Okkular’s e-Commerce Search Query Analysis – 2020

This report is divide into three parts; this is part one of three. 

Today, e-Commerce has increased by leaps and bound. Providing a superior shopping experience is key, however, when it comes to essential fundamental functions, some of the best eCommerce websites fail. One such area is ‘on-site search function‘. Optimizing on-site navigation is vital for any eCommerce. The lack of which can result in customer drop-offs, cart abandonment and poor user experience that ultimately result in a drop in conversions.

We took at a look at the top 100 eCommerce websites in Australia and found glaring gaps in the way they address users search queries.  

In this report, we’ll look at 8 types of search queries used by consumers for e-commerce search. These search queries represent the main principles behind how a consumer thinks and construct their search queries in an e-commerce context. 

As per Baymards our usability studies, beyond the standard keyword search for a specific product, users relied heavily on search queries that included a product type, a theme, or a feature. Yet surprisingly, 9 out of 10 Australia’s largest sites in 2020, reveal surprisingly low support at e-commerce sites for these search query. 

Our analysis of the top 100 eCommerce website reveals surprisingly weak support for essential e-commerce search query types, with 67% of all sites performing below an acceptable search performance. These websites directly misalign with the user’s actual search behaviour and expectations. Making matters worse, 8% of the sites were found to have a downright “broken” search query type performance. For example, among 100 the top-grossing Australian eCommerce sites showed the following results: 

57% of sites require their users to search by the exact same product type jargon the site uses, e.g. 43% even failing to return all relevant products for a search such as “red dress”, the search result worsens if the term search has a spelling error, e.g., if “redd dress” is used on the site, or “dress red” the search result drops to 32% in some cases even 0%. 

44% of sites don’t support thematic search queries such as “boho dress” or “retro-style”. 27% of sites don’t support symbols and abbreviations for even the most basic units, resulting in users missing out on entirely relevant products if searching for inch when the site has used “or, in” in their product data. 32% of sites won’t yield useful results if users misspell just a single character in a product title. 37% of sites don’t support non-product search queries, like “returns” or “deliveries”. 

This article will throw light on the search query findings for each of the 8 query types most common for e-commerce search. It will demonstrate the observed user behaviour, where and how it causes issues for e-commerce search, query samples, and the principled needed for how to best support each query type. 

Understanding the Consumers Mindset

When users search in an e-commerce context, they are mostly looking for specific products. Which prompts the users to search in a way that is different than when they are performing generic web searches. In particular, users will often include one or more criteria in their search which the product must meet.

Users will commonly combine the 8 query types when devising their search queries, with certain components of the query “setting the range” of the search while other components are included to refine and delimit that range. To read more about the types and kinds of search queries click here.

Exact Search: 

When users know the exact product they are looking for, they will typically rely on #1 Exact Search, entering the product’s title or model number, such as “red dress”. “Exact Searches” generally are the easiest to support technically, and most of the tested sites fared reasonably well.

While this may at first seem like an easy case of keyword matching against those two product attributes, the search engine must be a little smarter than that and there are a few conditions to take into account — refinements that will take the “Exact Search” query implementation from acceptable to great. For instance, good handling of phonetic misspellings is crucial since the user may only have heard the product title spoken and not know how to spell it, e.g. “redd dress” or “red drress”

Any misspellings (common when copy-pasted from user-generated content, such as social media posts), or localized or alternate spellings (common when copy-pasted from industry databases, magazines will, therefore, be pasted into the search and must be handled gracefully to provide a good search experience.

Product Type Searches

When users aren’t looking for a specific product but rather a type of product, they will rely on  Product Type Searches, querying for a whole category of products, such as “loungewear”.

When we tested this out on the Australian eCommerce market, a quick search for “loungewear” showed positive results and displayed all the images tagged under loungewear from track pants, T-shirts to lounge pants. However, a query for “leisurewear” showed little to no results on most of the websites. Typically, when users search for product types that aren’t an exact match for a site’s category labelling, only a fraction of the results display, which presents a missed opportunity since users aren’t presented with as many relevant results.

When used on their own, product type searches are generally an attempt by the user to access a category on the site quickly — either because it’s more convenient to search for it or because they are having difficulties finding the category via the main menu.

When the site can be sure of a 1:1 match with a product type search and an existing category, it’s worth autodirecting the user to the relevant matching intermediary category page, if one exists.

