Lina Chindamo is a Certified Credit Professional with over 25 years of experience in credit risk management. She has held senior leadership positions at companies like Sony Electronics, Maple Leaf Foods, and Mondelez Canada. Her extensive experience and current role, where she collaborates with c-suite partners and credit teams across various industries, make her a respected figure in the credit industry.
Only 1% of companies produce financial forecasts that are within 5% of actual results.
That sobering figure comes from a recent KPMG study that surveyed 540 senior executives across the world who are involved in the financial forecasting process for their businesses.
What this study indicates is that poor financial forecasting costs money. Many respondents felt that share prices had dropped by 6% over three years, representing a huge loss of market capital. Meanwhile, that tiny 1% of businesses that were accurate with their forecasting grew share prices by 46%.
The numbers don’t lie. Financial forecasting is a must-have for any organization interested in longevity. At its most basic, the process involves predicting a company’s financial future by examining historical data. This includes cash flow, expenses, sales, etc. It’s looking at business performance and there are plenty of external factors that have a hand in shaping the forecast.
In this article, we’re going far beyond the surface. We’ll show you different types of financial forecasting models so you can make an informed decision on what’s best for you. Plus, we’ll show you how you can build good financial forecasting models instead of models that could break your business.
Chapter 1
Why is financial forecasting important?
As the KPMG study details, without proper financial forecasting, companies run the risk of losing money. But it’s not the only reason why cutting corners is a bad idea. Here are just a few to consider.
- Poor goal setting: There’s no real path to follow or manage without financial figures to work from. Organizations are flying blind and don’t have an indication of where they need to put resources to grow.
- Wrong decision-making: A lack of benchmarks and quantifiable data means it’s hard to decide what’s best for business. This could lead to hiring the wrong staff, mismanaging budgets and even destroying customer loyalty.
- Stagnation: A company won’t be able to adapt to changing market conditions. This can lead to loss of revenue, sales, cash flow problems and more problems down the road.
- High credit risk: Investors and creditors expect to see strong financial figures and payment behaviors when reviewing business credit reports. Without them, a business can struggle to pay its outstanding bills on time, can be refused credit by lenders and could find itself in the precarious situation of having to file for bankruptcy.
How much risk are your customers exposing your business to?
Chapter 1
Different types of financial forecasting models
Financial forecasting model: Bottom-up forecasting
Bottom-up financial forecasting involves looking at existing financial and sales information. It’s reviewing a company’s finances from the lowest point and plotting future scenarios. Or it could be for predicting revenue by multiplying the average sales value by the potential sales per product offered.
So, put into a formula, it looks like this: Estimate of products/services expected to sell x average price = total sales.
Start-up companies or larger organizations may rely on the bottom-up financing forecasting model. Of course, there will be different requirements for every business. But the uniting factor is that the model ties directly to sales. If the forecast is inaccurate, then sales teams might not hit their targets. So, cash flow suffers and friction develops between sales, marketing and other teams.
Financial forecasting model: Top-down forecasting
When a company is focused on entering a new market, top-down financial forecasting might be the way to go. This is the formula to understand for this: Revenue = market size x market share assumption.
Put another way, market size refers to the complete revenue opportunity for a specific market. And market share assumption is the expected percentage of the market taken by the business.
Compared to bottom-down financial forecasting, this method is often best suited to larger organizations like Microsoft or Amazon. The reason is because they have diverse revenue channels and using a bottom-up approach would be too complicated because of the vast amount of products that are being handled.
With that said, top-down financial forecasting can offer some advantages for small companies that have only been trading for a couple of years. The lack of historical financial data can be an asset in this situation and help to identify investment opportunities.
Financial forecasting model: Statistical forecasting
As the name suggests, stats are an integral part of this model. It’s often used for finding and creating relationships between financial data and might be broken down into subcategories, such as:
- Percent of sales: This examines the future metrics of financial line items and how they correlate with sales figures. The accounts used are often inventory, cost of goods sold and net income because they’re likely to grow with sales.
- Moving average: This is taking the weighted average of previous periods and predicting the future. Usually a company will consider high and low demand. It works best for short-term forecasting.
