which queried keywords or search fields should take priority is a key part of effective search engine design. For example, deciding that a title field or description fields are more important than other fields. While weights are applied to fields, boosts are applied to field values.

A good search engine will assign numerical values to each term in a search query, which is then reflected in the retrieved results. This balancing act of prioritization is what separates the good search engines from the exceptional ones.

The proximity of the keywords in the content searched

When you are creating content, it is important to remember that the proximity of the keywords in the content you are searching for can affect your ranking. In particular, the closer the keywords are to the beginning of the document, the more likely they are to be found. This is because most people only look at the first few results, so if your keywords are near the beginning of your content, you are more likely to be found.

Synonyms and word variants with similar meanings

When people are searching for information, they typically use a variety of similar terms to find what they’re looking for. This is because people don’t always know the exact word or phrase that will best describe what they’re looking for. This is especially true when it comes to search engines.

Search engines take into account all of the synonyms and word variants that people use when they’re searching for something. This is why it’s important for businesses to use a variety of similar terms on their websites and in their content.

Spelling accuracy

Spelling accuracy has a significant impact on how your document is ranked by a search engine. If you have a lot of errors in your document, the search engine will rank it lower than one that is more accurate. This is because the search engine perceives the document with more errors as being less relevant to the user’s search query.

Proofreading your document before submitting it to a search engine is essential for ensuring that your document appears as high up in the search results as possible.

Geolocation

The relevance of a document to a specific user is not only determined by the keywords in the document, but also by the location of the user when performing the search. For example, a search for "coffee" near the campus of a university is likely to return different results than a search for "coffee" near the downtown area of a city.

This is because the algorithms that determine document ranking take into account the user's location when performing the search. The location is determined by the IP address of the user's device.

Popularity

The search engine uses popularity to determine how often previous users have clicked on the resulting item from the query. If an item or page is popular, it means that a lot of people in the past found the content helpful. This is a strong indication that new users might also find the content helpful.

Personalization/recommendation

Historic preferences of the user performing a search could be used as a signal to influence the search results. For example, if a user has searched for a particular product or service a number of times in the past, then it is likely that the user is interested in that product or service and the results for those types of searches should be presented higher up in the result list.

Natural language understanding

One of the most important of these factors is how well the search engine understands the natural language that a person uses. The more accurately a search engine understands user queries, the more relevant its results will be. This is why it’s important to use clear, concise language when you’re searching.

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Summary

In this article, we've covered how to better tune your search results to serve your users and give them what they're expecting. We hope this has given you some food for thought on how to improve your search functionality.

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