Natural language search
In short
Natural language search is a way of searching by describing what you want in everyday language rather than using keywords, operators, or a filter form. The system interprets the sentence and returns matching results.
In B2B prospecting, it turns a request like "SaaS companies in the UK with 50 to 200 employees using HubSpot" into the right filters or matches. It lowers the skill needed to build a list, but results still depend on the quality of the underlying data.
How it works
Interpreting the query
A language model reads the sentence and identifies the parts: industry, location, size, tools, or event.
Translating to filters
Some systems convert the sentence into structured filters you can see and edit. This is easier to verify.
Matching by meaning
Others compare meaning using semantic or vector search, which finds companies that fit a description even when exact words differ.
Natural language search vs. keyword search
- Keyword search: Matches the literal words you type. Predictable, but misses synonyms and concepts.
- Natural language search: Matches intent and meaning. Flexible, but can interpret a vague request differently than you meant.
Writing better queries
- Be specific: Name industry, region, size, and a trait or trigger.
- One idea at a time: Start with a core description, then narrow.
- Check the interpretation: If filters are shown, confirm they match your intent.
- Sample the results: Open ten results and judge fit before exporting thousands.
Common mistakes
- Trusting vague terms: Words like "innovative" or "fast-growing" mean different things to different systems.
- Skipping exclusions: State what to leave out, such as competitors or existing customers.
- Ignoring data freshness: A good search over stale records still returns outdated results.
Natural language search FAQs
What is natural language search?
Natural language search lets you look for information by typing a normal sentence instead of keywords or filters. The system interprets your meaning and returns matching results, such as companies or contacts that fit your description.
How does natural language search help with B2B prospecting?
It lets reps describe a target in plain words, such as a type of company in a region using a certain tool, and get a list without learning a filter interface. It speeds up list building, but results should be sampled for fit.
Is natural language search more accurate than keyword search?
Not always. It handles synonyms and concepts better, but it can misread vague requests. Keyword search is more predictable. Accuracy also depends on the quality and freshness of the data being searched.
What is a good example of a natural language search query?
Something like "B2B software companies in France with 100 to 500 employees that recently hired a head of sales." It names industry, region, size, and a trigger, which gives the system enough detail to find relevant matches.