- Simple linear regression: This focuses on a relationship between a dependent and independent variable. The former represents the forecasted amount, while the latter represents the factor that influences the dependent aspect. For example, you could determine the number of sales calls that turned to leads in a month. The number of leads is the dependent variable and the number of sales calls is the independent variable.
- Straight line: This method works on the understanding that company growth will be consistent. So, future revenue is predicted by multiplying a company’s previous year’s revenue by its growth rate. So, if the growth rate was 15%, then the straight-line model assumes a continued 15% growth into the next year.
Financial forecasting model: The Delphi method
This collaborative model focuses on gathering opinions from a panel of experts. A facilitator leads several rounds of questionnaires that analyze data and theories. After each questionnaire, the answers are aggregated and anonymous. Each participant has the chance to adjust their next set of answers based on the aggregation.
There are several advantages to doing this. First, as the answers are anonymous, participants can be completely open about their opinions without fear of negative backlash. Second, people from different teams can get together and bring new perspectives that might not have been considered before. Third, there’s a prevention of the ‘halo effect’ where the opinions of senior members are given priority over others.
On the other hand, the Delphi method can be drawn out and the value of discussion might be lessened. Other times, a live debate might be more appropriate as real-time data is broken down and assessed quickly.
Financial forecasting model: Asset and liability management forecasting
This method relies on risk management strategies to reduce various risk scenarios for a business. Changes in interest caused by recession. Worker strikes damaging supply chains and product circulation. Economic shifts that are simply beyond the control of an organization.
The process is built on the timing of cash flow and the payments of liabilities. Assets must be available to pay debt and assets can be converted into cash. Let’s take a look at a specific example of this model.
Let’s say a business has a pre-defined pension plan for employees. The business carries the risk that the assets invested in the plan aren’t enough to cover all benefits promised to staff. So, the company must forecast the dollar amount of assets needed to pay the benefits. This might be a figure of $1.2 million paid to staff over ten years. The rate of return on the dollars invested must be estimated for each year before the first payments start in ten years.
Chapter 1
Finding the right financial forecasting model for you
All these financial forecasting models have their place. But if you’re too rigid with any of them, you can still run into problems. For instance, you could end up misinterpreting the limitations of a certain model and be left with wildly inaccurate figures that hurt your bottom line in the long term. You could also favor a more time-consuming approach over a more efficient method. Or you might rely only on the calculations and not plan for any unexpected scenarios.
If you’re leading a finance team at a company, then there are certain traits and skills you’ll need to implement better financial forecasting. Let’s explore some of these.
Enhance quality beyond the basics
As the KPMG study reveals, CFOs need less time to train more finance staff in forecasting (11% see it as a priority compared to 21% at less accurate companies). Meanwhile, 87% hold managers accountable, while 25% incentivize managers to forecast accurately.
An example of this in action could be for a CFO to hold regular workshops with managers to understand their approaches and dig deeper into specific metrics like DSO, DIO and DPO. The key is to help the broader finance team get a full understanding of the impact on working capital.
Focus on productivity and profitability
The most financially stable brands have CFOs who:
- Update forecasts more frequently: 58% do it monthly compared to 44% that don’t.
- Forecast key balance sheet indicators: 83% compared to 78%.
- Make greater use of advanced forecasting software than spreadsheets: 48% over 37% prefer off-the-shelf forecasting/planning tools and 47% to 34% prefer bespoke software.
Making the most of AI and automation opens a new world of possibilities. CFOs should spend time doing a thorough review of different platforms and analyzing key features. Questions to ask include:
- How customizable is the software?
- What kind of financial forecasting model(s) does the platform cater to?
- What are the requirements for staff onboarding? How easy is it for end users to use the platform?
- How compatible is it with existing systems? Is integration easy or hard?
Make figures work for shareholders
Top-performing CFOs in the last three years have worked to make forecasting a key part of lowering the decline of share prices. This is a reminder that financial forecasting can’t just be left with one person or a team. Shareholders must be invited into the process. They need to see what’s going on as early as possible.
About the Author
Lina Chindamo, Director, Enterprise Accounts, Creditsafe Canada