FREE ECOMMERCE DIAGNOSTIC
Find where paid traffic stops turning into revenue
Run a five-minute diagnostic for established ecommerce brands. Map the risks across measurement, traffic quality, message match, conversion friction and unit economics—then leave with a prioritised action plan.
No account connection. No card. See your result before deciding whether to share your details.
CLICK → REVENUE
7 HANDOFFS
Ad impression
01
Click
02
Landing experience
03
Offer decision
04
Cart or lead action
05
Checkout or follow-up
06
Revenue signal
07
DIRECT ANSWER
What is a paid traffic leak?
A paid traffic leak is a measurable or suspected breakdown between an advertising click and profitable customer revenue. It can occur in campaign targeting, message match, tracking, the landing experience, checkout or customer economics. A useful audit checks these areas together before recommending more spend.
The diagnosis starts with evidence across the full journey—not with an assumption that the ad platform is the problem.
Updated 18 August 2026. This guide is educational and does not connect to ad accounts or commerce systems.
Assessment and calculator values are saved in local storage on this device only under a versioned key. Raw answers and financial inputs are not sent to analytics.
Assessment
Complete all 15 controls to generate the five-part leak map. Confidence reflects evidence completion, not predicted performance.
Progress: 0/15.
Personalised results
Please answer all 15 questions to view the final five-part leak map.
Opportunity calculator
Current orders = paid sessions × current conversion rate
Current revenue = current orders × average order value
Scenario orders = paid sessions × target conversion rate
Additional orders = scenario orders − current orders
Scenario revenue difference = additional orders × average order value
Scenario gross-profit difference = scenario revenue difference × gross margin
Current CPA = ad spend ÷ current orders
Effective CPC = ad spend ÷ paid sessions
This is a scenario based on your inputs, not a prediction or guaranteed recoverable revenue.
Please complete all required calculator inputs and provide a target value.
Interactive buyer journey
Ad impression
Definition: The moment an eligible user sees your paid creative.
Common evidence: Reach, frequency, placement quality, and audience fit.
First check: Check if impressions are concentrated in contexts aligned to buying intent.
Symptom lookup
| Symptom | Possible leak area | Evidence to inspect | False conclusion to avoid | First check |
|---|---|---|---|---|
| Clicks but no sales | Conversion journey / offer fit | Landing relevance, checkout drop-off, session quality by source | “The channel is broken.” | Validate mobile purchase path manually and check intent segments. |
| Platform conversions higher than commerce revenue | Measurement integrity | Attribution windows, duplicate events, refund timing | “Revenue disappeared.” | Reconcile directional trends by week across systems. |
| Strong desktop / weak mobile conversion | Conversion journey | Mobile UX, speed, form friction, payment methods | “Mobile users do not buy.” | Run device-segmented funnel diagnostics and usability tests. |
| High add-to-cart / low checkout completion | Checkout flow | Shipping surprise, trust signals, payment failures | “Pricing is the only issue.” | Inspect checkout exits and error logs by step. |
| Good first-order ROAS / poor margin | Economics and follow-up | Contribution margin by product/order mix | “ROAS means profitable growth.” | Model margin-adjusted acquisition thresholds. |
| Leads with slow follow-up | Follow-up systems | Response-time SLA and lead-contact outcomes | “Lead quality is poor.” | Measure speed-to-lead and contact attempt coverage. |
FULL DIAGNOSTIC GUIDE
Read the full diagnostic guide
Paid ads can get clicks and still fail commercially. The useful question is not only whether the campaign worked, but where intent, evidence or margin stopped carrying through to revenue.
The five chapters below separate reported controls from observed evidence, call out common false conclusions and give a first test for each leak area.
FIVE LEAK CATEGORIES
Where paid acquisition can break down
Each category answers a different diagnostic question. Read the definition first, then compare the symptom with evidence before deciding what to change.
01
Measurement integrity
02
Traffic and offer fit
03
Ad-to-page match
04
Conversion journey
05
Economics and follow-up
01 / MEASUREMENT INTEGRITY
Can you trust the conversion signal?
Measurement integrity is the degree to which the events, values and attribution signals used for decisions reflect what happened in the commerce or CRM system.
Common symptoms: duplicate purchases, missing revenue values, large platform-to-commerce gaps, unexplained swings after consent or site changes.
