--- name: subscription-monetization-audit description: 'Use when analyzing a subscription or recurring-revenue business (news site, paywall publisher, SaaS-light, membership platform, e-commerce with subscription tier) for monetization gaps and revenue-growth levers. Combines outside-in detection (paywall structure, tier visibility, ad density, trust signals) with optional inside-out import of MRR / churn / cohort data from a Stripe / Chargebee / Recurly CSV export. Triggers include "wir verbrennen Geld und wissen nicht woher das Wachstum kommen soll", "MRR stagniert", "Abo-Konversion ist zu niedrig", "subscription revenue model review", "premium tier evaluation", "warum wachsen wir nicht mehr", "Burn-Rate vs. Revenue-Lücke schließen". Anti-Use: NICHT für reine transaktionale E-Commerce-Shops ohne Subscription (`channel-economics-analyzer` ist dafür), NICHT für B2B-Enterprise-SaaS mit Custom-Pricing (anderes Playbook), NICHT für Pre-Revenue-Startups (Skill setzt eine bestehende Subscription-Basis voraus).' user-invokable: true argument-hint: '[domain | --csv path-to-export.csv]' allowed-tools: [Read, Write, Bash(node:*), Bash(curl:*)] license: MIT metadata: author: Max Schottke version: '0.5.2' category: marketing --- # Subscription Monetization Audit ## Overview A framework for diagnosing revenue gaps in subscription / recurring-revenue businesses and identifying the five to seven monetization levers that close the gap. Works in two modes that produce different depth of insight: - **Outside-In mode** — purely from the public website. Detects paywall structure, tier setup, ad-network density, trust signals, AI-shopping readiness. Lower confidence, no MRR / churn data. - **Inside-Out mode** — adds a CSV export from the subscription billing system (Stripe / Chargebee / Recurly / Shopify Subscriptions / custom dashboard). Computes MRR, ARPU, churn, cohort retention, plan distribution. Much higher confidence. The skill is consciously not vertical-specific. The same five-lever playbook applies to a news paywall, a SaaS plugin marketplace, a fitness membership site, or an e-commerce shop with a subscription tier — only the relative weights shift. ## When to use - Subscription business with stagnant or declining MRR - Need to identify ARPU-lift options (typically a Premium-Tier introduction) - Acquisition has slowed and the team needs to know where the next 50–100k EUR / month is coming from - Investor or board needs a structured monetization analysis with a 12 to 24 month outlook - Workshop or strategy session preparation where the question is "wo kommt das Wachstum her" **Don't use for:** - Pure transactional e-commerce without a subscription tier (use `channel-economics-analyzer`) - B2B Enterprise SaaS with negotiated custom pricing (different playbook entirely) - Pre-revenue startups (skill assumes an existing subscription base to optimize against) - Pure ad-supported sites with no paywall (the levers don't apply) ## YMYL notice If the subscription business is in a regulated industry (medical telemedicine, financial advisory, legal advice, gambling), the action plan output by this skill should be reviewed by a domain compliance expert before publication or implementation. The skill treats all subscription models uniformly at the monetization-mechanics level and does not validate industry-specific regulatory constraints. ## Two analysis modes ### Outside-In mode (always available) Anything the skill can detect from the public website without authenticated access: | Signal | What it tells you | |---|---| | Paywall present? Where? | Hard / soft / metered / freemium model | | Number of pricing tiers | 1-tier = ARPU-Lift potential, 3+ tier = mature optimization needed | | Tier price range (lowest to highest) | ARPU ceiling estimate | | Trial period length / Reverse-Trial signals | Conversion-funnel design quality | | Annual-vs-monthly visibility | Yearly-bias often missed, ARPU-stabilizer | | Ad networks detected (Google, Plista, Outbrain, Ströer, Taboola) | Ad-revenue diversification status | | Newsletter sign-up