One checkout investigation, from start to finish. All examples below are illustrative.
01 / CONNECT
Start with the context you already have.
Connect your analytics or warehouse, tell Tamda which outcomes matter, and add your codebase when you want help implementing changes. The investigation starts with your product’s actual context.
Import product events from Amplitude or Mixpanel, connect a supported warehouse, or send events through the API.
Choose the metrics and customer journeys you want to understand.
Connect GitHub for code-aware recommendations and proposed changes.
Your data, goals, and codeA working picture of your product
THE CONTEXTEXAMPLE
What does a good first purchase look like?
Goal
More completed first purchases
Evidence
Checkout and purchase events
Context
Customer type and checkout code
You choose the sources and scope of access.
02 / INVESTIGATE
Follow the signal to a useful question.
Tamda watches your metrics and investigates meaningful changes. It compares available segments and evidence to narrow down where the movement is concentrated.
Identify a change worth investigating, or start with your own question.
Look across customer groups and available dimensions.
Keep observed evidence separate from a hypothesis about the cause.
A change in your metricsAn evidence-backed opportunity
THE INVESTIGATIONEXAMPLE
Why are fewer new buyers completing checkout?
Overall conversion
8.5% → 5.8%
First-time buyers
315 → 126 purchases
Returning customers
281 → 280 purchases
A segment explains where the decline is. An experiment helps test why.
03 / DESIGN
Turn the question into a test.
Tamda proposes a hypothesis and an experiment: what should change, who should see it, what success means, and what must not get worse.
Define the control and proposed experience.
Pick a primary outcome and guardrail metrics.
Review the reasoning, assumptions, and measurement requirements before proceeding.
An opportunity and a hypothesisA measurable experiment plan
THE HYPOTHESISEXAMPLE
Would guest checkout help first-time buyers?
Change
Offer checkout without an account
Success metric
Completed purchases
Guardrail
Payment failures
A proposed explanation is something to test, not a fact to assume.
04 / REVIEW
Review the change before it ships.
With GitHub connected, Tamda reads the relevant code and drafts a pull request. Your team reviews the implementation, the experiment setup, and how outcomes will be tracked.
Ground proposed edits in actual repository files and conventions.
Use your existing feature flag assignments where configured.
Keep review and release in your team’s normal workflow.
An approved experiment planA proposed change for team review
THE REVIEWEXAMPLE
A small change, with a clear reason.
Proposal
Guest checkout experiment
Measurement
Exposure and purchase events
Status
Awaiting team review
A recommendation is not permission to deploy.
05 / LEARN
Measure the outcome. Keep the learning.
Tamda compares recorded outcomes, checks guardrails, and explains what the available evidence supports. The decision and its reasoning become part of your product’s history.
Compare the outcomes of customers exposed to each experience.
Check whether the intended metric improved and guardrails held.
Recommend a next step, or keep learning when the evidence is not sufficient.
Experiment exposures and outcomesA decision with a traceable history
THE LEARNINGEXAMPLE
Promising direction. More evidence needed.
Primary outcome
Early improvement
Guardrails
Continue monitoring
Next step
Keep the experiment running
The goal is a sound decision, even when that means waiting.
START WITH A QUESTION.
See what your product could do next.
A public-site teardown gives you initial hypotheses. Connecting your data lets Tamda investigate the evidence behind them.