Intelligent search is search technology that leverages artificial intelligence (AI) and machine learning to provide more accurate, relevant, and personalized results.
This smarter form of search functionality gets its name from its ability to “understand” the context and intent behind people’s search queries, enabling it to retrieve the best possible information.
As with AI in other areas, intelligent search technology is transforming how people search. Google has made significant advancements with generative AI capabilities, enhancing the relevance and context of search results.
Features like multisearch allow searchers to combine images and text in queries, a more natural and intuitive way to find information.
And this technology is not limited to web search; companies are using it to enhance their data management and the effectiveness of their enterprise search.
How is intelligent search better?
Traditional search relies on keyword matching and basic algorithms. Intelligent search uses AI and machine learning to understand the context and intent behind queries, which allows it to provide more accurate and relevant results.
For example, this is what happens when you search for “soccer” using each approach:
- Traditional (keyword) search: When you enter “soccer”, you’re presented with information about soccer from United States sources.
- Intelligent search: Using AI, in addition to soccer information, the search engine provides results for “football” (as Europeans call it), moves popular results higher on the list, and, because it considers the searcher’s location, prioritizes results related to the local team.
How does intelligent search work?
Intelligent search leverages advanced AI technologies to retrieve the most relevant content for the searcher. Here’s a summary of the process:
Query understanding
- Natural language processing: Intelligent search systems use NLP to interpret and understand the intent behind the search query.
- Semantic analysis: This involves understanding the meaning and relationships between words in a query. For example, a search for “apple” could refer to the fruit or the technology company. Semantic analysis helps distinguish between the two.
- Synonym recognition: Intelligent search recognizes and interprets synonyms and related terms. This ensures that a query like “laptop” returns results related to “notebooks” and “computers”.
Content analysis
- Indexing: Content from web pages and documents is crawled — broken down, with text and metadata extracted, and stored in a searchable format.
- Knowledge graphs are databases of interconnected entities and relationships that help a search engine understand the context of content and how different pieces of information relate to one another.
- Taxonomies and ontologies are hierarchical structures used to classify and organize information, allowing the search system to understand and retrieve data more effectively.
- Vectors are used to measure the similarity between queries and documents, ensuring that vector search results are relevant even if they don’t contain the exact keywords used in the query.
Processing the query
- Stemming and lemmatization reduce words to their root forms. For example, “running”, “ran”, and “runs” are reduced to “run”. This ensures that during search, word variations are recognized as equivalents.
- Entity recognition: The system identifies entities within the query, such as names of people, places, organizations, and products.
- Contextual relevance: By analyzing the context in which terms are used, intelligent search can provide more accurate results.
Retrieving and ranking results
- Relevance scoring: The search engine uses various algorithms to score the relevance of each piece of content based on the query. Factors include keyword matches, semantic relevance, and relationships between entities.
- Machine learning models: These models continuously learn from user interactions and feedback to improve the accuracy and relevance of search results over time.
Delivering results
- User interface: Results are presented in a user-friendly interface that may include filters, facets, and suggestions to refine the search further.
- Recommendations: Based on the searcher’s query and behavior, the search system might also enhance the customer experience by providing related recommendations.
Continuous learning and improvement
- User feedback: The system gathers feedback from people’s interactions to understand which results are most useful; it adjusts its algorithms accordingly.
- Behavioral analysis: By analyzing patterns in user behavior, intelligent search systems can identify trends and make better-informed decisions about relevance and ranking.
- Personalization: Intelligent search can tailor results to individual users based on their search histories, preferences, and online behavior patterns.
- A/B testing may be used to compare algorithms and configurations to optimize search performance.
Benefits of intelligent search
Intelligent search offers these upsides:
Better accuracy
Intelligent search engines improve accuracy through advanced NLP and machine learning technologies, which can significantly improve the quality of search results.
Example: if the managers of an online educational platform use intelligent search to expertly interpret student questions, they can potentially provide more useful study materials.
Better relevance
Higher relevance ensures that people receive the most pertinent information for their queries. This is achieved through semantic search, knowledge graphs, and advanced ranking algorithms that consider the context and quality of content.
Example: a financial services firm can supply intelligent search to help analysts quickly find relevant market data.
Saved time
With access to quick, relevant results, people can save time.
An intelligent search system can simplify this foray into multiple data sources by quickly pulling up the most relevant documents and email so people can move forward with the information they need.
Example: at a large company, employees might need to dig through silo repositories of digital content in search of specific data.
Consolidated, current information
Intelligent search can be integrated with enterprise tools and databases to provide an easy cross-platform experience that surfaces current material and weeds out outdated content.
The result: faster, better-informed decisions, which could lead to better care and operational efficiency.
Example: with traditional search, a doctor looking for patient records and treatment protocols might need to spend time consulting several databases, some of which contain outdated information, and reconcile various details. By contrast, intelligent search could quickly provide all the latest relevant information through a single search.
Higher conversion
Intelligent search can help online shoppers instantly find their desired products and services, leading to higher ecommerce sales and better conversion rates.
Studies back this up: 76% of customers are more likely to buy from sites that use personalization.
Example: an online retailer can use intelligent search to analyze shopper behavior and provide personalized product suggestions, increasing average order values and conversion rates.
Lower cart abandonment
Poor search experiences often lead to frustration and high bounce rates.According to the Baymard Institute, the average online shoppingcart abandonment rate is 70.19%. Intelligent search systems can help a company keep shoppers engaged by understanding their search context and delivering precise results.
Example: a travel booking site could implement intelligent search that allows shoppers to locate vacation packages using natural language queries. Providing accurate, comprehensive results based on shoppers’ preferences and past behavior could decrease cart abandonment and lead to more bookings.
Use cases and applications
Intelligent is being applied to improve efficiency and user experience across multiple industries and use cases, like:
Enterprise search
In large organizations, the data volume can be overwhelming, and productivity can be undermined by employees’ inability to find the right information at the right time. Intelligent search can help employees track down internal documents, email, and other information quickly, improving knowledge management and collaboration.
Ecommerce sites
Providing accurate product recommendations and facilitating easy product discovery is key to enhancing modern shopping experiences. Intelligent search understands user intent and preferences to make this possible.
An online fashion retailer, for instance, can integrate it to offer personalized product recommendations based on browsing patterns and previous purchases. Intelligent search can also help with managing inventory by predicting trends and demand, optimizing stock levels.
Large content platforms
Intelligent search improves the user experience on websites housing vast amounts of content, such as news portals and educational sites. A news website using semantic search capabilities to understand queries can quickly suggest relevant articles, videos, and multimedia content.
Customer service
Intelligent search chatbots can power support portals and efficiently answer questions without human intervention, reducing the load on customer service teams.
Considerations for implementation
When looking at intelligent search tools for your business, you’ll want to keep the following in mind:
- Scalability: As your business grows, the volume of data and the number of queries will increase, and your search tools must be able to handle this without compromising performance or speed.
- Connectivity: Your search application should allow for easy integration with databases, CMS and CRM systems, and other applications.
- Machine learning capabilities: Advanced ML features that enable continuous learning and improvement of search results are a must. This enables the platform to analyze user behavior and search patterns, refining its algorithms to provide better results over time.
- Analytics: Analytics tools track metrics such as search query trends, click-through rates, and user feedback. This can help you identify common search terms, popular content, and areas where your search experience could be improved.
Make a smart choice
Are you still using a traditional search engine for your website or apps? Now is a great time to upgrade to an intelligent search solution like Algolia’s AI-powered search.
Get started with us for free and discover the difference intelligent search could make for your bottom line.