prominence | Owned-channel-growth posture | | Live event / conference mentions | Event-revenue potential | | Affiliate / shop integration | Adjacent revenue streams | | Donation / membership CTAs | Genossenschaft-style stream | | Cancel-flow accessibility | Retention-engineering quality | ### Inside-Out mode (CSV import, optional) Hand the skill a subscription-export CSV (see CSV format below). The included `csv-import.example.js` script computes: - **MRR + ARR + ARPU** (total and per plan) - **Active subscriptions** by status, plan, billing period - **Churn 30/60/90 day rates** (logo + revenue) - **Cohort retention** (signup-month against active-after-N-days) - **Plan distribution** (yearly vs monthly vs other) - **Customer Lifetime Value** by plan (median + average) - **Pending cancellations and MRR at risk** - **Win-Back-Pool size** (customers with ended subscription, optionally still active on site) Inside-Out doubles or triples the analytical depth versus Outside-In, because the levers can be quantified against the actual user base. ## The five standard monetization levers Each lever has a real-market vorbild documented so it does not read as speculation. Each lever has a default sizing formula that the skill applies to the user's actual numbers (Inside-Out) or to industry-typical benchmarks (Outside-In). ### Lever 1: Premium-Tier introduction (largest ARPU effect) **Mechanik:** A subscription base on a single tier at low ARPU (e.g. 9-15 EUR / month) leaves significant pricing-power unused. Introducing a Premium-Tier at 2 to 3x the base price, with three to four clear additional benefits (exclusive content, audio-version, no-ads, early access, premium newsletter), typically converts 15-30 percent of existing subs and 25-40 percent of new subs to the higher tier. **Rechnung (Inside-Out):** New ARPU = (1 - premium_share) × base_arpu + premium_share × premium_arpu. With 25 percent premium share at 2.5x base price, blended ARPU rises 38 percent. **Vorbild:** WELTplus (Springer) WELTplus Premium tier, Bild Plus Bild+ tier, Apple News+, Politico Pro tier structure. **Aufwand:** 2 to 3 months product + content setup. Engineering is moderate, the bottleneck is the content pipeline for exclusive Premium content. ### Lever 2: Conversion-Pool activation (largest sub-count effect) **Mechanik:** Most subscription sites have a substantial active-non-subscriber base (users who read regularly but never converted). Structured soft-paywall sequences with progressive escalation (limit after 3 articles, reminder after 5, soft-sealing after 8 with first-purchase discount) typically lift conversion-rate from this pool by 2-5x over baseline. **Rechnung (Inside-Out):** Pool-size × conversion-rate-lift × ARPU × 12 = annual incremental MRR. Even a 1 percent monthly conversion improvement on a 10,000 active-non-sub pool yields 100 new subs / month, 1,200 / year, at ARPU × 12 ARR per cohort. **Vorbild:** Bild Plus 2023-2024 conversion-rate doubling, New York Times metered-paywall optimization, Politico EU member conversion funnel. **Aufwand:** 1 to 2 months, primarily conversion-optimization and marketing-automation work. ### Lever 3: Win-Back of churned subscribers **Mechanik:** Subscribers who cancelled and are still active on the site are a high-intent reactivation pool. Structured win-back-sequences (E-Mail "we miss you / here are 3 articles you've missed", 50-percent-off for 3 months, personalized editor offer for power-users) typically reactivate 10-25 percent of the still-active-churned cohort. **Rechnung (Inside-Out):** Churned-but-active-on-site × reactivation-rate × ARPU × average-tenure-after-reactivation. Smaller absolute numbers than Lever 1 and 2 but very high ROI because operating cost is low. **Vorbild:** Substack publisher win-back-flows, Netflix winback playbook documented across multiple Forbes / Variety pieces. **Aufwand:** 1 month, primarily marketing-sequence design. ### Lever 4: B2B Adjacency / Professional Newsletter **Mechanik:** For news, publisher, or content-driven subscription businesses, there is often