Linear Attribution Calculator
The formula
What the linear attribution tells you
Even division of conversion credit across every touch — the neutral starting point before evidence justifies weighting.
Calculation: Linear model: equal credit to every touch, revenue ÷ touches. Enter your inputs and every substitution is shown below the result.
Reading the result
Use it as the baseline: any channel arguing it deserves more (or less) than its equal share needs data — assist rates or incrementality.
Rules of thumb: The safest single model for multi-touch B2B reporting without causal data
Limitations and best practice
Equal weights assume equal roles — the form-fill that added nothing and the demo that won the deal split the credit like siblings.
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
Does journey length break linear attribution?
Linear vs U-shape?
Source & methodology
Attribution-modelling conventions.
Linear Attribution explained: formula, worked example and honest limits
This page was written for one job: turning your own inputs into a defensible figure, with the working shown. Searchers land on a Linear 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.
The discipline Linear Attribution enforces is naming your assumptions. Journey revenue ($) and Touches in the journey are guesses dressed as inputs; once they are on paper, the argument stops being about vibes and starts being about whether the guesses are defensible — which is how good decisions get made.
The useful way to hold Linear Attribution is as a ratio with a story attached: Linear model: equal credit to every touch, revenue ÷ touches says how the parts combine, and the $820.00 figure above is simply that sentence written in numbers. Everything that follows unpacks one clause of the sentence at a time.
Nothing below is stock text pasted around a number: the worked example computes credit per touch = $820.00 from this tool’s own defaults, and the sensitivity section ranks the inputs that move the result — Touches in the journey first, at ≈11.1 % of the headline for a ten-percent error either way in that input. Read the sections in order and you will be able to reproduce every figure by hand.
How Linear Attribution is computed, line by line
Here is the complete model this tool runs — Linear model: equal credit to every touch, revenue ÷ touches — with each input explained below. If a competitor’s page gives you a different answer from the same inputs, this is the text that settles which of you is right.
Journey revenue ($) (example value 4,100) is a quantity rather than a category — precision here is cheap and worth having. Replace the example with the figure from your own records — the tool recomputes instantly and the printed steps follow your numbers.
Touches in the journey (example value 5) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. This input feeds the formula directly; a defensible figure here is worth more than false precision anywhere else.
A fully worked Linear Attribution example
Abstract formulas are hard to trust, so here is the complete substitution using the default inputs, written the way an examiner would want it. Starting figures: Journey revenue ($), Touches in the journey. Run to the end, the credit per touch resolves to $820.00 — A 5-touch journey split evenly on the bands this tool uses.
- Per-touch = $4,100 ÷ 5 = $820
- Linear is the fair compromise when touch importance is unknown — and flatly wrong when it is not
Total credited — $4,100. If your inputs change, this is usually the first companion figure to move.
Finder’s share vs first-touch — 20.0 %. Compare it against the main result of $820.00 before drawing conclusions.
Closer’s share vs last-touch — 20.0 %. 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 Linear Attribution corridor: your result across a realistic band
Single-point answers invite false confidence, so the same engine was run across a band around the input that matters most here. Each row is a full recalculation of Linear Attribution, not an interpolation.
| Touches in the journey setting | Credit per touch |
|---|---|
| 80 % of the example value | $1,025.00 |
| 90 % of the example value | $911.11 |
| example inputs | $820.00 |
| 110 % of the example value | $745.45 |
| 120 % of the example value | $683.33 |
Across 5 settings of Touches in the journey, the result spans $683.33 to $1,025.00. A decision that only works at one row of this table is a decision waiting for an input to move. Reproduce it in the form above by editing Touches in the journey alone; the substitution steps will follow your figures exactly.
Sensitivity: which inputs change Linear 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 per touch. For Linear Attribution the ranking is measured rather than guessed, and it belongs to these defaults: shift the starting point and the order can shift with it.
- Touches in the journey: ten percent up gives $745.45, ten percent down gives $911.11 — ≈11.1 % of the credit per touch either way
- Journey revenue +10 % → Linear Attribution = $902.00 · −10 % → $738.00 (the credit per touch shifts ≈10.0 %)
Touches in the journey is the dominant lever here: ten percent either way moves the result by about 11.1 %, from $745.45 to $911.11. That is where a careless figure does the most damage, so check it against a source, and treat Journey revenue as the second priority (≈10.0 % 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.
