Forecasting (Budgeting) When You Have Little Solid Data

In an earlier post, I raised the challenge of forecasting revenues for budgeting noting that it's likely the most challenging step in budgeting for a new business.  One link that addresses this issue confirms that perspective.  In this link, the authors talk about ways to narrow the range of uncertainty - risk - around what you think will happen by focusing on reactions to your pre-launch marketing survey.

Another link contains an interesting give and take with various people on this issue.  One writer aptly notes that it's more art than science when you've really not got any good data.  A post by rogercbryan on 1-4-08 contains a good deal of wisdom.  Among his sage advice are the following:
- "Start with expenses, not revenues. When you're in the startup stage, it's much easier to forecast expenses than revenues"


He then includes some rules of thumb for cost forecasting that seem on point.  Note: Much the same advice is contained in this link.


On revenue forecasting, he proposes that you "Forecast revenues using both a conservative case and an aggressive case. If you're like most entrepreneurs, you'll constantly fluctuate between conservative reality and an aggressive dream state..."


What's good in his advice is the idea of making multiple revenue forecasts.  Very large multi-billion dollar businesses have made fatal errors by not forecasting multiple revenue scenarios.  These multiple scenarios reflect different sales possibilities.  Here in the Pacific Northwest, this very error was committed in the past with electric utility panning that ultimately led to billions of wasted dollars and mothballed and incomplete nuclear plants. 


One approach I like is to work your cost numbers hard and gin up several different cost scenarios.  Then, for each cost scenario, generate a revenue forecast - in terms of customers, product sold, prices, etc., that supports that cost scenario.  Then, you step back and examine the various product sales projections, examine their likelihood, or what would need to happen for it to be true.  At the beginning, this is again more art than science.


This is a way of being systematic and structured in your analysis, even in the presence of a great deal of uncertainty about both costs and revenues.  Through this process, you'll document your assumptions.  At this point, even though you have a great deal of uncertainty - risk - in your analysis, at least you've proceeded with a systematic approach.  Now, subject your analysis to rigorous review by some others not involved with your dream.  Use them to challenge your assumptions and analysis.  Use them to identify what is strong and what is weak in your analysis.