Learn about how an AI-powered semantic search ecosystem can help increase the revenue of your eCommerce platform.

Semantic Search is an AI-powered search engine that can increase conversions using autocomplete search to maximize the benefits of a business. Despite eCommerce platforms frequently using it, you shouldn’t take it for granted.

You may be wondering whether or not it makes sense to invest in a search bar that occupies a relatively small area of the website. Take note that a good 30% of visitors enter a website for the first time via search. 80% of those visitors leave without clicking anything. If you do the math, then almost a quarter of the total site visitors leave after using the search box. according to AdSearch, 68% of shoppers do not return to a site that provided a poor search experience.

The global eCommerce market blew up 16.3% in 2021. The internet has made it possible to buy just about anything with just a few clicks. A successful business is characterized by customer engagement. It creates interaction with consumers over several channels to strengthen the company’s relationship with them. Customer engagement, therefore, is an essential component of customer retention. Engaged customers buy more, promote more, and most importantly, remain loyal – which increases revenue for businesses.

The use of Semantic Search is crucial in sectors like travel brands, eCommerce, and online publishers. In this article, we focus on the eCommerce industry.

An eCommerce platform’s search functionality is one of the most important and influential components of user experience. But, those days are over when platforms use text-sequence matching or keyword matching as part of their search algorithm. The Apache Lucene Library, on which both Solr and Elasticsearch are based, is a fine example of a leading search engine based on keyword matching. Natural language search can now be used across all products and platforms because of recent developments in the Natural Language Processing (NLP) industry and computational power.

Keyword Matching and the Problems it Comes With

Text-sequence matching or keyword matching is a simple search – an ecosystem that takes your query, searches all available information and then presents the items that have an exact text match to the keywords you entered.

When a customer enters a search term, those queries are broken down into individual words. Each term is searched for in the product’s descriptive text using Boolean Logic. The results are then ranked based on word statistics.

But this method doesn’t work 100% of the time. Suppose you enter a query “dress for daughter” on Amazon. If Amazon search has a Keyword matching-based search backend, then the results will either be empty or completely irrelevant because of the word “daughter” since the product’s name or description rarely contain that word. This is the result we get with semantic search.

Semantic search capabilities


Other limitations of Keyword-based Searches include treating all words equally, not considering spelling mistakes from the users’ end, and unranked results.

Nowadays, Semantic Search algorithms are hidden in almost every major app and website’s backend. The Google search engine uses the most advanced AI-based search backend that heavily incorporates natural language-based semantic search capabilities.

Semantic Search Searches with Meaning

A Semantic Search is an information retrieval technique that focuses on the context of the query and the semantic meaning of words to go beyond mere keyword matching.
Semantic Search can be achieved through two approaches.
The first is through knowledge graphs. They represent knowledge in the form of subject-predicate-object triples, wherein the predicate indicates the relationship between two entities (subject and object).
The second approach, Semantic Vector Search, has attracted much attention in recent years. Here, the ML model learns from customer buying behavior to encode products and queries in a common vector space.

Examples of word embeddings that represent word relationships geometrically: gender (man/woman and king/queen), etc.)


In a good vector embedding, position (distance and direction) encodes semantics. These visualizations of real embeddings show geometrical relationships that represent semantic relationships, such as the relationship between a country and its capital and between gender-based roles.
The model continues to learn over time as product assortments and shopper behavior change. Considering its superior functionality, we will focus on Vector-based Semantic Search.

But how does it work internally?

The core problem: Given a large set of documents D (product catalog), we ask a query q and hope that the most relevant subset of D will be returned. The query q is a short natural language sentence (what user types). Document D resembles a JSON dictionary with several fields F. In an e-commerce catalog, each product item contains a title (T), description (D), and category C.

To semantically compare the text query q with all product documents D, we need a system that treats all words differently based on where it appears in sentences – what are the words next or near to them, and how often it appears. This is where Word Embedding Vectors come in.

This program creates a common vector space where goods and inquiries can be placed so that highly similar products and queries are grouped together, while dissimilar products and inquiries are placed apart. And that’s essentially what this model accomplishes. Because it encodes product names, descriptions, and inquiries into the common vector space, we call this type of model an Encoder. Its job is to keep items that are similar close together, and those that are distinct far apart.

The Query and all product documents get converted into word embeddings using different techniques and get compared to each other using cosine similarity or other vector similarity techniques


Shopping behavior teaches us what is similar and what is not. The Grocery Store comparison is a good real-life example. Grocery stores work very hard to order their products on the shelves such that when a customer looks for something, the closest that might match what they’re looking for is nearby. The purpose of the grocery store is to not only provide you with exactly what you asked for but to entice you to buy two or three other items that you didn’t realize you needed when you came in. This type of model, the Encoder, can learn from other signals as well. As a result, stores organize their products into categories to make browsing easier – arranging products on landing pages, all of which can be utilized as training data for the model. It’s not only about customer buying behavior, but it is the primary source.

Data – Fuel for AI powered semantic search

AI without data is like a vehicle without fuel. By ensuring that the AI system has enough data to learn and develop from, businesses can leverage the full potential of AI in their customer engagement.

Capturing signals is a way to capture data, which is then fed into Machine Learning models. It can dynamically record user behavior such as requests, clicks, views, purchases, and other actions. In order to create an optimized search experience for each user, these events are aggregated, analyzed, and applied to search results at the time of the query. The result is a truly optimized experience for the user. Signals can also include purchase history, previous searches, user profiles, device, language, location, and ratings.

Signal capturing for AI-powered search engines

The Benefits

The benefits of AI-powered semantic search for businesses in their daily interactions with users become more apparent as more use cases and opportunities for AI in search are discovered. Here are some of them:

  • Greater personalization for users
  • Improved search recommendations
  • Higher customer satisfaction
  • Large scale optimization
  • More customer conversions (more revenue)


Semantic Search is the new norm as gives us the ability to immediately understand customer intent and improve search results that can increase customer engagement.

AI can create a much smoother, seamless shopping experience that not only helps you increase conversion rates, but also gives you the insights you need to grow your business.

The eCommerce industry is moving in the direction of putting the customer in charge. This power is a result of functions like personalization. At Tasq.ai we help you deliver rewarding online experiences to company visitors and shoppers using powerful machine learning-driven semantic search.