When you think about the difference between a mediocre and stellar search experience, what stands out?
Subpar ecommerce product search engines miss subtleties, punish users for spelling errors, and even return the wrong or zero results — pushing shoppers to competitors like Amazon, where they know they’ll have a consistent experience.
Truly great search engines not only show relevant results despite errors, but also learn from users’ current actions to influence future shopping experiences. This improves brand loyalty while simultaneously driving key business metrics.
Do you know what else the best-in-class ecommerce search engines have in common? Find out below.
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What Qualities Do the Best Ecommerce Search Engines Share?
Search works best when it provides a personal, organic experience. Here are a few of the key building blocks all the best search engines for ecommerce share:
They Leverage Natural Language Search to Better Understand Search Intent
Natural language processing (NLP) enables ecommerce search engines to move beyond presenting results that simply match query keywords to presenting results that match on a query’s intent.
Providing a high-quality search experience means returning attractive, relevant results for first-time queries that have typos, unusual phrasing, and other errors. Search that returns poor results — or worse, zero results — due to misspelled words or synonyms can lead to higher bounce rates. (After all, users won’t blame themselves. They’ll assume your ecommerce doesn’t carry their desired products and shop elsewhere.)
In other words, natural language processing discovers the meaning behind your search, delivering a perfect result for an imperfect search.
For example, imagine a customer shopping for “women’s leather wedges” accidentally searches for “women’s lether weges” on Birkenstock’s website.
A product search engine that uses NLP will likely return similar first-query results that are close to the customer’s intent. It will identify common synonyms and misspelled words, all without any intervention on behalf of your merchandising team. (No more manually adding synonyms!)
And even if you do have the time and resources to painstakingly add synonyms and associations to your ecommerce site search, improvements are modest. Dramatic improvements — and a substantial revenue boost — from search accuracy don’t come from adding manual bandaids. They require a fresh approach using NLP and AI-based product search platforms.
They Optimize for Business Goals, Not Just Relevance
Understanding customer intent can help return a relevant set of products, but that set of products is often huge, especially when the search query is broad. Additional processing is required to return products to the customer based on their behaviors and business KPIs that matter.
The legacy approach to results ranking is with decades-old algorithms that use relevance scoring and exact keyword matches, dating back to B.A.I. (Before Artificial Intelligence). Using them for ecommerce today is akin to rubbing two sticks together to make a fire. They can work, but barely — and not without a lot of effort on your part.
In ecommerce, just because a product is relevant doesn’t mean it’s attractive and will lead to a conversion.
Merchandising staff is then tasked with manually layering AI solutions on top to override cases where the algorithm is off — i.e., re-ranking poor search results. This patchwork approach is educated guessing (at best) of user intent with little information on actual conversion outcomes.
Business strategy should determine the way your products rank, not your ecommerce search engine’s out-of-the-box algorithm.
And when strategizing hand-in-hand with a transparent AI-powered search solution, your merchandising team receives automated insights regarding any dashboard changes to more easily avoid oversights and make informed decisions that drive real business results.
This cuts down on countless hours of guesswork needed to make the search experience more valuable to customers and your business alike.
They Use Big Data to Personalize Search Results
Shoppers and merchants have always valued personalized experiences, but true ecommerce personalization is new. It goes beyond ranking results on business KPIs to also rank results for a specific user’s behavior in real time.
Machine learning (ML) is to thank for this, as it collects big data — notably first- and zero-party data — and personalizes shopping experiences at scale.
First-party data is collected anonymously and passively. It includes behavioral clickstream data, purchase history, and other actions users take on your site that the customer doesn’t explicitly share with you, but that your analytics platform captures and tracks.
For example, a shopper entering the search query “women’s coat” in the winter in New York benefits from seeing a different set of product recommendations than a shopper running that same search during the summer in Sydney.
On the other hand, zero-party data is gathered actively via information a shopper deliberately shares with a brand in exchange for a more personalized shopping experience.
For example, imagine a different shopper on a major grocer ecommerce site is looking for gluten-free snacks. At the beginning of their shopping journey, they decide to fill in a product recommendation quiz.
If that quiz collects zero-party data, that shopper would then be able to specify what they do and don’t want to see. Those answers would then automatically populate attractive results.
This not only drives conversions for the grocer, but also improves brand loyalty and the overall shopping experience.
They’re Built on Composable Commerce Technologies
Enterprise ecommerce companies with product search engines built on composable commerce architecture are able to pivot and adapt with the fast-changing times.
Composable commerce solutions are interoperable with existing ecommerce architectures. (Think of them as building blocks that function independently of one another.) And since they’re also API-first, your front- and back-end software can securely share data and access without your team’s intervention.
This means your in-house team (or contracted development firm) doesn’t have to devote countless hours of engineering time to ensure the installation of your new product discovery platform doesn’t break your entire ecommerce site.
And when you’re ready to change solutions, you simply swap them out — i.e., no technical debt.
Say goodbye to monolithic solutions that prohibit agility, scalability, and seamless omnichannel experiences and hello to composable MACH (Microservices, API-first, Cloud-native, Headless) technologies that provide ROI-boosting benefits.
They’re Made for Ecommerce Use Cases
In the same light, outdated search cores are better suited for content, not ecommerce — where success is measured in purchases.
Older search engines attempt to give relevant results by matching keywords in a search query to keywords in its result database. Back in the day, this one-size-fits-all solution worked as site search engines were widely used to pinpoint information within support docs, blogs, and other content.
But when the challenge to solve for a more limited use case like ecommerce arose, the relevance-based approach fell short of expectations.
And it still does.
Numerous huge retailers still rely on archaic, keyword-based product search engines to show the right ecommerce products to their customers at the right time, resulting in millions of lost revenue, overlooked opportunities for growth, and cumbersome merchandising work to make up for their shortcomings.
By opting for ecommerce search engines that use machine learning (ML) models and clickstream-first, vector-based algorithms, your team can focus on offering hyper-personalization for a customer-first shopping experience.
Ensure you’re choosing the right product discovery vendor by using the ecommerce RFP template.
Why Constructor is the Best Ecommerce Search Engine Out There
Constructor is an AI-powered product discovery solution specifically built for ecommerce use cases. Our Native Commerce Core™ employs advanced ML models to learn from every customer action, optimize against business KPIs, and empower merchandising teams with controls and insights to drive exceptional business outcomes.
And because our models leverage transformers, advanced algorithms, and large language models (LLMs), our search solution more accurately decodes complex patterns to understand customer intent.
All of this gives us certain advantages over the competition.
And it’s why customers like Bonobos have achieved a 9% increase in search revenue over legacy tools.
For more information and candid reviews on how we’re different from the competition, check out our G2 reviews.
Take Your Product Discovery to Heart This Year
Having an “OK” ecommerce search engine is no longer an option as we barrel into a tough economy. To move the needle while consumers are tightening their belts, you need a product discovery solution that not only revamps your search experience, but drives business results, too. A solution that encourages leveraging AI with human creativity and is built with ecommerce in mind.
How does your current solution measure up?
Product Discovery RFP Template
Ask the right questions to save hours of time with this free spreadsheet.