Accurate forecasting is vital for several reasons, including:
- Determining the right price point for products and services
- Estimating the correct size of production runs
- Determining sales quotas
- Planning the budget for the coming period
- Creating accurate and fair reports for shareholders or investors
Qualitative versus quantitative forecasting
There are two main approaches to financial forecasting, each of which has its own set of tools and techniques.
Qualitative forecasting uses expert analysis and judgement rather than relying on pure numerical data. For example, the Delphi method asks experts to answer a series of questionnaires. The results are aggregated and distributed to those same experts. They are then allowed to adjust their original predictions or estimations after reading the group response. This continues until a few discernible, unified patterns appear in the responses.
Other forms of qualitative forecasting include the use of focus groups and subjective surveys. Qualitative forecasting is especially useful in markets in which data is obscure or limited. This may include cases where a business operates in developing countries, where government bodies do not reliably track transactions.
On the other hand, quantitative forecasting is the use of trend analysis to predict future patterns. For example, a company could analyse sales over a period of five to 10 years to determine common up and down cycles. Today, quantitative forecasting is often assigned to data analytics experts or third-party data scientists, who may use proprietary algorithms to determine results.
The use of either the qualitative or quantitative approach is not exclusive. In practice, it is unusual for companies to rely purely on one method or the other. A company, for example, may do a trend analysis and also run focus groups. Assuming the data used is accurate, both forms of forecasting can be expected to arrive at a somewhat similar conclusion. If, however, the two results are divergent, a close re-examination of the data may be required.
Methods for improving the accuracy of financial forecasting
While there’s no way to be absolutely certain about one’s predictions of the future, business leaders can adopt strategies to improve the accuracy of financial forecasting.
1. Apply scenario writing to the forecasts
Rather than forecasting based on a single scenario, create multiple forecasts based on different possibilities.
For example, an ongoing Sino-US trade war is raising expectations of a recession in 2020. As such, many businesses are forecasting based on lowered demand, tighter liquidity, and the usual factors accompanying a downturn.
But it also pays to create forecasts based on an optimistic outcome. Separate trade deals might mitigate the effect of tariffs, or the two countries might resolve their trade dispute. By allowing for such a scenario, no matter how unlikely it currently seems, businesses can identify in advance the best strategies for making the most of the situation.
With the help of qualified scenario writers, such as consultants or industry experts, a business can formulate strategies by forecasting situations such as:
- Continued trade tariffs, but at a lower level than expected
- Lowered interest rates, as the US tries to maintain economic stimulus during the trade war
- Changing investor sentiment toward a sector specific to the business’ industry
This process is time-consuming, but having contingency plans allows businesses to respond quicker to changing scenarios.
2. Use a lateral rather than top-down approach
As far as possible, businesses must try to include input from different departments and at different levels of seniority when forecasting. Although forecasting is typically a top-down activity, it pays to talk to managers across the board to shed light on potential blind spots.
For example, senior management may forecast higher sales because of clearly rising demand. But logistics departments may be aware that production facilities or suppliers have yet to adapt to the new demand, thus limiting sales growth.
There are limits to how much input can go into a forecast—analysis paralysis can result if there are too many contributors. However, a business should, at the very least, seek input from more than a single department or level of management.
3.Factor in inherent biases
On a related note, businesses should factor in the inherent biases in departments contributing to a forecast.
For example, marketing and sales teams may have a tendency to predict significantly lower sales upon being told that the advertising budget is reduced. Likewise, manufacturing may exaggerate the impact on production capacity if there is a manpower cut on the facility floor.
Quantitative analysis can be used to balance this out. The business can check if sales fell as drastically as marketing projects during the last few instances when the advertising budget was cut.
4. Ensure forecasts are an ongoing exercise
Financial forecasting should be done at least once a year, but this should not be a straitjacket. It is sometimes feasible to create a new forecast based on changing circumstances, such as a sudden market upturn, restrictive policies enforced by a trade partner, or an epidemic that severely hampers supply (in the case of food and agriculture businesses).
Financial forecasting should not be conflated with long-term strategic planning, such as the company’s 10-year or 20-year plan. It is effective when used with a degree of immediacy, such as in setting plans for the next year or the next half of the year.
5. Check the integrity of the data being used
The quality of the data significantly affects the accuracy of a financial forecast. As far as possible, the data used in a forecast should:
- Come from a non-biased source
- Be as recent as possible
- Show consistency with multiple trusted sources
- Contextually correct (e.g. for fuel prices in a given country, it should clearly indicate whether prices are inclusive of subsidies)
6. Remember to factor in the availability and cost of financing
The use of financing and subsequent repayment obligations is a crucial part of forecasting. When making these projections, businesses must not ignore the operational impact of a credit crunch, or the effect of rising interest rates. Some businesses may also face refinancing risks if they need to roll over debt for operational reasons.
Today, there are many alternatives besides traditional business term loans that companies can factor into their forecasts. For example, account receivable financing provides a fast financing option without a need for collateral. This can allow a business to fund a needed expansion even if there’s no traditional bank loan available. It also gives greater flexibility to the business as the cost is incurred only to financed transactions.
7. Benchmark against comparable businesses
Besides monitoring the competition, businesses can also aid in the accuracy of their forecast by looking, if possible, at competitor sales, or the sales of tangentially related companies.
For example, if gold prices are rising, jewellery business could reasonably expect to raise prices in tandem.
The revenue of related businesses also provides a “reality check” on financial forecasts—if a business is projecting exponential growth when related companies are predicting the opposite, close scrutiny is called for. Such forecasts are not necessarily wrong, but management needs to explain and justify them.
Financial forecasting is both an art and a science.
This is evidenced by the continued need for expert opinion and intuition (the qualitative aspect), despite the advances in data science. There will be times when experience should trump the numbers, or when factors outside a business’ control affect an entire industry. As such, business leaders need to constantly review and refine their financial forecasts and explore different techniques to improve accuracy.