Time-Decay Attribution Calculator
The formula
What the time-decay attribution tells you
Recency-weighted credit: touches matter exponentially more the closer they sit to conversion, with a half-life knob as the tuning dial.
Calculation: Weight = 0.5^(days before conversion ÷ half-life); credit = weight ÷ Σweights × revenue. Enter your inputs and every substitution is shown below the result.
Reading the result
The right half-life equals your typical consideration window — a 14-day decay on a 90-day sales cycle is last-touch in a costume.
Rules of thumb: 30–60 day half-lives suit mid-market journeys; SMB 7–14
Limitations and best practice
For long enterprise cycles, aggressive decay starves the demand programs and overprices the closing email — tune half-life to cohort journey length, not to habit.
This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Frequently asked questions
How do I validate the half-life?
Does decay ignore what the first touch was?
Source & methodology
Attribution-modelling conventions (Marketo-style decay).
Time-Decay Attribution: what this tool answers
Time is the one input nobody can renegotiate, which makes getting the arithmetic right worth the minute it takes. Searchers land on a Time-Decay Attribution page for three reasons — to plan, to compare options, and to check that somebody else’s arithmetic is honest. This tool serves all three, and everything after the calculator explains the model instead of hiding it.
It helps to separate the mechanics from the interpretation. The mechanics are fixed: Weight = 0.5^(days before conversion ÷ half-life); credit = weight ÷ Σweights × revenue. Anyone, anywhere, entering the same inputs will get the same $3,038.43. The interpretation is where experience counts: whether $3,038.43 is good, normal or alarming depends on comparisons covered below, and that is the part most articles about Time-Decay Attribution never explain.
Business runs on numbers that are cheap to compute and expensive to ignore. Time-Decay Attribution in Business & Entrepreneurship is one of those: Weight = 0.5^(days before conversion ÷ half-life); credit = weight ÷ Σweights × revenue, and the gap between founders who check and founders who hope shows up exactly here.
Every figure in the prose is generated by the engine rather than written beside it — the worked example resolves to credit to closing touch = $3,038.43 on the default inputs, and the sensitivity section ranks the inputs that move the result — Journey revenue first, at ≈10.0 % of the headline for a ten-percent error either way in that input — which is the thread the rest of this guide pulls on.
How Time-Decay Attribution is computed, line by line
The engine implements a single stated model: Weight = 0.5^(days before conversion ÷ half-life); credit = weight ÷ Σweights × revenue. There is no hidden coefficient and no login-gated precision — the formula printed is the formula executed, which is also what makes the result citable.
Journey revenue ($) (example value 4,675) is a plain measured quantity — the unit shown in the label is the unit the formula expects. The example value is there to demonstrate the mechanics, not to describe your situation — overwrite it.
Days since touch 1 (first) (example value 45) is a plain measured quantity — the unit shown in the label is the unit the formula expects. Use the value as of today, not as of the last time you looked — time fields drift silently.
Days since touch 2 (nurture) (example value 31) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. Use the value as of today, not as of the last time you looked — time fields drift silently.
Days since touch 3 (closing) (example value 3) is a measured number in the unit named by the label; the formula does not convert for you. Be precise about the boundary (exact date, start of month); time inputs decide the exponent in most formulas.
Model half-life (days) (example value 18) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. Be precise about the boundary (exact date, start of month); time inputs decide the exponent in most formulas.
The default Time-Decay Attribution calculation, written out in full
Rather than describe the calculation, here it is end to end — every substitution in order, starting from the inputs above. Starting figures: Journey revenue ($), Days since touch 1 (first), Days since touch 2 (nurture), Days since touch 3 (closing). The endpoint of that chain is $3,038.43 of credit to closing touch, sitting in the “Finder still earns $603 under the decay” band.
- Weights = 0.5^(45/18) = 0.177; 0.303; 0.891
- Shares = weight ÷ 1.371 × $4,675
Credit to first touch — $602.90. If your inputs change, this is usually the first companion figure to move.
Credit to nurture touch — $1,033.67. If your inputs change, this is usually the first companion figure to move.
Weight ratio last:first — 5.0 ×. This is the number experienced users quote when the headline alone would mislead.
Overwrite any input above and every line of this example updates with it — the substitution is live, not a screenshot of somebody else’s numbers.
The Time-Decay Attribution corridor: your result across a realistic band
Read this table as the practical error bar on the headline figure. It answers the question people actually have after seeing a single number: how wrong could this be if the main input is off by ten or twenty percent?
| Journey revenue setting | Credit to closing touch |
|---|---|
| 80 % of the example value | $2,430.75 |
| 90 % of the example value | $2,734.59 |
| example inputs | $3,038.43 |
| 110 % of the example value | $3,342.28 |
| 120 % of the example value | $3,646.12 |
The corridor runs from $2,430.75 to $3,646.12 across that band — which is the range worth planning inside rather than the single $3,038.43 point estimate. Reproduce it in the form above by editing Journey revenue alone; the substitution steps will follow your figures exactly.
