What are leading and lagging indicators?

August 20, 2022
by James Stanier
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Q&A

Last week I wrote some thoughts on managing my energy better at work, through the lens of leading and lagging indicators.

When I was writing that post—judging by the amount of times I had to check and recheck the definitions—I realised that I often get them muddled up.

So, what better way of making sure I understand something than writing about it for a large audience?

Here we go…

Q: What are leading and lagging indicators? Why are they easy to get wrong?

Whether you are building a product, running a team, or managing a company, understanding the relationship between your inputs and outputs is essential for your success. 

In each of those scenarios, you are creating a function that accepts inputs, transforms those inputs in some way, and then, hopefully, outputs something of greater value.

We can think about what this looks like using some boxes and arrows. 

Let’s use building a product as an example. Any product that you build is going to have inputs and outputs.

Product inputs fall into categories such as:

  • Installs, such as the number of downloads, or purchases from a physical store.
  • Usage metrics, such as the number of daily, weekly and monthly active users.
  • Engagement metrics, such as the number of successful actions that have been performed in the product (e.g. ads created, workflows completed, amount of newsfeed items read, or number of tickets booked) or the amount of time spent in each user session.

These are all of the numbers that are feeding into the box. 

Then there are the outputs, which are important things like:

  • Stickiness, which is typically measured as a ratio of daily active users to monthly active users, giving you an idea of how often users are regularly coming back.
  • Retention, which is a longer term measurement of whether your users are continually active over meaningful periods, such as yearly renewal cycles or monthly billing periods.
  • Revenue, which is, well, revenue. It’s the amount of money that you are making—probably the most important metric of them all for any commercial product.

These inputs and outputs are what we call leading and lagging indicators. The inputs are leading indicators, and the outputs are lagging indicators.

Leading indicators

A leading indicator is a metric that allows us to better understand how something is going to perform in the future.

In our product example above, the leading indicators are the inputs into our box. 

We could reason that the number of installations that we are seeing of our product from week to week is one reasonable predictor of how it is going to perform in the future. After all, if nobody is installing it, we’re not going to make any revenue. The more people that install it, the greater the chance that we have of our revenue going up.

As per the examples in the previous section, there are often multiple leading indicators at play in any system. Exactly which ones you should pay attention to is something that can only be decided by you in the context of the system that you’re measuring.

The great part about leading indicators are that they are:

  • Quickly measurable, often in short feedback loops. For example, you can measure leading indicators daily or weekly in order to get an up to date view on your system.
  • Able to be affected by you, often in a short space of time. If your leading indicators are starting to drift outside of bounds that you deem acceptable, then you can make immediate changes to address that drift (e.g. make changes to the sign-up page).

Let’s take this example beyond a software product. 

Imagine that you were thinking about leading indicators for the performance of the engineering team which you are managing. 

One leading indicator could be the amount of critical production bugs that you are facing. This is because every time there is a critical bug, your team has to get interrupted away from what they are doing in order to triage and fix that bug, which in turn reduces their ability to get their current work done. I’m sure you can think of others.

Now, to another example. Imagine that you are gradually renovating an old house. 

You know that the energy efficiency of the house is poor (imagine that it is in the UK and has an energy rating of G—the worst!) so you look at a leading indicator which is the amount of windows that are in good repair and double glazed. 

That metric currently stands at 20%, so you decide that you are going to invest in replacing the windows over the next few years. The number of high quality insulated windows are your leading indicators of your energy rating.

Lagging indicators

A lagging indicator is a metric that allows us to better understand how something has happened in the past.

Looking back at our previous examples, we identified that:

  • Revenue was a lagging indicator for a product. If it has been doing well, then it will hopefully be making money. 
  • An engineering team’s lagging indicator could be shipping their milestones on time. This is because they’re happy, empowered and not getting interrupted by production issues.
  • A house’s energy efficiency lagging indicator may be an energy rating of A because it is well insulated and has no current structural issues that need fixing.

The tricky part about these lagging indicators is the inverse of the great part about leading indicators:

  • They require measurement over long periods of time to fully understand. For example, you might not know if your team is going to ship the new operating system on time until it has been finished in 3 years time.
  • You can’t directly affect them, since they’re an aggregated effect of leading indicators. If you make changes now, you might not see an impact in your lagging indicator for months or even years (e.g. think of annual billing cycles for software and your users churning at the end of the period).

