Leading Indicators in Volatile Environments
- pjwoolston
- Feb 21
- 4 min read
Whatever cycle we are operating on, there comes a regular moment when we can definitely measure the results of our hard work. In addition to the bottom line, those measures include numerous lagging indicators that help us understand how and why we ended up like we did. The challenge of lagging indicators of course, is that by the time you can measure them, there is nothing you can do to influence results!
More useful in any culture of accountability are leading indicators that we can watch during the cycle to maintain an evolving sense for how things are going to end up ultimately. We watch these data measures with increasing scrutiny and criticism as our cycles progress, partly because urgency is low at the beginning of a cycle (although the urgency will increases, often exponentially, as we approach the end), and partly because the earliest leading indicators are not nearly as reliable as later information. Thus we work hard to identify leading indicators that will be useful and actionable. These will often be defined by the market or by the industry, and often by logic. When we are creative, we can identify distinctive leading indicators that will provide tremendous insights and direction, often mixed metric leading indicators and often organization-specific. The challenge of identifying appropriate leading indicators is relatively straightforward. The real opportunity for us to add value in our work is in how we communicate and translate those leading indicators back to the organization and to leadership.
It is helpful to think about leading indicators like we do about weather forecasts. If we are planning an event or travel of some kind, we want to know what impact the weather might have on our activities. Usually our forecasts are reliable, repeatable, and predictable, especially at the macro level. Whether by geography or by topography or by season, we know what to expect in any given place and period. Sometimes however, the weather can become volatile and unexpected, and occasionally violent and expensive. For some of those moments, we have very little warning, vague at first and escalating quickly. We often talk of storms “coming out of nowhere.” That might be true in the immediate sense, but probably we also had an idea that something was possible. Our level of preparation for something like this depends on the way we monitored the leading indicators we had available to us.
For most of us, our work is also seasonal with periods of heightened risk and vulnerability. We often feel somewhat blindsided by the crazy things that happened to us and our organizations THIS year (every year!), but in fact every season is a “perfect storm” of some kind. There is always some new factor upending the status quo: new competition, shifting consumer priorities, changing market conditions, external influencers (style, politics), etc. This is similar to the weather in the sense that we have periods of higher susceptibility to major weather events like hurricane season, tornado season, and fire season. Less occasionally, but still often enough that we MUST plan for them, there are “black swan events” of greater or lesser magnitude, practically impossible to predict but nevertheless significant or consequential over the long-term. Sometimes it even seems like this is increasingly the case. In weather for instance, we often point to climate change as a long-term factor creating greater volatility. In our relative industries, we are also often able to identify the macro-developments that are changing the overall reality of our work.
Some work examples include:
Retail: Retail is often seasonal, with huge surges around big holidays, especially Christmas. This has implications for stock and staffing, for crowd volumes and opening hours.
Taxes: Many accountants plan significantly reduced demand on their personal lives during the height of “tax season,” most notably the run-up to April 15 every year.
Higher education: The admissions team has a freshman class cycle that culminates with each fall start.
Candy: Candy makers can count on the big holidays for a significant increase in demand, from Christmas to Valentine’s to Easter to Mother’s Day.
Music: Freelance musicians know that there will be more gigs around Christmas and Easter seasons.
Party planning: The end of the school year along with its graduation and commencement seasons generates a lot of events.
Each of these and countless other industries has reliable, predictable surges of and declines in business, and in fact much of the day-to-day operational organization is built around planning for and then successfully executing the navigation of those surges. Early in the cycle, leading indicators are much more broad and less reliable: We have less confidence in a direct translation of “leading indicator X” ensuring “lagging indicator Y.” As we get closer to our seasonal moment of reckoning, we have other leading indicators that emerge that allow us to assess the current cycle performance and gauge where we are likely to end up. These later leading indicators are generally more powerful and reliable, they are just not available as early as we would like. Incidentally, the point of “accountability” is to generate evaluation early and often. Accountability is often misinterpreted or misapplied punitively, creating a feeling of “heads are gonna roll.” But true accountability is about strategic appraisal of current market performance, not only to ascertain where we are likely to end up, but even more to influence that end result proactively.
What does all of this mean for how we communicate back to the organization and manage expectations? Leading indicators are a powerful tool to help us do that:
Reporting on leading indicators is part of the job description (a baseline expectation).
Translating those leading indicators comes from experience.
Contextualizing leading indicators is what makes us valuable to the organization.
It is absolutely essential that we understand both the power and limitations of leading indicators. They are limited in the sense that they operate from a historical baseline of understanding. Critically therefore, they need contextualization to make the metrics helpful, and incorporation of current developments and impacts to make them relevant. And without an indication of the level of confidence we should have in them, the power of leading indicators is greatly reduced.
