paywall-optimizer

Solid

AI-powered paywall optimizasyon. Sektore gore otomatik analiz, A/B test senaryolari, churn prediction sinyalleri, push notification stratejileri, agresif satis teknikleri ve RevenueCat Experiments entegrasyonu. paywall-strategy, revenuecat-patterns ve subscription-pricing skill'lerinin ustune insa eder.

AI & Automation 496 stars 41 forks Updated 1 months ago MIT

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Skill Content

# Paywall Optimizer (AI Layer) Bu skill, mevcut paywall-strategy, revenuecat-patterns ve subscription-pricing skill'lerinin ustune AI-powered optimizasyon katmani ekler. ## Sektore Gore Otomatik Analiz Pipeline ### Input ``` 1. App kategorisi (orn: "Health & Fitness") 2. Core feature listesi (orn: ["antrenman plani", "kalori takibi", "ilerleme grafigi"]) 3. Mevcut metrikler (varsa): DAU, MAU, trial rate, conversion rate, ARPU, churn 4. Hedef kitle (orn: "25-35 yas, fitness meraklisi, orta gelir") 5. Platform (iOS, Android, both) 6. Rakipler (orn: ["MyFitnessPal", "Nike Training Club"]) ``` ### Process ``` 1. paywall-strategy skill'inden kategori benchmark'ini cek 2. Benchmark vs mevcut metrikleri karsilastir (gap analizi) 3. subscription-pricing skill'inden optimal fiyat araligini hesapla 4. Rakip fiyatlarini ve modellerini karsilastir 5. Sektore ozgu oneri raporu olustur ``` ### Output ``` ANALIZ RAPORU: - Kategori benchmark ozeti - Gap analizi (mevcut vs benchmark) - Onerilen model + fiyatlama - ROI projeksiyonu (3/6/12 ay) ``` --- ## A/B Test Senaryolari ### Hazir Test Seti (6 Senaryo) #### Senaryo 1: Fiyat Testi ``` Hipotez: Daha yuksek fiyat, LTV'yi artirirken conversion'i fazla dusurmuyor Control: $9.99/ay, $49.99/yil Variant A: $12.99/ay, $69.99/yil Variant B: $7.99/ay, $39.99/yil Metrik: Revenue per user (birincil), Trial-to-paid (ikincil) Sure: Min 4 hafta, ideal 6 hafta Sample: Min 5,000 kullanici/varyant ``` #### Senaryo 2: Trial Suresi Testi ``` Hipotez:...

Details

Author
vibeeval
Repository
vibeeval/vibecosystem
Created
2 months ago
Last Updated
1 months ago
Language
C#
License
MIT

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