Still, a very important aspect of supporting #2 Product Type Searches is to return relevant results regardless of whether the searched-for product type exists as a category on the site or not. This not only requires detailed categorization and labelling of products but also proper handling of synonyms and alternate spellings of those groupings. Check out how Okkular’s Tag-Gen can help automate product tagging and improve on-site navigation.

A search for “t-shirt” should yield the exact same results as one for “tee shirt”, regardless of how it happens to be written in each product’s title or description. Other examples include “hair dryer” where the user might search for “blow dryer”, or users may type “multifunction printer” or even “copy machine” when looking for an “all-in-one printer”.

From a user’s point of view these everyday descriptions are just as correct as the industry jargon, and most of the users never thought of trying another synonym when they received poor search results but instead simply assumed that the poor or limited results for a search such as “copy machine” meant that was then the site’s full selection for such products.

Despite the severe impact on the user’s search experience, 96% of major e-commerce sites do not return all the relevant results, if any at all, when users search by a product type or synonym, e.g., “leisurewear” instead of “loungewear”.

The Product Type query is largely a missed opportunity within the industry and should always be one of the first things considered in any search improvement project due to the severe combination of Product Type searches being frequently used by users, the likelihood of users getting stuck and ultimately abandoning if synonyms aren’t well supported, with the fact that 96% of e-commerce sites currently don’t have proper synonym support.

A few key types of synonyms to consider when auditing or trying to improve a sites Product Type search capabilities are:

  • Near-identical word meanings, i.e., “shaping briefs” vs. “spanks”
  • Regional dialect synonyms, i.e., “daks” vs. “trousers
  • Regional spelling variations, i.e., “t-shirt vs. “tee-shirt”

Since #2 Product Type Searches are performed by users who are looking to browse a whole category of products, it’s also crucial that such queries enable relevant filtering and sorting options so the user can easily narrow down the list and compare products. Ideally such filters and sorting options are available directly from the search results (via faceted search filters — which 51% of sites still don’t have.

Symptom Search 

So far when users know the specific product they are looking for, they will use Exact search, and if they don’t know the exact product or aren’t sure about which one they want, they will often rely on Product Type searches.

However, sometimes users don’t even know the type of product they are looking for — all they know is the problem they’re experiencing and that they want a solution to it. In these cases, they will rely on #3 Symptom Searches, entering the problem they are experiencing, such as “slimming clothes” or “winter clothing”, in hopes of being presented with viable solutions and products to this problem.

Symptom search is essential because it will often be the user’s last recourse. If users don’t know what solution to look for and can’t search for products by entering their problem or symptom, it’s going to be almost impossible to find the relevant products on the site. The user should be able to search for anything. 

Furthermore, Symptom Searches often also have multiple different product types as relevant solutions, making it both difficult and inconvenient for users to find the best solution for them, if the Symptom Search query type isn’t supported. For example, a symptom query for “slimming inner” at a lingeire site should provide users with an array of different product types such as tummy tuckers, corsets, spanks, minimizing bras, etc. If search teams need inspiration for the complete range of possible solutions to a problem relevant for your industry, consider going to physical store in your same product vertical and actually talk to the in-store experts as users in physical retail will have the same product exploration approach. For sites where Symptom searches are critical, it may be a good idea to suggest its usage in the placeholder text of the search The final type of Query Spectrum is #4 Non-Product Searches, where the user is searching for something that isn’t a product, such as the return policy or shipping information. 

Non- Product Searches

While the primary function of search in an e-commerce context is obviously to find relevant products, the search engine shouldn’t be limited to just searching the product catalog, as we consistently observe that users expect the search field to search the entire website (not just the product catalog) – after all that is typically what a search field does on any other non-ecommerce website. 

While most websites fared well at non-product searches, almost 45% of the website were not optimized for the same. These search queries typically include “return policy”“unsubscribe”“cancel my order”, etc.

During testing, users would often search for this type of auxiliary content when they had difficulties finding navigational links to it. This is a logical consequence of this auxiliary content being secondary, and links to it, therefore, tend to be relegated to the page footer or nested deep within help sections.


These queries indicate the search spectrum, with which the user indicates the “range” of what should be searched. Depending on their particular needs and available information, this can go from highly specific #1 Exact Searches to broader #2 Product Type Searches, to the inquiry-like #3 Symptom Searches. Finally, users may be looking for non-product pages, in which case they will perform a #4 Non-Product Search field to let users know that they can search by symptom as it isn’t all users who think of it.