{"id":"7f900bbd-e6f0-4dd2-b6dd-a445a65c7376","shortId":"7SZy52","kind":"skill","title":"revenue-audit","tagline":"Audit an email marketing program for revenue leaks — missing flows, dormant high-value subscribers, under-segmentation, promo gaps, stale automations — ranked by estimated $ impact","description":"# Revenue Audit\n\nThe \"email growth consultant in a box.\" Scans your connected ESP for the revenue sitting on the table and ranks every finding by estimated dollar impact, so you know exactly what to fix first.\n\n**Requires:** Cogny MCP + a connected ESP. [Sign up](https://cogny.com)\n\nThis is the skill to run once per quarter, or whenever you inherit an email program and need to know where to start.\n\n## Usage\n\n`/revenue-audit` — full audit of the connected ESP\n\n## Prerequisites Check\n\nDetect connected ESP (check both `mcp__cogny__<svc>__*` and `mcp__<svc>__*` namespaces). If the user has multiple connected, run against each and produce separate reports.\n\n**Revenue data availability (cogny-mcp-proxy as of this writing):**\n\n| ESP | Has flows/automations? | Has purchase/revenue data? |\n|-----|------------------------|----------------------------|\n| Klaviyo | ✓ (`list_flows`) | ✓ (via `list_events` with metric=\"Placed Order\", `value` field) |\n| Mailchimp | ✓ (`tool_list_automations`) | ✗ — engagement only |\n| Rule | ✓ (`tool_list_journeys`) | ✗ — engagement only |\n| Get a Newsletter | ✗ — no flow concept exists | ✗ — engagement only |\n\nFor non-Klaviyo ESPs, revenue estimates must be either (a) supplied by the user (paste historical email revenue), or (b) flagged as \"engagement uplift only\" with no dollar figures.\n\n## Steps\n\n### 1. Establish baseline context\n\nPull once and reference throughout:\n- Total active subscribers\n- Average open rate + CTR (last 90 days)\n- **Klaviyo only:** Total email revenue (last 90 days) via `list_events` with `metric=\"Placed Order\"`, summed `value` field, filtered to events attributed to email campaigns. This is the denominator for every % impact claim.\n- **Klaviyo only:** Average AOV (total revenue / order count in period)\n- List growth rate (last 90 days)\n- Active flows / automations / journeys and their performance (Klaviyo / Mailchimp / Rule only — Get a Newsletter has no equivalent)\n- Last 90 days of campaign sends\n\n**If connected ESP is Mailchimp, Rule, or Get a Newsletter:** prompt the user for their approximate 90-day email revenue (or let them skip it). If they skip, use **estimated value per subscriber per month** = (industry benchmark $0.10–$2.00 depending on vertical) as a rough denominator, and clearly label every revenue estimate as \"based on industry benchmark, not your actual data.\"\n\n### 2. Run the nine checks\n\nFor each check, output: **finding title + estimated $ lift + evidence + recommended fix**.\n\n#### Check 1 — Missing core flows / automations / journeys\n\nMap \"flow\" to the right concept per ESP:\n\n| ESP | Object | Tool |\n|-----|--------|------|\n| Klaviyo | Flow | `list_flows` + `get_flow` |\n| Mailchimp | Automation / Customer Journey | `tool_list_automations` + `tool_get_automation` |\n| Rule | Journey | `tool_list_journeys` |\n| Get a Newsletter | **No equivalent** — skip this check and note the limitation |\n\nThe highest-ROI flows and their typical revenue contribution in healthy ecom programs (Klaviyo benchmarks):\n\n| Flow | Typical % of email revenue | Trigger |\n|------|---------------------------|---------|\n| Welcome series | 3–7% | New subscriber |\n| Abandoned cart | 5–12% | Cart created, not checked out |\n| Browse abandonment | 1–3% | Product viewed, no cart |\n| Post-purchase | 2–5% | Order placed |\n| Winback | 1–4% | No engagement in 60-120d |\n| Replenishment | 2–6% (consumables only) | Predicted reorder date |\n| Birthday / anniversary | 0.5–2% | Date token match |\n| VIP / thank-you | 1–3% | Top-tier LTV crossed |\n\nFor each missing or disabled flow, estimate lift: `total_email_revenue × benchmark_%` as the annualized $ left on the table (Klaviyo). For Mailchimp/Rule/Get a Newsletter without revenue data, frame the impact as engagement uplift (e.g., \"typically adds X–Y% to total email engagement volume\") and skip the dollar estimate unless the user provided a revenue baseline.