SERVICES // DATA INTELLIGENCE
Data-Driven Growth
Collecting behavioural data is easy and nearly worthless on its own. Value appears when a specific metric is wired to a specific decision that a named person makes on a known cadence.
Our position
We start from the decision, not the dashboard. Which choice is being made, how often, and what evidence would change it? Everything we instrument traces back to one of those answers.
Then we build the loop: clean event data, a metric definition everyone agrees on, an experiment framework, and the discipline to act on what it says.
What we build
- Product analytics and event instrumentation
- Conversion and retention measurement
- Churn analysis and early-warning signals
- Experimentation and A/B testing frameworks
- Operational dashboards tied to real decisions
- Predictive models for demand and behaviour
Typical stack
- PostgreSQL
- Python
- AWS
- Redis
- ML frameworks
WHAT YOU RECEIVE
Four things leave the engagement. All four are yours.
Measurement plan
The decisions, the metrics that inform them, and the events required to calculate those metrics — agreed before instrumentation starts.
Instrumented product
A consistent event schema implemented across web, mobile and backend, with tests that catch a broken event before release.
Reporting layer
Dashboards built for the person making the decision, not for the person who built the dashboard.
Experiment framework
The tooling and statistical guardrails to run a test, read it honestly and ship or revert on the result.
HOW WE MEASURE IT
Agreed before the first commit.
- A single agreed definition for every headline metric
- Conversion and retention tracked per cohort
- Experiments read against pre-registered success criteria
- Features retired when the data says they are unused
QUESTIONS WE GET ASKED
Answered before you have to ask.
We already have analytics. Why is it not useful?
Usually because the events were added feature by feature with no shared schema, so no two numbers reconcile. The fix is a measurement plan first, then a re-instrumentation pass.
How long before we see anything?
A measurement plan takes days. Clean data behind it takes weeks. Reliable experiment results take as long as your traffic requires, and we will tell you that number before you commit.
Do you build the dashboards or do we?
Either. We build the first set with the people who will use them, then hand over the model so your team can add their own without asking.
NEXT STEP
Start a data-driven growth conversation.
Tell us what the work needs to change in the business. If Data-Driven Growth is not the right answer, we will say so and point you at what is.