Guide has begun to use semantic search as a way to generate the most accurate search results possible based on the intent and context of user search queries. Unlike search methods that find literal matches to keywords, semantic search captures the meaning of search queries, helping end users and agents search for and locate content without prior knowledge of the exact keywords to use.
To understand how to tell if you have semantic search enabled and what the rollout plan looks like, see Understanding the semantic search rollout plan.
What is semantic search?
Semantic search uses artificial intelligence (AI) to process and understand the full meaning and intent of language used in search queries, similar to the way a human would. Specifically, machine learning (ML) and natural language processing (NLP) technologies work together in semantic search to help the search engine understand the user’s intent when they submit a search query. Understanding what the user is actually looking for, regardless of the keywords they use, helps the search engine return and rank the most relevant results.
For example, with semantic search, end users and agents can ask natural language questions instead of worrying about which keywords to use in order to get the best results. New agents can ask “how do I start using Guide?” instead of trying to think of the best keywords to use or the sequence of keywords to use in their query. With semantic search, they can enter their question in the way that makes sense to them, and still see the most relevant articles at the top of the results.
How semantic search works
Guide search has historically relied on keyword matches between queries and content (for example, articles, community posts, or external records). However, it didn’t capture the “meaning,” or semantics, of a query. Semantic search, on the other hand, deploys natural language processing to understand the content of the search query and content. This helps the search engine identify complex patterns that are otherwise missed. In many cases, semantic search is able to find relevant results even when there is not a strong word overlap between the query and indexed content.
Relying on language models, semantic search can translate queries and help center articles into vectors (a numerical representation), and measure the distance between them. Articles closer in the vector space are considered to be more similar by the language model. The language model is trained on many text examples, and through these examples has learned how to accurately interpret the meaning of text accurately.
When semantic search is used, the search engine boosts the relevance of semantically better matches. This pushes the most relevant content, based on intent and context of the search query, to the top of the search results. Each time a search is performed, the search results are ranked not only based on keyword matches but also boosted by semantic matches.
How Guide uses semantic search to improve the search experience
Semantic search offers a more intuitive search experience that lets help center visitors search using their choice of terms. Since Guide has begun to incorporate semantic search, an increase in search quality metrics such as mean reciprocal rank (MRR) and click through rate (CTR) has been observed. Specifically, MRR improves 7% on average with this first iteration of semantic search for English Zendesk help centers. More searches with clicks have also been observed.
By improving the search relevance, and ranking the most relevant results on top, semantic search produces the following help center improvements:
- End users can find the information they are looking for more easily, increasing the deflection power of your help center.
- Agents can be more efficient as they can find answers more quickly.
The degree of impact for semantic search depends on the search behavior of the user. Semantic search has particularly positive benefits for longer search queries, but still improves relevance for all types of search.