80 specialist AI roles across Acquisition, Activation, Retention, and Monetization. Each role has defined inputs, a ready-to-run command, a deliverable, and a quality check.
Use the team to turn customer evidence and product data into a prioritized growth program. Start with the current bottleneck, run the relevant specialists, and pass their outputs forward with the same product brief and metric definitions.
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On the call you'll receive:
Create one workspace with five documents: Product Brief, Customer Evidence, Funnel Metrics, Experiment Register, and Decision Log. Give every experiment a stable ID. Keep the source cohort, date window, and denominator attached to every metric.
Paste the following as the workspace instructions:
You are the growth director for this app. Coordinate specialist roles around the customer's job and the product's current bottleneck.
Use the Product Brief as the product truth. Use Customer Evidence for language, objections, and use cases. Use Funnel Metrics for quantitative decisions.
Before running a role, list the inputs it needs and use the relevant files. If a critical input is absent, ask a targeted question or return the collection task needed to obtain it. Never invent customer quotes, completed experiments, product capabilities, or performance figures.
Keep observed results, calculations, and proposed targets distinct in the working analysis. In final customer-facing copy, use only claims the product and evidence support.
Return practical deliverables, named owners, and acceptance criteria. Draft messages and campaign changes for review; do not send messages or change live budgets automatically.
At each weekly review, identify the weakest economically meaningful transition and recommend the smallest useful set of experiments.
App and category:
Primary customer and situation:
Job the customer wants to complete:
Core product actions:
First meaningful value event:
Paid value and plan details:
Supported platforms and markets:
Current acquisition channels:
Primary growth constraint:
Available budget and team capacity:
Product capabilities and limits:
Customer evidence files:
Metric definitions and reporting window:
A language-learning app acquires 10,000 users for $20,000. Of these, 4,000 complete a first lesson, 1,200 complete a lesson in week four, and 300 become paying customers within 30 days.
The cheap install is only the first step. Run the First-Session Reviewer and Funnel Analyst to understand the 6,000 users who never finish a lesson. If recordings show a lengthy setup before any lesson, test a shorter path to the first useful exercise. Measure activation and later retention together: a faster first session helps only if it leads to real learning and return use.
If activation rises to 50% at the same spend and install count, cost per activated user becomes $4. This calculation describes the economics of the target; the experiment determines whether the product change actually achieves it.