Forecasting in uncertain times

One senior executive of an organization pronounced during a planning session I was involved in that “one assumption for our 2021 plan is that we can have face-to-face engagements with consumers and customers by the fourth quarter.” This assumption was plugged into the 2021 financial budget.

Last month, news about a more contagious strain of Covid-19 in the United Kingdom broke out. In mid-January, the Philippines confirmed its first case of this strain. This came after several countries confirmed theirs and started banning travelers from the UK and other affected nations to contain the Covid-19 variant’s spread.

With this new information, and developments in the arrival and efficacy of available coronavirus vaccines, we need to revisit or redo our financial forecasts.

This is how uncertain the business environment is now; it has forced many companies to revise their financial forecasts. Severe economic and market volatility due to the evolving nature of the pandemic, coupled with the diverse government and business responses to it, make it virtually impossible for economic and financial planning teams to come up with accurate forecasts and budgets. This is further aggravated by inefficient forecasting processes, lack of an understanding of underlying business drivers, limited analytics capabilities, and capacity and talent constraints.


But one of the biggest influences on inaccurate business forecasts is the input of leaders and business executives that is filled with cognitive biases. Pressured to deliver the numbers, these business leaders unconsciously and irrationally shape business forecasts, only to miss them later and revise altogether.

As I wrote in a previous article, one of the cognitive biases that cloud our judgement is confirmation bias, which I described as “the tendency to search for, interpret, favor and recall information in a way that confirms or supports one’s beliefs or values.”

“Business owners are prone to confirmation bias, especially those who have been operating their businesses for decades, such as those in retail, travel and hospitality. They openly accept any positive indication of rebound and growth from optimists, instead of hearing the contrary and pivoting their business models to new ones,” I wrote then.

Another is optimism bias, which I wrote “causes some people to believe they are less likely to experience a negative event.” This influences forecasters and business executives to underestimate and even downplay the impact of the crisis on consumer demand and the business environment in general. Together with confirmation bias, optimism bias can be a powerful force in shaping highly inaccurate business forecasts.


These cognitive biases, alongside limitations in forecasting, create a blind spot in how we think, which may pose a great hurdle to realizing the forecast. To preclude this, we need to think clearly during the crisis and take steps to make our forecast as close to reality as possible.

First and foremost is to learn and understand your customer sentiments. Consumer and business buyer data can help your trach consumption and buying patterns. Talk to customers to get a first-hand account of how they see the market. These will help you remove biases in forecasting.

Another is getting real-time transaction data from customers (e.g., ecommerce purchases, deliveries, and orders). These would help you adjust your forecast as frequently as possible to adapt to shifts.

Also, getting the right people across the organization and even outside it is required to derive diverse views on the available data and produce a forecasting model based on an informed consensus view. You can include people from different backgrounds and experiences in the forecasting process to ensure assumptions are made holistically. Be open to a healthy level of skepticism about your forecasting model. All these will, again, remove your biases.

You can apply scenario testing to come up with multiple forecasts based on different scenarios. You also have to stress test each scenario to ensure rigor and validity, and that it is as close to reality as possible. One scenario we must stress-test is the worst-case scenario, which usually have a low probability of occurrence. Learn to embrace and accept the worst case, because any other scenario would represent a win, and thus reducing disruption within an organization.

All of these, when practiced and applied, will lead to an agile way of forecasting, one that is real-time data-based, devoid of biases and quickly adjusts to reality.

The author is the founder and chief executive officer of Hungry Workhorse Consulting, a digital and culture transformation consulting firm. He is a fellow at the US-based Institute for Digital Transformation and teaches strategic management in the MBA program of De La Salle University. The author may be reached at