a professional B2B audience (lobbyists, PR agencies, public-affairs managers, agencies, competitors) who would pay 5-20x consumer-ARPU for a paid professional version of the same content cleanly packaged. Examples: morning briefings, weekly trend reports, exclusive data access. **Rechnung:** Even 200 Standard + 50 Pro subscribers at 100 EUR + 500 EUR / month = 45,000 EUR / month = 540,000 EUR / year, at low ongoing cost. **Vorbild:** Politico Pro EU, Tagesspiegel Background, FT Confidential, Axios Pro, Stratechery Pro. **Aufwand:** 3 to 6 months for product + editorial design. ### Lever 5: Live Events / Conferences **Mechanik:** Subscription audiences with engagement and brand affinity convert into ticket buyers at high margins. One or two events per year, 500-1000 attendees at 250-800 EUR + sponsorship from category-aligned brands. **Rechnung:** First year 200,000-400,000 EUR brutto. Year 2 with two events plus livestream-tier: 800,000-1,500,000 EUR. **Vorbild:** Axios (30 percent of revenue from events), Stratechery (annual conference), every major publisher in DACH (Wirtschaftswoche, Welt, Tichys, Compact). **Aufwand:** 200,000 EUR setup year one, profitable from year two. ## Two bonus levers ### Bonus 6: Overdue invoice collection (immediate cash) If Inside-Out mode reveals significant overdue receivables (typical pattern: 10-15 percent of ARR sits in 30+ day-overdue status), a structured dunning process plus optional collections for 90+ day items typically recovers 60-80 percent of the overdue volume within 60-90 days. **Aufwand:** 1 month operations work. ### Bonus 7: Ad-stack optimization (where applicable) If the site monetizes with display ads alongside subscriptions and currently runs only Google Ads sparsely, adding 2-3 premium ad networks (Plista, Outbrain, Ströer, Taboola in DACH) and fixing Mobile-PageSpeed below the Google threshold (often a 30→65 PSI jump) typically lifts RPM from 2-3 EUR to 8-15 EUR, multiplying ad revenue. This lever is secondary for subscription-first businesses. It is a primary lever in `seo-outreach-report` for ad-supported sites. ## The negative-trend discipline **The rule:** Before applying any conversion-lever (1, 2, 3), if the signup trend is declining month-over-month, the underlying cause of the decline must be diagnosed and addressed first. **Why:** Conversion-rate improvements on a shrinking acquisition base produce shrinking absolute numbers. The hebel sizes in this skill all assume stable or growing acquisition. **Common causes of signup decline (in order of frequency):** 1. Marketing budget cut or campaign halted (verify with marketing-spend history per channel per month) 2. Channel algorithm change (YouTube, Facebook, Google Discover, Apple News) 3. Saisonality (US-election-cycle for political content, summer-slump for news, Black-Friday-anchor for e-commerce) 4. Competitive entry (a new site in the same vertical absorbing attention) 5. Editorial / programmatic shift losing audience-fit **Output:** the skill explicitly flags signup-trend in the report and prefixes lever-rechnung with the disciplinary note "lever sizing assumes acquisition stabilization." ## Premium-Tier ARPU calculator pattern For Inside-Out mode, the skill computes whether the user's stated goal (e.g. "100,000 subscribers by end of 2027") is consistent with the burn-coverage requirement. The 2-tier rechnung: ``` target_subs = 100,000 target_arr_needed = 16,000,000 EUR (= burn requirement) required_blended_arpu = target_arr_needed / target_subs / 12 = 13.33 EUR If current ARPU is 9 EUR, blended ARPU 13.33 EUR requires: - all-base scenario: 100,000 × 9 × 12 = 10,800,000 EUR (insufficient — 5.2M gap remains) - 30 percent premium at 25 EUR base 9 EUR scenario: 100,000 × (0.7 × 9 + 0.3 × 25) × 12 = 100,000 × 13.80 × 12 = 16,560,000 EUR (covers) ``` This pattern surfaces the most common subscription-business mistake: "more subscribers" is treated as the only growth lever, when "higher blended ARPU" achieves the same dollar goal with less acquisition risk. ## CSV import format The CSV-import script