How to Read the Evidence
Check evidence grades, denominators, samples, and conflicts of interest before trusting conversion figures, and establish rules for using benchmarks in forecasts.
Written and reviewed by Eric Han · Co-founder & CTO, reopt
Key takeaways
- Key figures in this book carry grades A–D. A means randomized experiments; B, observational studies and large primary datasets; C, vendor aggregates and self-reported surveys; D, individual cases and secondary citations.
- Conversion rates with different denominators are different metrics. Mixing sessions, users, messages sent, and recipients can produce differences of more than 10 times.
- Vendor benchmarks sample the vendor's customers. They include companies mature enough to measure performance, so new teams should base decisions on outcomes near the lower end of the range.
- Dividing average CPC by average CVR does not produce average CPL. These metrics are averaged separately; treat them as ranges.
- Do not put the double-digit lifts from case studies straight into a plan. An estimate based on experiments at Qubit customers placed 90% of treatment effects within ±1.2% (the authors describe this as a rough indicator).
Evidence Grades
| Grade | Meaning | Representative sources | How to use it in forecasts |
|---|---|---|---|
| A | Randomized experiment | Blake, Nosko, and Tadelis's eBay experiment | Use as a reference for effect sizes and adjustment multipliers. When only a summary was reviewed, label it A (summary). Company-reported A/B tests qualify as A only when they disclose arm-level samples and random assignment |
| B | Peer-reviewed observational research, large primary datasets, filings, transaction logs | Schmitt's banking data, RevenueCat subscription logs, ChartMogul billing data, SEC filings | Use as a baseline without interpreting it as causal; check the sample's scope |
| C | Vendor aggregates, self-reported surveys | LocaliQ, Littledata, ChartMogul surveys | Use as a reference range and record the sample and conflicts of interest |
| D | Individual case, secondary citation, opaque methodology | Agency blogs, founder presentations | Use only for direction, not as an input. Illustrative defaults that demonstrate a calculation are an exception and are labeled accordingly |
Methodology references (textbooks, model papers, and guidelines) do not provide numerical evidence, so they are either ungraded or assigned B. Meta and Google research with platform-affiliated coauthors is identified in the text.
Align the Denominator First
Check four things before comparing numbers.
| Question | Why it matters | Example |
|---|---|---|
| What is the denominator? | Different denominators mean different metrics | Cold-email reply rates of 0.45% per message sent and 4.5% per recipient |
| Is it a mean or a median? | A small upper tail pulls the mean upward | Shopify conversion: median 1.4%, mean 1.9% |
| Who is in the sample? | Only customers of the measurement tool are included | Littledata includes only stores with at least 1,000 sessions and 90 purchases |
| When was it measured? | Definitions and markets change | Naver Smart Store purchase conversion changed to an order-based definition in January 2026, according to an agency article |
Attribution and Incrementality Differ
Most vendor CVR, CPL, and ROAS figures are attributed results. They include people who would have bought without seeing the ad. In the eBay experiment, switching off nonbrand search ads reduced attributed revenue by more than 72%, but total revenue showed no clear change. See Reported and Incremental Conversions for adjustment details.
Six Rules for Using Benchmarks
Multiply values from the same table. Joining stage rates with different definitions and samples produces a plausible-looking number. If you must combine them, use a wide range.
Use a range instead of one value. Use published quantiles when available; otherwise, use differences between sources to inform the range.
Do not use ratios of averages as a consistency check. Dividing LocaliQ's legal-services CPC of $9.87 by its CVR of 5.55% yields $177.8, but the table's CPL is $131.63.
Compare the sample with your context. To use a US enterprise advertiser benchmark for an early-stage Korean startup, keep the mean and lower the concentration to widen the interval. There is no published discount factor for reducing the mean. Reflect a cautious starting point by making budget decisions at the 5th percentile rather than the median.
Discount reported lifts. For government nudges, published academic effects were 8.7 percentage points versus 1.4 points in government nudge-unit RCTs: roughly six times the absolute effect, or four times the relative lift. This is not a universal multiplier, but it illustrates how publicized effects can be inflated.
Record missing evidence. Do not fill gaps in evidence tables with guesses; write “measure with an internal pilot.” If a simulation needs an input, use a wide range and label it a judgment call.