So the art of bringing it all together is that you need to ensure that the leading indicators that you are affecting are proven to be responsible for the lagging indicators.

Bringing it all together

For many situations, this involves a combination of intuition, research and trial and error. 

You need to work out which of the leading indicators have an effect, and those that are red herrings.

Back to our box diagram:

There can be a lot to learn from prior art. 

For example, if you’re building a B2C SaaS product, there are lots of resources that can give you a head start in defining your leading and lagging indicators. Sequoia Capital has a detailed article on measuring product health which I also consulted while writing this article.

However, defining leading and lagging indicators gets more challenging when you are either measuring something that hasn’t been measured before (e.g. you are building a product in a completely new category) or you are dealing with a system that is hard to measure in the first place (e.g. what actually is the output of a good engineering team?)

This isn’t all bad, though.

Leading and lagging indicators can be the foundations of some valuable discussions within your team. For example:

  • What does it actually mean to perform well and to have great outputs? 
  • How can those be measured as lagging indicators, if at all? 
  • What are the leading indicators that contribute to those lagging indicators, and how can you be aware of them on a daily, weekly and monthly basis?

Why not go and chat to your team and find out?

Oh, and while you’re at it, why not try and apply leading and lagging indicators to other parts of your life? 

  • What are the leading and lagging indicators of healthy personal and family finance? 
  • What about your diet and your health?
  • What do leading and lagging indicators mean for the health of a community or city?

Leading and lagging indicators are a great model to think about how systems work in general, at all scales, shapes and sizes. All it takes is a box.

Extra bits

Before I go, I have a few extra bits to share.

I had an excellent article recommended to me by a reader after last week’s post about managing energy levels and burnout. 

It’s called “How to know when to stop” and it’s written by Andy Johns. It has some great models that cover our ranges of tolerance, career progression and life progression. It’s well worth your time.

If you’re interested in what the remote onboarding process is like at Shopify, then I published an article over on the Lead Dev blog that highlights my experiences of joining the team last year. Our onboarding program is fantastic. I outline what happens in the first few weeks in a way that will allow you to replicate the success at your own company.

And lastly, I’m really excited to be hosting and curating one of the tracks at QCon San Francisco —and virtually at QCon Plus—later this year. 

We’re going to be exploring the new world of hybrid work with a fantastic line-up of speakers:

If you’re interested in signing up, then you can get a $75 discount by using the code JamesSQSFPLUS2275 (yes, that’s quite a long code). 

The first 5 people to register with the code can get a free copy of my book, Effective Remote Work. Neat!

My energy is a linear function, until it isn’t

August 14, 2022
by James Stanier
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Growth

I’ve been thinking a lot about my energy levels at work this weekend, mostly because I’ve managed them badly over the last few weeks.

As a result, I am trying to be more mindful of habits or behaviours that I fall into in the hope that I can get better at managing them going forward.

In this article, I’ve tried to explore what I’ve observed in myself, in the hope that it might be of interest—or even of use—to those that read it.

Here’s what I’ve observed:: work generates energy and happiness for me, but only to a point, and I am very bad at identifying that point. I typically decide how much and how hard to work based on incorrect leading indicators, rather than the real, correct lagging ones.

Wonky predictions

Work is a core part of my life, much in the same way that my family is, and also my hobbies.

After all, why would I be spending part of my weekend writing this article? I am probably always going to be working in some way, shape or form, even if it isn’t for money. I just like making things.

I am also fortunate that I have a good job that I find meaningful and rewarding. When I am well-rested and in a good headspace, I know that my job is able to act as an energy flywheel: I do things, I feel good, and so I do more things to feel even better.

At the beginning of the working week—off of the back of a nice weekend—I know that I often work extremely hard, because there is a linear relationship between the effort that I put into my work and how good I feel about it.

It looks something like this.

This ideal represented by the graph is typically true for me on Monday through Wednesday. 

Monday is always a flurry of activity as people get a jump on the week ahead, and most of my 1:1s happen then as I like to have all of us aligned. This makes me feel good.

Tuesday is scheduled as a blend of meetings and focus time. I typically make a dent in whatever my big personal task is for the week on this day, which also feels good and feeds the flywheel.

Wednesday is no-meeting day at Shopify, so that day is characterised by a flexible schedule and an intense focus: I don’t have any meetings to attend, so I can have a full day of intense deep work that almost always makes me feel really good. This is usually the day where I’ll write that long proposal, or go deep researching something in the product or in our technology.

