Revenue Forecasting for Founders: How to Build a Forecast People Actually Believe

Why most founder forecasts fail#

The most common forecasting failure is the hockey stick — a flat line followed by a sudden steep upward trajectory that usually coincides with a planned product launch or marketing investment. Hockey stick forecasts fail because they are aspirational rather than mechanical. They do not show the specific assumptions that drive the growth. An investor or well-run finance function needs to understand the inputs — how many new customers, at what average order value, with what churn rate — not just the output revenue number.

Bottom-up vs top-down forecasting#

Top-down forecasting starts with the total market and applies a market share assumption: the market is £500 million and we will capture 2% = £10 million. This is almost never credible because it provides no mechanism by which the share is captured. Bottom-up forecasting starts with unit-level drivers: how many sales conversations per month, at what conversion rate, at what average contract value, with what churn rate. The resulting revenue forecast is the mechanical output of specific, trackable assumptions.

Building a bottom-up revenue forecast#

For eCommerce: Monthly revenue = (Existing customers × repeat purchase rate × average order value) + (New customers × first purchase AOV). For SaaS: MRR = Prior MRR + New MRR + Expansion MRR − Churn MRR. Every input is a business process you can track and improve. The forecast becomes a live dashboard, not a static document.

Accounting for seasonality and churn#

Two factors most forecasts handle badly. Seasonality: apply historical seasonal indices to your base forecast rather than assuming linear growth. A month-by-month seasonal index applied to a trend forecast is far more accurate than a straight-line projection. Churn: failing to account for customer attrition significantly overstates revenue. If you add 100 new customers per month but 15% of your base churns annually, your net customer growth is significantly less than gross additions suggest.

Using AskBiz to build and track revenue forecasts#

AskBiz builds a revenue forecast from your historical data — applying your actual growth rates, seasonal patterns, and churn rates to project forward. As each month passes, it compares actuals to forecast and surfaces the drivers of any variance. Ask it: what is my forecast revenue for the next 6 months at current growth rates, what would happen if my churn rate increased 3 percentage points, how much of my revenue variance this month is explained by volume vs average order value.