Boosting Ecommerce ROI With AI Site Search

By now, most people understand the importance of a good, accurate, and relevant search engine. Search is our entry point to the digital world. We search for information pretty much every time we connect to the internet. Search engines are the tour guides of cyberspace and this is especially true when it comes to buying products online.

According to global payments provider Klarna, 44% of shoppers start a given online shopping journey at the search bar. But ecommerce site search also has implications for offline purchasing. This same study found that over 80% of people sometimes research products online before buying in-store.

What this means for retailers is that effective ecommerce site search isn’t just a nice feature to have on your website – it’s essential. Ecommerce site search can be a particularly powerful tool when you add AI to supercharge its functionality. In this post, we’ll explain exactly what we mean by “AI-powered site search,” illustrate how it works, and give you some tangible tips focused on using AI site search to boost ecommerce ROI.

What is AI Site Search?

AI site search combines advanced artificial intelligence, machine learning, and the functionality of a traditional search engine to deliver personalized, relevant search results to online shoppers. AI improves the standard “keyword matching” search experience by using data and ML to interpret user intent and context. This makes the search experience, overall, much more intuitive. 

How Does AI Powered Site Search Work?

AI site search incorporates more than just the search bar, though it starts with making this important utility more useful with things like autocomplete suggestions and personalized search results. It works by combining some important ingredients including:  

• Data from customer behavior, preferences, and historical interactions
• Natural language processing (NLP) to interpret search queries
• Machine learning algorithms to continually optimize performance
• Semantic enrichment for improved understanding of product attributes

The above elements all contribute to AI-powered site search success. they essentially turn your ecommerce search tool into an intelligent shopping assistant. AI ecommerce site search creates a more engaging, user-friendly shopping experience that adapts to individual needs and preferences in real-time.

AI-powered search also supports merchandising strategies by allowing retailers to highlight specific products or content based on business goals. This means you can align search results with your business objectives, such as promoting high-margin items or clearing out seasonal inventory, while still providing relevant results to shoppers.

AI Site Search Tips for Boosting Ecommerce ROI

There are some proven ways to maximize AI-powered site search that improves the shopping experience for your customers, while also supporting your merchandising and business strategies. The following tips address both important objectives.

Tip #1: Create Personalized Experiences for Shoppers

You can – and should – use AI to analyze shopper data and activity, then personalize search results based on this information. Personalized site search uses AI to analyze various data points like a shopper’s previous purchases, browsing patterns, and current interactions to anticipate what products will resonate with the shopper.

 It then surfaces the products they’re most likely to buy (e.g., a gardener searching for “gloves” may see gloves suitable for the garden while a cyclist searching for “gloves” may see a range of cycling gloves. 

Tip #2: Make Search More Intuitive With NLP

Natural Language Processing (NLP) is a subset of AI that allows your search tool to interpret complex phrases, handle typos, and recognize synonyms. For example, a search for “comfy office chair” could return results for ergonomic desk chairs or a search for “watercolor supplies” surface paint brushes, paint pens, and watercolor paper.

Adding NLP capabilities to your site search make search more intuitive because it allows the search tool to go beyond exact keyword and phrase query matching.  It can understand the intent behind shopper queries and help them find relevant products, find them more quickly, and potentially introduce them to products they may not know about, all of which increases sales.

Tip #3: Optimize for Site Search for Mobile Devices 

Optimizing for mobile is an important one since, as of June 2024, 77% of global retail traffic came from smartphones. Mobile devices now generate more online orders than desktops and laptops. 

You can design an AI-powered search that caters to mobile shoppers by making the search bar easy to find and use on small screens. Features like voice search, image search, and auto-complete suggestions help mobile users find products quickly without having to type much. Adding facets can also help mobile users narrow down their search by category – style, color, brand, etc. 

Tip #4: Add AI-Powered Autocomplete to Your Search Bar

Autocomplete uses predictive AI to suggest what shoppers are likely looking for as they type. It displays the most relevant product suggestions, categories, and popular searches, again, by analyzing data including user behavior, historical purchase history, and things like popularity (e.g., looking at how similar audience segments search). Predictive autocomplete reduces search time and connects shoppers with relevant items more quickly and is particularly helpful on mobile devices. 

Tip #5: Use AI to Prioritize Product Ranking

AI-powered search results can be tuned to display more relevant items based on factors like customer behavior, current trends, and business goals. Results can be tweaked manually to match merchandising strategies, with the ML model dynamically prioritizing products for each shopper based on things like query context, past purchases, and seasonality. 

AI-fueled product ranking adds more intelligent automation to your product search approach, customizing results based on real-time search behaviors in a way that scales even for very large retailers.

Tip #6: Use AI to Analyze and Act on Search Data

Search analytics contains a wealth of insights that can help you understand what shoppers want – trends, needs, and in-the-moment whims are all tied to search. Search data can also help you spot gaps in your product offerings, for example, by analyzing no-result search queries. AI helps make this data more actionable by automatically identifying patterns and trends that might be missed by human analysis. It can then suggest or even implement optimizations to improve search relevance, product recommendations, and overall user experience in real-time.

This is a goldmine of information that can be used to inform inventory management strategies and craft more relevant messaging. You can also use the information to improve your website content (e.g., revise product descriptions, tweak titles, create more robust help pages, etc.)

Tip #7: Add Facets to Find Products Quickly 

Search facets act as filters that narrow search results down based on different criteria that a shopper selects. For example, facets for a clothing retailer might include various product attributes like size, color, price range, brand, and gender. 

An electronics retailer might include facets like OS, screen size, battery type, and memory. Facets can be combined to further refine results, helping customers narrow down their options quickly and find exactly what they’re looking for with less frustration.