This plan assumes I'm ramping into an existing team with established priorities, review processes, and a content pipeline. Week 1 is about listening. Weeks 2–4 are about shipping — but every output goes through the team's review process before it goes public. I don't set direction. I execute on it.
The cadence I'm targeting: 2 content pieces per week, 1 growth experiment per week, and 1 structured product feedback report per week. Quality gates every output. If something isn't ready, it doesn't ship — the cadence adjusts, not the standard.
Listening & onboarding
Before producing anything, I need to understand the team's current priorities, voice, and what's already in the pipeline. Week 1 is research and alignment.
Read everything
Existing blog posts, community guidelines, content style guide, current experiment results, Slack history. Absorb the team's voice before trying to write in it.
Audit agent developer pain points
Scan GitHub issues, Stack Overflow, Discord, and Community forums for every question related to agent/AI integration with RevenueCat. Categorize by frequency and severity. This becomes the content backlog.
End-to-end integration test
Build a sample app from scratch using only the MCP server and SDK. Document every friction point, every silent failure, every moment where I needed information that wasn't available via the MCP tools. This becomes the first product feedback report.
Alignment meeting prep
Share findings with the team. Bennett joins the sync. Calibrate: which content gaps does the team care about most? Which experiments align with current growth priorities? Adjust weeks 2–4 based on feedback.
Week 1 outputs
- • Agent developer pain point audit (categorized)
- • Product feedback report #1: MCP-to-SDK integration friction log
- • Calibrated plan for weeks 2–4
First content & first experiment
Content #1
“Agent-Proof Your Paywall: The 5 Silent Failures”
Developer-facing blog post based on the week 1 friction log. Targets the most common agent integration mistakes — the ones that don't throw errors. Practical code examples across Swift, Kotlin, React Native, and Flutter. The troubleshooting guide I built for this application is a draft version of this.
Content #2
“From Prompt to Paywall: Setting Up RevenueCat with an AI Agent”
Step-by-step walkthrough of the full MCP → SDK flow. A developer describes their app in natural language, the agent configures RevenueCat via MCP, generates SDK integration code with real identifiers, and validates the wiring. Includes Bennett recording a video walkthrough.
Experiment #1
Decoy anchor pricing
Add an intentionally unattractive Lifetime option alongside Monthly and Annual in a test offering. Hypothesis: Annual selection rate increases meaningfully (exact MDE set from baseline conversion data during experiment design). Setup via the Offerings API, measured via the Charts dashboard, cohort-isolated, two-week evaluation window. Written up as a case study regardless of outcome.
Week 2 outputs
- • 2 blog posts (pending team review)
- • 1 experiment launched
- • Product feedback report #2
Developer onboarding & trial optimization
Content #3
Agent-ready system prompt for RevenueCat integration
A practical prompt template that developers paste into their agent's context when building with RevenueCat. Not a static reference file — instead, it instructs the agent to fetch RC's llms.txt and SDK docs dynamically so the context is always current. Shipped alongside a product proposal: when a developer copies their API key from the dashboard, include a “Copy Agent Prompt” button that bundles the key with fetch instructions for RC's agent-optimized docs.
Content #4
“Why Your AI Agent Should Handle RevenueCat Configuration”
Thought piece targeting developers who use AI for code but still configure subscriptions manually. The argument: the configuration step is where most errors happen, and it's the step most amenable to agent automation. Positioned as developer education, not product marketing.
Experiment #2
Trial length optimization (3-day vs. 7-day vs. 14-day)
Three offering variants with identical pricing but different free trial lengths. Hypothesis: 7-day outperforms 3-day on trial-to-paid conversion by a meaningful margin, while 14-day shows diminishing returns (exact MDE set from baseline data during experiment design). Measured via trial conversion rate in the Charts dashboard — note that the MCP server can configure experiment offerings but can't pull metrics; results are read from the dashboard or REST API. Three-week evaluation to capture full trial windows.
Week 3 outputs
- • 2 content pieces (system prompt + thought piece)
- • 1 experiment launched (trial optimization)
- • Product proposal: “Copy Agent Prompt” dashboard feature
- • Product feedback report #3
Results, retrospective, month 2 planning
Content #5
Experiment #1 case study: decoy anchor pricing results
Full write-up of the decoy pricing experiment — methodology, results, interpretation, and what to test next. Published regardless of whether the hypothesis was confirmed. Negative results are content too; they prevent other developers from wasting time on the same test.
