The Covid-19 pandemic not only upended business strategies across industries, but also exposed pre-existing shortcomings in financial planning and analysis (FP&A). Kip Krumwiede, director of research at the Institute of Management Accountants (IMA), writes



According to a recent IMA report, financial executives in 2020 were less likely to agree than in 2017 that their company has a strategic long-range plan, that they clearly understand how operational projects and plans will impact financial results, that they identify real business reasons for plan-to-actual variances, and that they make course adjustments when they do not hit their financial and operational goals.

The study also found that by far the biggest FP&A challenge for most companies is predicting future revenues and cashflows – a challenge that became much harder during the Covid-19 pandemic.

Past results and old assumptions may no longer be sufficient, and new market factors need to be considered, making it hard to prepare for different scenarios. The IMA report provides nine ways that any company can use basic predictive analytics to reduce uncertainty and improve forecasting.


  1. Expand the data available

Management accountants can help to build a vast ‘data lake’ that can support a wide range of analytics, drawn from sources ranging from social media and website analytics to inventory and supply chain information. Integrating all this data into a more predictive and holistic FP&A process will be key to success.

  1. Start small and add FP&A tools

Predictive models should start small to include the most significant variables and approximate relationships among the variables. Once you establish a base model in Excel, then consider migrating to dedicated FP&A software programs to make the process more flexible and adaptive.

  1. Use scenario planning

In times of uncertainty, management accountants should identify potential scenarios circling around one or two critical uncertainties. Then give each scenario a name and compose a narrative as if it had already happened.

  1. Address the knowing-doing gap

There needs to be a stronger connection between predictive analytics and executing the business strategy. Predictions should be based on what the company plans to do. A common reason for variances from forecast is a failure to execute plans.

  1. Think about causality in model-building

To build a reliable predictive analytics model, think causality. A reliable predictive analytics model should be based on actual or expected causal relationships among resources, processes, customers, KPIs, external market factors and other leading or lagging measures.

  1. Establish data collection systems

Tying back to the first point, management accountants should establish mechanisms for constantly collecting valuable data from diverse sources to feed back into the FP&A process. Combining causal market analysis with well-developed models of revenue and cost drivers will strengthen the accuracy and speed of scenario planning processes that accountants and managers use to achieve key strategic goals.

  1. Improve assumptions and estimates

If you are going to develop a model for predictive forecasting, the assumptions and estimates should be reasonably accurate given the level of uncertainty at the time. Ask those providing forecast data to summarise their assumptions. There are also methods in the report to ‘calibrate’ future estimates.

  1. Monitor results and quickly identify the business reasons behind variances

In today’s volatile world, it is critical to monitor results continuously and to quickly determine business reasons for any significant differences from the forecast. Encourage a culture of accountability by holding people accountable for delivering both operational and financial goals.

  1. Improve analytical skills

Our study found the best-performing companies’ FP&A teams tend to have higher predictive analytics skills. These skills include drawing useful insights from the data and visually presenting and communicating them. Scenario modelling and what-if analysis, cost management and control, and cash forecasting and management are crucial.


All these ways of improving FP&A are doable now, with varying degrees of upskilling needed by finance professionals in FP&A teams.

Consider adding forward-looking variables to the forecast such as economic trends, consumer confidence, unemployment rate and online activity. If you have trouble identifying data that represents the critical variables for success, try hard to articulate what success would look like and then observe whether that happens. Anything that can be observed can be measured. And anything that can be measured can be modelled for forecasting.

As the world becomes more uncertain, the priority of investing in these skills will become more paramount.