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Grok Bot+18 agentsConversion Leak Detector — Discover Where Revenue Is Being
Diagnose revenue and margin leakage across conversion, pricing, billing, retention and measurement.
$9.99
Conversion Leak Detector — Discover Where Revenue Is Being
Example session with this skill installed
Illustrative scenario: Audit this one-time ecommerce funnel for one mature month. There were 1,000 checkout starts, 600 paid orders at $50 each, 60 full refunds, and 100 logged payment failures. We cannot tell whether failed attempts belong to the 400 non-buyers or customers who later paid. Gross margin rate before refunds is 40%; refund costs and other fees are unknown. Give a reconciled revenue view, leakage hypotheses and one experiment plan. Do not use external benchmarks or change billing.
- Read your context and instructions
- Compiled the conversion leak detector
Revenue reconciliation
One-time ecommerce model; supplied month is described as mature. Paid-order conversion is 600 / 1,000 = 60% only if starts and orders use a consistent cohort and deduplicated unit. Confirm those definitions before relying on the rate.
| Measure | Calculation | Result / qualification |
|---|---|---|
| Gross paid revenue | 600 × $50 | $30,000 |
| Full refund amount | 60 × $50 | $3,000 |
| Revenue after supplied refunds | $30,000 − $3,000 | $27,000 before other adjustments |
| Refund incidence | 60 / 600 | 10% of paid orders |
| Pre-refund gross margin proxy | $30,000 × 40% | $12,000; not net profit |
| Post-refund gross profit | Not safely calculable | Returned goods, unrecovered costs and fees unknown |
Diagnostic hypotheses
H1: payment problems may impede purchases, but 100 failure events are not 100 lost customers. Attempts may repeat or later succeed. H2: checkout friction or low-quality demand may explain some of the 400 starts without paid orders; no root cause is established. H3: refunds may reverse apparent conversion wins, so acquisition and checkout changes require refund and margin guardrails. H4: inconsistent event definitions could mimic leakage.
Evidence needed
Reconcile deduplicated checkout IDs to orders, payment-attempt sequences, final outcomes, refund timing and reasons. Segment by device, payment method, traffic source and new/returning buyer where valid data exists. Confirm costs, fees and cohort maturity. Do not add a separate $5,000 payment-loss estimate to funnel opportunity: overlap is unknown.
Transparent scenario
If a verified intervention produced 20 additional paid orders at the same $50 order value, gross revenue would be $1,000 before refunds and costs. At a confirmed 40% pre-refund margin it would represent $400 before refund effects and incremental expenses. This is a hypothetical scenario, not predicted or recovered value.
Proposed experiment
After identifying a reproducible payment error, test a truthful recovery path with approved copy and a clear alternative payment option where supported. Randomize eligible users if feasible. Primary metric: deduplicated paid orders; guardrails: refunds, chargebacks, gross contribution, support cost and complaints. Define sample size and observation window from actual baseline data.
No experiment ran, billing changed or recovered revenue was recorded. Start with data reconciliation, then choose a supported root-cause treatment.
Connects securely to your tools. The creator never sees your data.
About this skill
Conversion Leak Detector helps revenue, growth and finance teams find where valuable demand, customer value or margin is lost across the revenue system. It goes beyond funnel drop-off to examine pricing and discounts, checkout and payment failures, sales handoffs, activation, retention, renewal, expansion, refunds and attribution.
The workflow reconciles revenue definitions, maps the journey, segments supplied data, distinguishes measurement artifacts and healthy qualification from genuine leakage, estimates recoverable value transparently and proposes corrective experiments with downstream profit guardrails. File exports, screenshots and optional calculation tools support deterministic analysis of cohorts, waterfalls, churn, payment recovery and gross margin.
Authorized connectors are optional; without tools it provides formulas, schemas and manual audit procedures. It never invents transactions, benchmarks or actions. Prices, billing, subscriptions and communications remain subject to authorization; recommendations do not guarantee recovered revenue or profit.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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