The World Wide Web has grown exponentially over the past two decades, and it’s still showing no signs of letting up. Similarly, ecommerce and online shopping have seen explosive growth over the last decade as well. Such growth has meant that the problem of finding relevant products online has been becoming more and more acute. As a result, search engines have also had to keep evolving, and several relatively new technologies have emerged to provide Internet users with a better product search experience.
One such technology is what is called ‘semantic search’. Semantic search relies heavily on advances in artificial intelligence (AI), machine learning (ML) and natural language processing (NLP), the last of which is a sub-domain of AI. It leverages the power of ‘semantic matching’, which represents a major break from the traditional way of carrying out online searches (based mainly on matching specific ‘keywords’).
Moreover, several cutting-edge semantic search engines come with additional features that can help online business owners. For instance, they might allow search results to be ranked on the basis of various criteria, such as:
- their performance (as measured by metrics such as the click-through rate)
- the revenue they’ve generated
- the ratings they’ve received from visitors.
But how exactly is semantic search different from keyword-based search, and why is it better? Moreover, how can it help online store owners make their products more easily discoverable? Read on to find out.
The main drawback of keyword-based search
Before we dive into what semantic search is, let’s first get a rough idea of how keyword-based search works, and why it isn’t ideal.
Let’s suppose that you’re already on an online store that sells clothing, and you want to buy some baggy pants. Naturally, you’ll simply type ‘baggy pants’ into the store’s search bar (this is called ‘site search’ or ‘in-store search’). And voilà: you’ll get a list of the baggy pants that the store sells.
Assuming that the store’s search engine only uses keywords to deliver search results, let’s take a simplified look at what happens behind the scenes to give you those results.
First, the site search engine will consult its index (i.e. a database that maps words with the product entries that contain them). Through this index, it will get a list of all the products whose title, description, or metadata contain the words ‘baggy’ and ‘pants’. Finally, it will present these results to you in some particular order that is decided by certain algorithms.
So far, so good. But what happens if your store does sell baggy pants but has, unfortunately, decided to call them ‘loose-fitting trousers’ instead? Well, you’d be out of luck: your store’s search engine would fail to match the search keywords with the products on your store, and your ‘loose-fitting trousers’ would not appear in the search results.
But this is problematic, because it’s clear (at least to a human) that anyone who’s looking for ‘baggy pants’ should also be shown ‘loose-fitting trousers’, since they have a more or less identical meaning. Not showing listings for ‘loose-fitting trousers’ here is bad for the customer (because they miss out on potentially relevant products) and bad for the store owner (because they get fewer purchases than they could have).
This is the sort of problem that semantic search seeks to address, so that online search can be more human-like in general.
What is semantic search?
Search engines that use semantic search for ecommerce aim to first figure out the “shopping intent” behind each query. Once they “know” the shopping intent, they can provide search results that are much more relevant than those obtained by simply matching keywords.
But how do such search engines figure out the shopping intent in the first place? Well, they use various kinds of algorithms and information, including:
- An ML/NLP model that has been fed an incredible amount of data from websites, books, etc. Such models often use what is called ‘neural information retrieval’ to rank the search results that they produce.
Such models allow the associated semantic search engine to have knowledge of synonyms, of how user queries are to be handled, etc. In effect, such models make it possible for in-store search engines to better understand ‘natural language’, i.e. language as people naturally use it.
- Personal data (age, gender, locale, past searches, past purchases, preferred brands, etc.)
- Cohort-based data (how other people similar to the user have interacted with certain search results in the past)
- The context of each keyword (i.e. the surrounding words: the word ‘cool’ has very different meanings in ‘cool jeans’ and ‘cool beer’)
Thus, in effect, semantic search engines leverage huge amounts of structured data that represent logical associations among product titles, query words, their synonyms, and product metadata (such as tags or other kinds of product descriptions). It’s through such semantic matching that they are able to produce relevant results even if there are no keyword matches.
From the point of view of a user, a good semantic search engine seems to understand what the purpose of a given search query is, leading to a very satisfying and friction-free search experience.
The benefits of semantic product search
If you are an online store owner, you can provide a much better search experience to your customers by integrating a semantic search engine into your store. By making it easier and more convenient for your customers to find what they are looking for, you can make more and higher-value sales, resulting in greater revenue for your business.
Here are some of the benefits of using semantic site search on your online store:
1. Better search accuracy
Semantic search engines have several mechanisms in place to handle vague, imprecise, or incorrectly spelled queries. Moreover, they can usually also handle fully-formed sentences or questions! This reduces the cognitive load on your customers, as they can phrase their queries as naturally as they want without being worried about irrelevant results.
2. Multilingual capabilities
Some semantic search engines support multiple languages and even multiple scripts. This further simplifies the search process for customers.
3. Alternative suggestions
If a customer searches for a specific brand or product that you do not have in stock, a semantic search engine can suggest other brands or products that are as relevant as possible to the customer.
Several semantic site search engines offer a high degree of configurability, which enables you to align them with your business goals. For instance, you can set them up to redirect to a special page if a customer searches for a specified query. You can also prevent specific products from showing up in search results (for instance, if you want to reduce internal competition among your products).
5. Auto-suggestions and personalized results
Semantic site search engines can provide suggestions to customers as they type their query in. Moreover, as discussed above, they can also provide personalized search rankings based on the data they have about each customer.
6. Great user experience and higher conversion rates
The ability of semantic search engines to understand naturally phrased queries results in a much more effective and easy search experience for customers. Higher customer satisfaction with your store’s search results will typically translate into higher conversion rates.
7. Better inventory management
The data and analytics provided by several semantic site search engines enable you to keep better tabs on your inventories. You can monitor surges or drops in demand in real time, and can adjust your procurement accordingly.
Semantic product search: the future of in-store search
It is thus abundantly clear that there are many concrete advantages that online store owners can get by integrating semantic site search into their stores. One of the most easy-to-integrate semantic site search engines right now is Zevi. It provides all of the benefits discussed above, and can start delivering results on your Shopify store with just a few clicks. Learn more about Zevi here.