Lean Analytics

Lean Analytics

A data discipline for startups distilled from Alistair Croll and Benjamin Yoskovitz's Lean Analytics: separate metrics that change decisions from numbers that merely flatter, then point the whole company at the One Metric That Matters for your business model and stage. Use it to choose metrics, audit dashboards, set targets, and plan instrumentation.

Core Principle

Focus on the one metric that matters right now — everything else is noise that feels like progress. Startups die from lack of focus more often than lack of data. The discipline is knowing your business model, knowing your stage, and tracking the single number that tells you whether the riskiest part of the business is working. A metric earns attention only if it changes what you do next.

Scoring

Goal: 10/10. Rate metric choices, dashboards, and instrumentation plans 0-10 against these principles. Report the current score and the specific changes needed to reach 10/10.

• 9-10: One OMTM matched to model and stage, paired counter-metric, a line in the sand with a pre-committed miss response, cohorted and segmented data
• 7-8: Mostly actionable ratios and a plausible OMTM, but no explicit target, weak cohorting, or too many "key" metrics
• 5-6: Actionable and vanity metrics mixed; dashboard exists but rarely changes a decision; model and stage never named
• 3-4: Vanity metrics dominate — totals, cumulative charts, blended averages; metrics copied from other companies
• 0-2: No instrumentation, or numbers chosen to impress investors rather than drive decisions

Framework

1. Good Metrics vs Vanity Metrics

Core concept: A good metric is comparative (versus last week, versus another cohort), understandable (the team can recall and debate it), a ratio or rate (not an ever-growing total), and behavior-changing — if a number won't change what you do, stop measuring it. Vanity metrics — total signups, page views, cumulative anything — only go up and only make you feel good.

Why it works: The output of analytics is decisions, not data. Ratios are inherently comparative and operable, while totals hide decay: total registered users rises even while the product bleeds actives. Forcing every metric through the "what will we do differently?" test converts reporting into learning.

Key insights:

• Work the lens pairs: qualitative vs quantitative (interviews reveal why, numbers reveal how much), exploratory vs reporting (exploration finds your unfair advantage; reporting keeps the lights on), leading vs lagging (complaints predict churn before churn happens), correlated vs causal
• Correlation finds the lever; only an experiment proves it — find metrics that move together, then change one for a randomized group to test causality
• Cohorts make time honest: compare users by signup month, or real improvement vanishes inside blended averages
• Segments make comparisons honest: split by channel, plan, and geography — a flat aggregate often hides one segment soaring and another collapsing
• Averages lie under skew: whales and lurkers are different businesses, so read medians and percentiles
• A cumulative up-and-to-the-right chart is the single most reliable vanity tell

Applications: