AI and SEO: How brands can adapt to search algorithm changes
AI is impacting search engine optimization, a core aspect of digital marketing. Here's what brands need to do to make sure their websites rank in this new realm.
Imagine walking into a library where every book is meticulously organized to anticipate your every query, instantly guiding you to the exact novel you want. There’s a team of librarians who know not just the content of every book lining the shelves, but also understand your preferences and reading habits, based on past interactions, to be able to offer personalized book recommendations.
This is the promise of AI-powered site search functionality. While traditional keyword-based search often leaves users sifting through irrelevant results and feeling frustrated, AI can turn site search into a contextual, intuitive journey.
The technology actively learns from user behavior and delivers precisely what your visitors are looking for – sometimes even before they realize it themselves.
Before we get too far with site search optimization, however, let’s review the basics.
What is site search: Definition, components + benefits
Site search refers to the search functionality within a website that allows users to find specific content, products, or information by entering keywords or phrases.This internal search tool is an essential feature for most e-commerce sites, but particularly for organizations with extensive content or product catalogs as it helps visitors quickly locate what they’re looking for without having to manually click through multiple pages or menus.
Key components of site search:
- Search bar: The visible input field where users type their queries, usually located prominently on a website’s homepage or in a fixed position across all pages.
- Search algorithm: The behind-the-scenes technology that processes user queries and retrieves relevant results.
- Search index: A database that stores information about a website’s content, enabling quick retrieval of results.
- Search results page: The page that displays the most relevant matches to a user’s query, often with excerpts or snippets of relevant product content and links to the full listings.
For e-commerce websites, an effective site search can significantly boost conversion rates, as users who easily find the products they want are more likely to make purchases. Plus, by helping users discover content or products that match their interests, site search can increase engagement and encourage visitors to spend more time on the website.
More than 40% of site visitors go right to the search box, and these shoppers with strong intent are 216% more likely to convert than casual browsers.
A good site search should be able to:
- Offer filtering capabilities for different categories or groupings based on behavior of browsers and buyers and user-generated reviews
- Provide intelligent recommendations based on past searches and general site behavior
- Create clear, concise search results with the exact information that shoppers need to make an informed decision
- Provide autocorrect and error correction
- Never allow a shopper or user to hit a dead end; even if their exact search doesn’t come back with results, suggest something else that could be related
Before AI: The problem with standard site search
Traditional site search engines often face challenges that can reduce their effectiveness. Here are the top three:
Keyword dependence. Keyword-based site search focuses on finding content that contains the exact words or phrases entered by the user. While this approach might work for straightforward queries, it often falls short when queries are phrased differently than the indexed content or when they use synonyms, misspellings, or colloquial language. For example, a user searching for “running shoes” might miss out on relevant products labeled as “sneakers” or “athletic footwear” if the search engine doesn’t recognize these as similar terms.
Lack of context understanding. A user searching for “best running shoes for marathons” might be looking for specific product recommendations, but a keyword-based search engine might simply return any content containing those words, regardless of its relevance. Traditional search functions also tend to struggle with complex queries that require a nuanced understanding of language.
For example, a query like “best laptops for graphic designers under $1,000” involves multiple layers of context: the type of product, its use case, and a specific price range. Standard search engines may not parse this complexity, leading to poor results.
Static results. Standard site search often produces static results, meaning they deliver the same set of results for a given query to all users, regardless of their individual preferences, past behavior, or specific needs. This one-size-fits-all approach can significantly undermine the effectiveness of the search experience, leading to user frustration and reduced engagement.
Plus, it miss opportunities to boost engagement and conversion rates. Particularly for e-commerce, personalized search results can drive higher sales by presenting users with products that match their interests and previous browsing behavior. Without this dynamic personalization, traditional site search is less effective at guiding users towards products they’re likely to purchase, potentially resulting in lost sales opportunities.
Optimizing site search with AI
AI-powered site search addresses these challenges by using technologies such as natural language processing, machine learning, and user behavior analytics. These advancements enable the internal search engine to gain three new capabilities.
1. Understand context and intent
AI-driven search engines use advanced technologies like natural language processing (NLP) and machine learning to decipher the underlying meaning of a query, which allows them to deliver more accurate and relevant results.
AI-powered search engines go beyond simple keyword matching by understanding the semantic meaning of words and phrases, and recognizing synonyms, related terms, and context-specific meanings.
They can also take into account the broader context of a query, such as the user’s past behavior, preferences, and the current session context. Whether the user is seeking information, looking to make a purchase, or searching for a specific document, AI can adjust the search results to match the intention.
This also means that each search query and the user’s subsequent behavior (clicks, time spent on pages, etc.) provide valuable data that the AI uses to refine and improve its understanding of context and intent over time, so the search engine improves over time.
2. Personalized product content
The second benefit to AI site search is often overlooked: content localization. To a lot of people this means purely just translation, but there’s a lot more nuance to ensuring your content is ready to be taken to different markets.
AI-powered translation tools excel at understanding the nuances of language, cultural context, and regional preferences. Unlike traditional translation services, which may focus solely on linguistic conversion, AI takes into account the cultural tone, idiomatic expressions, and contextual usage of language. These algorithms have the capability to learn and adapt over time by analyzing user feedback and preferences.
AI can also work to ensure that the product data itself is enriched and complete by filling in missing or inconsistent data fields and optimizing product descriptions, metrics, and other tangible details.
3. Adaptive learning and improved insights
As mentioned earlier, AI-enabled search engines continuously learn and improve from user interactions. Over time, this continuous learning process results in increasingly accurate and personalized search experiences.
AI site search not only enhances the user experience, but also provides businesses with insights and analytics that can drive strategic decision-making. It captures a wealth of data about how users interact with the search function, including details about the queries they enter, results they click on, time spent on specific pages, and paths they take through the site. Analyzing this data helps businesses understand what users are looking for, how they navigate the site, and which content or products attract the most attention.
These insights can highlight gaps in a website’s content or product offerings. If users are consistently searching for terms that yield no or few results, it indicates an unmet need. Businesses can use this information to create new content, add new products, or optimize existing ones to better align with user demand.
Ultimately, the insights and analytics provided by AI-powered site search enable data-driven decision making across various aspects of the business. From content strategy and product development to marketing and customer service, businesses can make informed choices that are backed by real user data. This reduces the reliance on guesswork and intuition, leading to more effective and efficient operations.
How composable commerce technology unlocks the power of AI
Composable commerce helps brands achieve AI success by seamlessly making product data easily accessible to AI applications.
Charting a path for e-commerce success
Site search plays a crucial role in e-commerce success, and when enhanced with AI capabilities, it can revolutionize the product experience by offering personalization, improved searchability, and localization at scale.
More than just a functional tool, effective site search fosters trust and loyalty among consumers. It ensures that shoppers have access to the information they need at every touchpoint through advanced filtering options, intelligent recommendations, clear and concise results, and user-generated reviews.
Ultimately, implementing AI-powered site search not only boosts user satisfaction and engagement but also supports data-driven decisions, leading to more efficient and effective business operations. As technology continues to advance, embracing AI-powered site search is crucial for businesses aiming to provide a superior user experience and stay competitive in the digital marketplace.