Shipping a feature is just the beginning of the story. What happens next—how users engage, where they drop off, and which improvements make the most impact—defines product success. But for many teams, connecting feature delivery to actual outcomes remains a challenge.
The data that would help you connect these dots is often fragmented across tools, buried in custom dashboards, or delayed by pipeline dependencies. By the time results surface, it's too late to act with confidence: the loop between delivery and insight is broken, forcing product managers to rely on intuition, delay key learnings, and leave revenue-impacting blind spots.
That’s why we’re excited to announce the general availability of LaunchDarkly Product Analytics, designed to help you get closer to your users, uncover usage patterns, and optimize feature performance to drive adoption and growth.
Why warehouse-native analytics changes the game
More and more organizations are turning to data warehouses as their single source of truth for everything from account health and revenue metrics to product usage data—in a single, governed environment.
Yet most product analytics tools force you to extract and copy that data into a separate system. Every pipeline you build adds cost, latency, and risk; ETL jobs break, dashboards lag, and compliance teams raise red flags any time sensitive data leaves the warehouse.
By running analytics natively on your existing data platform, you eliminate duplication, help ensure accuracy, and simplify governance, so your product teams spend less time wrangling data and more time acting on it.
How Product Analytics delivers actionable insights
LaunchDarkly Product Analytics brings product usage data, experiments, and user behavior into a single, warehouse-native platform. By connecting directly to Snowflake, BigQuery, or Databricks, teams can finally bridge the gap between every release and real user outcomes.
- Correlate releases with real user behavior: See exactly how each feature rollout influences adoption, engagement, and retention without moving data into a separate system.
- Empower teams with self-serve analytics: Product managers and engineering teams can build charts, funnels, and cohorts on demand. There’s no SQL or engineering support required.
- Trust a single source of truth: All dashboards and reports reflect live data in your warehouse, creating consistency and removing the need for data reconciliation.
Comprehensive behavioral analysis
Dive deep into how users move through your product. Product Analytics lets you visualize trends, map user flows, and track cohorts over time—so you can pinpoint where users drop off or where engagement spikes. For example, if you see a sudden falloff in your onboarding funnel, you can immediately drill into that step, compare it across user segments, and uncover the root cause without stitching together multiple tools.
Self-serve analytics for every team
Give product managers, analysts, and growth teams the power to answer their own questions—no SQL or waiting on engineering and data science teams. With an intuitive UI, teams can spin up funnels, retention charts, or custom cohorts in seconds. Want to compare how new versus existing users engage with a feature? Just select your segments, apply a date range, and see the results instantly, all within the same dashboard.
Built-in security and governance
Keep sensitive data protected by running analytics where your data already lives. Product Analytics leverages your warehouse’s native access controls, audit logs, and compliance frameworks, so there’s no need to export customer data. You get the power of advanced analytics, while your data stays in the warehouse you already control, vetted and governed by your security and compliance teams.
Release-aware insights
Close the loop between feature rollouts and actual user behavior. As soon as you flag a new feature, Product Analytics ties every subsequent event back to that rollout or experiment variation. You’ll know at a glance how each release affects adoption, engagement, or retention, and you can launch follow-up tests or rollbacks directly from the insights you uncover.
Bridging analytics and experimentation
Once you’ve instrumented events and built dashboards, Product Analytics doesn’t just show you what happened, it empowers you to decide what to test next. By linking your experiment definitions to behavioral metrics, you can:
- Measure experiment impact in context: Drill into cohorts of users exposed to a test and compare adoption, engagement, or retention against control groups.
- Iterate on winning variations: When a variation drives better engagement, you can roll it out more broadly; if performance lags, you can quickly surface the data you need to refine your next hypothesis.
- Close the loop on learnings: Bring your experiment results back into dashboards and view trends, funnels, and retention, so every test becomes a source of actionable insight.
Get started with Product Analytics
Are you ready to bridge the gap between releases and results so you can optimize features, uncover adoption trends, and drive growth?
To request access, go to the ‘Product Analytics’ menu in LaunchDarkly or reach out at productanalytics@launchdarkly.com with any questions.
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