Everything You Need to Know About Intelligent Search

Searching for information on a company’s website should be a simple process – a visitor enters a query or keyword into a search tool and the system retrieves (relevant) results. But a lot can go wrong after a user hits “search”. Being bombarded with irrelevant results, getting no results at all, encountering outdated information – these are roadblocks that prevent customers from finding information and products quickly.  

Intelligent search engines alleviate many of these issues by using AI to help the system understand the intent of a user’s search query. Machine learning algorithms rank content more accurately, then produce search results that are relevant based on the context of the searcher.  There are a few different processes that work together to make an intelligent search engine smarter than a traditional one. This guide is meant to help you understand how intelligent search works and, importantly, how it differs from traditional search.

What is Intelligent Search?

Intelligent search combines several AI technologies that work together to enhance search capabilities and improve search results. Smart search engines like Google combine machine learning, natural language processing (NLP), user data, and search context (location, recent searches, etc.) to retrieve tailored results for the searcher.

This personalized approach to search adds context and intent into the search process, allowing users to search for information in a way that’s more natural to them and more accurate than keyword search alone. Advanced algorithms in smart search tools leverage machine learning to rank web pages based on relevance and quality. Intelligent search can handle queries that are ambiguous, misspelled, and complex, and still produce accurate, relevant, and personalized results.

How Does Intelligent Search Work?

Intelligent search uses leveraging AI and machine learning to understand user intent and context. Here’s a breakdown of the features involved in making it work:

• Better interpretation: NLP interprets complex, conversational queries. This is how the intelligent search engine can go beyond simple keyword matching and better understand user intent. It can process natural language queries, understanding them as a human would.
• User context: These tools use entity recognition to identify specific people, places, or things in queries, a process known as Named Entity Recognition (NER). This is a component of NLP. The system also learns from user interactions to provide personalized results.
• Advanced ranking: Advanced indexing and knowledge graphs analyze content so that it can be ranked appropriately. This is a data-heavy process which helps the search engine understand user behavior and patterns so that ranking is matched to learned patterns of user behavior. Vector search may also be used to measure similarity between queries and documents.
• Contextual understanding: Semantic analysis is used to understand relationships between words and concepts. This understanding allows the search engine to serve up more comprehensive and appropriate answers compared with old-school keyword search.
• Improved relevance: Relevance scoring algorithms rank information accurately, continuously learning and improving based on user feedback and interactions.
• Information integration: Intelligent search can integrate with multiple data sources and formats, providing a unified search experience across various platforms and databases.
• Simplification and refinement: Features like faceted and filtered search, query suggestions, and auto-completion further refine the search process and help users find information quickly.

Examples of Intelligent Search in Action

Intelligent search focuses on reducing irrelevant information, understanding the context of a user and their search, and smarter ranking of content which equals more relevant results. Here are three examples of what that looks like from the searcher’s perspective: 

1. Dealing with Limited Information

A shopper searches for “light summer dress”. The search engine understands this could mean lightweight fabric or light in color and produces a selection of pastel-colored dresses in fabrics like cotton, linen, and bamboo. It uses the shopper’s browsing behavior and past purchases to inform the results it produces, displaying sizes and styles they’ve shown interest in. Smart filters allow the shopper to drill down based on color, dress length, and brand. 

2. Understanding Context  

A customer types “seqwuin collar” into an online pet supply store’s search bar. Despite the typo, the system recognizes the misspelling of the word “sequin”. Since this customer has purchased supplies for their tiny dog in the past, the search engine  presents a range of style variations and collar options all aimed at small dogs. Someone’s pooch is going to look fabulous. 

3. Recognizing Entities

A customer service rep searches “return policy electronics over 30 days.” The system understands that this is an employee and pulls information from the company intranet. In this case, a word document that contains the specific policy for electronics returns beyond the standard 30-day window. The user also sees suggestions for related information like exceptions, required documentation, and refund processing times.

Intelligent Search vs. Traditional Search

Traditional search engines are simply not up to the task of producing the kind of relevant and customized results that consumers have come to expect. Here is a side-by-side comparison of the old versus the new:

Traditional Search – Keywords Rule

• Traditional search engines look for content that matches the keywords or phrases exactly as entered (or as closely as possible). Type in “wild bird supplies in Springfield” and you’ll get results that match those exact words – but you might miss out on that hidden birding boutique term “wild” on their website. Traditional search uses a one-size-fits-all approach, delivering the same results to all users without nuance or personalization. 

Intelligent Search – Context Rules

• Intelligent search uses AI to understand what the intent of your search – what you’re really looking for – even if your query isn’t perfect. Type in “wild bird supplies in Springfield” and you’ll get results for nearby stores that sell birdseed and supplies. Results may also include stores that don’t have the phrase “wild bird” anywhere on their website and will be ranked based on your specific context (e.g., location, past search behavior, etc.)  

Benefits of Intelligent Search 

By now we hope we’ve made it clear that intelligent search is an upgrade from the keyword-driven search engines of yore. It offers many benefits that go beyond simply finding information more quickly. Here are some advantages to present to your CEO when making the case for investing in a smarter site search experience. Intelligent search is better than traditional search because it:

1. Makes Search Easier

No one (but especially shoppers) likes sifting through pages of irrelevant and overwhelming results. Smart search engines solve this problem by using AI and machine learning to find the most relevant content and products based on user data. These tools analyze search patterns, user behavior, and contextual clues to deliver precise results, often anticipating user needs before they’re even expressed. Their advanced ML algorithms them make sure the most relevant results are presented to the searcher.

2. Gets People in a Buying Mood

Window shopping is great, but sales are better. Intelligent search showcases the right products based on the context of the shopper and where they’re at in their unique buying journey. By understanding user intent and past behavior, a smart search tool can surface items a shopper is more likely to purchase, even if they’re not explicitly searching for them. This subtle nudge can significantly boost conversion rates.

3. Supports Your Employees

Intelligent search is a powerful tool for employees, allowing them to quickly find relevant documents, data, and resources. It learns from each interaction, which means it gets better as it ingests more data. Results get even more useful over time and employees find the information they need to do their jobs with less time spent digging through files and switching applications in the eternal quest to find the right answer.

4. Uncovers Useful Insights

Your search data is a puzzle waiting to be solved. By examining search patterns and user interactions, intelligent search reveals trends, preferences, and pain points you might have missed. These insights can drive important business decisions and processes like product development, marketing plans, and customer service approaches. 

5. Scratches That Personalization Itch

Customers want to be recognized no matter where they happen to be in their buying journey – or what touchpoint/device they’re using. Intelligent search remembers user preferences, understands context, and adapts accordingly. Smart search can adjust to a returning customer looking for their favorite brand or a first-time visitor exploring your site. Each search experience becomes uniquely relevant to the searcher.