Today, 99% of eCommerce professionals believe their on-site search is working well and providing relevant results. But an astounding 80% of shoppers will abandon a merchant’s site due to a poor search experience. This research from Nosto suggests that not only can search negatively impact customer experience (CX), but it’s often the first touchpoint, with 69% of consumers going straight to the search box when visiting an online retailer.*
This guide will help you understand why eCommerce search is broken and what you can do to support a better search experience.
This guide is for B2C and B2B eCommerce leaders who want to learn about innovative search tools that can transform an eCommerce site into a powerful, accurate search engine that can deliver on CX goals to boost conversion, sales and average order value. We’ll explore a variety of tools, from the more basic to the most innovative and sophisticated options:
- Faceted search
- Semantic search
- Autocomplete search
- Predictive search
- Personalized search
- Conversational AI chatbots (GenAI)
- Product discovery or “merchandised” results
- Voice search
- Visual search
- Social search
Current eCommerce Search Challenges Merchants Face
Consumers today are diversifying how and where they search for products. 50% of consumers trust AI to provide them with product recommendations, according to the latest research from PwC, while 38% of consumers are discovering brands and products via social media ads, recommendations and brand updates—overtaking even traditional search engines (37%), GWI research suggests. Further, consumer experience of powerful search engines (Google) and marketplaces (Amazon) have created expectations for fast, reliable, and personalized search results. These expectations cross over to B2B buying as well, with 90% of buyers willing to switch to a competitor if they offered a better online experience.
Why standard search is broken
The default search box that comes with most eCommerce platforms requires your customers to enter the full text of what they are looking for, returning results in a fixed order—or no results at all. The problem is, you may actually have a product that’s perfect for your customer—they just can’t find it. That’s not a customer problem, that’s a search problem.
Does that sound like your spite? If so, it’s important to note that these standard search boxes have two major flaws:
- A user must type in text that matches how you’ve categorized or described your products
- Search results always display the same way for every customer
Common negative experiences with standard search
These two flaws are responsible for search experiences that do not meet customer expectations for fast, reliable or personalized search results. According to Nosto’s research, consumers say negative search experiences include:
- Irrelevant results
- Out-of-stock items in results
- A lack of filtering
- Slow search
- A lack of understanding – results do not encompass synonyms or correct for spelling errors
In order to get search results right for your store, you need to understand how your users search your site (and comparable sites) and how to improve their search experiences.
Types of eCommerce Search Queries
There are several different ways that users may try to search your site, each presenting different challenges for search optimization.
There are more ways that users can search (e.g. using voice and image) that can influence search, but this general overview demonstrates how you can layer search strategies to help optimize for a variety of different search queries. Next, we’ll examine
eCommerce Search Technologies That Optimize for CX
eCommerce search tools continue to innovate and add new features to engage users in new and unique ways. Many of today’s tools involve the use of artificial intelligence (AI) and machine learning (ML). In many cases, a single tool will include many different kinds of search solutions, but for the sake of this guide we will break out some of the latest innovations and how they translate into opportunities for merchants.
1. Faceted search (Guided navigation)
Faceted search, sometimes referred to as dynamic faceted search, faceted navigation or guided navigation, is the process of applying facets (predetermined factors) to narrow down search results based on the metadata of your products. This is better than a basic search, but only by a small margin. Facets are actions you design to narrow or re-order the list, but the results are the same for every customer who takes that action.
Facets vs filters: The two words are synonyms, but in commerce a filter is usually a sub-category (e.g. under “Shirts,” we have “tank tops,” “t-shirts,” etc), but the facet can filter by size, color, fabric or more.
Semantic search
Semantic search tools such as Coveo are a step up from basic search, adding a level of intelligence to understand common misspellings and longer phrases to deliver more relevant results. Typically, semantic search will leverage intelligence that relies on detailed metadata and examines context and previous search and purchase history to learn how to better match users with results.
Semantic search increases the relevance of search results and is one part of a successful personalized search tool.
Autocomplete search
Autocomplete search is the capability to anticipate user search queries as they type to offer autocomplete options based upon search query history. Autocomplete search both speeds up searches, removes some element of user spelling error, and can help shoppers navigate more quickly to areas of the site they may not have thought of.
Predictive search
AI search tools are designed to solve for the top user frustration with commerce search: irrelevant results. Predictive search tools such as Prefixbox build on autocomplete by adding intelligence to look at user intent and provide results as they type in the form of product suggestions instead of search results. These can take the form of specific product recommendations, expanded search options, or category suggestions to help increase conversion and sales.
Research from Prefixbox suggests that searchers use 60% of autocomplete suggestions. Predictive search is often part of a more comprehensive tool like Algolia or Prefixbox and may include capabilities to view prices and ‘add to cart’ directly from the search interface.
Conversational AI chatbots (GenAI)
Chatbots can serve multiple purposes on commerce properties, including sales and customer service. The ideal chatbot can understand natural language questions and have a conversation with users to personalize the results and present cross-sell and up-sell opportunities. There are chatbots specializing in B2C engagement, such as Ada and Zoovu, and those better suited for B2B, such as Talkdesk.
When placed in obvious places, chatbots can prompt users with familiar search boxes or with options to find products, track orders, or search for articles.
