FOR PRODUCT ANALYTICS
Product moves faster when data is in the room
Explore, test, and learn, so that every launch is faster, and more connected to what users actually need.
How do you prove which retention number is the one to trust?
Show your work as you go
Build your logic with the Hex agent, in a workbook that steps you through metric definitions, data sources, hypotheses, and decisions.
"I wrote zero SQL by hand, absolutely zero. Hex is the single most useful and magical tool that has ever existed to make my job better in 20 years.”
Tim Frietas Product Manager at Ramp
Did that feature shipped last month move the needle?
Give your team findings they can explore
Explore adoption, retention, and funnel changes after a launch. Share an interactive analysis where your team can compare segments and ask follow-ups to decide whether to iterate, expand, or change course.
10.6x
growth in data exploration
"I can have a thought, fire off a question through Hex's Slack integration, get an insight, and decide what to do — all in the time it takes to stand in line for coffee”
David Product Manager at Notion
Skip the upfront
Start with the context you already have
Hex reads what you've already written: table descriptions in your warehouse, dbt models, docs in Notion, rules in your repos. Endorse the tables your team trusts and monitor the answers the agents produce, that way you know where to build more guardrails and models.
"Context Studio changed how we manage agent performance. It surfaces gaps, proposes fixes, and suggests exactly where to add context. The agent improves without me auditing every conversation.”
Emily Hawkins Head of Analytics Engineering at Ramp
What if you knew about the drop-off before leadership asked about it?
Product updates find you before you think to check
Set up Tasks to push feature adoption updates, usage drops, or experiment results directly to you, so you know what changed without opening a dashboard or noticing something's off.
"Being able to scale decisions is not just about having a great data product, but putting it into places where people are actually looking and working every single day.”
Jordan Farrer Director of Data Science at Ramp
Where to start?
Product Funnel Analyzer
Configurable funnel from any event to any event — signup to activation, trial to paid, feature discovery to adoption. Step-by-step drop-off with segment filters. The most used product analytics starting point.
Retention Cohort Dashboard
Weekly or monthly cohort matrix with heatmap visualization. Filters by acquisition channel, plan, or any user property. Export-ready for leadership reviews.
Feature Adoption Tracker
Usage by feature, broken down by plan tier, company size, and user segment. Flags features with low adoption among high-value users — the signal that drives roadmap prioritization.
User Journey Explorer
Per-user event timeline with filtering by cohort, segment, or behavior. Used by PMs who want to understand qualitatively what high-retention users do differently in their first week.
FAQ
No. Business users typically ask questions in Threads, Slack, or published apps without opening a notebook. Notebooks are where your data team builds and governs the analyses those experiences draw on.
Yes. Threads and published apps let stakeholders explore in plain language while permissions and semantic context stay enforced.
Many teams replace legacy BI for self-serve and operational reporting while keeping the warehouse and dbt models they already trust. Hex consolidates exploration, apps, and AI on top of that stack.
Teams often connect a warehouse and publish a first app in days, then migrate high-traffic dashboards incrementally. There is no separate server to provision.
No. Hex starts from warehouse metadata, dbt, docs, and curated context you already have, then improves as you endorse tables and refine guardrails in Context Studio.
No. Answers respect the same warehouse and workspace permissions as the person asking. AI cannot bypass row- or object-level access controls.
Published apps, Threads, and agents inherit permissions, endorsed datasets, and audit trails so self-serve stays within policies you define.
Answers combine live queries with endorsed context, metric definitions, and feedback loops from Context Studio and Evals.
Give stakeholders governed self-serve in Slack and apps, while analysts build reusable logic once in notebooks instead of re-answering the same ticket.
Can't find your answer here? Get in touch.