The only certainties in 2021 will be that volatility across an uneven economic landscape will continue and will present new revenue opportunities for companies in every industry. Success will be dependent on a company’s ability to identify and capitalize on growth opportunities in the moments they are presented. How can your team set up a process for turning your data into revenue?
This blog takes a closer look at the process of building intelligent forecasts that address the needs and goals of your company.
Step #1: Define Your Goals
The first step for building intelligent forecasts is defining your goal. Business forecasts can be used to address a myriad of challenges and business needs. It is important to recognize that every company, and even departments within an organization, will have different reasons for building every forecast. Make sure to identify and start with your clearly defined business goal or goals from the beginning.
Based on the needs and goals that you are addressing, you will need to have a strategy for implementing the results into tangible actions within your company. For example, a three month supply chain forecast may look very different and have different drivers than a 12-18 month forecast used for FP&A. Does the forecast become part of a monthly S&OP discussion? Does it inform the sales managers ahead of quarterly sales planning?
Step #2: Understand Your Historic Success and Failure
After defining the business goals for the intelligent forecast, the next step is to review your historical data, including previous forecasts and results. While reviewing historical data, it can be helpful to start by identifying previous indicators that have acted as business drivers for your business. If an indicator was important before COVID, do not necessarily toss it out as it could become increasingly important again in a post-COVID world.
In addition to identifying key indicators, it is important to review data to determine whether your organization has previous blind spots when building forecasts. For instance, were there assumptions made about the performance of specific areas within your company that proved to be false? Or, maybe some indicators were not included because they were believed not to be relevant to your industry. Learn from these experiences and make sure that you know and account for previous blind spots that have affected your forecasts in the past. Finally, after reviewing historical data, it is important to find your “data sweet spot.” Confirm that you are looking at the right information within your data sets.
Step #3: Connecting the Data
Once you have defined your business goals and reviewed your historical data to find your sweet spot, the next step is connecting the data. This means taking all of the internal and external data sets and layering them, including all historical data that you can review. During this process, the key is to link trends from within your internal results to external data sets and identify unique trends and indicators that have impacted your business in the past.
It can be beneficial to look at relationships in different calculations, such as year-over-year or moving averages and at different offsets, to better identify leading and lagging indicators. As you spot those statistical matches, start thinking through the business logic of why these indicators would strongly relate to your past sales trends. This will help you separate statistical anomalies and spurious correlations from true business insights. These data points that pass both the statistical and the logical tests have the greatest possibilities of being true indicators for your business and will serve as the core indicators for your intelligent forecasting models.
Step #4: Apply Business Logic to Results
Of course, businesses cannot merely rely on the data points to dictate how they will perform. The key is to apply business logic to the results. For instance, you should not rely too heavily on a market demand based forecast if there have been significant changes to regulations or internal structures that would limit performance, despite ample demand.
Within that same thought, it is also important to know your limitations. Every company has limits for the amount of change that can realistically happen internally. Knowing those limitations and accounting for them is critical to making accurate forecasts. As an example, if the data indicates that there is an opportunity for market growth for a specific product within the next three months, but you know that it would take six-months to increase production of that product, then it would be better to focus your final projections on what is realistic from a production standpoint, versus what the market says is possible due to unconstrained external demand.
Step #5: Building Your Intelligent Forecasting Models
The final step is building your intelligent forecasts by leveraging the indicators you have identified as the strongest for your business. Once you take those indicators, you will want to build an intelligent forecasting model that projects the business results if the market stays consistent and the impact of potential market fluctuations, and build out the realistic scenarios. This is particularly important during times of economic uncertainty so that you can recognize and have a plan for adjusting your strategy at the earliest signs of volatility. The last step is to have a process for implementing the intelligence within your organization. When forecasting ends with the finance department, it becomes passive information. It can lose its real value of helping the organization capitalize on new market opportunities.
As Intelligent Forecasting becomes the new normal for strategic planning, leveraging global data, predictive A.I. technologies, and econometric modeling, these five steps will help you get started in augmenting your existing planning methods.
For more information, listen on-demand as Andrew Duguay, Prevedere’s Chief Economist, shares the best approach for your company to build intelligent forecasts in 2021. >>