SOP 01: Diagnosis

Goal of Diagnosis

The goal of diagnosis is to identify the highest-value revenue leaks in the current ecommerce system.

Diagnosis is not a general audit.

It is a focused pass to answer:

Where is revenue leaking, and which leaks are worth formal action now?

Required Inputs

• GA4 funnel behavior
• Shopify commercial data
• Meta Ads traffic quality context
• Clarity or Hotjar behavior evidence
• current merchandising or offer context
• known business constraints such as margin, shipping rules, or stock issues

What Counts as a Revenue Leak

A revenue leak is a friction point, mismatch, or missing signal that likely suppresses commercial performance.

Examples:

• low add-to-cart rate on high-intent product traffic
• weak product value clarity above the fold
• shipping cost surprise in cart
• checkout friction on mobile
• trust signal weakness near purchase decision
• poor search relevance causing high exit after internal search
• low repeat purchase rate after first order

What the Output Must Look Like

The output is a short ranked list of top leaks with evidence and first-direction logic.

Each leak should include:

• leak name
• affected step
• evidence
• likely cause
• suggested first move
• confidence note

Example of a Top Leaks Output

Leak 1: Weak value clarity on hero SKUs

• Step: PRODUCT
• Evidence: Product detail pages for top-selling SKUs have strong traffic but low add-to-cart rate relative to category traffic quality.
• Likely cause: Key benefits and use-case clarity appear below the fold, while the first screen is dominated by imagery and variant controls.
• First move: Move value bullets, delivery promise, and trust support above the fold.
• Confidence: High because GA4 shows low add-to-cart and session replay shows rapid scroll-and-bounce behavior.

Leak 2: Shipping cost surprise in cart

• Step: CART
• Evidence: Large drop from cart view to checkout start, especially for mobile sessions and lower basket sizes.
• Likely cause: Shipping thresholds and delivery cost are not clear until late in the cart.
• First move: Add shipping threshold progress and estimated delivery cost messaging in cart.
• Confidence: Medium-high because Shopify and behavior evidence align, but we still need basket-size segmentation.

Leak 3: Mobile checkout friction

• Step: CHECKOUT
• Evidence: Checkout start is healthy, but completion drops sharply on mobile relative to desktop.
• Likely cause: Too much form friction and weak reassurance during the payment step.
• First move: Reduce field noise where possible and improve reassurance around payment security and delivery timing.
• Confidence: Medium because the leak is clear but the root friction may have more than one cause.

What Not to Do

• do not start with redesign concepts
• do not list twenty issues with no ranking
• do not confuse interesting behavior with revenue impact
• do not treat every analytics anomaly as a leak
• do not write diagnosis outputs with no suggested next move

Short Diagnosis Checklist

• Is the leak tied to revenue, not just engagement?
• Do we have evidence from at least one strong source?
• Can we describe the problem in one sentence?
• Do we know which step of the funnel it affects?
• Is there a plausible first action?
• Is the confidence level honest?

Handoff

Every accepted leak moves into the matrix as one or more nodes.