If you’re a big baseball nerd like me, then you may have heard of the website fangraphs.com, which is dedicated to breaking down and analyzing the statistics of America’s Pastime. Ultimately, the site is trying to answer the fundamental question, “How do baseball teams most efficiently score more runs than their opponents?” Should teams prioritize acquiring players that hit lots of home runs? Should general managers (the CEOs of sports franchises) draft players that are better at taking walks and getting on base? Or perhaps the most optimal route is a mix of both options?
Sports, and baseball in particular, offer a particularly powerful metaphor for business strategy. Direct competition, limited resources, and day-to-day tactics all play a role in both running a sports franchise and running a business.
In this case, baseball is a great tool to discuss leading and lagging indicators.
What are leading and lagging indicators?
Baseball teams are trying to score more runs than the opposition. The number of runs a team scores is a perfect example of a lagging indicator. Runs are the EBITDA (or net profit) of baseball. They’re the final number that people care about.
Lagging indicators are defined as non-predictive measurements of objective performance. Generally more easily captured than their predictive counterparts, lagging indicators are often an uncontroversial indicator of objective success. For example, if a baseball team’s objective is to score runs, the measure “Number of runs” logically quantifies performance.
The performance of a lagging indicator should be driven by a corresponding leading indicator. Leading indicators should hypothesize a causal relationship with the lagging indicator. A baseball team may have a high “number of hits” which in turn generates a high “number of runs.”
Leading indicators are critical to a strategy management system because unlike lagging indicators, they allow ample time for course-correction. Baseball teams with low number of hits can forecast that their run totals will be insufficient, and could consider switching to a strategy that emphasizes scoring in other ways (such as walks, stealing bases, or home runs). The leading indicators that an organization choose neatly illustrate strategic choices, and reinforce the intent of KPIs, which is to set performance expectations and clarify the direction of objectives.
Effective strategy management requires a mix of both leading and lagging indicators
The big data revolution of the past twenty years has arguably shifted the traditional business lines of thought away from solely bottom-line-driven thinking to a greater appreciation for dashboards and operationalized key performance indicators. A strategy management system needs to combine both leading and lagging indicators in order to flexibly adapt to changing competitive environments.
The Balanced Scorecard naturally encourages thinking about cause-effect relationships through its four standard perspectives: Financial, Customer, Internal, and Learning and Growth.
For public or non-profit organizations that organize these perspectives differently, the underlying logic still applies – the bottom two perspectives are inputs (organizational changes) and the top two perspectives are the outcomes.
By virtue of being “outcome” perspectives at the top of the map, financial and customer objectives tend to have a higher number of lagging KPIs. That is not to say that leading indicators should be overlooked in the top perspectives. A top financial perspective KPI could be “revenue growth” which is driven by the lead indicator, “number of accounts.”
When to worry about leading and lagging indicators
Occasionally in the process of working with a client to draft a Balanced Scorecard, a client will start to evaluate their KPIs and realize that they do not have enough leading indicators. Leading indicators, being the more challenging to draft of the two, are the inevitable shorthanded group. The follow-up question becomes, “do we need to add more leading indicators to our Balanced Scorecard?” Rather than try to set a perfect ratio of leading to lagging indicators, I typically recommend that clients address any obvious missing leading indicators, but then follow a wait-and-see approach with their metrics.
KPIs will need to adapt and change with the strategy. Defective KPIs will not be readily obvious upon the first BSC draft; nor will all of the cause-effect driver relationships between them. In such a case it is better to gain some KPI baselines, then draw conclusions around which measurements are missing, and which need to be added.
In the hits-runs example above, the baseball team may decide that “number of hits” is insufficient to evaluate the total number of runs. So the team instead looks at the team batting average (AVG), which calculates the (total number of hits / total number of at bats). Unfortunately, batting average excludes walks, which are another way of getting on base, so instead of batting average, the team looks at on-base percentage (OBP). But OBP does not factor in extra base hits, which more efficiently score runs than singles. So the team looks at on base percentage plus slugging (OBPS).