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Imagine you’re about to embark on a weight loss journey. You cut a few calories here and do a few stretches there and step on the weighing machine to look at your progress every few days. Sometimes, it looks like you’re making great progress, but at other times, you feel stuck. Eventually, your efforts seem futile, and you lose interest and set it aside altogether.
What happened? Why didn’t it work? Let’s take a closer look.
Imagine your KPI dashboard is the weighing scale, and replace your weight with the quota percentage achieved in a quarter. You do your best to attain your quota, and the results? You either make it or you don’t. The problem with such a dashboard is that it merely measures what you’ve achieved so far. The percentage of quota achieved (or your weight in the previous example) is a lagging metric. And what you really need is something to help you understand how to do it better. And that’s what lead metrics are for!
Striking the balance between lead and lag metrics is precisely how Experian, the multinational consumer credit reporting company, saved about $10 million! They realized that employee turnover levels were 3-4% higher than expected and used a predictive model consisting of several lead metrics like team size, structure, length of commute, etc., to predict attrition. The insights they came up with helped refine their practices, bringing a 2-3% drop in attrition over the course of 18 months. So how are these metrics different from each other, and how do they affect each other? Let’s find out.
Lag Metrics vs Lead Metrics: The Difference
Lag metrics, or lagging indicators, are the KPIs that measure the results of an event after it has taken place. It tells you how successful (or unsuccessful) you were in that initiative. In the example above, the percentage of quota achieved is a lag metric.
The unique characteristic of lag metrics is that they are difficult to change as they are long-term trends influenced by several different factors. They also fail to explain what needs to be done differently so as to achieve different results. Even so, organizations continue to place the focus on lagging indicators because of the shiny numbers and flashy charts.
Here are some great examples of lagging indicators:
- Employee engagement rate
- Churn rate
- Retention rate
- Sales revenue
- Customer satisfaction
Lead metrics, or leading indicators, are the KPIs that help you achieve a desired business outcome. They can predict the result of an initiative. They look at what might happen, as opposed to lag metrics which look at what has already happened. In the sales example, leading indicators are the metrics that help you understand how to improve the percentage achieved of your quota. In this sense, they “lead” the way.
If the percentage of achieved quota is itself a lag indicator, then the lead indicators would be the number of calls made, the number of meetings booked, or the number of opportunities created—all of which have the power to influence and change the lag indicator in the future. Leading indicators are, thus, easy to change but difficult to measure.
Some more examples of leading indicators:
- Average onboarding time
- Number of sales calls booked
- Number of new customers
- Number of outreach emails sent
- Participation rate in L&D programs
“The goal is to turn data into information, and information into insight.”
— Carly Fiorina, Former CEO of HP
The Causal Relationship Between Lead and Lag Metrics
Lagging metrics, as we discussed earlier, are more attractive than lead metrics. It sure feels great to look at how far you’ve come and marvel at all your achievements so far. But what do these metrics tell you about why or how you achieved these results? Close to nothing. There are too many factors that affect any business outcome and trying to figure out which one initiative created the desired outcome is a wild goose chase.
They also take a long time to measure. You can’t measure lagging indicators until after an event or initiative is complete because these metrics are measured throughout the process. This is why they’re too late to rely on, especially in the case of time-sensitive deliverables. Even more importantly, by the time you’re done measuring them, they always beg the unceremonious question—“Now what?”
Lead metrics are the key to explaining why your lag metrics are what they are. Why? Because they are the very beings that cause lag metrics! Here’s how.
Lead metrics are the core initiatives that determine a particular business outcome. They happen first. All lag metrics that are measured later are influenced by the initiatives that were taken at the beginning. This brings us to one of the biggest advantages of lead metrics—they are flexible!
Let’s look at an example. A subsidiary of a large Dutch retailer in the FMCG industry used people analytics to determine the effect of training and development programs on business performance of a particular outlet. They found out that there was a positive financial impact on the performance of the shop when these programs were doled out to employees. Applying the same throughout their organization, they saw an ROI per training of over 400%! Thus, they effectively used lead metrics (increased training and development programs) to drive lag metrics (in this case, their financial performance).
Lead metrics can be changed with the changing environment. Say, at the start of the quarter, your desired outcome was to improve performance and productivity across your organization. And halfway through, your priorities change towards keeping employee retention in check. You can easily tweak your lead metrics from proper career pathing and training programs delivered to the number of employee engagement surveys and team-building events doled out.
In the weight loss example from the intro, if your weight is the lagging indicator, then the leading indicators you need can be the number of minutes of quality exercise in a day, the number of calories consumed per day, etc. It’s the changes that you make in your leading indicators that ultimately affect your lag metrics. Your lead metrics help you ascertain the root cause.
The Role of Lead Metrics and Lag Metrics in Your People Analytics
People analytics involves collecting, analyzing, and deducing meaningful patterns and trends from historical people data for better HR decision-making. In our previous blog on people analytics, we discussed three types of people analytics. There are descriptive, prescriptive, and predictive analytics.
Descriptive analytics is the part of people analytics that analyzes and “describes” historical data. Hey, isn’t that exactly what lag metrics do too? Lag metrics, therefore, come under the descriptive branch of people analytics. They describe what has already happened. However, they do not guide you to make better business decisions.
To obtain data-driven business insights, you need predictive analytics—this is where the information described by descriptive analytics is used to forecast trends in the near future. And who helps you make the right business decisions based on these predictions? Prescriptive analytics!
Predictive and prescriptive analytics ring a bell? Of course, they both sound a lot like leading indicators! Both of them look forward instead of backward and help you understand how to bring change into effect.
For effective people analytics, you cannot rely solely on descriptive analytics. To understand how to create a desired change, you need to find out the measures that need to be taken. And that’s what lead metrics help you do. If descriptive analytics (or lag metrics) show you that the employee engagement rate over the past quarter is down significantly, predictive analytics and prescriptive analytics provide you with lead metrics that you can use to recover the lost engagement.
Improve Your Lag Metrics By Improving Your Lead Metrics
The cause-and-effect relationship between lead and lag metrics is clear from the previous heading. Your lead metrics are what causes your lag metrics. And so, the key to improving your lag metrics is improving your lead metrics. If you let your lag metrics run your business, you’ll find your workforce chasing their targets over the business outcomes they are meant to create.
Lag metrics are of course, important indicators without which there’s no way to assess your success at a particular initiative. And lead metrics alone won’t help you much in this regard either. The key is to complement your lag metrics with the necessary lead metrics to create an outcome-focused workforce that drives your business to the next level of growth.
“If you torture the data long enough, it will confess.”
—Ronald Coase, Economist