Step 1: Define the outcome and the constraints
Start with a single primary objective (e.g. purchase conversion rate, lead rate, booked calls). Then list the constraints: traffic volume, seasonality, paid vs organic mix, checkout control, tech stack, and what you can actually change.
Why this matters
Clear objectives prevent scope creep and keep the audit focused on high-value fixes. Defining constraints up-front helps you create realistic hypotheses that are testable within your traffic and engineering limitations.
How to check: identify your current monthly sessions by channel, list critical integrations (checkout provider, payment gateways), and confirm which pages can be edited without a full redesign.
- Quick test: pull a 30‑day traffic overview and calculate baseline conversion per channel.
- Output: a single page objective and a constraints checklist.
Step 2: Validate tracking before you trust any funnel
If events are duplicated, missing, or misattributed, every “drop-off insight” is suspect. Validate core events first: view item, add to cart, begin checkout, purchase (or lead submits).
Why this matters
Bad instrumentation creates false positives and negatives. Before recommending changes, make sure the data you rely on actually reflects user behavior.
How to check: run a p-card purchase or test lead submission and verify the event appears in GA4, your A/B tool, and server-side logs. Check for duplicate events (fired twice) and missing parameters (currency, value).
- Quick test: instrument a test purchase and trace the event through GTM/GA4 and your backend.
- Output: a short instrumentation report listing missing/duplicate events and recommended fixes.
Step 3: Find the leak with 3 evidence layers
We use three layers because any single one lies: quantitative funnels (analytics), qualitative signals (surveys & recordings), and technical validation (logs, devtools).
Why this matters
Combining evidence reduces false leads. For example, analytics may show a drop on checkout, recordings reveal a slow payment widget, and logs confirm payment gateway timeouts — together they identify the true root cause.
How to check: correlate funnel drops with session recordings, look for JavaScript errors in the same timeframe, and review server logs for payment or API failures.
- Quick test: pick a high-traffic day with a known drop and correlate across layers.
- Output: a prioritized list of candidate leaks with supporting evidence for each.
Step 4: Write hypotheses that can be proven wrong
A useful hypothesis includes: the user problem, the change, why it should work, and the measurement. Example: “If we show explicit shipping timing above the CTA, purchase completion will increase because users cite shipping uncertainty in surveys.”
Why this matters
Good hypotheses make experiments interpretable. They force you to pick measurable outcomes and define the minimum detectable effect given your traffic.
How to check: ensure each hypothesis has a clear primary metric, secondary metrics (AOV, bounce), and an expected direction.
- Quick test: convert the top three qualitative themes into testable hypotheses this sprint.
- Output: a hypothesis document ready for engineering handoff.
Step 5: Prioritize by impact × confidence × effort
The output of your audit should be a ranked backlog. We typically score each item by expected revenue impact, confidence (evidence), and engineering effort.
Why this matters
A ranked backlog helps stakeholders agree on what to ship next and prevents busy teams from chasing low-value ideas.
How to check: apply the scoring to the top 10 hypotheses and validate that the highest-scoring items are feasible within the next sprint.
- Quick test: score the top 10 items with a cross-functional group and publish the resulting roadmap.
- Output: a prioritized test calendar for the next 4–8 weeks.
Want us to run the audit?
We’ll audit your funnel, implement fixes, and turn the findings into a prioritized test plan. Our engagement includes an instrumentation review, qualitative sampling, and a prioritized roadmap. Book a call.
Implementation checklist
- Run qualitative research: surveys & session recordings
- Validate tracking & reconcile events across sources
- Produce 8–12 testable hypotheses from evidence
- Prioritize and schedule experiments for the next 4–8 weeks
- Deliver experiment exports, analysis, and a handoff document