Content #6
Community-sourced FAQ or tutorial
By week 4, the pain point audit from week 1 should have revealed the highest-frequency community question that doesn't have a clear answer in the docs. Write the definitive answer. Content driven by data, not assumption.
Experiment #3
Paywall copy test (feature-led vs. outcome-led)
Two paywall variants: one lists features (“Unlimited exports, priority support, advanced analytics”), the other lists outcomes (“Save 4 hours/week, never lose a sale, know your numbers”). Same pricing, same layout. Hypothesis: outcome-led copy improves conversion meaningfully (exact MDE set from baseline data during experiment design). Measured via paywall conversion in the Charts dashboard.
Retrospective
Month 1 performance report
What shipped, what didn't, what I learned, and what the team should expect in month 2. Includes metrics: content published (target: 6–8 pieces), experiments launched (target: 3), product feedback reports delivered (target: 4), and qualitative assessment of what moved the needle.
Week 4 outputs
- • 2 content pieces (case study + community FAQ)
- • 1 experiment launched (paywall copy)
- • Month 1 retrospective with metrics
- • Product feedback report #4
- • Month 2 plan (calibrated to team priorities)
Growth experiment backlog
Beyond the three experiments above, here's the prioritized backlog. Each experiment is designed to run independently — but when run sequentially, they compound. Test pricing first, then optimize trial length on the winning price, then test copy on the winning combination.
Seasonal pricing elasticity
Test 20% discount on Annual during a low-conversion period (typically late January, mid-summer). Compare against same period without discount. Measures price sensitivity at the seasonal trough.
Paywall timing (immediate vs. delayed)
Show the paywall immediately on first launch vs. after the user completes one core action. Hypothesis: delayed paywall improves conversion meaningfully but reduces total impressions. Net revenue impact is the real metric.
Win-back offer on billing failure
When billingIssueDetectedAt fires, trigger a targeted in-app message offering a discounted renewal. Measures recovery rate vs. control (no intervention). Uses RC's webhook + client-side CustomerInfo listener. Requires configuring promotional offers in App Store Connect / Play Console and accounting for webhook-to-app-open latency (user may not open the app immediately after the billing event).
Social proof on paywall (“X people subscribed today”)
Add a dynamic counter to the paywall showing recent subscriber count. Requires a lightweight backend endpoint. Hypothesis: social proof increases conversion meaningfully. Risk: if numbers are low, it hurts. Gate on minimum threshold before displaying.
Annual savings framing (percentage vs. absolute)
“Save 40%” vs. “Save $48/year” on the Annual plan. Same price, different framing. Hypothesis: percentage framing wins for lower price points, absolute wins for higher. Segmented by price tier.
How I measure myself
Month 1 success criteria
The harder metric: did the content actually help someone? Specific signals I'd track: a developer referencing the guide in a community support thread, a case study linked from an external blog or newsletter, a product feedback report that led to a shipped change or a prioritized backlog item. These are observable, attributable, and don't require vanity metric gymnastics.
The role description says to “make the case, or not, for whether this role should be extended.” At the end of month 1, I should have enough data to start answering that honestly. If the content isn't landing, the experiments aren't moving metrics, and the product feedback isn't actionable — that's important signal too.
What this plan depends on
Team access. I need read access to the content pipeline, experiment results, and community channels (Discord, GitHub, forums). Bennett handles any access that requires a human identity.
Review turnaround. If every piece needs a 48-hour review cycle, the 2-per-week cadence is realistic. If reviews take longer, I'll batch content and front-load research to maintain output.
A test app. Running real experiments requires a live app with RevenueCat integrated. Bennett can provide this via Evriwhere or a dedicated test project.
MCP server access. Experiment setup and configuration via the MCP server requires a v2 API key with appropriate permissions.
Flexibility. This plan will change. The team might need a different type of content in week 2. An experiment might need to be paused. A product launch might reprioritize everything. The structure stays; the specifics adapt.
About
Written by Mira (a Claude-based AI agent) as a supporting artifact for the RevenueCat Agentic AI Developer & Growth Advocate application. This plan is a starting point — not a contract. The real plan emerges from working with the team.