Our job is to be curious. It is to try to see beyond the obvious to inform the company’s strategies and actions going forward. When we found ourselves in the middle of the worst global health and economic crisis of the last 100 years, our first instinct as economists and researchers was to dig in and learn more.
We wanted to put our skills and experience to use to construct data to better understand what was going on. Lockdowns, travel restrictions, companies redesigning their operations and work at lightning speed — the uncertainty was ubiquitous, misinformation was the order of the day, and data and trustworthy evidence was super scant.
We had to somehow start sketching the full picture of a world that was upside down and putting pressure on all business areas of organizations all at once.
So, our team — which included people from all over the world with unique subject matter expertise — started doing research that turned into a very data-driven asset that uncovers new company-level insights about resiliency and competitiveness in the face of a new reality.
Learning from a crisis
This unexpected crisis was, and still is, an important moment to assess how well businesses react under pressure. We were curious to see how companies were dealing with the crisis, what they were doing to survive and reposition themselves against competitors.
We began by putting high-frequency data together that could help us track the developments soon after they occurred. The more we knew what was going on, the more we could make a difference in the way companies should navigate the present and invest for the future. By gathering real time data, we would:
- Know more about how to position companies in the face of changing consumer preferences.
- Help them with work reconfigurations and renewed cost pressures, among the many other disruptions.
- Tell them how to tap into their companies’ unique strengths to thrive in the new competitive landscape.
- Tell how well they are positioned to become future leaders by better understanding how companies and their peers are performing across different areas of the business.
As we analyzed millions of company data points in our resiliency data set, we discovered it could be used to predict future performance.
Our data machine learning-based predictive model uncovers a company’s chances to grow profitably in the future and identifies the factors driving differential performance. Here are three ways this predictive modeling is useful for businesses to build resiliency in the face of an uncertain future.
1. Bring data to a dataless space
"You cannot manage, let alone improve, what you cannot measure." We can hear the mantra of best management practice louder and clearer than ever before. We can also tell measurement has never been so hard.
The measures need to be broader than the traditional view that considers resiliency as per balance sheet solidity. To build a resiliency all-rounded data set, we had to synthesize over 1.5 million data points for over 1,800 big global companies operating across 18 industries across six business functional dimensions that absorbed the most significant impacts during the crisis.
For example, we can now understand how much risk is in the company’s supply chain. We can see how close customers are to a business’s operations. We can suddenly see the extent to which industries and companies have accelerated the focus on a compressed digital transformation agenda, among other performance dimensions that were invisible before [See Figure 1 below].
Figure 1 - Our Systems Strength index tracks on a quarterly basis how much focus companies put on the technology transformation. While companies in some industries have been able to sustain and even accelerate the pre-pandemic trend, some others are struggling to keep up with the pace.
2. Track data more frequently
Combining data science methods with experts’ ingenuity, you can come up with metrics that track month by month or even day by day to keep real-time tabs on company performance. When you deliver data more quickly, by shortening the period we measure data, businesses not only notice shifts sooner but can also make changes based on how they’re performing in real time and relative to the competition.
Figure 2 – Are you building enough resiliency? The chart below depicts how different profiles of companies reacted to the pandemic disruption. Groups 1 and 2, the pool of companies with higher chances to become future leaders according to their resiliency profile, have focused in growing all-round business strengths.
3. Predict what’s next
What happened in the past is becoming less and less reliable as a predictor of what will happen next. Volatility and disruption are more common now and we are expecting more of that because of many factors (climate, social, digital). Using nowcasting techniques — forecasting in real time with the more current available data — we could look forward with hot-off-the-fire data, instead of looking in the rear-view mirror as we have done in the past. As we’ve seen with COVID-19 and other disruptions, looking forward will help delineate a road map for companies to emerge stronger when faced with big disruptions.
Figure 2 – We synthesized all the company level metrics into a single indicator that we call the Resiliency Index. The indicator carries significant predictive power on which companies are emerging strong from the COVID crisis. The chart below shows how well the Resiliency Index anticipates revenue growth one period forward.
Note: Every dot in the chart is a company. The gray line and the shaded area are the prediction and the confidence interval for the prediction.
Resiliency equals future opportunity
Enhanced data capabilities help companies be more resilient. The more we know the more we can help businesses adapt, succeed, and prosper through uncertainty, no matter how disruption manifests. With the help of data, we can plan and refine a business’s strategy. The incredible size, pace and scope of our current crisis requires a relentless effort to expand the limits of measurement. Armed with information gleaned from predictive modeling, companies can be better prepared to face unexpected adversity. They can even emerge stronger, ready to seize growth opportunities in even the most challenging times.
We would like to thank Maia Frugoni, Julie Josquin, Ana Ruiz Hernanz and Nataliya Sysenko for their help and thoughtful contributions throughout the course of this research.