Product Analytics, Growth & Experimentation
Know what's working. Act on what matters.
Growth teams spending on paid acquisition often can't tell you with confidence which channels are actually working. The data exists — in AppsFlyer, Google Ads, your CRM, your warehouse — but it's fragmented. There's no single reliable number for cost per retained user by channel. Decisions get made on partial data and instinct.
We connect the attribution stack, model campaign-level LTV, and build the reporting that lets growth teams act rather than guess. We also work with product teams that need to understand what users actually do: which features drive retention, where funnels break, and which experiments are genuinely moving the needle.
We're vendor-agnostic and not affiliated with any attribution vendor, CDP, or analytics tool. Our recommendations are based on what's right for your stack and stage — nothing else.
What we deliver
Select a service area to see how we approach it.
Capture every meaningful user action, correctly and completely, from day one.
- 01
Audit current event coverage: identify missing events, inconsistent naming, and bad property values.
- 02
Define the instrumentation framework: taxonomy, naming conventions, required vs optional properties.
- 03
Write the tracking plan — a living document that engineering implements against.
- 04
Work with your engineers to instrument web and mobile surfaces correctly.
- 05
Validate the implementation: QA each event end-to-end from trigger to warehouse.
Run rigorous experiments that drive decisions, not just fill dashboards.
- 01
Audit current experiment process: how tests are designed, analysed, and actioned.
- 02
Set up the experimentation platform (LaunchDarkly, Split, or Statsig) and connect it to your warehouse.
- 03
Define the statistical framework: significance thresholds, minimum detectable effects, sample size requirements.
- 04
Configure feature flags for both experiment delivery and safe deployment rollbacks.
- 05
Document the experiment review process so the team runs rigorous tests independently.
Know which channels actually drive revenue, not just clicks.
- 01
Map every paid channel, attribution tool, CRM, and CDP currently in your stack.
- 02
Identify the gaps: where data is missing, duplicated, or unjoined between systems.
- 03
Connect sources into the warehouse and build a unified user journey model.
- 04
Build campaign-level CAC and cohort-level LTV models by channel.
- 05
Deliver the reporting layer that lets growth teams make channel-mix decisions independently.
Give every team the data access they need without creating a bottleneck.
- 01
Survey the stakeholders: what questions do they need to answer, and where do they currently get stuck?
- 02
Select and configure the BI tooling and semantic layer appropriate to your stack.
- 03
Define governed metric definitions so every team is measuring the same thing.
- 04
Build the core dashboards and exploration surfaces for product, growth, and finance.
- 05
Run enablement sessions so teams can answer their own questions without data requests.
Full visibility on cost per quality user by channel. A product analytics system your team can query without engineering support. Experiment results with statistical rigour, not gut feel.
Founders and Heads of Growth spending on paid acquisition without clear attribution. CPOs and Heads of Product who need more than vanity metrics.
We publish in-depth playbooks on data engineering best practices at handbook.bottomlinedata.co. Detailed guides related to this practice area will be linked here.
Start a conversation.
Every engagement begins with a focused discussion of your current data environment and priorities. To schedule an initial consultation, reach out directly.
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