Hire your AI Forecasting Analyst

Revenue forecasts, cohort analysis, and scenario models — on demand.

Updated 2026-04-18

This is a role guide, not an installed employee. It explains what the role owns and how Tycoon runs it. Nothing is hired or activated until you choose it.

What your AI Forecasting Analyst does

01Maintain the monthly revenue forecast with baseline, upside, and downside scenarios
02Run cohort retention analysis and surface net revenue retention (NRR) by segment
03Build scenario models for pricing changes, new channels, and potential hires
04Calculate CAC, LTV, payback period, and gross margin per cohort
05Flag leading indicators of churn (engagement drop, usage decline, support ticket spike)
06Model fundraising scenarios — dilution, runway extension, milestone targets
07Maintain the sales and pipeline forecast with stage conversion by source
08Run the monthly board-of-one metrics review with written commentary

Workflows on autopilot

Monthly forecast refresh

First business day rebuilds the forecast with last month's actuals, updates assumptions, publishes baseline-upside-downside with written narrative on what changed.

Cohort retention report

Monthly cohort analysis: retention curves, NRR, churn drivers. Identifies best and worst cohorts and what distinguishes them.

Scenario sprint

When a pricing change, channel investment, or hire is on the table, builds the model in under a day: assumptions, outputs, sensitivity table, recommendation.

Leading indicator watch

Daily scan for usage drops, support spikes, and payment failures. Flags at-risk accounts to the CSM and the CEO before they churn.

Board-of-one monthly review

Monthly metrics pack with the 12 numbers that matter: MRR, ARR, NRR, CAC, LTV, payback, gross margin, runway, pipeline, conversion, cohort deltas, top issues. Written commentary per metric.

Fundraising scenario model

When raising is on the table, builds dilution tables, milestone targets, and runway scenarios for conservative/base/optimistic outcomes.

Without vs With a AI Forecasting Analyst

Without

  • Revenue forecast is whatever the founder said at breakfast
  • Churn is discovered at quarter-end when the cohort finally fails
  • Pricing changes are guessed at and hoped to work
  • CAC and LTV are rumors you quote at pitch meetings
  • A fractional finance analyst runs $4-8K/month for output you can't verify

With Tycoon

  • Forecast has baseline/upside/downside with documented assumptions
  • Leading indicators flag at-risk accounts 60 days before they churn
  • Every pricing change is modeled with a sensitivity table first
  • Numbers are calculated monthly per cohort with the math shown
  • AI Forecasting Analyst ships the same work with reviewable models

A day in the life of your AI Forecasting Analyst

07:00Monthly close just happened. Refreshes the forecast with March actuals. MRR beat plan by 7%, NRR slipped from 112% to 108%.
09:30Drafts the scenario for raising the Team plan from $49 to $69. Models elasticity from two prior pricing experiments, projects net MRR impact.
11:30Runs cohort analysis on February signups. Identifies the LinkedIn-sourced cohort as 2.1x better retention than cold-email cohort. Flags for CMO.
14:00Reviews Stripe dunning queue. 14 failed payments, $3,200 at risk. Hands to the AI CSM for outreach, writes the expected recovery number.
16:00Founder asks 'if we hired a second AE, when would they pay back?' Builds the model in an hour: 5.2 months with current conversion rates.
18:30Logs: 1 forecast refreshed, 1 scenario built, 1 cohort insight actioned.

Tools your AI Forecasting Analyst uses

Stripe as the canonical revenue sourceBigQuery, Snowflake, or Postgres for the data warehouseHex, Mode, or Metabase for interactive modelsGoogle Sheets for lightweight scenario modelingHubspot, Salesforce, or Attio for pipeline dataPostHog or Mixpanel for usage and engagement signalsNotion for narrative commentary and exec memosTycoon skill marketplace for forecasting, cohort, and scenario modeling skills
FAQ

Frequently asked questions

Clear answers about wallet credit, usage, subscriptions, and how Tycoon charges for work.

Is this the same as an AI CFO?

Related but distinct. The AI CFO owns the financial strategy — fundraising, capital allocation, board narrative, cash management. The AI Forecasting Analyst owns the numbers underneath — the models, cohorts, scenarios, and metrics the CFO uses to make decisions. On a larger team you hire both. On a small team you often start with just the AI CFO and expand to include a dedicated forecasting role when the volume of scenario work justifies it. Most founders running under $5M revenue find the AI CFO alone sufficient; above that the analyst becomes a force multiplier.

What data does it need access to?

Minimum: Stripe (or your equivalent billing system), your CRM for pipeline, and any product telemetry (PostHog, Mixpanel, Amplitude) that captures engagement. Better: a data warehouse (BigQuery, Snowflake, Postgres) that centralizes everything. If you do not have a warehouse yet, the AI Forecasting Analyst can run on direct API reads for the first 12 months, then guide the migration to a warehouse when the scale demands it. It does not require expensive data infrastructure to start.

How accurate are its forecasts?

For typical SaaS businesses with 6+ months of history, monthly forecasts land within 5-10% of actual on the baseline scenario. Accuracy comes from two places: the AI Forecasting Analyst actually runs the model (not a vibes estimate) and it updates assumptions when they break rather than defending the previous forecast. Most founder forecasts are wrong because they stop being updated; the AI updates monthly without ego. If your business is under 6 months old, forecasts are wider bands and the analyst is honest about that.

Can it model non-SaaS businesses?

Yes. E-commerce (cohort-based LTV, contribution margin, ad spend payback), marketplaces (supply/demand modeling, take rate scenarios), services (utilization, bench time, project margin), and hybrid businesses are all supported. The modeling approach differs by business type but the workflow is the same: pull real data, build the model, document assumptions, publish with commentary. The AI Forecasting Analyst defaults to SaaS templates because that is the most common case but adapts quickly.

Who reviews its work?

The CEO reviews the monthly forecast and the major scenarios. The AI CFO reviews the methodology and flags anything that does not match prior assumptions. For high-stakes work (fundraising models, material pricing changes) most founders also send the output to an advisor or board member for a sanity check. The AI Forecasting Analyst is comfortable having its work reviewed and updates when corrected; it does not defend bad models. That posture is the reason the outputs become trustworthy over a few months.

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