5 Methods to Make Realistic Financial Estimates

date: 2021-06-13 14:57:40


Reliable financial forecasts are truly considered a necessity. They help in decision making, setting strategic goals and attracting investors.

However, having inaccurate or insensible financial forecasts could lead to bad cash management, angry investors and most importantly the possibility of running out of cash.

We have complied a few tips to help you make your financial forecasts as realistic as possible:


Use conservative, but realistic assumptions

Almost always people are drawn in using overly optimistic assumptions in their forecasts. As a result, to counter this, some people sometimes tend to use overly conservative assumptions. In reality, both methods are incorrect. Forecasts should be more skewed towards conservatism; however, it should also be realistic. This is because that most people tend to underestimate the uncertainty of factors that could impact any business, for example, competitors, customer demand or the overall economic market. To overcome this, it is always recommended you use two scenarios, one optimistic and one conservative, but again both should be as realistic as possible.


Begin with forecasting your fixed expenses

On an overall level, it is much easier to estimate your fixed expenses, then your revenues. You can start by outlining your fixed expenses, like utilities, salaries or rent.

Afterwards, you can start forecasting your revenues, it is always recommended not to forecast revenue as a bulk figure but understand the metrics behind that figure. For example, if you are a subscription-based company, you can forecast your number of annual recurring subscribers and then after the expected annual payment per customer. This method will enable you to understand the accuracy of that assumption as opposed to just stating your budgeted revenue figure that you wish to generate.

Finally, most expenses afterwards, will be mainly related to revenue and therefore could directly fluctuate with revenue.


Understand your cash conversion cycle

The cash conversion cycle is a metric that expresses the time (measured in days) it takes for a company to convert its inventory into cash flows from sales. For example, if a company purchases supplies and pays the supplier 30 days after the purchase is made (that is called Days payables outstanding), then-after, the supplies stays in the inventory for 50 days before it is sold to a customer/client (Days inventory on hand) who then pays the company in 40 days (Days sales outstanding), in that sense the cash conversion cycle would be 60 days (40+50-30 days).

 In general, from a cash flow perspective, the lower the cash conversion cycle the better.

This is essential to understand that any given time, your company could have a cash deficit that could impact its operations during a given period.

The Days sales on hand, days inventory on hand and days payable on hand, could be derived from the market average in that specific industry, historical figures and/or contracts already signed with customers / suppliers.


Compare your results with other comparable companies

Assess the accuracy of your forecasts by comparing your estimates with other comparable companies in your industry. 

If you are operating in a certain industry where comparables financials are not easily attainable. You can try to research and know your industry’s EBITDA profit margin, number of customers/subscribers, average price per customer (this should be your own price of course, but it should be close to the market average price) and average salary per employee (or per job title). Afterwards, you can compare these figures to your own estimates to make sure that they are inline.


Regularly review your forecasts

Forecasts are never even 90% accurate at the beginning, unless you have very good knowledge of your industry or you have a good number of historical figures that you can base your assumptions on. In that sense, forecasts should be constantly reassessed in order to evaluate how accurately your assumptions were compared to your actual results. This will result in you becoming more skilled in the process and will decrease the number of inaccurate forecasts going forward.



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