Innovation Accounting Metrics: Tracking Real Progress

Track innovation accounting metrics: set a baseline, pick actionable over vanity metrics, read cohorts and keep a scorecard that shows real progress.

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At a Glance

Overview

Innovation accounting is the lean startup's way of measuring progress when ordinary accounting cannot. Revenue forecasts and milestone plans assume the business model is known. A new product has no such model yet, so Eric Ries lists innovation accounting as one of the method's principles: measuring progress, setting up milestones and prioritizing work in a way built for startups (The Lean Startup principles). This skill covers how to choose innovation accounting metrics, set a baseline, read results by cohort and keep a scorecard that shows whether the product is getting better.

The central distinction is between actionable metrics and vanity metrics. Ries describes vanity metrics as numbers that "look good on paper but aren't action oriented," such as website hits or message volume (Ries, Beware of vanity metrics). The Wikipedia summary gives new users gained per day as a typical example: the number can rise while each user costs more to acquire than they bring in. Actionable metrics show cause and effect, so they tell you what to do next.

Ries's slides describe three learning milestones for innovation accounting. First, use an MVP to establish a baseline for the key metrics. Second, tune the engine: run experiments to move those metrics toward the values the business model needs. Third, decide whether to pivot or persevere, based on whether tuning is working (RailsConf 2011 slides). The metrics in this skill are the instrument for all three.

The Lean Startup method explains where innovation accounting fits in the wider approach. This page covers the practical work of choosing lean startup metrics, setting them up and reading them honestly.

The output is an innovation accounting scorecard: a handful of metrics, each with a baseline, a target derived from the business model and a trend by cohort. It is the evidence pack for each pivot or persevere decision.

How It Works

Good metrics pass Ries's three A's: they are actionable, accessible and auditable (Beware of vanity metrics). Actionable means the metric shows cause and effect clearly enough to guide a decision. Accessible means the whole team can understand it without a specialist. Auditable means it can be checked against real data, down to individual customers. A metric that fails any of the three will either be ignored or argued about.

Cohorts are the main tool. Instead of one cumulative line, group customers by when they joined, for example by week, and track what share of each group reaches each step: sign-up, activation, repeat use, payment, referral. Ries recommends this kind of cohort analysis, along with per-customer metrics and split tests, in his guest post on actionable metrics (Vanity Metrics vs. Actionable Metrics). If a change helped, later cohorts perform better than earlier ones. Cumulative totals hide this, because they rise even when each new cohort does worse.

The metrics to track depend on how the business is meant to grow. Ries describes three engines of growth, summarized by Lars Lofgren (engines of growth). A sticky engine grows when new customer acquisition outpaces churn, so retention and churn are the key metrics. A viral engine grows through referrals and needs a viral coefficient above one. A paid engine grows when each customer's lifetime value exceeds the cost of acquiring them. Pick the engine you are betting on and track the metrics that govern it.

The baseline comes first. Before tuning, measure where the MVP stands on each key metric with real customers. Then write targets that the business model needs, such as the retention that would make paid acquisition affordable. The gap between baseline and target is what tuning experiments try to close, and it gives each experiment a clear yardstick.

Reading the scorecard is a judgment about trend. One cohort that improves may be noise. Several consecutive cohorts improving after a series of changes is evidence that tuning works. Cohorts that stay near the baseline despite many experiments point to a strategy problem, which is the signal for a pivot discussion. Tristan Kromer's advice to set success and failure criteria before looking at the data applies to the scorecard too (Kromer).

Step-by-Step Guide

Step 1: Name your engine of growth

Decide whether the business is betting on retention, referrals or paid acquisition, using Ries's three engines as Lofgren summarizes them. Write down why you believe that engine fits the product. If you cannot choose, pick the one your riskiest growth assumption depends on and revisit later.

Step 2: Choose a few actionable metrics

Map the customer journey from first visit to the behavior that drives your engine, and pick one metric for each key step. Keep the list short enough to fit on one page. Test each metric against the three A's and replace any that fail. Drop totals and averages across all customers in favor of rates by cohort.

Step 3: Instrument and audit the data

Make sure each step is recorded per customer, so any number can be traced back to real people. Check a sample of records by hand. Where a metric depends on data you cannot yet collect reliably, note the gap rather than filling it with estimates.

Step 4: Establish the baseline

Run the MVP with real customers and record where each metric stands. Use at least a few cohorts so you see the typical range. This baseline is the measured starting point that later experiments are judged against. Keep it separate from any forecast.

Step 5: Set targets from the business model

For each metric, work out the value the business needs to be viable, such as the retention or payback that makes your engine run. Write the target next to the baseline on the scorecard. The gap between them shows where tuning experiments should focus first.

Step 6: Tune and track by cohort

Run experiments aimed at the biggest gap, one change at a time, and compare the cohorts before and after each change. Use split tests where traffic allows. Record each experiment's effect on the scorecard, including experiments that moved nothing.

Step 7: Review the trend at each decision point

Before each pivot or persevere meeting, summarize the trend for each metric: how far it has moved from the baseline and how much of the gap to the target remains. Bring the experiment history alongside. Let the trend, judged against criteria set in advance, drive the decision.

Best Practices

• Keep the scorecard small. A few well-chosen metrics are easier to act on than dozens of reports, which is Ries's advice to "measure what matters" in his actionable metrics post.
• Report rates by cohort, never cumulative totals, for any metric used to judge progress.
• Make every number traceable to customers. Auditability lets the team check a surprising result by talking to the people behind it.
• Show the baseline and target on every chart. A metric without them shows motion without telling you whether it is enough.
• Record experiments that failed to move the numbers. They are part of the evidence that the current strategy is or is not working.
• Revisit the metrics after a pivot. A new engine of growth or customer segment usually needs a new baseline and new targets.

Common Mistakes

• Celebrating vanity metrics: Total sign-ups and page views can rise while the business gets worse. Judge progress by cohort rates tied to your engine of growth.
• Tuning without a baseline: Without a starting point, you cannot tell whether an experiment helped. Measure the baseline before running tuning experiments.
• Tracking too many metrics: A long dashboard lets every team find a number that went up. Agree on a short scorecard and use it for decisions.
• Mixing cohorts after a change: Comparing all customers before and after a change blends old and new behavior. Compare the cohorts that joined after the change with those that joined before.
• Setting targets by instinct: A target picked because it sounds ambitious says nothing about viability. Derive targets from what the business model needs.

References

• Examples: Worked examples and scenarios
• FAQ: Frequently asked questions
• Parent Method: Lean Startup

Related Skills

• Pivot or Persevere: When to Pivot a Startup
• Running the Build-Measure-Learn Loop
• Designing Validated Learning Experiments
• How to Build a Minimum Viable Product (MVP)
• Lean Startup Hypothesis Template: Testable Hypotheses
• Types of MVP: How to Choose the Right Format
• Customer Discovery Interview Questions and Technique

Sources

• The Lean Startup: Principles
• Eric Ries: Beware of vanity metrics
• Eric Ries: Vanity Metrics vs. Actionable Metrics
• Eric Ries: The Lean Startup, RailsConf 2011 slides
• Lars Lofgren: The three engines of growth
• Tristan Kromer: Pivot or persevere decision
• Wikipedia: Lean startup

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