How AI is silently transforming e-commerce search and discovery

At the age of Google and now ChatGPTwe take the pursuit of quality for granted. We are used to having information at our fingertips, but this ecosystem of instant gratification hides complex processes that allow us to find what we are looking for.

Perhaps due to the ubiquity of research, good research and discoveries are often overlooked in e-commerce. But getting this right can have a huge impact on your business. Corresponding effectively customers and products increases conversions and builds trust and loyalty as customers realize you meet their needs. Increasingly, AI is driving a revolution in search and discovery, being implemented by everyone from Amazon to Shopify to personalize search results and enable deeper categorization of search results. products that go beyond standard keywords.

Think of search as a two-way street, an interaction between your products and your customers. AI allows us to better understand the two points on this line and plot the shortest possible distance between the two. Here’s how AI can help you upgrade your search and discovery.

Go beyond keywords

Research and discovery are traditionally driven by keywordsreturning products that most closely match the search words chosen by the user. The problems with this approach are obvious: first, it requires precise classification of products. It also requires your customers to know pretty much exactly what they want to buy! While keyword search remains at the heart of most algorithms, notably Google and Amazon, in e-commerce it has contributed to a failure rate on first searches of up to 17% And more than two-thirds of consumers see irrelevant results.

For an online fashion brand or a food delivery app, keyword searching is a functional starting point because customers often begin their search with a good idea of ​​what they’re looking for, whether it’s jeans or chinese food. However, brands need to be mindful of the diversity of customer needs and keywords should include broader categories and common phrases like “healthy eating,” “fast delivery,” or “relaxed fit.”

At Atom.com, we focus on areas that can be marked. These often contain unique naming styles that make discovery less linear than that provided by a simple keyword approach. The main selling point of our domains may be metaphor or emotional invocations, so the connection to keywords is tangential or opaque. In e-commerce, brands may have product categories for which keyword relevance is unclear; Additionally, an intelligent search algorithm must read between the lines of the consumer’s keywords to present additional products that the customer may want without yet realizing it.

AI and the future of search and discovery

In September 2024, after months of testing, we fully integrated AI into our research. This allowed him to continually criticize and improve his own results. By training AI models to think like buyers, we were able to identify irrelevant results across thousands of keywords, which helped us eliminate results that didn’t match buyers’ expectations. Since this implementation, we have already seen clear improvements: 17.4% improved engagement and a 14.6% increase in conversions. Here’s how AI enabled an optimized search and discovery process.

In-depth classification

AI enables deeper and more complex keyword classification than a manual process could ever achieve. Our buyers typically start by researching broad industry themes, such as fashion or beauty, as they work to build brands in these categories. With tens of thousands of domain names in these categories, it is impossible to reliably recommend the most relevant names, even with a huge root keyword base.

We implemented AI to not only create a deeper set of relevant and interrelated keywords, including themes, emotions, and name style, but also to generate specific use cases for our domains. This means that if a shopper searches for use cases like “sustainable clothing brand” or “natural skincare brand,” our search will now prioritize discovering names like “PurityCompass” that are highly relevant in relation to brand intentions without being rooted in a direct context. keywords. Instead of returning names with the user’s search terms forced into them, our AI-powered discovery process offers an intuitive solution based on end-use.

Deeper classification is essential for any ecommerce brand, even when many of your products can be found by a more direct keyword search, and it allows your search algorithm to return highly relevant answers even when the words buyer’s keys lack specificity. Additionally, by offering customers diverse yet relevant products, you will learn more about their behavior, enabling individual customization and precise segmentation.

Connection with the purchasing journey

Deep classification – and the precise categorization it requires – is only one side of better discovery. The other side is your customers, the ones doing the research. You need to associate deep classification with data on types of buyers and behavior. In other words, how buyers browse your marketplace. This will allow you to create meaningful associations based on real end-user search terms.

In most industries, it’s likely that buyers will return to use your search function multiple times before making a purchase, as well as after an initial conversion. Data collected through these touchpoints can be integrated into detailed user profiles. For example, if a buyer searches for a certain keyword and selects 5 different names, our AI starts creating dynamic associations based on search intent and these domain attributes. With a data set of 3 million unique monthly visitors, we are able to aggregate insights to improve the discovery experience across the board.

Deep classification is an integral part of this, as it allows our search algorithm to cross-reference products that spark buyer interest and present personalized suggestions based on how similar users have interacted with those names in the pass.

Of course, every client is different. Advanced machine learning allows our dynamic user profiles to adjust in real time, based on search behavior. The result is ever-improving search results that help shoppers find the right products at the right time.

Research is about data and people

Effective search and discovery requires a two-pronged approach. First, deeper and more sophisticated product classification. Second, understand your customers and their search intentions.

Once you’ve perfected both, research is no longer a coin flip for your customers but an accurate and efficient tool for getting the right products to the right buyers. Search should be able to manage customers at all stages of the buying journey and deliver relevant and desirable results to those with varying degrees of knowledge and information about your inventory. It could be the last step in finding and purchasing a product, or a mid-funnel test of what you have to offer. So it truly is an essential part of your business as an e-commerce provider!