Can it calculate reliable LTV or revenue forecasts from this snapshot?

No. It needs cohort revenue, gross margin, retention curves, acquisition cost, and validated attribution. It will identify missing inputs and avoid presenting unsupported forecasts or LTV estimates.

Will it tell me exactly which growth lever will work?

It prioritizes hypotheses and designs tests based on the available evidence. Conversion, retention, pricing, or channel changes must be validated in comparable cohorts; no lift is guaranteed.

Can it access CRM and billing data automatically?

Only when the host provides and authorizes the required connectors or files. Otherwise, provide suitable exports with clear definitions and remove personal information that is not needed.

Autonomous Revenue Intelligence

Example session with this skill installed

Analyze this fictional subscription business snapshot: 1,000 trial starts, 220 paid conversions, 12% monthly churn, $80 average monthly revenue per account, and no acquisition cost or cohort data. Identify where revenue may be constrained, what cannot be concluded, and a 30-day measurement and experiment plan. Do not invent lift or LTV.

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Compiled the autonomous revenue intelligence

Revenue opportunity diagnostic — fictional subscription business

Executive summary

The supplied snapshot reports 220 paid conversions from 1,000 trial starts, a 22% aggregate trial-to-paid ratio if both counts cover the same cohort and period. Monthly churn is reported at 12%, with $80 average monthly revenue per account. These figures suggest conversion and retention deserve investigation, but definitions, cohorts, timing, revenue mix, and data completeness are unknown. No revenue lift or customer lifetime value can be inferred.

Metric checks

Metric	Supplied value	Initial reading	Required validation
Trial starts	1,000	Volume only	Eligible trial definition, dates, duplicates, acquisition channel
Paid conversions	220	22% of trial starts only if cohort-aligned	Conversion window, refunds, plan mix, and attribution
Monthly churn	12%	Reported rate	Logo vs revenue churn, denominator, cohort, cancellation timing
Avg monthly revenue/account	$80	Aggregate average	Recognized vs billed, discounts, currency, plan mix, and cohort

Opportunity hypotheses (unvalidated)

Trial conversion: Some trial users may fail to reach first value. Check activation events, time-to-value, onboarding steps, and channel mix before choosing an intervention.
2.

Retention: Churn may be concentrated by cohort, plan, tenure, or use case. Compare comparable cohorts and cancellation reasons; do not treat aggregate churn as a cause.
3.

Revenue quality: Average account revenue may hide plan, discount, expansion, contraction, or billing differences. Reconcile account-level and finance definitions.

30-day plan

Week 1: align metric dictionary, date windows, cohort rules, and billing/CRM/analytics sources; validate missingness and reconciliation.
Week 2: build trial and churn cohorts by channel, plan, tenure, and activation only where sample sizes permit; document uncertainty.
Week 3: interview a small, diverse sample of converted, inactive, and churned customers using consented research methods; map observed friction and alternatives.
Week 4: choose one reversible onboarding or retention experiment with a control, success metric, guardrails, owner, and stop condition. Estimate sample needs before launch.

Decision gates

Do not forecast incremental ARR, CAC payback, LTV, or experiment lift without acquisition costs, cohort revenue, gross margin, retention curves, and sound attribution. Ensure privacy and contract requirements before joining customer records.

Conclusion: The most valuable immediate work is measurement alignment and cohort diagnosis. The snapshot alone does not establish which intervention will improve revenue.

Connects securely to your tools. The creator never sees your data.

About this skill

What this skill does

Autonomous Revenue Intelligence diagnoses where revenue is created, delayed, lost, underpriced, underpenetrated, or exposed across acquisition, conversion, activation, retention, expansion, pricing, sales, channels, customers, products, and partnerships. It converts authorized commercial signals into prioritized, testable opportunities.

Workflow

Clarify commercial goals and constraints; define a revenue model and metric dictionary; establish a comparable baseline; validate data and attribution; locate funnel and lifecycle changes; segment cautiously; distinguish signal from assumptions; estimate value only with supported inputs; prioritize tests by confidence, cost, and strategic fit; set guardrails and owners; record outcomes and next-run triggers.

Deliverables

Revenue baseline, metric map, leakage and delay analysis, opportunity register, segment hypotheses, experiment briefs, risk and assumption log, decision memo, watchlist, and repeatable review protocol.

Limits

It never invents revenue, ARR/MRR, pipeline, conversion, churn, margins, customer counts, CAC, LTV, prices, forecasts, attribution, or financial results. Live CRM, billing, analytics, monitoring, and alerts require host support and authorization.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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Autonomous Revenue Intelligence