A semantic search is one step more complex than a keyword search. Considering that shoppers, when they visit ecommerce websites - are browsing or buying. Unlike Google, Yahoo, or Bing, ecommerce site search is very specific to the environment the search is built for. It considers the meaning, context, concept, and history of words and phrases used by searchers - essentially the shopper's intent. And that’s why semantic search becomes complex - if you don’t show shoppers what they are looking for, in their language, you’re missing out on conversion opportunities.  

The semantic search involves vector matching of the keywords. In this case, a word like "strap dress" is matched differently from "dress strap". Semantic search is therefore capable of understanding natural human language, the intent behind the search, essentially.

To comprehend what the shopper is attempting to convey to all intents and purposes, semantic search makes surfing and product discovery more detailed and effective rather than just looking to match the keywords in the search query to a product or category page

The meaning of a search is different from its intent. Intent describes what the searcher expects to see. Meaning describes what the searcher is looking for. 

Keyword-based inquiry in a search engine is a method of matching text using words that are present in the query. TF-IDF (Term Frequency-Inverse Document Frequency) is a statistical measure that evaluates how relevant a word is to a document in a collection of documents. This is done by multiplying two metrics: 

• Word frequency in a document 
• Inverse document frequency across a set of documents

This is basic keyword matching. 

With TF-IDF the intent of the customer is not taken into account. Shoppers often have problems finding relevant products on eCommerce websites because search results aren't sorted by relevance. By mapping the query only to the TF-IDF factors, the shopper's intent is lost.

Here are a couple of examples that explain the working of semantic search (what is searched and what happens post-search)

The search queries “lace shoe” and “shoelace” should have a different set of results. But not all site searches have the capabilities to identify the difference.

In the first case, the search algorithm performs keyword-based inquiry and shows the same results. The shopper will leave your site and go to your competitor.

In the second case, an ecommerce site equipped with the semantic search will understand that both queries have different semantic meanings in an automated way. There is absolutely nothing that you have to do to put a semantic search into action. Hence, different sets of results will be displayed. Therefore, retain shoppers.

Semantic site search is indexing products and then defining them based on the tokens a.k.a attributes and tags attached to them. For example:

You’re looking for pajamas. Your search query is “red silk pants for home”.

Following are the tags as mapped on the catalog

Brand: Mark and Spencers

Product: Pajama

Color: Red, Maroon

Material: Silk, Linen

Front Pocket: No

Back Pocket: Yes 

Now the above categories will be facets assigned to text - tokenized through the AI.  If a shopper enters the query “red silk pant for home”, the attributes from the above facets are “color: red, maroon” and “material: silk, linen.” The shopper has entered “product: pant” instead of “product: pajama” and the human intent here is “home.”

A semantic search combines with NLP to show intent-driven results - in this case, “30 red silk pants that can be worn at home” will be displayed.

A semantic search is to a query about what a parent is to a child’s rambling. Without a parent to understand the child, the words will not make much sense. Semantic search affects your ecommerce business in more than one way. Let us see how.

• Semantic search paired with personalization is guaranteed to increase your revenue. 63% of consumers will stop buying from brands that use poor personalization tactics. While semantic search understands the intent of the shopper, personalization recommends the closest match of the product to the shopper, overall making the purchase journey enjoyable.

• Natural Language Processing (NLP) used in semantic search helps create unique shopping experiences by understanding human language, therefore, displaying search results that lead to customer loyalty, and customer retention. 

• Semantic search impacts your ecommerce business directly. Shoppers will jump off to another retailer if they do not find the product they are looking for. Your AI should be smart enough to understand and make use of user behavior using personalization.

a. Engaging visitors, resulting in sales, is the goal

Driving visitors to your website is simply one aspect of the puzzle. Making your online presence as seamless and appealing as you can is equally crucial.

Trust is gained through listening carefully to the digital journey and experience provided to the users. When they find what they're looking for easily, they're likely to buy it right away and return again with the same intent next time.

And if during this process they discover new products that suit their taste, it will increase your Average Order Value (AOV). 

b. Personalize the shopping experiences

Implementing smart search on the platform itself is one way to increase this trust, particularly for those with generally larger product libraries.

Each customer has a unique preference and semantic search capitalizes on this. It makes it easier for you to find what you’re looking for. It should make you feel that you do not need to think very hard and just by typing simple words on the search bar, you will be shown the items you want. It’s a seamless process.

c. Improves the quality of your catalog

A catalog with hundreds of synonyms for several words is cluttered and difficult to maintain. A website’s semantic search understanding is enhanced if the search algorithm understands synonyms, user intent, context, and conceptual meanings, rather than stuffing your catalog with irrelevant datasets.

d. Helps you address long-tail searches

Over four years, Unbxd has seen a steady increase in the number of words used in search queries from 2019 to 2022.

About 22.74% of searches in the United States return results with three keywords, which is an average length.  More words mean more complex queries. Complex search queries can be addressed appropriately using semantic search. And in most cases, long-tail keywords bring in high revenue.

The paradigm shift in search technology today places greater emphasis on context and intent rather than just raw keyword percentages.  Semantic search strategies give your visitors something of value, like responding to customer inquiries using structured data, creating personalized product discovery experiences, and lots more. The idea is to create an in-store-like experience. 

Site search in ecommerce is moving in the direction of putting the client in charge. Because that’s the shoppers’ first point of contact. This strength is a result of functions like personalization and suggestion-based browsing for shoppers, as well as the extraordinary ease of access to a vast amount of information for retailers. 

One of the fundamental components that gave rise to this new era of customer experience was semantic search. Google first patented semantic search back in 1999. It’s high time to make your eCommerce site search smarter.

Book a demo with us to enhance your ecommerce site search today.