AI helps in improving website USABILITY

Today, an online presence is a must for even the startups and the small businesses. And, a website is essential for it. Without a website, the business is considered simple and not doing well. Almost, all the businesses have one, including all your competitors. The question is — does it really bring about more business? Do your potential buyers or regular shoppers have a good time using your website?

All of that comes down to the user experience (UX) or usability of your website. That is, if your website isn’t user-friendly, it won’t be effective.  To make it an effective one, the navigation is expected to be simple and various features can be accessible instantly. If they can’t easily navigate your website or find it not very appealing, they’ll just return back to the search engine and find an alternative website that fulfill their needs.

Website usability is a crucial factor and it plays a significant role in any business. That’s why website usability — delivering a pleasant user experience to your website visitors — should be your top priority in 2020.Now, as you probably know, artificial intelligence (AI) is making great improvements in recent years, with positive influences in various industries such as healthcare, hospitality, automotive, and even online usability (UX).

To illustrate the latter, here’s how AI can improve a website’s usability:

Semantic Search in Search Engines

Usability of the website can be improved by implementing the Semantic search in the search engines.Search bars on any website allow visitors to quickly navigate to what they’re looking for. These users have a specific intent in mind, such as finding a piece of information or shopping for a particular product.In traditional search, known as lexical search which involves character-matching wherein the results were limited to literal matches of the query words or variants of them.

That’s where AI helps — with semantic search, the search engine can figure out the intent and contextual meaning of the search query, thus improving the accuracy of search results, and consequently increasing the user satisfaction. Semantic Search is defined as the search for information based on the intention of the searcher and contextual meaning of the search terms, instead of depending on the dictionary meaning of the individual words in the search query.

Semantic search denotes search by understanding the overall meaning of the terms as they appear in the search query, to generate more relevant results. Semantic Search in search engines mean the search engine would provide relevant search results based on the intent and contextual meaning of the search terms.

Factors considered in SEMANTIC search

A smart search engine would consider several factors to provide the most relevant and useful search queries, including:

Current trend – If the world cup match finals was just over in the country and someone is searching for ‘Who won the world cup’, the semantic search system should be able to understand the query and give relevant results based on the current trend and news.

Location of search – If a person is searching for ‘what is the temperature’, the semantic search engine should be able to provide results based on the current location of the search.

Intent of the search – Semantic search engines should be able to give appropriate search results based on the intent of the search and not based on the specific words used in the search query.

Variations of words in Semantic Search –Semantic search should consider tenses, plural, singular etc and provide relevant search results for all semantic variations of the words. For example, words like devicedevicesdevice’s etc.

Synonyms and Semantic Search – A semantic search engine should be able to understand the synonyms and give more or less the same search results on any synonyms of the word users search for. For example, try searching for “biggest mountain” and “highest mountain”. You would get pretty much the same results since both of them means the same in this particular query, even though the “biggest” and “highest” could mean different things in different cases.

Concept matching – This is a sub-set of context matching in semantic search. Semantic search should understand the broad concept of the query and return relevant results. For example, a query on “Traffic problems in a particular city” could return relevant results including the topics “narrow roads”, “nonfunctioning traffic lights”, “lack of roadside assistance” etc. because in a broad conceptual point of view, all of these lead to traffic problems.


Semantic search will enable even the non-technical persons to get the more relevant results based on the intent and contextual meaning of the search terms. Bing and Google are not pure semantic search engines. However, both of the search engines incorporate many elements of semantic search in to their search algorithm to provide more relevant and useful search results.

To explore , further about this topic, please check the following link :