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The Three Metrics Redfin's Hot Home Actually Needs
Accuracy, utility, and trust — why you need all three and why most measurement frameworks only get one.
THE QUESTION
How do you measure success of the Hot Home feature in Redfin?
You've established that accuracy matters. Now the question shifts to what success looks like for each stakeholder — and why their definitions can pull in opposite directions.
DECISION 1 OF 2
You're designing the measurement framework. What's your north star metric for Hot Home?
THE PLAYBOOK
WHAT THIS QUESTION IS REALLY ASKING
This question is really asking whether you can design a measurement system that tests the feature's core claim — not just tracks how much it's used.
HOW TO THINK ABOUT IT
Build your metric stack in layers: accuracy at the base (is the prediction correct?), utility in the middle (does accuracy create value for each stakeholder?), and trust at the top (does repeated accuracy build lasting behaviour change?). Diagnostic questions flow down the stack — when a top-level metric drops, start by checking the layer below it.
THE ANALOGY
"A good metrics framework for a prediction feature is like a diagnostic tree for a car engine warning light — you don't fix the light, you trace the signal to its source."
EXPLORE FURTHER
Once you can measure a prediction feature well, the harder version is designing the feedback loop so the model improves over time — how does user behaviour become training signal without creating a filter bubble?
See more questions →← Product Thinking
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Execution & Trade-offs →
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