Leading with Data - Cascading Metrics
Leading with Data - Cascading Metrics
It's surprisingly hard to lead a company with data. There's a lot written about how to set good goals and how to avoid common pitfalls (like Surrogation) but I haven't seen much written about the practicalities of taking action on these metrics.
I spent most of this year working with our executive team to understand our corporate goals and to track our progress against these goals. I found that setting rock-solid goals didn't do much good if individual employees didn't know how they could contribute.
The big and ambitious goals we set for our company as a whole can be overwhelming to a single employee. It's hard to know where to start, so instead, overwhelmed employees go back to whatever they were working on before. We have to do more if we want to create behavior change and get everyone working toward the same goal.
This article introduces a new framework for breaking down corporate goals into metrics that are relevant and tractable for individual teams. I call it Cascading Metrics.
Let's start with a case study to illustrate.
An Example: 2020 Firefox Goals
Firefox is losing users. We have been for a while. Obviously, we want to turn this around. We started by setting a goal for 2020: Slow the loss of Firefox users.
This is vague, so we decided on a metric to track our progress and set targets that we wanted to hit by the end of the year. Specifically, at the end of the year we want to have 238 million Monthly Active Users (MAU).
That's a good start. By specifying a metric and a target we've made the goal specific, measurable, and time-bound. Slowing the loss of Firefox users is obviously relevant. In the SMART framework we're almost there. The only remaining question is whether this goal is attainable.
Increasing MAU is a huge undertaking. It's unwieldy. It's hard to decide where to start on such a massive project. To make this simpler, our leadership set a strategy to narrow the scope. The team decided we're going to improve MAU by finding a way to keep our new users around longer.
New Firefox users are at high risk of installing Firefox and never returning again. We decided to track this goal by measuring New User 1-Week Retention. At a high level, this metric measures: of the people using Firefox for the first time today, what portion use Firefox again next week? Currently, it sits around 45-50%, meaning a little more than half of new users don't return in the following week.
This is a more manageable goal. Increasing retention is still a big undertaking, but we have narrowed the scope a bit.
Leading vs Lagging Metrics
We have two metrics in this case study, MAU and Retention. I'd call Retention a leading metric and MAU a lagging metric.
Our main goal is to improve MAU, but to make the goal more manageable we focused our strategy on improving retention. Our hope is that improving retention will, in-turn, improve MAU.
I've seen this concept of leading and lagging metrics discussed pretty often in leadership literature, so I won't go too deep into it here 1.
The important point is that this is a very powerful pattern. In the ideal case, employees can see consistent progress against our leading metric. That's encouraging! If we fail to set good leading metrics, employees can get discouraged trying to make progress against a metric that just won't budge.
I came across a particularly elegant explanation of this pattern in a Steinbeck novel (of all places):
So often men trip by being in a rush. If one were properly to perform a difficult and subtle act, he should first inspect the end to be achieved and then, once he had accepted the end as desirable, he should forget it completely and concentrate soley on the means. By this method he would not be moved to false action by anxiety or hurry or fear. Very few people learn this.
- John Steinbeck, East of Eden
Here, our lagging metric would describe the "end" and our leading metric the "means". Put another way: plan the work, then work the plan.
There's a plot hole in this story though. Increasing retention is still a very difficult goal to achieve. A individual team might be able to improve Firefox retention, but most won't be able to. Our leading metric didn't do enough to make our lagging metric tractable.
This is where Cascading Metrics can help. When creating cascading metrics, we repeatedly apply this pattern of breaking down difficult to move metrics into easier to move metrics until we have an appropriately-sized project for an individual team. Let's look at an example:
Let's say Alice is a senior leader at Firefox and is responsible for improving MAU. Alice identifies retention as the leading metric she wants to focus on improving. Alice can then delegate responsibility for improving retention to Bob. So far, nothing's changed.
Now Bob has the goal of improving retention. He thinks that new users will be more likely to keep using Firefox if we make the browser faster. Accordingly, he identifies a leading metric to measure how quickly Firefox loads websites on average.
In this example, Alice's leading metric becomes Bob's lagging metric. This pattern can continue as needed until we have an achievable goal and our strategy has become a tactic.
Here's a visual of what this flow might look like:
At each level of delegation, there's a chance to match an employee's responsibility with their influence. For example, increasing MAU might be a fine goal for a C-Suite executive, but it would be an unachievable goal for a single manager with only a few reports. Similarly, an executive shouldn't be setting goals for individual teams.
Developing Leading Metrics
Something I really like about this framework is that it's explicit about where these leading metrics come from. The leading metrics describe a product strategy.
We've looked at a goal, thought deeply about the product, and decided on a strategy that will help us achieve that goal. This decision-making process should be informed by data, but it's probably not driven by data. It's driven by product intuition.
A very common failure case is to skip over the "strategy" part of this process and hope our leading metric will just fall out of the data. We focus on finding analytical solutions for increasing our metric. Maybe we run some broad machine learning exploration to identify a leading metric.
These approaches are only occasionally successful. More often than not, we end up finding obvious truths: "Users who use the product frequently retain better! We should get people to use the product more!". If we do find a meaningful way to move our lagging metric, it's very often something we don't have agency to change, which limits its value as a leading metric.
We're almost always better off if we lean on our product experts to lead the way. They can combine all of the tools at our disposal to find a way forward: data science, user research, and their own product intuition.
Data science can support product in a couple of ways. In the early stages of developing a strategy, data science can help with opportunity sizing and helping product test their intuition against the data (e.g. do we have enough users in Germany to focus all of our efforts in one country?) Later in the process, data can help product develop a leading metric to describe their strategy.
Setting numeric goals is a great way to give a company direction. However, these ambitious corporate goals often fail to create any real behavior change in practice. If we want people to change their behavior we have to make our goals relevant to individual teams. Cascading metrics gives us a framework for turning our big corporate goals into something relevant to front-line engineers.
 If you want to read more, I can recommend The 4 Disciplines of Execution which is referenced heavily in Deep Work.