Paddle

Customer Success Playbook for Solo SaaS Founders

A solo-founder-focused customer success playbook recommends a lightweight, weekly-updated 5-signal health score and concrete outreach/rescue sequences that a single founder can run using a spreadsheet. The five signals are Core Feature Activation, Usage Breadth, Support Sentiment Trend, Champion Engagement, and Billing Health, each weighted and scored to classify accounts as Healthy, At Risk, or Critical. The author presents specific triggers and a multi-step churn-rescue sequence (email, short Loom, call, discount/re-onboarding). A case study reports reducing a fitness-scheduling tool's monthly churn from 8% to 3.1% and moving net revenue retention from 84% to 102% over four months. Benchmarks from ProfitWell and Baremetrics are cited to contextualize churn norms.

Stripe to Mollie Migration: What Breaks

A developer recounts migrating their SaaS billing from Stripe to Mollie after Stripe disabled their account. The author found one-off payments straightforward but spent two weeks rebuilding subscription infrastructure because Mollie is a thin EU payment provider rather than a full billing platform. Key gaps included no native subscription object (subscriptions must be assembled from Customer + Mandate + first payment), webhooks that only provide an id requiring reconcile-by-id patterns, and the absence of proration, dunning/retries, hosted customer portal, tax computation, and invoice generation. Card mandates cannot be transferred between providers, so existing subscribers must re-subscribe. The post is a practical case study on operational work required when leaving a feature-rich PSP.

AI Wellness App Lessons: Why Habit Trackers Fail

A developer of the AI-powered habit app Wishyze analyzed engagement from 28,547 users and identified a four-phase behavior-change model: Spark (days 1–7), The Void (weeks 2–6), Alignment (weeks 6–12), and Manifestation (week 12+). The analysis found 73% of users who quit do so in weeks 2–6, a period the author calls the Void. The piece argues most AI wellness apps fail because they wrap generic LLM outputs in a wellness UI without phase-aware personalization, and that structured, four-component rituals (Affirmation, Visualization, Action, Sign) outperform freeform journaling. The author also describes a 'ritual engine' and phase-detection logic and lists the technical stack (Next.js 14, TypeScript, Supabase, DeepSeek V4 Pro, Paddle). The article recommends phase-aware personalization engines, outcome-focused metrics beyond streaks, and psychologically nuanced re-engagement for future AI wellness tools.