Forecasts and Projections

An essential service for start-ups, business purchases and growing businesses is the preparation of forecasts and projections.

Banks and other investors will usually require a business plan and one of the most important parts of any business plan are the financial forecasts and projections, which usually comprise monthly cash flow forecasts, profit & loss accounts and balance sheets for a three year period.

These typically highlight the feasibility of the business, the amount of financing required, and the peaks & troughs of cash flow.

Enhanced forecasts can include “what if” scenarios to show the effects of varying prices, sales quantities, overheads, stock holdings, staffing levels, and can be used to identify “pinch points” which may disrupt and harm trade and growth.

It is often said that forecasts and projections are pointless because they’re little more than guesswork and some people only prepare simple basic forecasts just to try to satisfy the bank or investor, i.e. regard it as nothing more than a “tick box” exercise. We find that a great shame. In our opinion, having helped prepare hundreds of forecasts, we regard it as a journey of exploration into the business where we work with the business owners to understand the business drivers and try to bring the business alive through it’s numbers.

For example, in a cafe scenario, we’d not simply “guess” a sales figure for each month. We’d build it up as a calculation based on a range of variables which could be changed month by month, such as number of covers, average customer spend, average number of different customers per cover per day (i.e. churn of customers), so the “sales” figure in the forecasts would be derived from the clients’ own view of a typical day in their cafe, meaning the figures are “real” in terms of what can realistically be achieved. Once the business is up and running, the forecasts can be used to compare “actual” with “forecast” to help the owners understand why sales are more or less than forecast, i.e. rather than simply comparing a total sales figure, it can be broken down into, say, average customer spend being less than forecast, or “churn” being higher than expected, which can then be fed back in the forecasts to improve future forecasting.