Proxy metrics, what are they, when and how to use them
From Guessing to Data-Informed Decisions: How Leading Indicators Can Transform Your Product Strategy
"Most metrics we use are only an approximation of the real thing we care about." — Sophie Alpert
As product managers, we're constantly seeking ways to make better decisions faster. One powerful technique is using proxy metrics—measurable indicators that predict movement in more important but slower-to-capture outcomes.
What is a Proxy Metric?
A proxy metric is a stand-in measurement that reliably predicts a more important outcome that might be:
Too slow to measure (like annual retention)
Difficult to quantify directly (like product experience quality)
Not generating enough data yet (in early-stage products)
Think of proxy metrics as the pulse check you do while waiting for comprehensive blood test results. They explicitly acknowledge: "I'm not the ultimate thing you want to measure, but I'm the best practical surrogate for it right now."
Why You Need Proxy Metrics in Product Management
In the Pyramid of Evidence framework I wrote about previously, proxy metrics help you climb higher by providing faster signals about what's working. Here's when they're most valuable:
At Booking.com, we used proxy metrics extensively to make product decisions without waiting for full booking cycles to complete. This approach allowed us to move much faster while maintaining confidence in our direction.
Characteristics of Effective Proxy Metrics
Based on my experience implementing hypothesis-driven product development at companies like Eneco and Foodics, effective proxy metrics should have:
Strong correlation with your target outcome (backed by historical data)
Causal potential—moving the proxy should move the outcome (validate with A/B tests)
Timely sensitivity—it moves quickly enough to inform sprint-level decisions
Team control—your product team can directly influence it
Simplicity & integrity—easy to understand and resistant to gaming
These align with decision frameworks I've shared previously about reducing decision fatigue—the right proxy metrics simplify decision-making by providing clear signals.
How to Implement Proxy Metrics: A 6-Step Process
1. Define Your North Star
Start with your ultimate goal—the outcome that truly matters. This might be annual retention, revenue per user, or customer lifetime value. A proxy never replaces this North Star; it just helps you navigate toward it faster.
2. Map Your Value Path
Identify the customer behaviors that must occur before your outcome happens. This is where the Jobs-to-be-Done framework becomes valuable—understanding the steps in your customer's journey helps identify potential measurement points.
3. Brainstorm Candidate Metrics
For each critical behavior, ask: "What can we measure quickly that indicates this is happening?" Focus on ratios rather than absolute numbers (e.g., percentage of users who activate rather than total activations).
4. Run Correlation Analysis
Use historical data to determine which candidates best predict your outcome. This is where the data-driven decision-making approach I've advocated for becomes essential.
5. Test for Causality
Run controlled experiments where you intentionally move the proxy metric to verify it impacts your ultimate outcome. The hypothesis-driven product development approach I detailed previously works perfectly here.
6. Implement and Monitor
Set up dashboards and alerts at your sprint cadence. Establish guardrails to ensure the proxy isn't optimized at the expense of your ultimate goal.
Real-World Examples from My Experience
At Booking.com, we found that "Users who supplised a credit card at the time of their booking that was 6 months or less about to expire, were 2X more likely to cancel their reservation”. By reminding customers to update their credit card 6 months before its expiry, we decreased cancellations of that customer segment by around 3%.
In product management training programs, I've seen "completion of practice exercises" serve as an excellent proxy for eventual skill application. Programs that optimize for this proxy consistently produce more effective product managers.
Avoiding Common Proxy Metric Pitfalls
Evolving Your Metric Strategy
Proxy metrics are like training wheels—crucial when you're starting but meant to come off eventually. As your product matures and you collect more data, you'll be able to move to more direct measurements.
The product trio should regularly evaluate the effectiveness of proxy metrics and be ready to graduate to more sophisticated measurement approaches when appropriate.
Key Takeaways
Proxy metrics compress the feedback loop between action and learning
They must be correlated, causal, timely, and team-controllable
Use a structured selection and validation process to ensure they remain reliable
Regularly reevaluate and replace proxies as your product and data capabilities evolve
When implemented thoughtfully, proxy metrics give your product team the confidence to move quickly without guessing in the dark—turning every sprint into measurable progress toward your true North Star.
What proxy metrics have you found most valuable in your product management work? Share your experiences in the comments!