Clear baseline
Know what is underperforming and why
EXPERIMENTATION AND PERFORMANCE ANALYTICS
Know what worked before you scale it.
We build experimentation and measurement systems that help teams separate promising movement from reliable evidence, then turn each release into a clearer next decision.
Focused tests
Change one valuable variable at a time
Better economics
Improve cost, speed or conversion
Continuous learning
Use evidence to compound performance
What this solves
What you get, in plain language.
Start with the business problem. We define and deliver the product, workflow or transformation needed to solve it.
At a glance
A practical learning system
| Stage | Core question |
|---|---|
| Outcome | What business or customer behaviour should change? |
| Hypothesis | Why should this intervention create that change? |
| Design | What comparison can test the claim fairly? |
| Instrumentation | Are the events, exposure and quality signals trustworthy? |
| Decision | What will we do if the result is positive, negative or inconclusive? |
At a glance: Define → Instrument → Prioritise → Test → Interpret → Scale or stop
What this changes for you
- Fewer opinion battles: Give teams a shared standard for evidence and decisions.
- Cleaner measurement: Align events, metrics, attribution limits and decision rules.
- Faster learning: Prioritise tests by value, evidence and practical feasibility.
- Responsible scaling: Expand changes only after the result and uncertainty are understood.
What you receive
- Business outcome and metric tree
- North-star, guardrail and diagnostic metrics
- Analytics and event-tracking audit
- Experimentation principles and governance
- Hypothesis and prioritisation framework
- Experiment design and sample considerations
- A/B, multivariate or alternative test plan as appropriate
- Product and growth performance dashboard
- Attribution and incrementality assessment
- Experiment readout and decision template
- Learning repository and ongoing test cadence
Our experimentation process
- Define the decision: Agree what the team needs to learn and what action the result will unlock.
- Build measurement confidence: Validate events, exposure, metric definitions and data quality.
- Form a causal hypothesis: State the mechanism, audience, expected outcome and risk.
- Choose the right design: Use an A/B test, phased release, holdout, quasi-experiment or qualitative method according to the environment.
- Interpret honestly: Report effect, uncertainty, guardrail movement and limitations before deciding to scale, refine or stop.
Integration examples
Experimentation and analytics can work with approved web analytics, product analytics, feature-management, CRM and warehouse data environments. Server-side or product-code experiments may be preferable when platform constraints, performance or governance require them. Access and implementation feasibility are confirmed during discovery.
How we work
Small-team speed. Enterprise-level discipline.
You always know what is being decided, built and measured.
Map
Understand the current flow, exceptions and baseline.
Prioritise
Choose the smallest shift that can move the metric.
Implement
Connect intelligence, systems and human checkpoints.
Optimise
Observe real usage and improve continuously.
Built for reality
Connected. Governed. Ready to operate.
The new system should fit the business you already run, not create another isolated tool.
SystemsCRM · ERP · data · APIs
ControlsAccess · review · audit
PeopleRoles · handoffs · adoption
Relevant exampleClient engagementRead the case study ↗
Forecast demand without overproducing.
A demand planning and margin guardrail designed to balance customer demand, shelf life, factory capacity, inventory and the economics of every batch.
20%forecast error
96%fill rate
FAQ
Questions about Experimentation and Performance Analytics
Straight answers before you decide what to do next.
Do you only run A/B tests?
No. A/B testing is one method. Low traffic, operational workflows or enterprise environments may require phased releases, holdouts, interrupted time-series analysis, matched comparisons, usability research or other evidence designs.
How do you choose which experiments to run?
We rank hypotheses by expected business value, evidence strength, reach, risk, effort and learning value. The backlog should serve a strategic question rather than become a list of cosmetic changes.
Can AI optimize our experimentation programme?
AI can accelerate analysis, hypothesis development, segmentation and monitoring, but it cannot repair weak instrumentation or remove the need for a valid comparison. Human judgement remains essential for causal interpretation and business trade-offs.
How do you handle attribution?
We define what the available data can and cannot prove. Where attribution is weak, we may recommend incrementality tests, holdouts or a decision framework that reports uncertainty instead of presenting a false single answer.
How long does a typical engagement take?
A focused diagnostic or prototype can take a few weeks. A production build or wider transformation is phased around complexity, integrations, risk and the evidence required at each gate.
What determines the cost?
Scope, workflow complexity, data readiness, integrations, security requirements and the level of production support determine investment. We define the smallest credible first phase before proposing a wider programme.
Can this work with our existing systems?
Usually, yes. We design around your current CRM, ERP, data, communication and operational tools, then recommend replacement only when an existing constraint genuinely blocks the outcome.
How do you handle security and human oversight?
We define access, approval, escalation, audit and monitoring requirements with the workflow. High-impact decisions retain the right human checkpoints and visible accountability.
How will we know whether it worked?
We agree a baseline and a small set of business measures before delivery. Success can include adoption, speed, quality, conversion, cost, revenue or risk depending on the service.
Have a problem worth fixing?
Tell us what needs to change.
We will help you find the clearest route from business need to working system.
Talk to our team