Evidence to inspect — test orders, transaction IDs, event timestamps, consent states, deduplication, cross-domain journeys and directional weekly reconciliation.
Common false conclusion — a platform-reported conversion count is automatically the commercial truth.
First test or correction — complete a manual purchase journey, record every expected event and reconcile the resulting order across the ad platform, analytics and commerce system.
When specialist help is justified — event logic, consent mode, server-side tracking or cross-domain flows cannot be explained and tested by the team that owns them.
02 / TRAFFIC AND OFFER FIT
Are you buying the right visit for the offer?
Traffic and offer fit describes whether the campaign objective, audience intent and destination match the commercial action the business actually wants.
Common symptoms: cheap clicks with weak product engagement, irrelevant search terms, poor placement quality, low-intent audiences and homepage-heavy destination paths.
Evidence to inspect — campaign objective, search terms, audience and placement reports, device mix, exclusions, post-click product engagement and destination relevance.
Common false conclusion — a lower cost per click is always a better acquisition outcome.
First test or correction — isolate a high-intent campaign, remove obviously weak sources and send it to the most relevant product or offer page.
When specialist help is justified — the team cannot distinguish media efficiency from commercial quality or has no clear taxonomy for intent, exclusions and destination mapping.
03 / AD-TO-PAGE MESSAGE MATCH
Does the page continue the promise made by the ad?
Message match is the continuity of product, promise, price, offer and next action from the paid creative into the first landing experience.
Common symptoms: strong click-through with fast exits, visitors searching for the promoted product, expired sale language, missing inventory and unclear mobile first viewports.
Evidence to inspect — exact ad and URL pairs, first-viewport screenshots, offer expiry, product availability, price consistency, trust signals and the visible next action.
Common false conclusion — a high click-through rate proves the destination is doing its job.
First test or correction — compare the three highest-spend ads with their exact mobile landing view and score continuity across product, promise, price and offer.
When specialist help is justified — campaigns and landing pages are owned by different teams with no shared offer inventory, QA process or message-match standard.
04 / CONVERSION JOURNEY
Can a ready buyer complete the next step?
Conversion journey quality is the absence of avoidable friction from product understanding through form, cart, checkout, payment or lead follow-up.
Common symptoms: strong add-to-cart with weak checkout completion, mobile drop-off, surprise shipping costs, payment errors, unclear returns and slow lead acknowledgement.
Evidence to inspect — device and stage conversion, form-field abandonment, checkout errors, payment methods, delivery messaging, returns clarity, recordings and manual journey tests.
Common false conclusion — checkout abandonment always means the price is too high.
First test or correction — complete the mobile journey on a real device using common payment paths and record every unresolved question, delay and error.
When specialist help is justified — the journey spans theme code, apps, payments and CRM handoffs that no single owner can test or prioritise end to end.
05 / ECONOMICS AND FOLLOW-UP
Does the conversion create acceptable customer value?
Economics and follow-up connect acquisition cost to contribution margin, repeat purchase, recovery and the speed or quality of the response after intent is expressed.
Common symptoms: acceptable ROAS with weak cash contribution, heavy discount dependence, low repeat purchase, unmeasured cart recovery and slow or generic lead follow-up.
Evidence to inspect — contribution margin by order type, allowable acquisition cost, discount and refund effects, first versus returning orders, recovery coverage and response timing.
Common false conclusion — platform ROAS on its own proves profitable growth.
First test or correction — calculate a margin-aware acquisition threshold for first orders, then separate returning-customer economics and test one recovery or follow-up sequence.
When specialist help is justified — media, finance and lifecycle teams use different definitions of acceptable acquisition and no one can trace a click to contribution or follow-up quality.
A completed leak report—without invented proof
The company, inputs and scenario below are fictional. They demonstrate the format of the action plan and calculator; they are not a client result, benchmark or prediction.
FICTIONAL COMPANY / MODELLED MONTH
Harbor & Field
Starting condition — 42,000 monthly paid sessions, 1.10% paid-traffic conversion rate and £68 average order value.
What the demonstration plan changes — validate the purchase event, align the three highest-spend ads with their exact product pages and clarify delivery costs before checkout.
Measurement window — one modelled month. No observed post-change result exists because this is not a real engagement.
Attribution limitation — seasonality, traffic mix, promotions, refunds and margin could materially change the commercial outcome.
