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How to Calculate Success Rate

Updated September 2026 · Written and maintained by the Progression Agency strategy team

Success rate is the number of successes divided by the number of attempts, times one hundred. The arithmetic is trivial and almost nobody gets it wrong. What goes wrong is everything that happens before the arithmetic: what counts as a success, what counts as an attempt, and over what period. Get those three wrong and two people can compute the same figure from the same data and both be right, which is how a metric stops being useful. This page covers the formula, the denominator problem, worked examples across sales, support, email and payments, and how to tell an honest success rate from a flattering one.

The short answerSuccess rate = (number of successes ÷ number of attempts) × 100. Before you calculate it, write down two definitions: what counts as a success, and what counts as an attempt. The second is where almost every error lives, because excluding inconvenient attempts inflates the rate without anybody noticing. Always state the period, always show the sample size alongside the percentage, and always report the rate next to the raw volume — either number alone can be made to look good while the business gets worse.

Success rate, in five points
The formula is trivial. Everything that goes wrong with a success rate happens in the two decisions before the arithmetic: what counts as a success, and what counts as an attempt.
S / A — The formula. Successes divided by attempts.
x100 — For a percentage. The trivial part.
Define — Success first. In writing, before counting.
Define — An attempt. The denominator; where errors live.
State — The period. Always, with the number.
Show — The sample size. A percentage without one is a claim, not data.

What is success rate?

Answer first: success rate is the proportion of attempts that achieved a defined outcome, expressed as a percentage. It measures efficiency — how well attempts convert — as distinct from volume, which measures how many succeeded in total.

Success rate versus success rating

The two phrases are used interchangeably in everyday speech, and where they differ it is usually this: a success rate is a calculated percentage, while a success rating is frequently a subjective score or a band derived from one. If somebody quotes you a success rating, the useful question is whether there is a rate underneath it and what its denominator was.

Why efficiency and volume are different questions

A team that makes ten attempts and succeeds nine times has a ninety per cent success rate and nine successes. A team that makes a thousand attempts and succeeds a hundred times has a ten per cent rate and a hundred successes. Which is performing better depends entirely on what an attempt costs and what a success is worth, which is why both numbers must be reported together.

The formula

Answer first: divide successes by attempts and multiply by one hundred. That is the whole calculation, and it is not where the difficulty is.

Success rate = (S ÷ A) × 100

Where S is the number of attempts that met the defined success criterion, and A is the total number of attempts made in the same period.

Failure rate = 100 − success rate.

How to calculate a success rate
Steps one and two take longer than the rest combined and they determine whether the number means anything. Skipping them produces a figure that two people can compute differently and both be right.

A worked example

A sales team made 240 qualified approaches last quarter and closed 54 of them. The success rate is (54 ÷ 240) × 100 = 22.5 per cent. Reported properly, that reads: 22.5 per cent close rate on 240 qualified approaches, Q1 — because the percentage, the sample size and the period together are the claim, and any one of the three removed makes it unverifiable.

The denominator problem

Answer first: almost every misleading success rate comes from a denominator that quietly excludes inconvenient attempts. Deciding what counts as an attempt is a more consequential decision than deciding what counts as a success.

Exclude — Nothing convenient. Dropped attempts inflate the rate.
Count — Abandoned attempts. They are failures, not absences.
Fix — One definition. Used by every team, written down.
Watch — Small samples. Six attempts is not a rate.
Watch — Changing definitions. A rate that improves when the definition changed did not improve.
Report — Rate and volume. Together, or either can mislead.
Common denominator errors and their effect
What gets excludedJustification usually givenEffect on the rateWhy it matters
Attempts abandoned partway‘They never really started’InflatesAbandonment is a failure mode worth seeing
Attempts by new staff‘They were still learning’InflatesOnboarding quality is exactly what you want measured
Attempts on difficult cases‘Not representative’InflatesThe difficult cases are where improvement lives
Attempts that fell outside the period‘Timing issue’Either directionCreates a moving denominator nobody can audit
Attempts where the customer went quiet‘Not a real loss’InflatesSilence is the most common loss in most funnels
Duplicate attemptsLegitimate, if definedEither directionFine, provided the rule is written down

The conclusion: the only defensible position is a written definition of an attempt, applied consistently, with any exclusions stated explicitly and the excluded count reported alongside. If an exclusion cannot survive being written down, it should not be made.

Defining success before you count it

Answer first: write the success criterion down in one sentence, and make it binary. A criterion that requires judgement produces a rate that changes depending on who counts.

