In the current state of unprecedented global change, financial forecasts provide the key to business stability and predictability. Companies rely on these business forecasting techniques to define business goals, set KPIs and create plans for the future. Traditionally, forecasts use historical company data, macroeconomic trends and market research to project future company performance.
While these forecasts are crucial for business planning and growth, traditional business forecasting techniques can be limiting.Traditional models utilize a relatively small data set and look to historical data to predict future outcomes, which can create potentially inaccurate assumptions. As technology and forecasting tools grow more advanced, businesses are looking to increase predictability and proactively address changing environments.
Why businesses are shifting away from traditional forecasts
While traditional forecasting models have been the industry best practice for some time, they are not as robust or accurate as the more advanced predictive analytics tools that have been developed in the last decade. Though traditional forecasts are still standard, businesses are evolving their forecasting methods and looking towards emerging tools to increase predictability and remain competitive. One survey by GTreasury and Strategic Treasurer found that among 250 enterprises, deployment of AI and machine learning technology for cash flow forecasting is expected to increase 450 percent between 2021 and 2023.
The reasons for moving to more advanced forecasting methods are abundant. Traditional forecasting:
- Relies on potentially incorrect assumptions: Traditional forecasting models rely on a set of assumptions about the future state of the company and overall macroeconomic conditions. As soon as business conditions or the global environment changes, those assumptions become inaccurate, and the forecast is no longer relevant.
- Leads to reactivity rather than proactivity: Advanced forecasting techniques can update forecasts at more frequent cadences—even in real time. Traditional forecasting, however, requires decision makers to evaluate the accuracy of older assumptions on a less-frequent basis.
- Uses relatively small amounts of data: Many mid-to-large sized companies have large amounts of data about their enterprise and their customers at their disposal. However, traditional forecasting methods are limited in the type of data that can be used. Thus, these forecasts make predictions about the future without considering all of the information available.
- Isn’t easily actionable: Forecasts built with predictive analytics can attribute business performance to the exact business lever driving it (e.g., marketing spend or operational inefficiency). Traditional forecasts that take a top-down approach don’t have this ability, as a business will be able to see overall forecasted performance but not the granular details of the cause and effect.
Businesses that rely solely on traditional business forecasting methods may not maintain a competitive advantage as those who invest in methods with increased accuracy, predictability and granularity. More progressive techniques, in contrast, can help your business reduce risk and increase profits in the long term.
Augment your current method with additional techniques
Not all forecasting techniques are appropriate for every business. Investing in the most sophisticated tools will not be beneficial if your company has little data or a very limited budget. However, any business can benefit from using a wider range of solutions and techniques. For example, if you are currently doing simple historical analysis based on past financial statements, you may consider using the expertise of a financial analyst who can also incorporate new modeling tools into your practice.
Scenario and sensitivity analyses can add more dynamic insights into your current forecasts. By plugging certain “what if” scenarios into your forecasts, you can determine the potential outcomes of certain business decisions or major events, such as supply chain disruption. Then, you can determine the level of impact or “sensitivity” that a single input has on your business. These techniques allow you to be proactive and prepared for a greater number of scenarios than you would by looking solely into your historical data.
You may also alter your current forecasting method and try a new approach. Consider a bottoms-up approach, for example. This approach can tell a business what their performance will be, but also what key drivers of the business lead to that outcome. From there, businesses can perform scenario analyses and measure the predicted outcome if a company makes a specific business decision. For example, a business can use these advanced methods to determine how much their profit would increase if they hired an additional salesperson or increased investment in their manufacturing capabilities.
What is intelligent forecasting?
Intelligent forecasting includes advanced and predictive analysis. Similar to traditional forecasting techniques, predictive analytics uses historical data and statistical modeling to predict future outcomes for the business. However, predictive analytics provides increasingly accurate results through data mining, machine learning and artificial intelligence (AI) techniques. These techniques allow businesses to use the forecast to achieve predictable business growth even in times of uncertainty.
Unlike traditional forecasts, advanced forecasts update daily as additional data is fed into the model. Since the forecast is continuously learning and adjusting to changes in business conditions and the macroeconomic environment, it can achieve roughly 95% accuracy. In contrast, a traditional forecast may achieve closer to 75% accuracy if all of the model’s assumptions are correct.
While AI-enabled forecasts may seem highly technical and difficult to implement, there are a growing number of avenues for making these techniques accessible. Whether a company invests in a ready-to-use cloud and software package or seeks the expertise of financial analysts with experience in advanced forecasting techniques, companies today are investing in business forecasting methods that fit their capabilities.
Finding predictable growth with AI forecasting
At Paro, we have put these techniques into action and have seen the direct results of advanced business forecasting techniques. In 2020, we introduced an AI solution that allowed us to drive internal decision making toward our company’s own growth goals and become a product-based company and growth platform for our clients and fractional experts.
Supported by internal and external data, our AI-based forecasts provide operational and optimal investment recommendations to guide our sales and marketing strategy. Advanced business forecasting methods allow us to identify and understand key change drivers of our business performance on a regular basis and at a granular level to help determine strategy.
In moving past traditional forecasting models, we more effectively answer the question of what strategies to prioritize and what targets we need to hit to reach our goals. Our AI solutions continuously provide over 97% quarterly forecast accuracy, resulting in reliable recommendations and more predictable growth.
Explore new business forecasting methods for your business
Interested in learning more about innovative forecasting methods for your business? At Paro, our fractional experts are experienced in providing data-driven forecasts that are highly tailored to your business. Contact us to learn more about advanced forecasting techniques and the possibilities for your business.