What a Linear Attribution of $820.00 means in practice
A number on its own is trivia; a number against a range is intelligence. The verdict attached to the default calculation — “A 5-touch journey split evenly” — is one band on a ladder your own figure may sit somewhere else on. How much of that band is luck? On Touches in the journey alone ten percent either way covers $745.45 to $911.11. The practical reading rules: ask what would have to change to move you one band. If the answer is “a small, plausible input difference”, the band itself is not the finding — the uncertainty is.
Where Linear Attribution sits next to similar measures: this tool answers “how much is the Linear 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.
Why Linear Attribution results go wrong — and how to prevent it
Equal weights assume equal roles — the form-fill that added nothing and the demo that won the deal split the credit like siblings. 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.
Copying a single headline number without checking the companion outputs. The tool reports Total credited, Finder’s share vs first-touch and Closer’s share vs last-touch alongside the main result precisely because Linear Attribution in isolation misleads — the secondary columns are where outliers and trade-offs show up.
One mistake is specific to this engine: assuming every input matters equally. The computation above shows Touches in the journey dominating — a careful figure there beats false precision everywhere else.
A final trap is presentation: reporting Linear Attribution to more decimal places than the inputs support. The tool prints what the arithmetic gives; you should quote only what your least precise field justifies.
Before you trust your Linear Attribution: a short checklist
- Recompute once with the dominant input deliberately set low, and see whether the decision changes.
- Verify each input against its original source document, not memory.
- Check Touches in the journey before anything else: it is the measured dominant input, ≈11.1 % of Linear Attribution per ten percent.
- Note the date and the inputs beside the number so the figure can be audited later.
- Check that every field the formula needs actually arrived from a document, not from recollection.
- Repeat the ±10 % test on the inputs that matter and see whether the decision — not just the number — still holds.
Five steps from raw inputs to a figure you can defend
The tool does the arithmetic; the routine below is what makes the arithmetic trustworthy. Five steps, a minute total — and afterwards you will know not just what the number is, but which assumption produced it.
Step 1 — one document, one pass. Open the statement, label or record that holds journey revenue ($), touches in the journey and transcribe straight into the form. Transcription from a single source removes the mismatch errors that appear when figures are assembled from three different places. Compare the calculation to the rough guess you arrived with, and ask why they differ.
Step 2 — start from the case you can defend. Enter pessimistic-but-honest figures first; the optimistic run is a ceiling, not a plan. A conclusion that holds on conservative inputs holds everywhere. A figure with a date beside it becomes evidence; without one it stays an anecdote.
Step 3 — play with the inputs on purpose. Ten percent up, ten percent down, one field at a time. Within a few minutes you will know this calculation better than most people who quote its results. Write the figure down before moving on; memory flatters whichever number you saw last.
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. Re-running a number costs seconds; retracting a decision costs considerably more.
Step 5 — schedule the next run. In Business & Entrepreneurship the inputs drift quietly. Put a reminder a quarter out, re-enter the same fields, and read the change rather than the level. Repeating a calculation with the same numbers proves only that the arithmetic is reliable — which is worth knowing.
From calculation to decision: where Linear Attribution earns its keep
Numbers earn their keep in conversations, and Linear Attribution is no exception. In Business & Entrepreneurship discussions around Marketing & Growth, this figure decides whether an option stays on the table: it converts an opinionated debate into an arithmetic one, and arithmetic is the only referee both sides accept. Write the figure down before moving on; memory flatters whichever number you saw last.
The companion outputs — Total credited and Finder’s share vs first-touch — exist for the second stage of that screen: the headline sizes the prize, while the companions describe the trade-off you are accepting to get it. Precision is a promise you make about your data; make sure your inputs can keep it.
Re-running this page costs nothing and catches the slow drifts that never announce themselves. In Business & Entrepreneurship the expensive mistakes are rarely sudden; they are small changes in Journey revenue ($) that nobody recalculated for a year. Suspiciously tidy answers deserve the same scrutiny as untidy ones.