  1. Make it binary. Did it happen or not. ‘Partially successful’ outcomes need their own category, not a share of the numerator.
  2. Make it observable. Somebody other than the person who made the attempt should be able to verify it from a record.
  3. Make it time-bound. A sale that closes eleven months later — does it count in the quarter of the attempt or the quarter of the close? Decide once.
  4. Write it where everyone can see it, not in one team’s spreadsheet.
  5. Record the date you defined it. If the definition changes later, the trend before and after that date is not comparable and somebody needs to know.

Where success rate is used

Answer first: the same formula appears across sales, marketing, support, email, payments and hiring, with entirely different normal ranges. A three per cent conversion rate and a ninety-eight per cent deliverability rate can both be excellent.

Where success rate is used, and what it usually means
Illustrative figures showing the range, not benchmarks. Notice that a 3 per cent conversion rate and a 98 per cent deliverability rate can both be excellent — the number means nothing without its context.
Sales — Close rate. Deals won over qualified opportunities.
Marketing — Conversion rate. Conversions over sessions or visitors.
Support — First-contact resolution. Resolved first time over total tickets.
Email — Deliverability. Delivered over sent.
Payments — Authorization rate. Approved over attempted.
Hiring — Offer acceptance. Accepted over offers made.
The same formula, six contexts
ContextSuccess isAn attempt isWhat usually corrupts it
Sales close rateA closed-won dealA qualified opportunityLoose qualification inflating the denominator
Marketing conversionA defined conversion eventA session or a visitorCounting sessions but deduplicating conversions
Support resolutionResolved without escalationA ticket receivedReclassifying hard tickets as a different type
Email deliverabilityMessage accepted by the receiving serverA message sentSuppressed sends excluded from the denominator
Payment authorizationApproved by the issuerAn authorization requestRetries counted as separate attempts
RecruitmentAn accepted offerAn offer extendedInformal offers not recorded as offers

The conclusion: when comparing a success rate against any external figure, check whether both used the same definition of an attempt. In most cases they did not, and the comparison is meaningless rather than merely imprecise.

Success rate against the metrics it gets confused with

Success rate against the metrics it gets confused with
Volume of successes is the metric people fall back on when the denominator is inconvenient. It always rises with effort and tells you nothing about efficiency, which is precisely why it is popular.
Rate — Efficiency. How well the attempts convert.
Volume — Scale. How many succeeded in total.
Both — Always together. Either alone can be gamed.
Rising rate — Falling volume. Usually means fewer, better attempts.
Falling rate — Rising volume. Usually means looser qualification.
Ask — Which changed, and why. The interesting question is always this one.
Four related metrics
MetricWhat it measuresWhen it misleads
Success rateEfficiency of attemptsWhen the denominator is manipulated
Volume of successesAbsolute outputAlways rises with effort; says nothing about efficiency
Conversion rateA specific step’s efficiencyWhen the step boundaries move
Win rateCompetitive outcomes onlyWhen non-competitive losses are excluded
Failure rateThe inverse of success rateNever; it is the same number
YieldOutput per unit inputWhen inputs are measured inconsistently

The most important row is the second. Volume of successes is what people report when the denominator is inconvenient, because it always rises with effort. A team can double its successes while halving its success rate, and only reporting both reveals it.

How to tell an honest success rate from a flattering one

Signs a success rate is honest
The seventh row is the commonest problem in business reporting: a percentage with no sample size attached. Ninety per cent of ten is a very different claim from ninety per cent of ten thousand.
Small — Samples mislead. Under ~30 attempts, treat with caution.
Trend — Beats a snapshot. One period is an anecdote.
Segment — Rates hide variation. An average across segments hides the answer.
Compare — Like with like. Different definitions are not comparable.
Record — Definition changes. With dates, or trends become meaningless.
Publish — The method. So anybody can reproduce the number.
  1. Ask for the denominator. A percentage without one is a claim rather than data.
  2. Ask for the sample size. Ninety per cent of ten is not the same statement as ninety per cent of ten thousand.
  3. Ask what was excluded. If anything was, ask how many and on what rule.
  4. Ask when the definition last changed. A rate that improved when the definition changed did not improve.
  5. Ask for the trend, not the snapshot. One period is an anecdote.
  6. Ask to see it by segment. An average across segments frequently hides the finding.

Small samples

Below roughly thirty attempts, a success rate is unstable enough that a single outcome moves it materially. That does not make it useless, but it means it should be quoted with the raw numbers attached — ‘four of six’ rather than ‘sixty-seven per cent’ — because the second sounds far more authoritative than the evidence supports.