\n\n#### Check 2 — Dormant high-value subscribers (Klaviyo only)\n\nSegment current active list by:\n- **Ever purchased** × **engaged in last 60 days**\n\nPull purchase history via `list_events` with `metric=\"Placed Order\"`. Group by `profile_id`, take max `value` sum per profile as LTV. Cross-reference against `list_profiles` last-engaged date.\n\nCount subscribers who have purchased at least once but haven't opened/clicked in 60+ days. Estimate:\n`count × avg_AOV × 3% reactivation rate × 2 orders/year = annualized recoverable revenue`\n\n**For Mailchimp / Rule / Get a Newsletter:** purchase data is not exposed. Degrade this check to \"dormant deeply-engaged subscribers\" — pull subscribers with high historical open+click counts who haven't engaged in 60+ days, present as engagement-uplift opportunity without a dollar figure.\n\n#### Check 3 — Under-segmented broadcasts\nScan last 90 days of campaign sends. For each campaign, check if it was sent to:\n- **\"All subscribers\" / full list** (bad default)\n- **A meaningful segment** (engaged, purchased, category-preference, geography)\n\nFlag any campaign sent to >80% of list where segmentation would plausibly apply. Estimate lift: `(segment_ctr - broadcast_ctr) / broadcast_ctr × revenue_of_that_campaign × number_of_similar_sends_per_year`.\n\n#### Check 4 — Promo calendar gaps\nBuild a 90-day send calendar from actual sends. Look for:\n- **Weeks with zero broadcasts** (dead zones)\n- **Dead zones around proven peak buying windows** (BFCM, Mother's Day, back-to-school — vertical-specific)\n- **Over-send weeks** (>4 broadcasts to the same segment in 7 days — fatigue risk)\n\nFor dead zones during peak windows, estimate: `avg revenue per send × N missing sends`.\n\n#### Check 5 — Flow decay\nFor every active flow, check:\n- **Last modified date** — flag if >6 months\n- **Open/CTR trend** — flag if the flow's rolling 30-day rate is >20% below the flow's historical rate\n\nRecommend: refresh subject lines, update product references, re-evaluate offer.\n\n#### Check 6 — Suppression leaks\nCount subscribers who have:\n- No open in 180+ days\n- No click in 180+ days\n- No purchase in 365+ days (if ecom)\n\nSending to these hurts deliverability. Quantify the deliverability drag: \"Removing these X subscribers typically lifts overall open rate by 1-3pp on remaining list, which compounds to ~Y% revenue lift across all future sends.\"\n\n#### Check 7 — Post-purchase upsell / cross-sell gap (Klaviyo only)\n\nPurchase-data-dependent — only runs for Klaviyo. Uses `list_events` with `metric=\"Placed Order\"` to compute repurchase cadence.\n\nFor brands with purchase data:\n- Does a post-purchase flow exist? (Check 1)\n- Does it include category-appropriate upsell or accessory recommendations?\n- What's the 30-day repurchase rate? If <15%, post-purchase is underbuilt.\n\nEstimate: `customers_per_month × current_repurchase_rate_gap × avg_AOV`.\n\n**For Mailchimp / Rule / Get a Newsletter:** skip this check with a one-line note (\"skipped — no purchase data exposed by this ESP's MCP; rerun with Klaviyo or paste purchase data manually\").\n\n#### Check 8 — List capture inference\nCompare list growth rate to benchmarks:\n- Ecom site with steady traffic: 2–5% monthly list growth is healthy\n- <1% monthly growth = under-capturing\n\nIf growth is weak, flag: \"Your site traffic (if paired with analytics MCP) vs. your list growth implies a capture rate of X%. Industry benchmark is 2-5%.\" Recommend: exit-intent popup, post-purchase capture, content offer gate.\n\n#### Check 9 — Over-reliance on discounting\nScan subject lines and body content of broadcasts for % off / $ off / \"sale\" / \"discount\" / promo codes. If >60% of broadcasts lead with a discount:\n- Flag as margin leak + audience training (they wait for sales)\n- Recommend: content-led sends, product drops, exclusivity, early access\n\nEstimate: margin recovery = `current_margin × (promo_share_reduction × conversion_retention)`.\n\n### 3. Rank every finding by estimated $ impact\n\nBucket into 🔴 High (>5% of annual email revenue or >$10k/yr), 🟡 Medium ($1k–$10k/yr), 🟢 Optimization (<$1k/yr or hard to quantify).