Definition
A natural language query is a question or instruction written in a conversational form that a software system can interpret. For example, someone might ask, “Which mobile landing pages had the highest conversion rate last week?” rather than writing SQL or navigating several filters manually.
In analytics, an NLQ system typically translates the wording into structured operations such as selecting metrics, applying filters, comparing segments, or grouping results. It may then return a table, chart, narrative explanation, or a follow-up question when the request is incomplete.
Natural language queries are not magic access to every answer. Results depend on the data available, the definitions used for metrics, the system’s ability to resolve ambiguity, and whether the question is specific enough to be interpreted consistently.
What does that mean
It is like asking a helpful analyst a question in plain English. The system works out which data to look at and brings back an answer, instead of making you learn a special computer language.
Why this matters
Natural language queries lower the barrier to asking data questions and can shorten the path from curiosity to insight. Marketing, product, and UX teams can explore patterns more independently, while data and IT teams can help make sure the underlying data, permissions, and definitions are trustworthy.
Use cases
Explore performance without writing queries
Teams often need a quick answer about traffic, engagement, or conversion but do not know the exact report or query syntax to use. With a natural language query, they can describe the metric, audience, time period, and comparison in everyday language, then refine the request through follow-up questions. This can make routine exploration faster and help more people participate in data-informed decisions.
Relevant audiences: Marketing; Data
Investigate journeys and prioritize experience improvements
A product or UX team may see a drop-off but need to understand which devices, channels, pages, or user groups are most affected. Asking targeted questions in natural language can help compare segments, surface likely friction points, and identify where deeper analysis is needed. The expected benefit is a faster path from an observed change to a focused hypothesis and an actionable improvement plan.
Relevant audiences: Product; UX/UI
Bring analytics into existing AI workflows
Insight work can slow down when people must switch between an AI assistant, an analytics platform, a warehouse, and other business systems. Natural language queries let teams ask questions in the tools where they already work, when integrations and permissions support it. This reduces unnecessary tool switching and makes it easier to connect an analytics question to the next decision or task.
Relevant audiences: IT; Data
See it in practice
The alexanderwang team uses Contentsquare’s AI to surface unexpected insights, speed up analysis, and make behavioral analytics more accessible to non-technical team members. The customer story describes natural-language-driven analysis as part of that broader workflow.
How Contentsquare helps
Sense lets teams ask questions in plain English and use conversational analysis to explore behavioral data and identify next steps.
MCP Server connects Contentsquare insights to compatible AI tools so teams can query experience data from the workflows they already use.
Data Connect exports behavioral, performance, and error data to a warehouse so data teams can combine it with other sources and support broader analysis.
Heatmaps provide a visual way to investigate where users click, tap, hesitate, or scroll after a natural-language question points to a page or element.
FAQs on natural language query
A question such as “Which product pages had the highest add-to-cart rate on mobile this month?” is a natural language query. It states the subject, metric, audience, and time period without requiring the user to write SQL or use a formal query syntax.