But, as Wednesday turns to Thursday, there seems to be an inflection point for me in terms of my energy, but I am rarely able to perceive it at that time.

I’ve come to the conclusion that because, subconsciously, I experience three days where intensity is linearly correlated with feeling good, and I then ignore the fact that I am tiring because I have failed to pace myself, slow down and rest more on the preceding days.

The data from Monday to Wednesday produces a model like the graph above: linear and limitless. I am then using that model to justify ploughing on through the remainder of the week at the same high intensity. 

However, not being more self-aware means that I typically seem to experience Thursdays and Fridays that look like this.

The issue is that I am making decisions in the present moment based on a model of how I am going to feel in the future that has been trained on data from the previous few days where my energy was high from the weekend. 

But this model doesn’t take into account lack of ample rest, my cumulative tiredness, all of the usual curveballs that a usual working week and family week will bring. 

On Thursday afternoon I know that I’m already starting to get a bit irritable and impatient, and by Friday I’m still working hard but I’m having to dig into my reserve tank of fuel, despite my best interests.

I write my weekly internal newsletter on Fridays, and I really enjoy writing, so it should—in theory—be a rewarding activity at the end of a long week. Instead, if I’ve managed my energy badly, I find myself bashing it out in haste just trying to get the damn thing done rather than taking any pleasure in it. 

This isn’t a pattern that can sustain itself over long periods of time.

Something better in the long run

What I want to aim towards is something more like this:

In this model, my intensity is purposefully kept capped—much like the speed on powerful electric cars—to stop me attacking my work too hard, even if I feel like I want to. 

This is because I think I’ve realised that my own subconscious mind can’t always be trusted to make the right long-term decisions when the short-term data suggests otherwise.

With that in mind, here’s a bunch of things that I’m going to be trying over the coming weeks.

  • Purposefully trying to work 10% slower. I have noticed that I have a habit of trying to work at breakneck speed. I think that this has something to do with my “Completer Finisher” personality (for what it’s worth, my Enneagram is type 3) and that I get a great feeling when a task is done, hence the intensity I generate to complete it. I’m going to try my best to mindfully work 10% slower to see whether treating the week like a marathon, rather than a race, leaves me feeling better and less stressed at the end of it. It’ll be interesting to see whether I get the same done, or more.
  • Being stricter with my input. I can’t deny that messaging applications feed the dopamine habit loop. There’s so much compelling discussion and information across countless Slack channels and mailing lists that it can become addictive to keep chasing inbox zero whenever there’s a spare moment. However I’m aware that context-switching can take its toll after several days in a row. I’m going to try to see whether I can limit checking messages to just a few intentional times every day, and then purposefully focus on input-free deep work—wherever possible—in between those times.
  • Limiting checking messages to within working hours. Related to the above, I’ve found myself falling into “busy waiting” behaviours with my input feeds because there is so much communication happening during the evening time in the UK (the critical mass of the company is in Canada). There’s no expectation for me to read any of it, but I tend to try to keep on top of it to “complete the task” of inbox zero. But this prevents me from recharging in my downtime.
  • Deferring non-essential requests and tasks into the following week. Another habit that I have is “cramming”, where I overestimate how much I can get done in a day, and underestimate how much I can get done in a week. This feeds a continual bin-packing loop where I try to clear as much as I can off my to-do list every single day to optimise all available time, even if some of those tasks could easily wait a little longer to get done with no ill effect. I’ll be trying to schedule less important things in the future using the Eisenhower matrix, giving myself more time to do the important work in a more considered manner.
  • Getting back on the tomatoes. When I was doing my Ph.D., I often used the Pomodoro Technique to make sure that I got enough breaks within my working day. It works by chunking your time into 25-minute sprints (pomodoros) with 5 minute breaks in between them, and then having longer breaks between contiguous blocks of pomodoros. I’m aware that often when I get deep into something I can work intensely for hours, but like the graphs above, too much intensity over too long a period can lead to irrecoverable energy drains. So I’ll try to pace myself more with some structure.

Let’s see how it goes. 

How about you? What sort of ways do you try to control your energy as the week goes by? 

Do you start strong, but fade towards the end of it? Are you checking your Slack messages on your phone whilst trying to relax in the evening because you think it helps you out tomorrow, or have you mastered a separation (or integration) of your work and your recreational time?

I know I’ve still got a long way to go, so I’d love to hear your tools and tips if you have any.