PAID TRAFFIC LEAK REPORT
Completed example / redacted demonstration
V1.0
01
Measurement — unverified
02
Traffic — partly covered
03
Message — reported gap
04
Conversion — reported gap
05
Economics — unverified
Three actions to investigate first
1. Test and reconcile the purchase event before changing spend.
2. Compare the three highest-spend ads with their exact mobile destinations.
3. Make delivery cost and timing visible before checkout begins.
MODELLED SCENARIO
A target conversion rate of 1.35% would model 105 additional orders and a £7,140 revenue difference for one month.
SEVEN-DAY INVESTIGATION
One focused correction in seven days
Do not attempt to fix every reported gap at once. Use this sequence to improve the evidence, rank the risks and launch one measurable correction.
DAY 1
Validate revenue and conversion events
DAY 2
Review campaign objectives and traffic quality
DAY 3
Compare ads with their exact destination pages
DAY 4
Test mobile product, cart and checkout paths
DAY 5
Calculate contribution economics
DAY 6
Rank findings by evidence, impact and effort
DAY 7
Launch one measurable correction
Useful direction without pretending to observe your account
The free audit organises what your team reports, identifies missing evidence and gives you a practical inspection order. It cannot validate configuration, prove causality or diagnose a performance issue it has not observed.
FREE / SELF-REPORTED
What the assessment can establish
It can show reported control coverage, reported No answers, partial gaps, evidence gaps and the confidence of the diagnosis based on supported answers.
CAN ESTABLISH
— Which controls the respondent says are present, partial, absent or unknown.
— Which evidence should be inspected first and who should own the next check.
— A transparent scenario from visitor-supplied calculator inputs.
CANNOT ESTABLISH
— Whether a tag, campaign, page or checkout is configured correctly.
— How much revenue is being lost or what result a correction will produce.
— Whether a reported symptom is caused by media, site experience, inventory, economics or another factor.
OPTIONAL / EVIDENCE-LED
What a human review adds
A human review can inspect selected configuration, source data and live journeys, then separate observed facts from reported context and professional judgement.
— Manual validation of agreed conversion and revenue events.
— Review of campaign intent, destination mapping and selected traffic-quality evidence.
— Mobile journey inspection and evidence-backed priority ranking.
— A practical correction plan with owner, effort, confidence and measurement window.
How the audit reaches a conclusion
The audit converts self-reported control answers into coverage, gap and confidence summaries. It does not infer unobserved account performance, use an industry benchmark or calculate lost revenue.
Scoring and evidence rules
Yes = 2, Partly = 1, No = 0. Not applicable is excluded. Don’t know is excluded from coverage and counted as an evidence gap.
Control coverage summarises the supported answers. Diagnostic confidence reflects the share of applicable questions with a supported answer.
Observed means directly inspected evidence; reported means information supplied by the respondent; unverified means the evidence is not yet established. The free tool produces reported and unverified findings only.
Priority combines lower reported coverage, reported No answers and evidence gaps. It is an investigation order—not a scientific performance benchmark.
Authorship and governance
Author — Dylan Flockline, Orchidea
Reviewer — pending assignment and approval
Method version — 1.0 · Materially updated — 18 August 2026
Privacy — assessment and calculator values remain in local storage on the visitor’s device unless the visitor deliberately opens an email draft. Raw answers and financial values are not included in analytics events.
V1.0 / 18 AUG 2026
COMMON QUESTIONS
Questions before you use the audit
The short answer appears immediately below each question so the important limitations stay visible.
What is a paid traffic leak?
A paid traffic leak is a measurable or suspected breakdown between an advertising click and profitable customer revenue. It can occur in targeting, message match, tracking, the landing experience, checkout or customer economics.
Does this replace a platform account audit?
No. It checks controls and evidence across the whole customer journey without connecting to an ad account. A specialist review can inspect the underlying configuration and data.
Is the control coverage score a performance benchmark?
No. It summarises the controls you report having in place. It does not predict revenue, ROAS or conversion performance.
Will Orchidea store my performance data?
Not by default. Assessment and calculator values stay in local storage on this device. Raw answers and financial inputs are not sent to analytics.
Can the calculator tell me exactly how much revenue I am losing?
No. It models a scenario from the values and target you enter. It is not a prediction or a claim that the difference is recoverable.
What should I do with a low-confidence result?
Validate measurement and investigate the unanswered areas first. A low-confidence result means the evidence is incomplete, not that performance is necessarily poor.