Introduction to AI Search

The Synerise AI Search Engine is a powerful tool that can boost the search results relevancy on your website to the next level. There are numerous ways to improve your customers’ experience as well as shorten the path to conversion, from the basic search configuration, through synonyms, ending on query rules. The search engine can also detect typos and perform a search for the corrected query. By using our award-winning algorithms, personalization reranks search results to highlight products that interest your customers the most. Semantic search is better than traditional methods, because it analyzes complete queries to comprehend search intent and meaning. In Hybrid mode, which merges typical keyword approaches with semantic search, it improves overall metrics.

Benefits

• Higher conversion
• Increased revenue
• Strengthened relationship with your customers
• Shortened customers’ path to conversion
• Enhanced searching experience
• Gather more information about your customers’ behavior
• No-code optimization
• Personalized search results
• Ability to monetize product boosting

About search engine

The search engine is based on indices to speed up the search process. Indices are built from the provided item feeds or catalogs. When creating an index, you can choose from three search methods: keyword searching, semantic searching, or a combination of both.

1. Textual ranking, this includes:
   • analyzing keyword frequency, semantic analysis, and natural language processing to determine the ordering of search results (depending on the selected search method).
   • indexing - the process of locating relevant documents or data that match the search terms.
   • low match enhancement - if this option is enabled in the index settings, it adds more items to the search results if there are fewer than 5 items returned.
     You can find more information how to enable and use this option in “Matching words” section.
2. Item ranking based on item visits and transactions.
3. The following operations happen at the same time:
   • Executing rules condition: Boosting by attribute
   • Personalization
   • Applying dynamic re-ranker - if this option is enabled, it prioritizes displaying the most popular results for the search query according to the ranking from the last 12 hours.
4. Executing rules condition: Filter query rules.
5. Executing rules condition: Promoting and/or hiding items.
6. Applying query suggestions.

Search methodology

Searching involves different stages and elements, they can be divided in the following way:

• searching method
• providing a search query
• presenting results

Searching methods

When creating a search index, you can choose the search methods:

• Keyword - searching for information using specific words or phrases that are relevant to the desired topic or subject. To take full advantage of it, request full access.
• Semantic - connects words and phrases to interpret digital content similarly to human comprehension, offering personalized, accurate results. Governed by search intent and semantic meaning, it aims to decode content contextually for precision. By taking a holistic approach, it assesses word meanings and relationships, similar to human language interpretation. The goal is to eliminate irrelevant results for an enhanced user experience. To take full advantage of it, request full access.
• Hybrid - combines the two approaches, with a weighting ratio of 70% for keyword-based and 30% for semantic search. In practice, this involves the weighted average of two normalized scores. We highly recommend opting for the Hybrid search method as semantic search proves invaluable in situations where keyword-based searches fail to yield satisfactory results. To take full advantage of it, request full access.

Methods of providing search query

• Full-text search - When a request is made with a query parameter, a full-text-search can be conducted. Read more about implementation.
• Auto-complete search - The search engine also has an in-built option of auto-completing the searched query. This means that with every keystroke, it can predict what a customer is entering into the search. Read more about implementation.
• Visual search - A user can search by providing an image. Find out more about visual search

Methods of presenting results

Listing - In order to retrieve a list of products, send a search request with no query. The listing can be filtered by any given attribute, included in the items catalog. This means you can create, for example, a list of products filtered to a specific brand and sorted by color and price. You can utilize this feature to create brand or category pages. Moreover, the listing can be personalized, meaning the items returned will be sorted so that the items returned first will fit the customers preferences. The personalized listing is sorted in a different way for each customer, based on their interaction with your business.

Best practices for semantic search implementation

• Ensure every product in your item feed has a comprehensive and detailed description. Usually, a few sentences.
• Include relevant attributes such as size, color, material, product designation, and unique features.
• Use natural language to describe products, avoiding jargon and overly technical terms where possible.
• Use the hybrid search mode to combine the strengths of keyword and semantic search.
  Example: A hybrid search for “summer dresses for beach vacation” returns results both for specific keywords, such as “summer dresses”, and for semantically related items, like “lightweight sundresses.”
• Conduct A/B tests on different search index configurations (for example, keyword vs. hybrid) to determine the best-performing setup. Use the integrated preview and comparison tools to analyze the results in a single view.
  Example: Test the effectiveness of semantic search on queries like “laptops for graphic design” versus traditional keyword search and compare user engagement metrics.
• Regularly monitor the performance of your semantic search implementation. Use the Analytics module to track search success rates and user satisfaction.
  Example: Track metrics such as click-through rates, conversion rates, and search abandonment rates to gauge the effectiveness of semantic search.