\n\n### 4. Output\n\n```\nRevenue Audit: <brand> via <ESP>\nPeriod analyzed: <date range>\nTotal email revenue (90d): $<X>     Implied annualized: $<Y>\n\nEstimated annual revenue left on the table: $<Z>   (<Z/Y>% of current)\n\n────────────────────────────────────────────────────\n🔴 HIGH IMPACT — start here\n────────────────────────────────────────────────────\n\n1. Missing abandoned cart flow\n   Est. lift: $<X>/yr   (7% of email revenue benchmark)\n   Evidence: No flow with trigger \"cart created, not converted\" in <ESP>.\n             Current cart sessions on connected store = <N>/month at\n             <conv rate>%. Industry abandoned-cart flow recovery ≈ 10–15%.\n   Fix: Build 3-email cart flow (1h / 24h / 48h). I can draft this now — run\n        /welcome-series and tell it \"abandoned cart\" or ask me to generate.\n\n2. <N> dormant high-value subscribers (LTV > $<X>)\n   Est. lift: $<X>/yr\n   Evidence: <N> past purchasers with avg LTV $<Y>, no engagement in 60+ days.\n             At 3% reactivation × 2 orders/year × $<AOV> AOV.\n   Fix: Run /winback-engine for a tiered winback series.\n\n3. ...\n\n────────────────────────────────────────────────────\n🟡 MEDIUM IMPACT\n────────────────────────────────────────────────────\n\n4. ...\n\n────────────────────────────────────────────────────\n🟢 OPTIMIZATION\n────────────────────────────────────────────────────\n\n7. ...\n\n────────────────────────────────────────────────────\nSuggested 90-day plan\n────────────────────────────────────────────────────\nMonth 1: <high-impact items 1-2>. Est revenue unlock: $<X>.\nMonth 2: <high-impact item 3 + medium 1>. Est: $<Y>.\nMonth 3: <remaining>. Est: $<Z>.\n\nTotal 90-day estimated unlock: $<X+Y+Z>.\n```\n\n### 5. Create a finding for every item\n\nEvery finding in the output also becomes a `create_finding` in Cogny, so the dashboard has the same ranked list the user just saw:\n\n```json\n{\n  \"title\": \"Missing abandoned cart flow (est. +$<X>/yr)\",\n  \"body\": \"<evidence + fix>\",\n  \"action_type\": \"flow_build\",\n  \"expected_outcome\": \"Recover 10-15% of abandoned carts\",\n  \"estimated_impact_usd\": <annualized impact>,\n  \"priority\": \"high\"\n}\n```\n\n### 6. Save the audit summary\n\nPersist to context tree under `insights/email/revenue-audit/<date>` so the next audit can show progress:\n- \"Last audit: 2026-01-15 — identified $120k/yr unlock; 2 items now implemented.\"\n\n### 7. Offer next steps\n\n```\nNext steps I can help with right now:\n\n  /welcome-series          — draft any missing flow\n  /winback-engine          — execute #2 (dormant high-value)\n  /subject-line-lab        — lift open rates on existing broadcasts\n  /email-report weekly     — start tracking progress against this audit\n\nOr: ask me to draft a specific flow by name.\n```\n\n## Notes\n\n- **Benchmarks are rough.** Real uplift depends on list quality, brand, vertical, and offer. Always frame estimates as \"reasonable industry range\" not \"guaranteed.\"\n- Don't double-count. If \"missing abandoned cart\" and \"post-purchase gap\" both hit, check that the benchmark assumptions aren't overlapping.\n- **ESP-specific check coverage (cogny-mcp-proxy):**\n  - Klaviyo: all 9 checks run with dollar estimates.\n  - Mailchimp: Checks 1, 3, 4, 5, 6, 8, 9 run normally. Checks 2 and 7 degrade to \"engagement-only\" framing.\n  - Rule: same as Mailchimp.\n  - Get a Newsletter: Check 1 is skipped (no flow concept). Checks 2 and 7 are skipped (no purchase data). The remaining checks (3, 4, 5, 6, 8, 9) run on engagement data only.","tags":["revenue","audit","claude","code","marketing","skills","cognyai","agent-skills","ai-agents","claude-code","claude-skills","cluade-mcp"],"capabilities":["skill","source-cognyai","skill-revenue-audit","topic-agent-skills","topic-ai-agents","topic-claude-code","topic-claude-skills","topic-cluade-mcp","topic-cursor","topic-geo","topic-growth-hacking","topic-llm","topic-marketing","topic-mcp","topic-seo"],"categories":["claude-code-marketing-skills"],"synonyms":[],"warnings":[],"endpointUrl":"https://skills.sh/cognyai/claude-code-marketing-skills/revenue-audit","protocol":"skill","transport":"skills-sh","auth":{"type":"none","details":{"cli":"npx skills add cognyai/claude-code-marketing-skills","source_repo":"https://github.com/cognyai/claude-code-marketing-skills","install_from":"skills.sh"}},"qualityScore":"0.474","qualityRationale":"deterministic score 0.47 from registry signals: · indexed on github 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