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reopt Handbook
reopt Handbook
Customer Conversion Simulation

Reading Guide

How to Read the EvidenceConversion Rate Intervals

Conversion Atlas

Conversion Atlas by ChannelConversion Atlas by ProductConversion Atlas by RoleKorean Conversion Atlas

Paid Marketing

Paid Channel BaselinesReported and Incremental ConversionsCreative, Retargeting, and Saturation

Referral Marketing

Referral Funnel MetricsReferral Experiments and Limits to Spread

Partners and Influencers

Influencer Marketing ConversionPartnerships, Collaborations, and Sponsorships

Ecommerce

Ecommerce Conversion BaselinesEcommerce Funnels and CartsEffect Sizes of Ecommerce Levers

SaaS

Self-Serve SaaS ConversionEarly-Stage SaaS ConversionSales-Led Conversion

Apps

Mobile App Acquisition and RetentionApp Subscription ConversionDesktop App Conversion

Consultation and Application Forms

Building Forecasts

Expert Forecasts and CalibrationChoosing a Forecasting ModelLLM Synthetic Customers

Korean Case Studies

Korean Cases: Advertising and CommerceKorean Cases: Consultations, Finance, and Public Experiments

Appendix

Handbook›Customer Conversion Simulation›How to Read the Evidence
한국어KOEnglishEN

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

GradeMeaningRepresentative sourcesHow to use it in forecasts
ARandomized experimentBlake, Nosko, and Tadelis's eBay experimentUse 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
BPeer-reviewed observational research, large primary datasets, filings, transaction logsSchmitt's banking data, RevenueCat subscription logs, ChartMogul billing data, SEC filingsUse as a baseline without interpreting it as causal; check the sample's scope
CVendor aggregates, self-reported surveysLocaliQ, Littledata, ChartMogul surveysUse as a reference range and record the sample and conflicts of interest
DIndividual case, secondary citation, opaque methodologyAgency blogs, founder presentationsUse 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.

QuestionWhy it mattersExample
What is the denominator?Different denominators mean different metricsCold-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 upwardShopify conversion: median 1.4%, mean 1.9%
Who is in the sample?Only customers of the measurement tool are includedLittledata includes only stores with at least 1,000 sessions and 90 purchases
When was it measured?Definitions and markets changeNaver 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

TermMeaning
Conversion, CVRThe share taking a specified action. Always state the denominator
FunnelStages from visit to purchase. Each stage rate is relative to the immediately preceding stage
AttributionConversions credited to a platform or code, including some that would have happened without advertising
IncrementalityConversions that would not have happened without the activity, measured against a control group
LiftIncrease relative to the control group
Reported-to-incremental multiplierPlatform-reported conversions divided by incremental conversions. Divide benchmark conversions by this value
Concentration kkkControls the spread around the mean. At the same mean, larger values give narrower intervals. Choose it using mean × kkk; do not compare confidence using kkk alone
5th, 50th, and 95th percentilesThe 5th, 50th, and 95th values in 100 ordered draws. The 50th percentile is the median
Prior, posteriorBelief before measurement and belief after incorporating observations
Odds ratio, RRRatios comparing odds or risks between two groups. A value of 1 means no difference
p-valueThe probability, under no difference, of a result at least this extreme. Below 0.05 is commonly called significant
ElasticityPercentage change in the outcome for a 1% change in an input
Difference-in-differencesThe treatment group's before/after change minus the control group's before/after change
Propensity-score matchingAn observational method matching similar subjects. It is not randomization
R2R^2R2The proportion of observed variation explained by a model. Values closer to 1 indicate more explained variation
Last click, last touchAttribution assigning all credit to the touchpoint immediately before conversion
HoldoutA control group deliberately excluded from ads or messages
KKKNew customers brought in by one customer: the viral coefficient. Distinct from concentration kkk
FreemiumA model with an ongoing free plan, from which some users move to paid
Trial, reverse trialTime-limited free use. A reverse trial starts with paid features and falls back to free when the trial ends
Self-serve, PLGCustomers sign up and pay themselves without sales involvement
MQL, SQLMarketing-qualified lead and sales-qualified lead
CTR, CPC, CPMClick-through rate per impression, cost per click, and cost per 1,000 impressions
CPL, CPI, CPA, CACCost per lead, install, conversion, and acquired customer
ACVAnnual contract value
ROAS, ROISRevenue divided by ad spend; net revenue divided by influencer spend. Neither measures margin
EMVExposure and engagement expressed in dollars. It is not revenue
GRR, NRRRevenue retention from existing customers. NRR includes expansion revenue
D1, D7, D30Share returning 1, 7, or 30 days after installation
ATTiOS app tracking consent
VendorA tool provider or agency publishing a benchmark, usually from its own customers and with a commercial interest
CohortCustomers who started during the same period

Numerical Conventions

  • Grades appear in a table's Grade column or at the end of its evidence cell; Korean case tables put them at the end of the first cell. Prose uses (grade C).
  • Calculated means this book divided, multiplied, or converted source values; the result does not appear verbatim in the source.
  • Secondary citation means the original publication and underlying data were not both reviewed; the value was confirmed only through another article. It is grade D.
  • Via press coverage means public data or vendor aggregates were reviewed only through reporting. The grade follows the nature of the underlying data.
  • Summary means only an abstract or institutional summary was reviewed.
  • Author judgment marks 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.

Related docs

Conversion Atlas by Channel

Compare click-through rates, conversion rates, and costs across Google search, Meta, TikTok, LinkedIn, email, and referrals with evidence grades.

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.

Metrics and Dashboards

CRM Standard · Connect CRM operations to a KPI tree and decision-ready dashboards.

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.

On this page

Evidence GradesAlign the Denominator FirstAttribution and Incrementality DifferSix Rules for Using BenchmarksTerms Used in This BookNumerical ConventionsNext Steps