eCommerce search seems like an easy process. One enters a query such as “black running shoes” and expects relevant results to pop up. However, a lot of work takes place in the backend to ensure relevance based on a shopper’s intent, keywords, prices, products’ features, inventory status, brand preferences, sizes, popularity, and time.
Keyword search alone does little when it comes to determining the relevancy of the products. It would be able to find products featuring the exact keywords in their titles, but not the intent behind the searches. For example, someone typing in “work bag” may be looking for a laptop tote, a backpack, or a leather briefcase.
With behavior-based eCommerce search, this problem goes away. Behavior data shows the actions people take after entering the queries, including clicking, filtering, comparing, placing items into the basket, ignoring them, coming back, and purchasing.
The Importance of Behavior Data in eCommerce Search
Students researching digital commerce, UX or consumer behavior should understand the link between search design and shopping behaviors. When a student requires writing assistance for such a subject matter, saying, “I need my paper help”, they’ll learn that behavior is one of those great topics for college essays as it combines psychology, data research, marketing and even design.
According to Annie Lambert, a prominent educator, the research on eCommerce is strengthened when one takes into consideration not only what the users state, but also what they actually do. This applies to the topic of search, for instance, customers could say they want more variety of options, while in actuality, they may be looking for filters, better product naming or categorizing by price.
Baymard Institute’s eCommerce search UX benchmark for 2026 revealed that 56% of eCommerce websites have mediocre or worse search UX.
What Is Considered To Be Behavioral Data?
Behavioral data can be defined as the data on consumer actions. In other words, it cannot be considered a secret since many eCommerce teams track behavioral data via analytics, site search logs, product pages, cart activity, purchases, etc.
Examples of behavioral data include:
- Keywords typed by consumers.
- Products clicked following searches.
- Filters applied after search.
- Products added to the shopping cart.
- Products were deleted from the shopping cart.
- Products purchased after searches.
- Searches that returned no results.
- Viewing of products before purchase.
- Consumers’ time spent searching.
- Duplicated searches within the same session.
- Return rates and review activities.
Behavioral data is important since it allows for identifying consumer intentions. For instance, when customers type “desk lamp” into the search bar, use a filter of “adjustable” lamps, click several times on compact items, and finally buy a USB-powered lamp, they help stores identify consumer intentions.
Improvements To Search Results Via Behavioral Data
There are multiple areas where behavioral data can help in improving eCommerce search:
It is easier to rank products in a more useful manner. If there is high traffic on a particular keyword (e.g., “summer dress”) and consumers end up clicking mostly on linen midi dresses, then it makes sense to give them priority in search results.
It can also be used to build better synonym relationships. For example, if people use the keyword “trainers” but they actually buy sneakers, then the search engine must find out about that relationship.
It can be used to identify dead ends, which are searches where there are no results returned at all. They provide useful information on missing product names, ambiguous keywords, or consumer language that the website doesn’t know yet.
Lastly, it can be helpful for personalizing search results. A visitor who regularly purchases activewear will probably get a different list of results for “shorts” than one who prefers business attire. However, personalization must not become intrusive.
Improving Product Ranking Using Click And Purchase Data
Product ranking is one of the most apparent examples where behavior can play a part. A search engine understands which products suit the request better.
For instance, let users type in the phrase “waterproof backpack.” They receive ten backpacks as search results. The first backpack in the list includes the words “waterproof backpack” in its title, while the third and fifth are chosen by users for their good images, quality reviews, and additional information about laptop compartments. Behavioral data reveal that the initial product ranking was poor.
An improved search system considers:
- Which products are clicked.
- Which products are skipped.
- Which products are put into the shopping cart.
- Which products are bought.
- Which products are returned.
Returns are necessary since a purchase does not necessarily mean success. If users often buy certain items and immediately return them after a search request, it might be due to misfitting, misunderstanding, or wrong placement among other results.
More Clever Synonyms And Customer Language
The customer doesn’t speak the way a product team does. The brand might name an item a “performance quarter-zip.” The customer will likely search for “gym pullover.” The furniture shop might label its offering a “media console.” The customer will likely type “TV stand.”
Customer behavior will help bridge the gap between terms. If a customer searches a term and clicks items with a different term many times, the site can establish synonyms.
Examples include:
- “Sofa” and “couch.”
- “Sneakers” and “trainers.”
- “TV stand” and “media console.”
- “Baby stroller” and “pushchair.”
- “Raincoat” and “waterproof jacket.”
It’s not just about solving a problem. It is about respecting the customer’s way of speaking. It’s absurd to make customers memorize naming conventions before they purchase a lamp on your site.
Improving Filters Using Actual Behavioral Data
Filters make or break eCommerce search performance. A consumer searching for jeans might be interested in size, fit, waist-rise, color, pricing, brand, stretch, and length. Another consumer searching for laptops could be interested in screen size, RAM, memory storage, processor speed, price, and intended use.
The behavioral data collected indicates which filters the consumers use after every search. This way, it becomes possible for the store to offer filters based on actual usage behavior.
If a fashion retail store finds out that people searching for wedding guest dresses are mostly interested in color and size, then these two filters will come first in the category of wedding dresses. If the consumers of hardware stores searching for paint show interest in finish and room type, then these two filters should come first when filtering paint products.
Conclusion
Behavioral data helps improve the search process by providing insight into how the customer behaves. This data enables stores to provide appropriate ranking, better synonym suggestions, improve filters, fix no results search, and personalize search results better.
The most intelligent search engines will not simply focus on matching words with the right query but will incorporate click-through rates, cart behavior, purchases, filter behavior, refinement behavior, and search failure behavior.
The takeaway for eCommerce stores is very clear: all search boxes on eCommerce websites are potential sources for behavioral data. This information needs to be listened to by the eCommerce site owners.