accepts a generic format that maps from most major subscription-billing systems (Stripe, Chargebee, Recurly, Shopify Subscriptions, custom). Minimum required columns: ``` subscription_id, customer_id, plan_name, billing_period, amount_eur, currency, start_date, end_date, status, churn_date ``` Optional columns the script uses if present: - `last_login_date` — for active-non-subscriber pool detection - `trial_end_date` — for trial-conversion tracking - `payment_provider` — for provider-distribution breakdown - `country` — for geo-segmentation - `coupon_code` — for promo-effectiveness tracking - `cancellation_reason` — for churn-cause analysis The script: 1. Validates column-presence and rejects with clear error if minimum columns missing 2. Computes the KPI snapshot (MRR, ARR, ARPU, churn 30/60/90, cohort retention, plan distribution) 3. Writes a `subscription-summary-.json` to `~/.cache/seo-rescue/` 4. The skill reads that summary and constructs the lever-rechnung against actual data Run as: `node csv-import.example.js path-to-export.csv` A worked example CSV with synthetic data is at `csv-import.example.csv`. ## Common rationalization traps | Statement | Reality | |-----------|---------| | "We just need more subscribers" | Without ARPU lift, even 10x subscribers may not cover the burn | | "Premium-Tier will cannibalize our base subs" | Empirical data from Welt Plus, Bild Plus, NYT shows the opposite: Premium-Tier accelerates total subscriber growth because it gives a higher-intent commitment-path | | "We can't introduce a B2B tier, our content isn't B2B" | If your content reaches a professional audience (journalists, agencies, lobbyists, competitors), there's a B2B version. The question is packaging, not content | | "Events are too operations-heavy" | First year is. Year two onwards is 30-50 percent margin if format is right | | "Win-Back is a small lever, not worth it" | Wrong unit-economics. ROI per hour of work is highest of any lever because operating cost is near-zero | | "Our paywall is hard-walled, that's why ARPU is low" | Hard-walled paywalls minimize the conversion-pool (Lever 2). Soft / metered models almost always outperform hard models on total revenue | ## Output In Outside-In mode: a structured Markdown report covering the seven detection signals, the five-lever sizing against typical benchmarks, the negative-trend discipline check, and four open questions the team would need to answer for a full Inside-Out analysis. In Inside-Out mode: same structure but with quantified hebel-rechnung against the actual numbers, plus the Premium-Tier-ARPU-Goal calculator output, plus a cohort-retention diagnosis. Both modes can be rendered into a PDF via the `make-pdf` skill or `seo-outreach-report`-style pipeline if the user wants a workshop-grade deliverable. ## Related skills - `seo-outreach-report` — for the SEO + visibility side of the same domain. Often run together. - `channel-economics-analyzer` — for pure transactional e-commerce without subscriptions. - `ai-search-rescue` — for the AI-visibility layer that increasingly drives subscription-acquisition. - `post-core-update-recovery` — if subscription growth has slowed because of a Google Core Update affecting acquisition. - `make-pdf` — to render the analysis as a workshop-grade PDF deliverable. ## Real-world anchor data (anonymized) The skill's lever-sizing benchmarks (Premium-Tier 25-30 percent conversion, Conversion-Pool 1-2 percent monthly, Win-Back 10-25 percent on still-active-churned cohort) come from a combination of: - One DACH news-site case (mid-five-digit subscriber base, recent monetization-gap diagnosis, full Inside-Out access) - Two SaaS-light memberships (subscriber bases 1,000-5,000) - Published Substack publisher win-back data - Springer / Burda / Madsack public investor-report disclosures about ARPU and tier-conversion - Axios and Politico publicly documented event-revenue and B2B-newsletter performance These are observation-based starting hypotheses for the lever-sizing. Calibrate against your actual numbers in Inside-Out mode.