Averages across segments

A single success rate across several segments can conceal the entire finding. If one channel converts at fifteen per cent and another at two, the blended figure of eight tells you nothing actionable and quietly protects the failing channel from scrutiny.

Improving a success rate, honestly

Answer first: there are only three levers — improve the attempts, improve the execution, or narrow what you attempt. The third improves the rate without improving the business, which is why it needs watching.

The three levers, and what each does
LeverWhat it changesEffect on rateEffect on volumeHonest?
Better qualification of attemptsDenominator qualityUpMay fallYes, if volume is watched
Better executionNumeratorUpUpYes, unambiguously
Narrowing what you attemptDenominator sizeUpFallsOnly if stated
Excluding hard cases from countingDenominator definitionUpUnchangedNo
Redefining success more looselyNumerator definitionUpUnchangedNo
More attempts, same qualityBothUnchangedUpYes

The conclusion: rows four and five raise the number without changing anything real, and they are common precisely because they are cheap. Reporting rate alongside volume, with the definition and its change history, makes both visible.

Answer first: a single period’s success rate is an anecdote. What tells you anything is the trend across several periods against a definition that did not change, with the volume shown alongside.

How many periods before a trend is real

Answer first: three at minimum, and more if the volumes are small. Two points make a line and a line is not a trend — one unusual month between two ordinary ones produces two apparent trends pointing in opposite directions, both of which will be explained confidently at the next meeting.

What to do when the definition changed mid-trend

Answer first: break the series. Show the periods before the change and the periods after as two separate lines with the change date marked, rather than a single line that appears to move. A continuous line across a definition change is the most common way a chart tells a lie without anybody intending one.

Seasonality in success rates

Answer first: compare like periods. Many success rates are seasonal — close rates fall in holiday periods, support resolution rates fall when volumes spike — and comparing December to November produces a finding about the calendar rather than about performance. Year-on-year comparison of the same month is usually the honest view.

Reading a success rate trend
What you seeLikely causeWhat to check
Rate up, volume upGenuine improvementNothing; this is the good case
Rate up, volume downTighter qualificationWhether total successes also fell
Rate down, volume upLooser qualificationWhether the extra successes were worth the effort
Rate down, volume downA real problemSegment it before concluding anything
Rate jumped at one periodOften a definition changeWhen the definition last changed
Rate perfectly stableSometimes a reporting artefactWhether anybody is recalculating it

Building a success rate into a report nobody argues with

Answer first: publish the method alongside the number. If anybody can reproduce the figure from the raw data using your written definition, the argument moves from the number to the performance — which is where it should be.

  1. Publish the definition of success and of an attempt, dated.
  2. Publish the exclusion rules, if any, with the excluded counts.
  3. Publish the raw counts next to every percentage.
  4. Publish the period and use consistent period boundaries.
  5. Publish by segment as well as in aggregate.
  6. Publish the change log so a shift in the line can be attributed.

Reporting a success rate properly

Answer first: the percentage, the numerator, the denominator, the period and the definition. Five elements, one line. Anything less and the reader cannot check it.

Good: “Close rate 22.5% (54 of 240 qualified opportunities), Q1 2026. Qualified opportunity defined as a recorded discovery call with a named budget holder.”

Bad: “Close rate 22.5%.”

  • The percentage is the headline, and it is the least informative part.
  • The raw counts let the reader assess reliability instantly.
  • The period prevents comparison across incomparable windows.
  • The definition prevents two teams reporting different numbers for identical performance.
  • Any exclusions, with counts, stated rather than assumed.

Related reading on the surrounding measurement discipline: what retention means and how to calculate it and how performance is measured in marketing.

Updated August 2026. The example figures on this page are illustrative and chosen to show the range across contexts; they are not benchmarks and should not be used as targets. Normal ranges differ enormously between industries and between definitions of the same metric.

