Investigate checkout completion for Google traffic on /starter-kit

Synthetic demo. This report was written from an actual run of the bundled analyzer against generated data. It is not a customer result or a recovered-revenue claim.

The decision

Prioritize the /starter-kit × google checkout path. Purchase conversion fell from 4.00% to 2.00%, with the decline localized to checkout completion. Verify measurement first, then investigate the checkout experience. A payment or checkout defect is a hypothesis, not an established cause.

What to do next

Work through this action list in order. Owners and timing below are suggested; the report has not assigned work or made changes.

1. Verify that the drop is real

Priority: first · Timing: today · Suggested owner: Analytics

• Do: reconcile the August 29–September 4 export for /starter-kit × google with the source analytics report: 14,000 sessions, 280 purchase sessions, and a 2.00% purchase rate. Confirm both periods use the same timezone, session attribution, purchase-event definition, and settled date boundaries. Count sessions with a purchase, not raw purchase events.
• Done when: the counts reconcile and the event mapping is documented, or a specific measurement discrepancy is recorded with supporting evidence.
• Next decision: if tracking or export mapping is wrong, correct it and rerun before diagnosing the page. If measurement checks out, continue to action 2.

2. Reproduce the affected checkout path

Priority: next · Timing: today, after measurement checks · Suggested owner: Engineering

• Do: open /starter-kit using the affected Google campaign parameters and follow the purchase path in a test environment. Compare it with an unaffected path under equivalent conditions. Inspect checkout and payment errors, purchase-event firing, and changes released around August 29. Confirm the affected campaign values from the export before testing.
• Done when: there is a reproducible failure with steps and evidence, or a documented clean test with the environments and paths checked. A clean test does not rule out an intermittent or device-specific problem.
• Next decision: a confirmed defect goes to action 3. If no cause is found, inspect available device/browser breakdowns, consented session replays, and support feedback before proposing a change. Keep the cause marked unresolved.

3. Fix the confirmed cause, or write a test brief

Priority: after diagnosis · Timing: once evidence supports a change · Suggested owner: Engineering; CRO/Marketing for an experiment

• Do: for a confirmed defect, record the affected behavior, proposed fix, regression check, and rollback plan; ship through the team's normal approval process. If the evidence instead supports a usability hypothesis, write one experiment brief with the observation, proposed change, purchase-conversion metric, guardrails, and a sample-size and stopping plan.
• Done when: the defect fix passes its regression check and its release time is recorded, or the experiment brief is ready for review. A proposed experiment is not a verified fix.
• Next decision: do not redesign the page or cut Google spend solely because this report flagged a decline. Traffic mix is unchanged in this example, and the report does not establish campaign inefficiency.

4. Verify the result and record the outcome

Priority: after the change · Timing: after a complete, settled comparison window · Suggested owner: Analytics

• Do: rerun the analyzer with the prior scan state. Compare the same page/source segment, checkout completion, purchase conversion, volume, and data quality. Use the unaffected comparison path to help distinguish a local recovery from a broader change. Evaluate an experiment using its agreed plan, rather than treating a before-and-after comparison as causal proof.
• Done when: a follow-up records the new dates, sample sizes, rates, remaining alerts, and a decision to close, continue investigating, or revert. If evidence is insufficient, keep the action open and state what data is still needed.
• Success check: look for sustained improvement with valid tracking and no guardrail regression. The historical 4.00% purchase rate is context, not a guaranteed target; the modeled $28,000 is not a recovery commitment.

Evidence

The comparison covers August 29–September 4, 2026, against a 28-day baseline of August 1–28, 2026.

For the affected /starter-kit × google segment, the engine reports:

• $28,000 revenue at stake in the comparison window. This is a modeled baseline difference, not observed lost sales or guaranteed recovery.
• 4.00% → 2.00% purchase conversion, a 50% relative decline.
• 14,000 current sessions and 280 purchase sessions, compared with 56,000 baseline sessions.
• 40.00% → 20.00% checkout completion. The earlier funnel transitions are unchanged in this generated scenario.
• A 12.97% relative minimum detectable effect for this segment. The planted decline is larger than that sensitivity estimate.

The site-wide purchase conversion is 3.67%, compared with 4.00% in its baseline. The scan tests 84 cell/step comparisons and identifies one root finding. Four overlapping findings on the page, source, medium, and campaign are attributed to the same affected segment. Their revenue figures must not be added together.

The engine reports zero traffic-mix contribution in its tested dimensions and no tracking-break or data-quality findings in this run. These are observations about the controlled input, not proof that an arbitrary live export has sound tracking.

Reproduce it

The download includes the generated CSVs, their deterministic generator, configuration, metadata, baseline state, raw findings, and exact commands. No analytics account or credentials are needed for the example.

The public edition passed 28 bundled synthetic checks and 4 showcase regression checks. The latter verify that the headline uses the declared purchase conversion, the correct page/source is identified, checkout is localized, and overlapping aggregates are explained.

Scope

This example demonstrates a planted checkout-completion change. It uses session attribution, fixed traffic composition, and synthetic revenue. It does not test a live integration, establish causation, validate your export mapping, or demonstrate multi-month drift. The broader bundled suite separately exercises mix shifts, drift, tracking breaks, incomplete data, and restatements.