Sensitivity: which inputs change Time-Decay Attribution the most
Nobody should have to guess which input matters. The engine was re-run once per field below — ten percent up, ten percent down, everything else frozen — and the results ranked by how far they moved the credit to closing touch. Read it as a map of where Time-Decay Attribution is fragile — drawn for the example inputs, which is the map you want when deciding what to measure carefully.
- Nudge Journey revenue by ±10 % and Time-Decay Attribution lands between $2,734.59 and $3,342.28 (sensitivity ≈10.0 %)
- Model half-life: ten percent up gives $2,912.62, ten percent down gives $3,185.53 — ≈4.8 % of the credit to closing touch either way
- Nudge Days since touch 2 by ±10 % and Time-Decay Attribution lands between $2,955.57 and $3,115.96 (sensitivity ≈2.7 %)
- Days since touch 1: ten percent up gives $3,102.08, ten percent down gives $2,966.06 — ≈2.4 % of the credit to closing touch either way
- Days since touch 3: ten percent up gives $3,026.12, ten percent down gives $3,050.70 — ≈0.4 % of the credit to closing touch either way
Journey revenue is the dominant lever here: ten percent either way moves the result by about 10.0 %, from $2,734.59 to $3,342.28. Spend your verification effort there first, and treat Model half-life as the second priority (≈4.8 % per ten percent). The rest of the inputs earn only a rounding-error of attention, and that asymmetry is the practical payoff of measuring rather than asserting.
Interpreting the Time-Decay Attribution verdict
Nobody acts on a bare figure — they act on a figure with a comparison attached. The verdict attached to the default calculation — “Finder still earns $603 under the decay” — is one band on a ladder your own figure may sit somewhere else on. Before treating the band as fact, note its width: ten percent either way on Journey revenue already spans $2,734.59 to $3,342.28. The practical reading rules: read the band as a starting hypothesis, then test it: change the two dominant inputs by ten percent and see whether the verdict survives. Verdicts that survive stress are the ones worth acting on.
Where Time-Decay Attribution sits next to similar measures: this tool answers “how much is the Time-Decay Attribution”, while the related calculators answer “what do I do about it”. Reading the result alone tends to over-weight a single snapshot; pairing it with the companion tools above turns the number into a decision.
How people get Time-Decay Attribution wrong
For long enterprise cycles, aggressive decay starves the demand programs and overprices the closing email — tune half-life to cohort journey length, not to habit. This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Reading Time-Decay Attribution from a single month. Business figures have seasonality and noise; compare across a full cycle before believing the trend.
Attention is also misallocated between fields. Having measured this engine, we can say where it belongs: Journey revenue first, everything else second.
Another is comparing across contexts: a Time-Decay Attribution computed with one set of conventions should never be lined up against a figure computed with another. Check the inputs behind both before ranking them.
Time-Decay Attribution verification checklist
- Record the date and the inputs alongside the number so future-you knows what was assumed.
- Compare the result with one independent method or published benchmark.
- Re-verify Journey revenue against a source document — ten percent of error there moves the headline by ≈10.0 %.
- State, in one sentence, which convention each rate, unit or period in the form is using.
- Recompute once with the dominant input deliberately set low, and see whether the decision changes.
- Repeat the ±10 % test on the inputs that matter and see whether the decision — not just the number — still holds.
Step by step: getting a trustworthy Time-Decay Attribution out of this tool
Using a calculator well sounds trivial, but the difference between a decision-grade figure and a throwaway one lives entirely in the routine. The routine for Time-Decay Attribution takes a minute to learn and applies to every input above.
Step 1 — collect, then type. Everything this tool asks for is listed above (journey revenue ($), days since touch 1 (first), days since touch 2 (nurture), days since touch 3 (closing), model half-life (days)). Pull each figure from something you can point at — paperwork, a dashboard, a label — before touching a field. Guesswork at the input stage cannot be repaired at the interpretation stage. The test of a good estimate is not that it feels right but that it changes what you do.
Step 2 — enter the conservative case first. Resist the flattering scenario. Type the inputs you would defend in front of a sceptical reader, read the Credit to closing touch ($3,038.43 for the example values), and note the verdict band. If a result feels obviously right, that is the moment to check the inputs hardest.
Step 3 — measure what deserves measuring. The ranked levers above tell you where a careful figure pays off. Everything else can stay approximate, which is a licence to stop hunting for data you do not need. Precision is a promise you make about your data; make sure your inputs can keep it.
Step 4 — ask what would have to be true. Take the result you want and work backwards through the inputs until you find the assumption that produces it; then decide honestly whether that assumption is defensible. Write the assumptions in the same file as the result, and future arguments take minutes instead of meetings.
Step 5 — record the context, not just the number. Note today’s date and the inputs alongside the $3,038.43 output. Six months from now the context is what turns this figure into a trend. Comparisons are cheap: run the identical calculation for the alternative and look at the gap.
Where Time-Decay Attribution matters in practice
In Marketing & Growth the figure computed here is usually the first one somebody asks to see. Having it — with the formula behind it — changes your position in the conversation from participant to reference. Half of a good calculation is knowing which input you would hate to be wrong about.
Used properly this is a five-minute filter that saves weeks. Compute Time-Decay Attribution, reject what fails, and only then bring in the expensive analysis for the survivors. Half of a good calculation is knowing which input you would hate to be wrong about.
