Doing a site search in your eCommerce store is more than just typing the words you think in mind and then hitting "Enter." eCommerce site search is a function on your online store and a revenue-generating machine. As the store owner, you'll need to dig deep into how to implement site search in eCommerce, especially with the power of AI technology.
In this article, we outline several eCommerce site search best practices and techniques recommended by site search optimization specialists that can help online merchants get the most out of their on-site search experience.
What makes well-designed autocomplete & search suggestions?
In eCommerce site search, one of the most common terms is autocomplete or search suggestions. This term is also known as recommended searches, suggested search terms, or typeahead. Whatever the name is, it refers to a search feature that predicts the input search term and the following words or phrases as a site user types.
The suggested phrases appear in a dropdown menu below the search box. The more site user types, the more information is collected and analyzed. Site users can select a suggested phrase if they think it makes sense to the information they seek.
Similarly, there are instant search results in eCommerce site searches. Instead of predicting phrases, the search bar displays a list of best-matched results or product items for searchers. Note that the search terminology is interchangeable, and these site-search features all aim to provide relevant search results.
An exemplary implementation of autocomplete and search suggestions must ensure both are relevant and responsive.
Now, let's head to the more important questions: what makes a good autocomplete and search suggestion feature? How can eCommerce merchants implement site search well into their default site search?
🪄 For this feature to stay relevant and responsive to users’ search queries, your autocomplete, and suggestions need to be able to automatically learn which searches are popular or closely match based on the user’s recent search and then prioritize them, burying the ones that don't match.
4 must-haves in a well-designed autocomplete and search suggestion feature
- Speed: In an era of a hyper-digital world, 2 seconds can feel like an eternity if your search results don't return immediately.
The more extensive your database, the more "painfully slow" complaints you'll see from your shoppers if you don't optimize your search feature.
How much instantaneous your autocomplete and suggestions should be? 500ms or less is acceptable. If it takes longer than that, you'll need to upgrade your search solution.
- Insight: Your search suggestions should provide users insights into products they might not think you have available in store.
You can utilize AI synonyms and related keywords to broaden the user's search results.
- Meaning: No searchers should be left empty-handed with a No Results Found message.
A well-designed search feature must ensure that users always get something back from their search queries.
- Efficiency: Your autocomplete and search suggestions should be sufficient.
It must predict and generate suggestions based on search log queries in frequency order, prioritize popular suggestions, and prevent having different versions of a word, including misspellings, etc.
Leverage your store’s data points to improve autocomplete suggestions
To stay relevant to the user’s search intent, autocomplete and suggestions should be deeply tailored using your eCommerce store’s authenticated customer data.
There are a few more effective eCommerce site search best practices that you can follow to boost search relevance:
- Tailor search feature based on the visitor's location on the site:
When a visitor is browsing the men's section of your website and types in the search term "shoes," your autocomplete and suggestions should display men's shoes related options, such as dress shoes, sneakers, or boots.
On the other hand, when a visitor is browsing the women's section of your website and types in the same search term "shoes," it should suggest items in women's shoes, such as high heels, flats, or sandals.
- Use the user’s on-site search history to predict and suggest results:
Let's take another example. When a visitor types in a search term like "mystery novels" while browsing and purchasing books by a particular mystery author, your store should include other books by the same author or books similar in style or genre in the search suggestions.
Nothing beats an intelligent AI-powered search machine in deeply analyzing and integrating site users' search logs into autocomplete and search suggestions.
Do more than generate simple results! Nail your store's search experience with Boost AI's intent-understanding, per-customer-tailored predictive search!
What you’ll get:
- Accuracy & performance boosting autocomplete & search suggestions
- Machine learning models integrated into the search bar
- Scalable search efficiency thanks to AI power for big inventory data
How to use scoped search to boost eCommerce site search experience
Experts recommend applying grouped or scoped search to enhance keyword matching for eCommerce site search engines.
Scoped search (or grouped search) refers to the practice that limits search boundaries to a specific subset of products or categories such as categories, brands, collections, etc.
However, note that only some eCommerce stores can use the scoped search with all its efficiency. So far, eCommerce stores like fashion stores are utilizing scoped search the best.
What is scoped search, and how to use it effectively on your eCommerce site?
Although the more specific scoped queries can be helpful to site users, they need to be noticed. Adding styling or grid layout to your scoped search results is one of our expert team’s eCommerce site search best practices.
Visual cues such as using italics (not bold - your suggested search terms are already in bold), a different font color, or text indentation help your site users quickly identify scoped search results.
When entering a keyword in the search bar on Louis Vuitton's website, it'll return results by categories, product items, and other content like Magazine. See more option is also available.
How to improve eCommerce site search to understand long-tail search queries?
We all know site searches must be relevant to provide good product discovery for our online shoppers.
However, how can you achieve that level of “relevance”? Is being semantically relevant enough? Is simply ingesting keywords entered by your site users and returning matching results enough?
Unfortunately, the standard content search is inadequate for the relevance of your eCommerce store needs.
Whether to design a search autocomplete system or an instant search box in general, as a store owner, you’ll need to think product discovery, not just standard site search, primarily when users use a long-tail keyword to search for their desired products.
In this case, context helps you define the user’s intent and guess their behavioral desire when performing a long-tail search query (of course, short tail too). And AI can help you with that.
Semantically relevant results are sometimes DIFFERENT from what your shoppers want. You need to show matched AND attractive results to the shopper's preferences.
How can you make your eCommerce site search process thousands, even millions of search queries a day, yet keep the results irresistible to potential buyers? An AI-powered search tool might do the wonder.
With real-time customer search data, an AI search model can learn to become conversational, "think," and speak as your customers do. It helps combine customer intent nuances with collective consumer interests and process results accordingly.
Use predictive analytics for search suggestions & recommendations
Analyzing customer search behavior is a critical strategy in how to implement site search in eCommerce to improve the search experience on your website.
By collecting and analyzing search data from your customers, you can gain insights into their search patterns and preferences.
Predictive analysis can help you periodically forecast what they might be looking for and provide relevant recommendations matching their interests and needs.
For example, a gardening store can notice that during the fall, there's a spike in searches for bulbs and other winter-hardy plants. To take advantage of this seasonal trend, you can tie your search results and "Trending Now" product recommendations by promoting your fall gardening selection on the search bar and homepage.
Let your site search flow with context understanding - Boost AI can help
Don't settle with just a sufficient search! With the right tool and tips, you can boost the product discovery experience for all potential users visiting your eCommerce store.
We hope that with the eCommerce site search best practices mentioned above, you can better understand what weaves your site search effectiveness AND attractiveness.
Everyone has a search box that can return "matching results."
Boost AI search helps you move beyond "semantically sufficient" results to understand customers' search intent based on their behaviors and search context by providing search results that can answer both the "why" and "who" (not just "what") we make your product discovery a unique advantage for your eCommerce store.
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