We all talk a lot about the future. We talk about what the weather will be like in ten days, who’s going to win that election, and what the stock market will do in Q4. We have more tools at our disposal for predicting the future than ever before in history — sophisticated climate models, data-driven political insights, financial algorithms. We feel like we really should know where we are going and what will be coming at us along the way.

Of course, the reality is that we cannot predict the future with 100% certainty. When predicting future organizational success, we can, however, seek out those indicators that can best help us anticipate future conditions and apply the forecasting practices that are most likely to tell us where we are going before we get there. Here’s how to end uncertainty and use forecasting to predict future performance with accuracy and expediency.

1.   Have a Forecasting Plan

Unlike the insights it’s meant to provide, forecasting is not a contrivance of the future. In fact, many of the methodologies and proven approaches of business forecasting have been in practice since at least the early 1970s. Generally, it is understood that a superior forecast is:

• Accurate — as much as possible, given available data
• Reliable — again, as much as possible with the data at hand
• Timely — centered on a given length of months or years
• Easy to use and understand — often for a range of users
• Cost-effective — the forecast must provide insights that are valuable enough to justify the cost of the forecasting process

For a forecast to meet these qualifications, it needs to be approached methodically. Best practice for a forecasting plan includes six steps:

1. Determine the forecast’s purpose: What are you going to do with the information provided by the forecast? In general, forecasts are designed to predict future revenues, expenses, and capital costs. Are you looking to use this information to ensure stability or to fuel growth? Different goals may mandate different forecasting techniques.
2. Establish a timeframe: Do you need short-term projections that will allow you to respond to immediate conditions and look ahead at the next 3-12 months? Or are your forecasting needs more aspirational and growth-oriented, looking forward one, two, three, four, or even five years?
3. Select a forecasting method: Models can be either qualitative or quantitative. We outline some of the key methods below.
4. Gather and analyze data: Your forecasting method is often only as good as the data you have on hand, so make sure it’s accessible and credible.
5. Make the forecast: This isn’t something you have to do on your own! A trained business analyst can be enormously helpful to organizations looking to understand their enterprise trajectory.
6. Monitor and change course as needed: Forecasts need to stay flexible to be usable. Just as a weather radar changes from one minute to the next, your business conditions can shift in a heartbeat. Your forecasting approach needs to stretch to include new variables as they arise.

2. Deploy a Variety of Predictive Models

One way to build flexibility to your forecasting approach is to try on several different predictive models. In general, forecast approaches are either quantitative or qualitative.

Quantitative approaches are designed to be objective, based on deep historical data. They rely on lagging measures, including sales data or its equivalent. Quantitative methods include “naive” forecasting, where past performance is assumed to be a straight-line predictor of future performance. For example, if 600 people attended last year’s event, you would anticipate that 600 would attend this year’s. Other quantitative methods combine sales data with insights about trends, seasonality, cycles, and anomalies (for instance, a pandemic) to predict future performance more accurately. They might rely on averaging or more complex mathematical formulas — what’s known as econometric methods — to anticipate demand.

Qualitative methods are more subjective, often happening earlier in a product or service life-cycle when less historical data is available. These include:

• Market research
• Salesforce opinion
• Executive opinion
• Delphi or Nominal Group models where multiple external forecasters provide their insights and the consensus of their opinions constitutes the final forecast.

If two or three different models give you vastly different visions of the future, you might need to conduct more research, adjust your leading measures, or examine your business intelligence.

3. Rely on Business Intelligence Tools to Ensure Data Credibility

There are plenty of challenges inherent to the forecasting process. These include differing priorities across business units that make the process unwieldy (for instance, if sales and marketing aren’t aligned in their goal-setting); the impossibility of factoring in unknown variables (c.f. the pandemic); and most of all, ease and speed of access to fresh, credible data.

Quantitative forecasting methods are particularly reliant on accurate data as a measure of future performance. That’s where a business intelligence platform can be hugely beneficial, providing rapid access to and visualization of important data for users across an organization. Sure, you can keep everything in an Excel spreadsheet that you manually update regularly. But just because forecasting has been around for decades doesn’t mean the tools haven’t changed radically and for the better. For real-time availability of actionable data insights, BI software is a game-changer.

4. Assess, Assess, Assess

Accurate forecasting can help reveal seasonal sales or donor trends, rationalize cash flow, inform supply chain decisions, understand how outside factors influence internal processes, and ultimately prepare your organization for the future. However, it can be incredibly difficult to get the level of accuracy you desire out of your forecasting approach. That’s why that 6th step in the plan is essential: monitor and change course as needed.

A few assumptions underlie the majority of forecasting efforts. We assume that the past repeats itself (so past sales will predict future ones). We know that it’s easier to forecast in the aggregate rather than at the individual level. For example, we can predict the volume of all donations better than just one type of donation, such as a legacy gift. Finally, we understand that as the horizon shortens, as the future gets closer, accuracy increases. To ensure that accuracy, it’s best to regularly assess how adept your near- and long-term predictions are. When you see less accuracy in your forecasts, it is time to devise new approaches when you see less accuracy in your forecasts.

Sometimes You Need Help Finding the Right Crystal Ball

Given the variety of predictive methodologies and outcomes, it’s easy to find forecasting overwhelming. Momentum helps government, corporate, and nonprofit clients successfully engage in forecasting to predict future performance, ensure stability, and plan for growth.

Get in touch to learn more about how we can help find your crystal ball.