name: revenue-audit description: Audit an email marketing program for revenue leaks — missing flows, dormant high-value subscribers, under-segmentation, promo gaps, stale automations — ranked by estimated $ impact version: "1.0.0" author: Cogny AI requires: cogny-mcp platforms: [klaviyo, mailchimp, rule, get-a-newsletter] user-invocable: true argument-hint: "" allowed-tools:

Cogny Cloud (aggregated) namespace

• mcp__cogny__klaviyo__*
• mcp__cogny__mailchimp__*
• mcp__cogny__rule__*
• mcp__cogny__get_a_newsletter__*
• mcp__cogny__create_finding
• mcp__cogny__write_context_node
• mcp__cogny__read_context_node

Cogny Solo / Lite (per-ESP direct) namespace

• mcp__klaviyo__*
• mcp__mailchimp__*
• mcp__rule__*
• mcp__get_a_newsletter__*
• WebFetch
• Bash
• Read
• Write

Revenue Audit

The "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.

Requires: Cogny MCP + a connected ESP. Sign up

This is the skill to run once per quarter, or whenever you inherit an email program and need to know where to start.

Usage

/revenue-audit — full audit of the connected ESP

Prerequisites Check

Detect connected ESP (check both mcp__cogny__<svc>__* and mcp__<svc>__* namespaces). If the user has multiple connected, run against each and produce separate reports.

Revenue data availability (cogny-mcp-proxy as of this writing):

For 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.

Steps

1. Establish baseline context

Pull once and reference throughout:

• Total active subscribers
• Average open rate + CTR (last 90 days)
• 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.
• Klaviyo only: Average AOV (total revenue / order count in period)
• List growth rate (last 90 days)
• Active flows / automations / journeys and their performance (Klaviyo / Mailchimp / Rule only — Get a Newsletter has no equivalent)
• Last 90 days of campaign sends

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."

2. Run the nine checks

For each check, output: finding title + estimated $ lift + evidence + recommended fix.

Check 1 — Missing core flows / automations / journeys

Map "flow" to the right concept per ESP:

The highest-ROI flows and their typical revenue contribution in healthy ecom programs (Klaviyo benchmarks):

For 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.

Check 2 — Dormant high-value subscribers (Klaviyo only)

Segment current active list by:

• Ever purchased × engaged in last 60 days

Pull 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.

Count subscribers who have purchased at least once but haven't opened/clicked in 60+ days. Estimate: count × avg_AOV × 3% reactivation rate × 2 orders/year = annualized recoverable revenue

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.

Check 3 — Under-segmented broadcasts

Scan last 90 days of campaign sends. For each campaign, check if it was sent to:

• "All subscribers" / full list (bad default)
• A meaningful segment (engaged, purchased, category-preference, geography)

Flag 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.

Check 4 — Promo calendar gaps

Build a 90-day send calendar from actual sends. Look for:

• Weeks with zero broadcasts (dead zones)
• Dead zones around proven peak buying windows (BFCM, Mother's Day, back-to-school — vertical-specific)
• Over-send weeks (>4 broadcasts to the same segment in 7 days — fatigue risk)

For dead zones during peak windows, estimate: avg revenue per send × N missing sends.

Check 5 — Flow decay

For every active flow, check:

• Last modified date — flag if >6 months
• Open/CTR trend — flag if the flow's rolling 30-day rate is >20% below the flow's historical rate

Recommend: refresh subject lines, update product references, re-evaluate offer.

Check 6 — Suppression leaks

Count subscribers who have:

• No open in 180+ days
• No click in 180+ days
• No purchase in 365+ days (if ecom)

Sending 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."

Check 7 — Post-purchase upsell / cross-sell gap (Klaviyo only)

Purchase-data-dependent — only runs for Klaviyo. Uses list_events with metric="Placed Order" to compute repurchase cadence.

For brands with purchase data:

• Does a post-purchase flow exist? (Check 1)
• Does it include category-appropriate upsell or accessory recommendations?
• What's the 30-day repurchase rate? If <15%, post-purchase is underbuilt.

Estimate: customers_per_month × current_repurchase_rate_gap × avg_AOV.

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").