Nothing here is stored or transmitted, so the habit of checking is free: open the page, re-enter current figures, compare with the number you saved last time. Rounding early is the quietest way to introduce a permanent error.
The questions that come up most about Time-Decay Attribution
What affects Time-Decay Attribution the most?
Time-Decay Attribution is driven above all by Journey revenue. Perturbing only that field by ten percent either way produced $3,342.28 against the $3,038.43 baseline — a ≈10.0 % response, while Model half-life manages ≈4.8 %.
Which matters more: model half-life or journey revenue?
By the numbers, Journey revenue: a ten-percent change in it moves the result ≈10.0 %, versus ≈4.8 % for Model half-life. Spend your measuring effort in that order — precision on the weaker lever buys almost nothing.
What is a typical Time-Decay Attribution?
With the example inputs on this page the result is $3,038.43, labelled “Finder still earns $603 under the decay”. “Typical” varies with situation, which is why every result here ships with its verdict band and its sensitivity list: they turn one figure into a range you can locate yourself inside.
How do I calculate Time-Decay Attribution by hand?
Use the stated model — Weight = 0.5^(days before conversion ÷ half-life); credit = weight ÷ Σweights × revenue — and substitute in the order the tool prints (it starts from Weights = 0.5^(45/18) = 0.177; 0.303; 0.891). On the example inputs the hand route lands on $3,038.43, exactly what the calculator shows. A spreadsheet reproduces it in two or three cells; doing it once by hand is what makes the automated answer trustworthy.
What happens to Time-Decay Attribution if journey revenue changes by 10 %?
The engine was re-run with that field moved by ten percent while everything else stayed fixed: the credit to closing touch goes across $2,734.59 to $3,342.28, a swing of roughly 10.0 % around the $3,038.43 baseline. That ratio is the practical definition of how much this input matters.
Is the Time-Decay Attribution result of $3,038.43 good or bad?
The tool answers “what”, not “whether”: $3,038.43 carries the verdict “Finder still earns $603 under the decay” for the default case, which is a starting interpretation rather than a verdict on your situation. Re-run with your own figures and compare the bands.
Should I use Time-Decay Attribution before launching?
Yes — a Time-Decay Attribution run on honest inputs before spending is the cheapest consulting an entrepreneur can buy. Re-run at every pricing change; the number moves more often than founders expect.
Is Time-Decay Attribution accurate?
The arithmetic is exact: Weight = 0.5^(days before conversion ÷ half-life); credit = weight ÷ Σweights × revenue is evaluated with full floating-point precision and every substitution is printed in the worked example. What limits accuracy is input quality and the model’s assumptions, so treat the $3,038.43 result as the correct answer to the question you typed — and check the assumptions section before acting on it.
How do I validate the half-life?
Fit it: pick the value whose implied channel mix best matches geo-holdout results — the decay curve becomes a testable hypothesis.
Does decay ignore what the first touch was?
Yes, it only times — which is its weakness for brand work that pays back months later through direct-traffic surges.
Can this tool see the numbers I type?
It cannot. There is no submission step: the calculation happens in your browser and the result disappears when you close the page. Treat the fields above as a scratchpad rather than a form.
Glossary for Time-Decay Attribution
- Credit to first touch
- $602.90 on the example inputs — a companion figure printed beside the headline of Time-Decay Attribution so that no result is read in isolation.
- Credit to nurture touch
- $1,033.67 in the default Time-Decay Attribution calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Weight ratio last:first
- 5.0 × for the Time-Decay Attribution worked case; it sits next to the main result because it carries context the headline leaves out.
- Credit to closing touch
- The primary output of this tool: $3,038.43 for the default inputs, computed exactly from Weight = 0.5^(days before conversion ÷ half-life); credit = weight ÷ Σweights × revenue.
- Worked example
- The step-by-step substitution printed above, so the arithmetic can be re-derived by hand or in a spreadsheet.
- Verdict band
- A named zone on the range of plausible results; the example inputs land in “Finder still earns $603 under the decay”, and your inputs may not.
- Sensitivity (±10 %)
- How far the Time-Decay Attribution moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.
Method, sources and what this tool cannot tell you
There is no black box on this page — the engine evaluates Weight = 0.5^(days before conversion ÷ half-life); credit = weight ÷ Σweights × revenue. Source of record: Attribution-modelling conventions (Marketo-style decay). Any other implementation of this model should return the same figure for the same inputs. If one does not, the difference is in its assumptions — which this page prints and that one probably does not.
Nothing here replaces a qualified professional, and nothing here is approximate either — the arithmetic is exact, and the honesty is in the inputs you supply. Nothing is smoothed, weighted or adjusted behind the scenes; where judgement enters, this page says so and names the input responsible.
Turning this Time-Decay Attribution into a decision
Mark the computed date or span in your calendar with its assumptions attached; plans drift when the inputs do.
This page was written for people planning around a point or span in time, and the promise is that nothing above required trust. Bookmark the tool, re-run it whenever an input moves by more than a few percent, and keep the date with the number.