Personalized search
78% of consumers will choose a brand or pay more for a brand that includes a personalized shopping experience on-site and through site search. Further, retailers that get personalization right stand to make 40% more revenue than those that do not.
Personalized search leverages data gathered about the user to supplement search queries, increasing both search accuracy and delivering results that are more tailored to a user’s needs. The more data points that are used to inform personalization, the more accurate the search results will be.
For example, a search for “jacket” in California could suggest a denim jacket while in Idaho a winter jacket. With purchase history, results may further refine the choice to be a blazer instead or to only display in-stock blazers in size Medium, based upon purchase history. There’s also a growing demand for personalized search results to go beyond just products to include FAQs, articles and reviews to support the buyer journey.
Top tools in this “personalization engine” category include Algolia, Algonomy, Adobe Target and Klaviyo.
Tip: to increase trust in AI recommendations, be transparent about the source of the recommendations. For example, you could say “Based on your purchases of business attire, we think you’d like the following.”
Product discovery or “merchandised” results
Product discovery and merchandising tools are used to surface products that shoppers didn’t know they wanted. Product discovery uses buyer behavior and queries to suggest products alongside additional prompts, including categories, recommendations, and other recommendations in a taxonomy (e.g. “bed” may suggest frames, mattresses or sheets). Some product discovery tools are also personalized, such as Bloomreach, Constructor, Coveo, or GroupBy.
In this category, look for the following features:
- Ability to include ‘related products’ or ‘frequent combinations’ to support upsell and cross-sell opportunities or alternate products in the case of stock outs
- Display top-sellers or top-reviewed items
- Dynamic category and collection creation, based on buyer behavior
- Merchandising controls, which allow brands to, to boost or bury specific products within results to support campaigns, trending sales, etc.
- Autocomplete
To boost sales with this type of search, look for products that allow for “add to cart” and “quick preview” opportunities to change the colour, see the price, or read a summary without clicking to the product page.
Visual search
Visual search tools such as Coveo and Nosto leverage user-generated images as the basis for a search input. While users often plug images directly into search engines, shoppers can benefit from visual search to find specific or similar products directly on your site. In broader terms, it can also help users match a specific aesthetic (e.g. in furniture).
Voice search
Voice search involves using voice commands on a voice-enabled device such as a smartphone or a smart speaker such as the Apple HomePod, Google Assistant or Amazon Alexa. Tools such as Algolia, Azure AI Search and Google App Actions (for Android apps) incorporate contextual understanding and natural language tools to find the products or information they need.
Social search
Social commerce has seen dramatic growth, with 42% of Gen Z users using social media primarily for shopping.
To optimize your store for social search, including:
- Create shoppable content, creating a custom store for social media and leveraging tools such as Bazaarvoice that turn any photo into a shoppable opportunity or incorporate links within live reels (e.g. Checkout on Instagram and Instagram Shopping, Firework)
- Create shareable content, incorporating more images and videos and using relevant hashtags and keywords to appear in search results
- Leveraging influencers / creators for social validation, to boost social search priority, leveraging their accounts on sharing tools such as LTK
- Invest in social ads, optionally leveraging tools such as Smartly.io
- Leverage AI tools that capture social trends (e.g. Quid), track content performance, interact with users (AI chatbots), and link seamlessly with your on-site tools to fuel personalized experiences (omnichannel continuity)
6 Critical Steps to Boost Search Effectiveness
The quality of your product data and site information can directly impact the effectiveness of your search results. Here are some practical tips you can use today to increase the ROI on your chosen search tools:
- Enrich Product Data: Include detailed and relevant information in product titles, descriptions, and metadata. Make sure to enrich product descriptions with details that satisfy all types of search queries. Use hidden menus to hide details (e.g. model number, use instructions, etc) that would overwhelm the eye. Include tags in your database that add further details (e.g. slang or abbreviation terms) that may be used in search, but that you want to hide on the product page.
- Audit your categorization: Ensure you have categories that are simple and make sense, making them easier to navigate. Using metadata combined with AI, product discovery tools can also assemble categories on the fly to better suit search results.
- Invest in non-product information: 34% users search for non-product content, making it crucial for your organization to assess, improve, and fortify your knowledge documents.
- Examine your index frequency: Frequent re-indexing and updating of the product catalog is critical to reflect updates such as new products, discontinued items, or changes in stock level.
- Use Structured Data: Structured data is data embedded within the HTML of a web page, only visible to search tools. Structured data helps search engines understand the relationship between products and attributes and gives you an opportunity to add additional data such as conversational terms, abbreviations and more.
- Optimize for mobile and social: How users search on mobile devices is different, with an increased use of voice search and branded apps. Leverage mobile apps or headless storefronts to ensure you have customized your app interface to make search obvious and simple to navigate.
Ready to Get Started
When search results don’t match user intent, you lose out on opportunities to turn browsers into loyal shoppers. With powerful new tools in search, you have the chance to change the growth trajectory of your online store and also gain powerful insights into how users are searching to keep improving over time.
If you are unsure which tools to choose or how to integrate them into your eCommerce platform, we can help. Net Solutions has been a trusted eCommerce development company for brands for well over 20 years for building bespoke solutions and integrations, helping you optimize every aspect of your presence in the marketplace.
