Forecast
AI-powered revenue forecasting
Thesis
Revenue forecasting is spreadsheet hell. The data already exists in tools, AI can predict cash flow automatically.
The Problem
Revenue forecasting is a mess: Half the data is in your CRM, half in spreadsheets, half in someone's head. Close rates are guesses. Project end dates slip. You're surprised when cash gets tight. Agencies need to know: what's coming in, when, and how confident should we be? The data exists. The synthesis doesn't.
Implementation Approaches
Pipeline + Project Integration (Recommended)
Combine CRM pipeline with active project data for full picture
Implementation
- →Connect CRM (HubSpot, Pipedrive) for pipeline
- →Connect project tools for active work
- →AI adjusts close rates based on historical patterns
- →Predict: revenue by month with confidence intervals
- →Scenario modeling: what if this deal slips?
Pros
- +Complete picture: pipeline + in-progress + renewals
- +Learns from your actual close rates, not guesses
- +Directly actionable for capacity planning
- +High value, directly affects business decisions
Cons
- −Requires clean data in CRM and project tools
- −Complex integrations to build
- −Accuracy depends on data quality
Spreadsheet Enhancer
AI layer on top of existing forecasting spreadsheets
Implementation
- →Import existing forecast spreadsheet
- →AI suggests adjustments based on patterns
- →Highlights: overconfident deals, missing renewals
- →Generates scenarios automatically
- →Export back to spreadsheet or visualize
Pros
- +Works with existing workflow
- +No need to change tools
- +Lower adoption friction
- +Can show value quickly
Cons
- −Depends on spreadsheet quality
- −Doesn't fix underlying data problems
- −Less automated than full integration
Invoice-Based Projection
Forecast from invoicing patterns and payment history
Implementation
- →Connect invoicing tool (Stripe, QuickBooks, FreshBooks)
- →Analyze: when do clients actually pay?
- →Project cash flow based on historical patterns
- →Flag: at-risk invoices, slow payers
Pros
- +Based on actual money, not projections
- +Simpler data source
- +Clear, concrete output
- +Helps with collections too
Cons
- −Backward-looking, misses new pipeline
- −Doesn't help with sales forecasting
- −Limited strategic value
Validation Plan
Hypothesis to Test
Agency owners will pay $79/mo for accurate revenue forecasting that updates automatically
Validation Phases
Historical Analysis
1 week- •Get 12 months of actual revenue data from 3 agencies
- •Get their forecast spreadsheets from same period
- •Compare: how accurate were their forecasts?
- •Show: where AI could have improved predictions
Spreadsheet MVP
2 weeks- •Build spreadsheet import and analysis
- •Add AI suggestions and scenario modeling
- •Test with 3 agencies on current forecasts
- •Measure: do suggestions improve accuracy?
Integration Beta
3 weeks- •Add CRM integration (HubSpot first)
- •Automate pipeline to forecast flow
- •Test accuracy over 2-week period
- •Validate $79/mo pricing
Kill Criteria
Stop and move on if any of these become true:
- ✕Forecast accuracy not meaningfully better than spreadsheets
- ✕Agencies don't have clean enough data to work with
- ✕Too much manual work to set up and maintain
- ✕Price sensitivity below $50/mo