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Frequently asked questions

How do you calculate success rate?
Divide the number of successes by the number of attempts and multiply by one hundred. The arithmetic is trivial; the difficulty is in the two decisions before it — what counts as a success and what counts as an attempt. Write both definitions down before you count anything.
What is success rate?
The proportion of attempts that achieved a defined outcome, expressed as a percentage. It measures efficiency — how well attempts convert — as distinct from volume, which measures how many succeeded in total. Both should be reported together, because either alone can be made to look good while the business gets worse.
What is the success rate formula?
Success rate equals successes divided by attempts, times one hundred. Failure rate is one hundred minus the success rate. Both require the same denominator, which is why defining an attempt matters more than defining a success.
What is the difference between success rate and success rating?
A success rate is a calculated percentage with a denominator. A success rating is frequently a subjective score or a band derived from one. If somebody quotes a success rating, the useful question is whether there is a rate underneath it and what its denominator was.
Why does the denominator matter so much?
Because almost every misleading success rate comes from a denominator that quietly excludes inconvenient attempts — abandoned attempts, difficult cases, attempts by new staff, prospects who went quiet. Each exclusion inflates the rate without changing anything real, and none of them is visible in the percentage.
What should I include when reporting a success rate?
Five things on one line: the percentage, the numerator, the denominator, the period and the success definition. ‘Close rate 22.5% (54 of 240 qualified opportunities), Q1 2026’ is checkable. ‘Close rate 22.5%’ is a claim.
What is a good success rate?
It depends entirely on context, and cross-industry comparison is usually meaningless. A three per cent marketing conversion rate and a ninety-eight per cent email deliverability rate can both be excellent. What matters is your own trend against your own consistent definition.
How large does the sample need to be?
Below roughly thirty attempts a success rate is unstable enough that one outcome moves it materially. That does not make it useless, but quote it with raw numbers attached — ‘four of six’ rather than ‘sixty-seven per cent’, because the percentage sounds far more authoritative than the evidence supports.
How do I define success?
In one sentence, binary, observable and time-bound. Binary because partial outcomes need their own category rather than a share of the numerator. Observable because somebody other than the person who made the attempt should be able to verify it. Time-bound because otherwise late outcomes get counted twice or not at all.
Can a success rate be manipulated?
Easily, and usually without anyone lying. Narrowing what you attempt, excluding hard cases from the count, or redefining success more loosely all raise the number without changing performance. Reporting rate alongside volume, with the definition and its change history, makes all three visible.
What is the difference between success rate and conversion rate?
Conversion rate is a success rate applied to a specific step in a defined process — sessions to enquiries, enquiries to sales. Success rate is the general form. Both are corrupted the same way, by moving the boundaries of what counts in the denominator.
Why report volume alongside the rate?
Because a team can double its successes while halving its success rate, and either number alone hides that. Rate measures efficiency, volume measures output, and the interesting management question is always which of the two changed and why.
What if success takes months to determine?
Decide once whether the outcome counts in the period of the attempt or the period of the resolution, write it down, and apply it consistently. Both conventions are defensible; switching between them is not, and it makes trend data meaningless.
Should I exclude abandoned attempts?
Generally no. Abandonment is a failure mode and frequently the most informative one — in most sales funnels, going quiet is the commonest loss. If you do exclude anything, state the rule and report the excluded count alongside the rate.
How do I compare my success rate against an industry benchmark?
Carefully, and usually you should not. Check whether the benchmark used the same definition of an attempt, the same period and the same success criterion. In most published benchmarks at least one of the three differs, which makes the comparison meaningless rather than merely imprecise.
What is failure rate?
One hundred minus the success rate. It is the same number expressed the other way round, and the choice between them is presentational. Teams sometimes report whichever sounds better, which is worth noticing when a metric switches direction between reports.
How often should the success definition be reviewed?
Rarely, and every change should be dated and recorded. A definition that changes frequently makes trend data useless. If a rate improves in the same period a definition changed, the improvement cannot be attributed to performance.
Should success rates be segmented?
Almost always. A single rate across several segments can conceal the finding entirely — one channel at fifteen per cent and another at two produce a blended eight that tells you nothing actionable and protects the failing channel from scrutiny.
What is the most common mistake?
Quoting a percentage with no sample size. Ninety per cent of ten and ninety per cent of ten thousand are wildly different claims presented identically, and the first appears in business reporting constantly without anybody asking.
How do I improve a success rate honestly?
Three levers only: qualify attempts better, execute better, or narrow what you attempt. The first two improve the business. The third improves the number while reducing volume, which is legitimate if stated and misleading if not.
Is a rising success rate always good?
No. It can mean better execution, or it can mean fewer and easier attempts. A rising rate alongside falling volume usually means tightened qualification, which may be exactly right — but it is a different story from improved performance and should be reported as one.
What is yield and how does it differ?
Yield is output per unit of input, which may not be a simple count of attempts. Success rate is the special case where the input is a countable attempt. Yield misleads when inputs are measured inconsistently across periods, which is the same failure mode in a different form.
Where should the definition live?
Somewhere every team can see it, not in one team’s spreadsheet. The single most common cause of two teams reporting different numbers for identical performance is two definitions, each perfectly reasonable, neither written where the other team could find it.

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