Input metrics are so hard that most product teams give up

Input metrics are so hard that most product teams give up
Via Midjourney prompt "elaborate dashboard of meters/metrics inside a submarine, against the backdrop of a map of a large ocean with a clear destination."

Data-driven product teams need good input metrics because the business KPIs that ultimately matter (revenue, retention, etc.) are lag measures that cannot be directly influenced. Teams can’t usually launch a product or marketing update and immediately see the business impact. They need input metrics to measure the results of their efforts.

Good input metrics are:

  1. predictive of business KPIs
  2. possible to influence with work
  3. lead measures that are sensitive enough for quick learning loops

To illustrate, here’s one of our favorite examples from when Netflix was a DVD business:

Credit to Amplitude’s North Star Playbook's telling of Gibson Biddle’s Netflix story.

When a product team has good input metrics, they are off to the races in running experiments and placing bets to create maximum impact. Solid input metrics allow teams to creatively solve the most important business problems without being told what to build by the top of the org. In short, input metrics enable aligned autonomy.

In the absence of clear, data-driven input metrics, decision-making can become subjective, driven by internal politics or the highest-paid person's opinion. In that kind of environment, the people doing the hands-on work can lose confidence in the priorities and get disillusioned. Sadly this is the norm for most companies.

Problem: Input metrics are hard

Figuring out good input metrics is so hard that most product teams give up trying to use them. Here are some reasons why:

  1. Specificity to Product and Strategy: Unlike business KPIs which are often directly derived from a company's business model (like revenue, profit margins, customer acquisition costs), input metrics are deeply tied to the specific nature and strategy of a product. This means they vary significantly from one product to another. For instance, an input metric for a social media app might be user engagement time, while for a SaaS product, it could be the adoption rate of a new feature.
  2. Difficulty in Linking to Larger Goals: Teams risk optimizing metrics that don't matter. It can be challenging to demonstrate how specific input metrics tie back to overarching business objectives. This linkage is crucial for justifying the focus on certain metrics and for aligning team efforts with broader company goals.
  3. Dynamic and Evolving Nature: Input metrics that were relevant at one point may become obsolete or less impactful over time. Teams need to continuously review and update their input metrics, which adds to the challenge.
  4. Risk of Misinterpretation, Misuse, and Gaming (Goodhart's law): If not carefully selected and monitored, input metrics can lead to misguided efforts. For example, focusing too much on a specific metric like user sign-ups without considering user retention can drive the wrong product decisions.

DoubleLoop’s solution for input metrics

At DoubleLoop, we’ve worked with hundreds of companies to identify input metrics that unlock the potential of their product teams. Here’s how it works:

Phase 1: Map your business drivers & candidates for input metrics

To get started, teams create a qualitative map of their business drivers; the inputs and outputs of their product. Based on the map, they identify candidates for input metrics.

Example impact drivers for a music subscription service

Phase 2: Validate your input metrics with data

Teams often make bad assumptions about their input metrics (see our post on how SpotHero used DoubleLoop to reveal a flawed input metric). We’ve seen teams target input metrics that even hurt their business. Therefore, it’s important to validate your input metrics with data. When teams plug their data into DoubleLoop, they can narrow down their input metrics to the ones with the most influence on the business KPIs they want to impact. 

Correlation scores in DoubleLoop

Phase 3: Place data-driven bets

Identifying good input metrics is only half the battle. Teams must then figure out how to move their input metrics in the right direction. The DoubleLoop platform is designed for teams to record their bets and monitor impact on their input metrics so they can learn what’s working and what’s not.

Bets and work mapped to input metrics in DoubleLoop
A metric graph annotated with bets and work activity in DoubleLoop

Let's identify and move your input metrics!

To guide you through the process, DoubleLoop provides a service specifically for input metrics. Schedule a 30-minute discovery chat.