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The importance of search relevance and how to improve it

Posted January 1, 2021
Illustration depicting a search bar and magnifying glass, with various iconography in the background

Have you been getting a lot of visitors to your site, but not a lot of sales? Search relevance and improving the strength of your site’s search engine may be the answer to your problem. The user experience of search engines can feel quite simple and slick, but behind the scenes, building a search engine is an intricate and difficult process.

The moving parts behind the curtain require enormous amounts of data to train and improve the search engine’s capabilities. One of the most practical and useful applications of machine learning, search engines can be found on almost every website — from personal blogs to international conglomerates like Amazon and eBay. The strength of these search engines and the user experience of these websites rely heavily on search relevance.

What is search relevance?

Simply put, search relevance refers to the performance of a search engine and the relevance of its fetched results. It is the user’s ability to search for information on your website quickly and easily. When a visitor types in a query, how accurately do the results match what they were looking for?

The results your search engine displays can depend on a variety of factors including, but not limited to:

  • Text analysis: the process of searching for items and pages on your site that include the searched keywords. Matching words in a search query to words in an article or product listing may seem simple. However, the inner workings of that process are intricate, complicated, and extremely important.
  • Term weighting: the process of deciding which queried keywords or search fields should take priority. A numerical value is given to each term in a search query which is then reflected in the fetched results. This balancing act of what should take priority separates the good search engines from the exceptional ones.
  • Popularity: the number of times previous users have clicked on the resulting item from the query. The popularity of an item or page tells your search engine that many people in the past found this content helpful and therefore new users also might find it helpful.

Why is search relevance important?

Often the search function will be the first element used on your website. Therefore, it is the first interaction a potential client or customer has with your company. A strong search engine shows professionalism and reliability. On the other hand, a weak search engine exudes carelessness and makes visitors feel frustrated.

If a new user searches for something on your site and receives completely mismatched results, they probably won’t be coming back. As an example, for eCommerce website owners, an average of over 96% of visitors will leave your website without making a purchase. With conversion rates of visitors into leads already on the low end, how do you raise your conversion rates above the global average?

Using recommended and related search results to keep online traffic

Recommended or related search results are the items your search engine shows the user when nothing matches their query. For example, let’s say a customer searches for a product on your site, but you are sold out of that specific product. Or perhaps you run a blog about reviewing cosmetics, but haven’t reviewed the specific product that the user is looking for. What happens next is what will keep the customer on your site or send them searching elsewhere.

Instead of simply displaying a “no matches found” or “sold out” result, display whatever products or pages you have on your site that most closely resembles the user’s query.

The power of Amazon

A perfect example of this is Amazon. As one of the world’s largest eCommerce sites, Amazon receives millions of search queries daily. Imagine, for example, that someone searches for “mocha blend coffee from Brand A” on Amazon. Unfortunately, either Brand A doesn’t have any mocha blend coffee on the Amazon marketplace or they are completely sold out. Instead of giving them bad news, Amazon might fill the user’s search results with mocha coffee from Brand B and mocha coffee from Brand C.

Alternatively, the system might show other coffee blends from Brand A. Now, the user is presented with a decision. Do they go out to the store to look for that specific product, try another online marketplace or simply settle for another brand or product? Chances are they won’t want to leave the warmth of their home or shop on a less reputable marketplace.

Basically, recommended search results are the equivalent of a good sales rep who tells your users, “Actually, we don’t have that, but this is just as good.” Whether you have a personal blog or online business, implementing recommended results can keep visitors on your site instead of sending them to your competition.

Strengthening your search relevance

At the same time, recommended results can be a double-edged sword. If the recommendations are completely unrelated, the user will become frustrated and your website will lose credibility. The power of recommendations depends on the relevance of those recommendations. Likewise, the strength of your search engine depends on how closely the system can predict what users are looking for.

How can you train your search engine to display the most useful recommended results?

To improve search relevance, developers utilize large datasets, created by thousands of examples of correct input and output for a given task. Due to all the complex semantics that goes into natural language processing, there isn’t an AI program out there yet that can 100% accurately tag text data. This is especially true when your project has unique needs or requires custom data. Therefore, the only way to provide the best possible data for machine learning is by using human annotators. These annotators should be native in the languages and well-versed in the cultures the machine needs to learn. By running each piece of data through the algorithm multiple times, the search engine learns a set of rules defined within the data.

Human knowledge is the most important resource for improving search relevance and that’s where TELUS International can help. While technology is growing exponentially, the key to providing the most accurate search results requires a human touch and human evaluation. That human touch is exactly what we provide. Instead of learning how to train your search engine and spending weeks or months training it yourself, we can help. Our team and communicately can work quickly and accurately to evaluate your search queries, help you build a strong search engine and provide the best possible customer experience. Reach out to learn more.

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