People also ask about Linear Attribution
What affects Linear Attribution the most?
Linear Attribution is driven above all by Touches in the journey. Perturbing only that field by ten percent either way produced $745.45 against the $820.00 baseline — a ≈11.1 % response, while Journey revenue manages ≈10.0 %.
Which matters more: journey revenue or touches in the journey?
By the numbers, Touches in the journey: a ten-percent change in it moves the result ≈11.1 %, versus ≈10.0 % for Journey revenue. Spend your measuring effort in that order — precision on the weaker lever buys almost nothing.
What is a typical Linear Attribution?
There is no honest universal average for Linear Attribution, but there is a reference point: the defaults on this page produce $820.00 (A 5-touch journey split evenly). Replace the inputs with your own and compare — that comparison is the useful meaning of “typical”.
How do I calculate Linear Attribution by hand?
Linear model: equal credit to every touch, revenue ÷ touches is the entire method. Work the inputs in that order — Per-touch = $4,100 ÷ 5 = $820 — and you will reach $820.00 on the default figures, the same number this tool prints.
What happens to Linear Attribution if touches in the journey changes by 10 %?
The engine was re-run with that field moved by ten percent while everything else stayed fixed: the credit per touch goes across $745.45 to $911.11, a swing of roughly 11.1 % around the $820.00 baseline. That ratio is the practical definition of how much this input matters.
Is the Linear Attribution result of $820.00 good or bad?
Neither, on its own — the label “A 5-touch journey split evenly” is the band this figure falls in for the example inputs, and your own inputs will place you elsewhere on the same ladder. Judge the number against your history and your alternatives, and remember that a ten-percent error in Touches in the journey alone moves it by ≈11.1 %.
What does $820.00 tell me about the business?
It answers the narrow question mechanically and the broad question only in context: Linear Attribution is one dashboard light, and it pairs with unit economics and cash timing before any bet the business size.
How often should I check Linear Attribution?
Whenever an input changes by more than a few percent, and otherwise on the rhythm of your decisions — before a purchase, a review, a renewal. Linear Attribution is a snapshot, not a subscription; stale snapshots are the most common silent error in Business & Entrepreneurship planning.
Does journey length break linear attribution?
It redistributes it: longer journeys dilute each touch, so the same channel looks weaker in longer funnels even at constant effectiveness.
Linear vs U-shape?
U-shape gives 40/40 to first and last, 20 across the middle — closer to how committees actually decide; linear is the conservative default when roles are unproven.
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.
What the labels on this page mean
- Total credited
- $4,100 for the Linear Attribution worked case; it sits next to the main result because it carries context the headline leaves out.
- Finder’s share vs first-touch
- 20.0 % in the default Linear Attribution calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Closer’s share vs last-touch
- 20.0 % for the Linear Attribution worked case; it sits next to the main result because it carries context the headline leaves out.
- Credit per touch
- The primary output of this tool: $820.00 for the default inputs, computed exactly from Linear model: equal credit to every touch, revenue ÷ touches.
- Worked example
- Every substitution in order, from the example inputs to $820.00 — the audit trail for the headline figure.
- Verdict band
- The plain-language label — here “A 5-touch journey split evenly” — mapping the computed figure onto experience-based ranges for Business & Entrepreneurship.
- Sensitivity (±10 %)
- How far the Linear Attribution moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.
Methodology, sources and limits
There is no black box on this page — the engine evaluates Linear model: equal credit to every touch, revenue ÷ touches. Source of record: Attribution-modelling conventions. Where a professional version of this calculation differs, it differs in inputs and jurisdiction-specific coefficients, not in the algebra — which is why re-checking your inputs beats hunting for a second calculator.
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. The design constraint was set by usefulness rather than by comfort: an estimate you can interrogate beats an answer you have to trust.
After the number: what to do with it
Save the number together with its inputs and today’s date; a Linear Attribution figure without context is unusable the moment an input changes.
The audience here is people who need one trustworthy number before their next decision. Everything you need to defend the result — formula, substitution, sensitivity, source — is printed above, so the only remaining variable is the honesty of your inputs.