Lesson 5: Strategic metrics
In this lesson, you learn about the strategic metric hierarchy and how different types of metrics serve different purposes. You explore KPIs, composite metrics, proxy metrics, and local metrics, and understand how they connect to business goals.
The metric hierarchy
Not all metrics serve the same purpose. Some represent the most important measures of business health. Others measure specific features or user flows. Understanding this hierarchy helps you choose the right metrics for different decisions and communicate effectively across the organization.
Think of metrics as forming a three-layer pyramid. At the top sit your strategic measures: the KPIs that define organizational success. In the middle are proxy and local metrics that measure team and product area impact. At the base are feature and functionality metrics for day-to-day optimization. Each layer connects to the one above it, creating a path from tactical improvements to strategic outcomes.
This pyramid is one of many frameworks for structuring metrics—others include HEART, AARRR (Pirate Metrics), OKRs, and more. Use whatever fits your organization. The underlying principle is always the same: every metric should connect, directly or indirectly, to something your business ultimately cares about.
Key Performance Indicators (KPIs)
At the top of the pyramid are Key Performance Indicators—the metrics that define success for your organization or product. These are the "North Star" metrics that senior leadership tracks and that drive strategic decisions.
A good KPI is directly tied to business objectives and reflects meaningful user or business value. It's stable enough to track over time, understandable by all stakeholders, and actionable—teams can actually influence it. Monthly active users, subscription conversion rate, and revenue per customer are common KPIs because they meet these criteria.
KPIs across industries:
A streaming service tracks monthly active users, paid subscribers, and listening hours per user. An e-commerce platform measures gross merchandise value, customer acquisition cost, and average order value. A SaaS company focuses on monthly recurring revenue, net revenue retention, and customer lifetime value. A gaming company monitors daily active users, average revenue per user, and player retention.
These metrics represent fundamentally different businesses, but they share the same characteristics: they're tied to business success, leadership pays attention to them, and teams can influence them through product decisions.
In experiments, KPIs most commonly serve as guardrail metrics to ensure product changes don't harm core business outcomes. Less frequently, they're used as success metrics when a change is expected to directly impact these high-level measures.
Composite metrics
Composite metrics combine multiple individual metrics into a single measure. They're tempting when success requires improving multiple dimensions simultaneously—you can simplify reporting by reducing many metrics to one number, capture multidimensional success, and reduce multiple testing problems in experiments.
But composites come with serious trade-offs. When a composite metric moves, it's hard to interpret which component drove the change. They can hide important trade-offs between components—one metric improving while another degrades, with the composite showing a neutral result. And they require careful design and validation to ensure the weighting actually reflects what matters.
The middle layer: Proxy and local metrics
Between KPIs at the top and daily operations at the bottom sits the middle layer: proxy and local metrics. These measure team and product area impact.
Local metrics measure behavior in specific areas: checkout completion rate, dashboard usage, or level completion rate. These help you understand how particular product areas perform and give teams clear signals about their impact.
Proxy metrics fit here because they connect tactical work to strategic outcomes. Week 1 retention proxies for long-term retention. Add-to-cart rate proxies for purchase intent. These metrics bridge the gap between what teams can directly influence and what the business ultimately cares about.
Proxy metrics are often genuinely necessary—no team can run a six-month experiment to directly observe long-term retention. Their value lies in making outcomes measurable on an experimentally practical timescale. Always validate proxies with historical data—a proxy that seems logical but doesn't actually correlate with the real outcome will lead you to optimize for the wrong thing. Revisit that validation as your product and user base evolve, since the relationship between a proxy and its underlying outcome can drift over time.
The bottom layer: Feature and functionality metrics
At the base of the pyramid are feature and functionality metrics, the day-to-day optimization tools.
Feature metrics measure interaction with specific features: wish list additions, collaboration invites sent, or power-up purchases. These tell you whether users are adopting and using individual capabilities.
Functionality metrics describe whether features work as intended from a technical perspective—page load times, search response times, API error rates. These are the quality assurance and performance monitoring tools.
The three layers connect: improvements in feature and functionality metrics should drive proxy and local metrics, which should ultimately contribute to your KPIs.
How layers connect
The power of this hierarchy comes from understanding how the three layers connect. Improvements at the bottom should cascade upward, ultimately contributing to your strategic measures at the top.
How improvements cascade through the pyramid:
Bottom layer: You reduce page load time from 2 seconds to 0.5 seconds (functionality metric) and see increased feature usage as users engage with the faster experience (feature metric).
Middle layer: The improved feature usage drives higher checkout completion rates in that product area (local metric). This becomes a validated signal that faster experiences improve conversion (proxy metric).
Top layer: The improved conversion rates across product areas contribute to increased gross merchandise value—a key strategic measure (KPI).
Each improvement connects to the layer above it, creating a chain from daily optimization to strategic business impact. In practice, these causal chains are rarely this clean—faster page load, for instance, may attract both high- and low-intent users, and the net effect on GMV depends on the composition of that additional traffic. Treat the hierarchy as a reasoning framework for building and testing hypotheses, not as a guarantee that bottom-layer improvements will propagate upward.
The right layer for your question
The layer of metric you choose depends on what question you're trying to answer. For strategic planning, use the top layer—KPIs that set organizational direction and goals. For team road maps, use the middle layer—proxy and local metrics that measure team impact on the business. For feature development and quality assurance, use the bottom layer—feature and functionality metrics for day-to-day optimization.
In experimentation, you typically mix layers. Your success metric is often from the middle layer (a local or proxy metric)—specific enough to detect the change's impact, but meaningful enough to matter. Your guardrail metrics typically come from the top layer (KPIs), ensuring your optimization doesn't harm core business outcomes.
In Confidence, required metrics are automatically added to all experiments on a surface and checked continuously for deterioration. Add Strategic KPIs and general app quality metrics as required metrics on the global surface to ensure all experiments check for deterioration of these metrics. Add more local metrics as required metrics on associated surfaces.