Even prior to the COVID-19 pandemic’s stranglehold on the global economy, corporate leaders increasingly embraced advanced analytics and artificial intelligence (AI). These capabilities were highly warranted and were expected to offer between $9.5 trillion and $15.4 trillion in annual economic value. Then came the pandemic with its devastating impact on businesses of all sizes. While some companies did fare better than others, almost all were impacted in some way and rendered most forecasts obsolete,thus rendering other FP&A processes even less effective.
Predicting future revenues and cashflow is difficult at the best of times, let alone after a pandemic when the continuing uncertainty can make forecasting very tricky. This holds especially true for sectors that bore the brunt of the pandemic, seeing drastically lower revenues or, worse, closure with almost no revenues. As the economy opens, companies in sectors such as travel, hospitality, retail, logistics, and ecommerce,which saw wild swings in consumer behaviour – be it in the form of cancelled vacations and business trips to a surge in online shopping and home deliveries—are dealing with chaotic trend lines that complicate forecasting efforts. As historical data became an unreliable peg following the pandemic, many companies started leaning more heavily on real-time data to feed predictive software models, including live website activity, online, and mobile searches.
Given the disruption of everything – from consumer behaviour to supply chains, and the resultant economic fallout, very few organizations are facing business as usual or as expected. Consequently, the data analytics field faces a complicated problem: how to use past data, and predict future behaviour, in the face of uncertainty. Here is where predictive analytics come in to help finance and accounting professionals to use data, statistical algorithms, AI,and machine learning techniques to identify the likelihood of future outcomes based on any relevant data.
The goal is really to go beyond knowing what has already happened to decrease uncertainty about the future and associated risk. The fact is that predictive analytics can be used to get clarity on what the future can look like. In its simplest form, it is the science – even artform – of taking historical data and using it to project future results.For finance and accounting professionals looking at harnessing the power of data to inform business decision making amid these volatile times we live and work in, here are a few key ways to facilitate effective predictive analytics:
Expand the data ecosystem–we live in a whole new data-intensive marketplace with vastly more ways of data collection than ever before. Good predictive analytics requires that a wide range of data be available. New types of data being included by companies includes carbon footprint, individual credit card transactions, social media statistics, and Google trends index data.Data integration is central to being able to combine data from various sources and making them equally available. It can help build a “data lake” holding a vast amount of data in its native format until it is needed for management purposes, like forecasting.
Tools: time for an upgrade–An IMA study showed that well over 40% of the FP&A survey respondents said one of their biggest challenges is that they are still doing most FP&A in Excel (consistent with other surveys in this area). Of course, the upside for spreadsheets is that they are cheap, easy to use, and quite flexible.However, in a post-pandemic world, it is certainly time for companies to migrate to a dedicated FP&A software program to automate the update and reporting process.
Put scenario planning to work– Scenario and crisis planning (also known as scenario analysis) is used to better prepare the organisation for potential future scenarios be they negative or positive. It is especially useful at this time of market uncertainty, both to identify opportunities, as well as potential risks, and to prepare for them. It involves conducting research on forces that could have a big impact on the success of the company; focusing on a small number of potential scenarios; articulating each of the scenarios, including both direct and indirect effects; and developing strategies to enable the organization to succeed in each of these scenarios.
Address the knowing-doing gap – There needs to be a strong connection between predictive analytics, competitive strategy, and operational execution. Examples of these planned actions include investing in R&D to launch new products, managing the life cycle around end-of-life for others, re-prioritizing certain products in the portfolio and the marketing investment behind them, and doubling the number of sales associates. Performance measures linked to the planned actions need to be built into the predictive analytics model.
Model building: 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. The goal is long-term accuracy, not short-term precision. Consider adding forward-looking variables to the forecast such as economic trends, consumer confidence, unemployment rate, website, or other online activity.
An important lesson for corporate leaders to remember is that their organisations are,more often than not, more agile than they think! A McKinsey survey showed that companies realizing higher returns from AI and scaling it more broadly were much more likely than others to assemble cross-functional teams to solve business problems. Organizations, regardless of analytics maturity, would do better if they assemble cross-functional crisis-response teams with all relevant stakeholders to develop analytics solutions for faster response.
If there is one thing that the pandemic has shown, it is that rapid change is both possible and pivotal for business survival. While not every complex challenge will be solved overnight, leaders who pay heed to the lessons from the pandemic and acknowledge that the future will only be different from the past if they build on the new and pragmatic ways of working, have the potential not only to survive but to also thrive.
(The author is Kip Krumwiede, Researcher and consultant, and former IMA director of research and the views expressed in this article are his own)