Leading product metrics are ways to define and measure likely business performance in advance of hard revenue or churn scorecards.
Product metrics are used by many teams across the business to understand product performance: marketing teams, product teams, operations and finance teams all commonly review these metrics.
Leading product metrics are early indicators by which you can hope to impact lagging outputs, like revenue and cohorts. They’re frequently more attractive to try to move since they’re easier and quicker to show impact than later stage outputs.
Leading product metrics are often data points such as traffic or sign up metrics, activation and activity metrics, and customer surveys.
However proving causality between input and output data points can be tricky: it’s not uncommon for teams to work hard on moving a leading metric only to realize limited output uplift.
In this article we’re going to take you through some common leading product metrics, discussing their strengths and weaknesses as leading indicators. We’ll talk about where they’re useful and how to read and calculate them.
Let’s get into leading product metrics now.
Check out the Hustle Badger Product Metrics Cheat sheet
Why are leading product metrics important?
Leading product metrics provide answers to questions like: Are we growing possible future users? If we bring them to the site do they engage with it? Are we providing good service and delighting customers?
Tracked all together they should provide a picture of the health of your business before it shows up in revenue outputs or cohort tables:
- How users are brought to the product: Do users bring users via word of mouth, a traffic asset you built, or are you dependent on paid media?
- How users progress through the funnel: Do they really like the product and are they prepared to use it?
- How well you’re doing at providing a good service: Are you meeting their needs and creating delight among your ideal customers?
It’s important to realize that
“Good metrics aren’t about raising money from VCs—they’re about running the business in a way where founders know how and why certain things are working (or not) … and can address or adjust accordingly.” - a16z
How can you choose good leading product metrics?
By selecting leading product metrics, or input metrics, over lagging, or output product metrics, your goal is to measure changes today in the hope of influencing tomorrow.
Typical output metrics are things like revenue or user churn. Customer trends can take months to surface in these metrics.
Input metrics can be influenced today
Check out the Hustle Badger article on choosing the right success metrics
Let’s go through some other key components.
True driver of outputs
Moving your input metric should ultimately result in a positive monetary impact. This can either be a growth in revenue or a cost saving.
Establishing a firm link between an input and an output metric is often one of the hardest things to do. You should not assume common input metrics are linked to monetary outputs without proving they are a driver in your business.
There’s only 2 ways to discover if an input metric is a driver of an output metric such as churn or revenue:
- Analysis: techniques such as granular segmentation or regression analyses can identify tipping points, or user groups which drive revenue and retention
- Trial and error: it’s common not to have big data sets or access to data teams. In this scenario, you have to test and learn.
Normalized for periods
Time windows can be deceptive: things can look bad month on month, but great year on year.
Hourly, weekly, monthly or quarterly metrics can all have high volatility and be subject to seasonality. You can use year on year comparisons, or moderate KPIs and targets to take this into account.
However the best way to display accurate product metric growth is to use a compound measure to normalize across growth periods, such as CMGR, or Compound Monthly Growth Rates.
Compound measures allow you to understand the true underlying periodic growth rate of the business.
It’s more accurate (and usually lower) than a mean or an average and it allows you to benchmark growth rates across different initiatives more easily.
CMGR smooths out fluctuations
The time period can be whatever you want - decades, seconds - the calculation is the same.
Investors often ask for compound growth metrics since they benchmark across company types, industries and stages. This can be a useful comparison point.
Ratio or a rate
Ratios or rates are another way that you can normalize for fluctuations. A percentage rate view of how many new users register in a given period is more illustrative of underlying health than the absolute numbers.
Equally well a ratio allows you to balance a metric which might otherwise tell a misleading picture. For example, you might look at daily active users compared to monthly active users to ensure that you’re growing your total long term active user base.
Commonly understood
Metrics can quickly become an opaque language.
The metric should be one that everyone understands and agrees is important, across the stakeholder pool. You should avoid metrics that people don’t comprehend.
Influence behavior
By picking this product metric you’re trying to ensure attention and action. It has to be that when you look at it, you are able to make decisions about what to do. It shouldn’t be a confusing or a ‘Could be this, could be that’ metric.
In your remit
The metric should be something that your team can move by taking action; it should not sit with other teams, nor should there be major barriers to moving it.
Check out the Hustle Badger article on choosing the right success metrics
Which metrics are product managers responsible for?
When selecting product metrics, the most common mistake is to think that some metrics aren’t relevant.
The business is the product and the product is the business. How much users value your product shows up in every metric.
Investing time in understanding product metrics and working with colleagues in adjacent functions to harvest their insights and understand where they have challenges leads to the best long term product outcomes.
However, not all metrics are important all the time. Learning which metrics to pay attention to when is a core skill. This is where clarity around the strengths and weaknesses of metrics helps.
Common mistakes when it comes to selecting product metrics
Avoid mistakes such as:
- Selection bias: Don’t pick the metrics that look good or where you feel more affinity. No one metric is the answer alone and lots of metrics are meaningless - either intrinsically or in certain contexts. Only by immersing yourself and interrogating metrics can you discover drivers.
- Vanity metrics: You should be looking to have business impact. Moving a metric alone has no value.
- Not benchmarking: Moving a driver metric is not enough. You should review where you sit versus industry, sector and customer benchmarks, which are widely available.
“I’m not going to tell you, as a product manager, not to track vanity metrics. I know you’re going to. And in some cases, your leadership team may want you to track and report on these numbers. That’s not great, but it’s common. Your job, though, even if you track vanity metrics, is to avoid making decisions off those numbers because, ultimately, they are not actionable” - Ben Yoskovitz, author of Lean Analytics: Use Data to Build a Better Startup Faster
Advice for selecting product metrics as KPIs
Final pieces of advice are:
- Make sure you really understand your product metrics: How it’s calculated, why it matters and how it helps the business
- Never stop exploring and refining metrics: It’s common to start out in a low data environment, pick a metric which you hypothesize is a key driver and then discover it doesn’t do what you thought. You should continue to explore and refine to laser in on the actual driver of value.
- Use your data science team if you have one: If you’re a large enough company, they can figure out driver metrics for you via regression analyses. This can take a while but it can be a helpful exercise if exploring isn’t getting you to the so-what metric.
- Don’t forget qualitative inputs: This article is focused on quantitative data points, but insights are easier to harness from qualitative research. Keep doing discovery.
Let’s get into common leading product metrics now.
Check out the Hustle Badger Product Metrics Cheat sheet