Check 8 — List capture inference

Compare list growth rate to benchmarks:

• Ecom site with steady traffic: 2–5% monthly list growth is healthy
• <1% monthly growth = under-capturing

If 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.

Check 9 — Over-reliance on discounting

Scan subject lines and body content of broadcasts for % off / $ off / "sale" / "discount" / promo codes. If >60% of broadcasts lead with a discount:

• Flag as margin leak + audience training (they wait for sales)
• Recommend: content-led sends, product drops, exclusivity, early access

Estimate: margin recovery = current_margin × (promo_share_reduction × conversion_retention).

3. Rank every finding by estimated $ impact

Bucket into 🔴 High (>5% of annual email revenue or >$10k/yr), 🟡 Medium ($1k–$10k/yr), 🟢 Optimization (<$1k/yr or hard to quantify).

4. Output

Revenue Audit: <brand> via <ESP>
Period analyzed: <date range>
Total email revenue (90d): $<X>     Implied annualized: $<Y>

Estimated annual revenue left on the table: $<Z>   (<Z/Y>% of current)

────────────────────────────────────────────────────
🔴 HIGH IMPACT — start here
────────────────────────────────────────────────────

1. Missing abandoned cart flow
   Est. lift: $<X>/yr   (7% of email revenue benchmark)
   Evidence: No flow with trigger "cart created, not converted" in <ESP>.
             Current cart sessions on connected store = <N>/month at
             <conv rate>%. Industry abandoned-cart flow recovery ≈ 10–15%.
   Fix: Build 3-email cart flow (1h / 24h / 48h). I can draft this now — run
        /welcome-series and tell it "abandoned cart" or ask me to generate.

2. <N> dormant high-value subscribers (LTV > $<X>)
   Est. lift: $<X>/yr
   Evidence: <N> past purchasers with avg LTV $<Y>, no engagement in 60+ days.
             At 3% reactivation × 2 orders/year × $<AOV> AOV.
   Fix: Run /winback-engine for a tiered winback series.

3. ...

────────────────────────────────────────────────────
🟡 MEDIUM IMPACT
────────────────────────────────────────────────────

4. ...

────────────────────────────────────────────────────
🟢 OPTIMIZATION
────────────────────────────────────────────────────

7. ...

────────────────────────────────────────────────────
Suggested 90-day plan
────────────────────────────────────────────────────
Month 1: <high-impact items 1-2>. Est revenue unlock: $<X>.
Month 2: <high-impact item 3 + medium 1>. Est: $<Y>.
Month 3: <remaining>. Est: $<Z>.

Total 90-day estimated unlock: $<X+Y+Z>.

5. Create a finding for every item

Every finding in the output also becomes a create_finding in Cogny, so the dashboard has the same ranked list the user just saw:

{
  "title": "Missing abandoned cart flow (est. +$<X>/yr)",
  "body": "<evidence + fix>",
  "action_type": "flow_build",
  "expected_outcome": "Recover 10-15% of abandoned carts",
  "estimated_impact_usd": <annualized impact>,
  "priority": "high"
}

6. Save the audit summary

Persist to context tree under insights/email/revenue-audit/<date> so the next audit can show progress:

• "Last audit: 2026-01-15 — identified $120k/yr unlock; 2 items now implemented."

7. Offer next steps

Next steps I can help with right now:

  /welcome-series          — draft any missing flow
  /winback-engine          — execute #2 (dormant high-value)
  /subject-line-lab        — lift open rates on existing broadcasts
  /email-report weekly     — start tracking progress against this audit

Or: ask me to draft a specific flow by name.

Notes

• Benchmarks are rough. Real uplift depends on list quality, brand, vertical, and offer. Always frame estimates as "reasonable industry range" not "guaranteed."
• Don't double-count. If "missing abandoned cart" and "post-purchase gap" both hit, check that the benchmark assumptions aren't overlapping.
• ESP-specific check coverage (cogny-mcp-proxy):
  • Klaviyo: all 9 checks run with dollar estimates.
  • Mailchimp: Checks 1, 3, 4, 5, 6, 8, 9 run normally. Checks 2 and 7 degrade to "engagement-only" framing.
  • Rule: same as Mailchimp.
  • 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.