A reality check on effect sizes
Browne and Jones (2017) analyzed about 6,700 experiments on retail and travel sites. Their estimate placed 90% of absolute effects below 1.2% (described by the authors as a rough indicator); even the strongest category, scarcity messages, averaged +2.9%. The authors were affiliated with Qubit, no journal publication details were provided, and independent verification by PwC UK was stated (grade B).
Terms Used in This Book
| Term | Meaning |
|---|---|
| Conversion, CVR | The share taking a specified action. Always state the denominator |
| Funnel | Stages from visit to purchase. Each stage rate is relative to the immediately preceding stage |
| Attribution | Conversions credited to a platform or code, including some that would have happened without advertising |
| Incrementality | Conversions that would not have happened without the activity, measured against a control group |
| Lift | Increase relative to the control group |
| Reported-to-incremental multiplier | Platform-reported conversions divided by incremental conversions. Divide benchmark conversions by this value |
| Concentration | Controls the spread around the mean. At the same mean, larger values give narrower intervals. Choose it using mean × ; do not compare confidence using alone |
| 5th, 50th, and 95th percentiles | The 5th, 50th, and 95th values in 100 ordered draws. The 50th percentile is the median |
| Prior, posterior | Belief before measurement and belief after incorporating observations |
| Odds ratio, RR | Ratios comparing odds or risks between two groups. A value of 1 means no difference |
| p-value | The probability, under no difference, of a result at least this extreme. Below 0.05 is commonly called significant |
| Elasticity | Percentage change in the outcome for a 1% change in an input |
| Difference-in-differences | The treatment group's before/after change minus the control group's before/after change |
| Propensity-score matching | An observational method matching similar subjects. It is not randomization |
| The proportion of observed variation explained by a model. Values closer to 1 indicate more explained variation | |
| Last click, last touch | Attribution assigning all credit to the touchpoint immediately before conversion |
| Holdout | A control group deliberately excluded from ads or messages |
| New customers brought in by one customer: the viral coefficient. Distinct from concentration | |
| Freemium | A model with an ongoing free plan, from which some users move to paid |
| Trial, reverse trial | Time-limited free use. A reverse trial starts with paid features and falls back to free when the trial ends |
| Self-serve, PLG | Customers sign up and pay themselves without sales involvement |
| MQL, SQL | Marketing-qualified lead and sales-qualified lead |
| CTR, CPC, CPM | Click-through rate per impression, cost per click, and cost per 1,000 impressions |
| CPL, CPI, CPA, CAC | Cost per lead, install, conversion, and acquired customer |
| ACV | Annual contract value |
| ROAS, ROIS | Revenue divided by ad spend; net revenue divided by influencer spend. Neither measures margin |
| EMV | Exposure and engagement expressed in dollars. It is not revenue |
| GRR, NRR | Revenue retention from existing customers. NRR includes expansion revenue |
| D1, D7, D30 | Share returning 1, 7, or 30 days after installation |
| ATT | iOS app tracking consent |
| Vendor | A tool provider or agency publishing a benchmark, usually from its own customers and with a commercial interest |
| Cohort | Customers who started during the same period |
Numerical Conventions
- Grades appear in a table's
Gradecolumn or at the end of its evidence cell; Korean case tables put them at the end of the first cell. Prose uses(grade C). Calculatedmeans this book divided, multiplied, or converted source values; the result does not appear verbatim in the source.Secondary citationmeans the original publication and underlying data were not both reviewed; the value was confirmed only through another article. It is grade D.Via press coveragemeans public data or vendor aggregates were reviewed only through reporting. The grade follows the nature of the underlying data.Summarymeans only an abstract or institutional summary was reviewed.Author judgmentmarks judgments, interpretations, or estimates that do not follow directly from the evidence.- Dollar amounts are preserved as published because there is no basis for converting them into Korean-market benchmarks.
- Measurement periods are included. Do not mix figures from different editions.
Next Steps
- Find relevant figures in the Conversion Atlas.
- Turn them into distributions in Conversion Rate Intervals.
Customer Conversion Simulation
Use research and public benchmarks across paid, referral, influencer, ecommerce, SaaS, app, and consultation funnels to forecast conversion ranges before spending.
Conversion Rate Intervals
Turn a benchmark into a beta distribution, simulate funnel uncertainty with Monte Carlo, and update the forecast with pilot results using interactive calculators.