Broadly speaking, there are two categories of forecasting techniques: qualitative forecasting and quantitative forecasting.

Qualitative forecasting methods

Qualitative forecasting methods are based on opinions and judgements. Executives and sales teams often give forecast opinions based on broad market trends, industry expertise, and knowledge of individual customers buying plans. Qualitative forecasting can be strengthened if done collectively and iteratively – i.e. if a whole team of informed experts makes an estimate, then reviews and revises the estimate, it is more likely to be accurate. Qualitative forecasting is valuable and should not be overlooked. Blindly following data can get you into trouble.

Quantitative forecasting methods

Quantitative methods use historical data to attempt to predict the future, by building predictive models.

• Time series methods are among the most simple. Time series forecasting in management of small businesses is most commonly done monthly, and starts by plotting or listing a series of historical data points over time. Examples can include monthly revenue, employee count, units produced, etc.. Some businesses simply use the previous period’s monthly value as its forecast for the next month – this is called the Naive method. It can be appropriate for some data where there should be little expected change, such as employee count. For data where there is a lot of fluctuation, it can be appropriate to use a moving average (also known as a trailing average) to predict the next month. If you want to get even more fancy, apply exponential smoothing which is like trailing average but gives more weight to more recent periods.
• Causal models attempt to find the ‘drivers’ behind the numbers. For instance, it may be appropriate to assume that travel expenses scale up as some function of the number of employees, or that “units sold” increases proportionally to store traffic. One of the most common shortcuts for forecasting in management is to assume that a certain expense line will hold as a percentage of revenue. A more sophisticated approach is to run regression analysis and other statistical methods for forecasting. This can allow you to more accurately determine the relationship between variables.
• Pattern analysis leads to trend forecasting, seasonality forecasting and cyclical forecasting.
• Trend analysis is a trendy buzzword (foregive the pun), that actually means looking for a consistent upward or downward movement over time. Trends are most commonly linear, geometric or exponential – a common mistake for founders’ forecasting in management is to assume a trend is exponential (that hockey stick curve looks great on the pitch deck!) whereas it’s actually linear. Remember, past trends don’t always predict future trends.

Period of Time for the Forecast

Startup founders and executives often find the concept of KPIs exciting but fail to implement effective ones for the following reasons:

1. Using someone else’s KPIs – there are some popular KPIs in each industry and there’s nothing wrong with using them, but they aren’t written for your specific goals. Use or create KPIs only after considering your own business’s strategic objectives. To that end, there are no shortcuts here.
2. Definition struggles – a KPI concept might be easy to understand but when it comes down to the nitty gritty, it’s often difficult to decide ‘what to include’ and ‘from what time period’. Certainly, experience is imperative here.
3. Data problems – KPIs must stem from real data from financial systems, CRM, and other business applications. As a result, it’s common for startups to have poor data quality or lack the specific type of data they need.
4. Visibility and accountability – despite good intentions, KPIs are sometimes abandoned or reviewed infrequently because of the data analysis effort required to populate them on a regular basis. Worse still, leaders sometimes fail to adequately share the KPI and hold their teams accountable to progress on a regular basis.
5. Too many KPIs – humans can only focus on a few things at a time. If there are 20 KPIs, you can be sure that the most important ones will be lost.
6. Failure to refresh KPIs – as business conditions change, businesses should update their KPIs immediately following any strategic planning process. This is often forgotten.

Relevant Data

A great deal of effort should be taken in forecasting to first include the relevant historical data to inform predictions. For example, should we consider the average Cost of Goods Sold Percentage (COGS%) average from the last 6 months to build our forecast, or 12 months, or 36 months? Is last year’s seasonal pattern relevant now that we’ve launched into Canada, USA and Australia? To what extent should we include or exclude launch expenses for the new product in our forecasts? Generally speaking, forecasters must identify assumptions that can be reasonably expected to hold true, and separate those from conditions that have changed. At the same time, forecasts that are too granular are difficult to update and that can make them less useful.

In a dynamic business like a startup, a forecast is out of date the day after it is released. In order to be useful it must be updated regularly. Founders and CEOs often make the mistake of only updating a forecast when they need to report to the board. Forecasts should be updated at least monthly. This requires your financial house to be in order – the books must be closed monthly and reviewed for accuracy, inventory counts done, sales forecasts made, etc.

It is extremely important that management not introduce their own optimistic biases into their own forecasts. For this reason, forecasting should be done by a third party – CFO services or an internal financial analyst – that understands the business and can construct the forecast objectively.

In finance, if your budget is your map then your financial statements are your GPS coordinates and your forecast is your compass. Your forecast tells you where you are heading and should inform management decisions. The forecast answers the question “if we keep going the way we are, what will be our total sales, our salary expense and our closing bank balance for the year?”

While forecasts are not perfect, they can allow management to alter course. Many founders have been able to act quickly to changing conditions as a result of exceptional forecasting clarity. Having a 6 months heads’ up on a cash crunch can mean the difference between life and death for a small company. If sales aren’t quite going as well as expected, it can help the CEO communicate to managers about delaying hires or purchases. If the forecast is favourable, the business may be able to advance their plans.

On at least a monthly basis, management should review the forecast and find areas for improvement of execution of the plan. On at least a quarterly basis, management should examine the forecast and determine if a change of plan is required.

Forecasting in management is part art, part science. It requires that financial and sales data be always clean and up to date. A well constructed forecasting model contains reasonable assumptions and is easy to update as conditions change. Using forecasting correctly means looking at the forecast at least monthly. To get help with building, updating and maintaining forecasting for your startup or growth-stage company, talk to one of our outsourced CFO services help to ensure high quality and low-bias.

• The Three Way Forecast
• Cash flow forecasting – a guide for small businesses
• Forecasting vs. Budgeting for Small Businesses