Linear Attribution

Who this is for

• Marketing Analysts who need a fair, simple way to credit all touchpoints in a conversion path.
• Performance marketers building baseline attribution before testing advanced models.
• Generalists who must report channel impact without heavy modeling.

Prerequisites

• Basic understanding of marketing channels and conversion tracking.
• Comfort with percentages, fractions, and simple aggregation.
• Access to conversion paths (from an analytics tool, CRM, or exports).

Why this matters

Real tasks you will do on the job:

• Report each channel’s contribution when multiple channels touch a customer.
• Build a baseline model to compare against last-click or time-decay.
• Align stakeholders by showing that mid-funnel channels also matter.
• Create budget conversations grounded in fair-share credit.

Concept explained simply

Linear Attribution splits conversion credit equally across all touchpoints in the path. If a path has n touchpoints, each touchpoint gets 1/n of the conversion.

How to calculate (step-by-step)

1. List each conversion path in order (e.g., Paid Social → Email → Direct → Conversion).
2. Count touches in the path (n).
3. Assign 1/n credit to each touchpoint in that path.
4. If the same channel appears multiple times, add its per-touch credits in that path.
5. Aggregate credits across all conversions to get channel totals.

Worked examples

Example 1: Single path

Path: Email → Search → Direct → Conversion.
Touches: 3 → Each touchpoint gets 1/3 ≈ 33.33%.

• Email: 33.33%
• Search: 33.33%
• Direct: 33.33%

Example 2: Repeated channel in a path

Path: Paid Social → Email → Email → Organic → Conversion.
Touches: 4 → Each touchpoint gets 25%.

• Paid Social: 25%
• Email: 25% + 25% = 50%
• Organic: 25%

Example 3: Multiple conversions, aggregate

We have 3 conversions with these paths:

1. Paid Search → SEO → Direct
2. Email → Paid Search → Email → Direct
3. Paid Social → Paid Search → SEO → Direct → Direct

Per-path credits:

• Path 1 (3 touches): each 1/3 → Paid Search 0.333, SEO 0.333, Direct 0.333
• Path 2 (4 touches): each 1/4 → Email 0.5 (two touches), Paid Search 0.25, Direct 0.25
• Path 3 (5 touches): each 0.2 → Paid Social 0.2, Paid Search 0.2, SEO 0.2, Direct 0.4 (two touches)

Totals (out of 3 conversions):

• Paid Search: 0.333 + 0.25 + 0.2 = 0.783 (~26.1%)
• SEO: 0.333 + 0 + 0.2 = 0.533 (~17.8%)
• Direct: 0.333 + 0.25 + 0.4 = 0.983 (~32.8%)
• Email: 0.5 (~16.7%)
• Paid Social: 0.2 (~6.7%)

When to use (and when not)

• Use when you want a simple, fair baseline that values every touchpoint equally.
• Good for long, exploratory journeys with many mid-funnel touches.
• Not ideal when recency clearly matters (consider time-decay), or when first or last touch should dominate due to your business model.

Data requirements and assumptions

• Ordered touchpoint sequences for converting journeys.
• Consistent channel grouping (e.g., normalize naming for “Paid Search” vs “PPC”).
• Assumes each touchpoint’s importance is equal within a path.

Implementation in practice (tool-agnostic)

1. Extract conversion paths with channel labels.
2. For each path, count touches and assign 1/n to each touchpoint.
3. Aggregate by channel across all conversions.
4. Optionally, compare results with last-touch and time-decay to create a triangulated view.

Exercises

These mirror the exercises below. Try here first, then open the solutions.

Exercise 1 — Single path with repeats

Path: Paid Social → Email → Direct → Email → Organic → Conversion.
Question: What percent credit does each channel receive?

Exercise 2 — Aggregate multiple paths

You have 3 path templates with volumes:

• Path A (3 conversions): Organic → Email → Direct
• Path B (2 conversions): Paid Search → Paid Search → Direct
• Path C (1 conversion): Referral → Organic → Email → Email → Direct

Compute total channel credit across all 6 conversions.

Checklist to self-check

• I counted total touches correctly per path.
• I assigned equal credit per touchpoint (1/n).
• I aggregated repeated touches within a path correctly.
• I summed credits across conversions and verified the total equals the number of conversions.

Common mistakes (and how to catch them)

• Forgetting repeated touches: If a channel appears twice, it earns two shares. Fix: explicitly count appearances per path.
• Mixing channel names (e.g., “PPC” vs “Paid Search”): Fix: standardize naming before aggregation.
• Dropping conversions with missing steps: Fix: include the path with whatever steps exist; still split equally among present touches.
• Comparing totals across models without consistent datasets: Fix: use the same time window and channel grouping when comparing to last-touch or time-decay.

Practical projects

• Build a linear attribution calculator in a spreadsheet: input a list of touchpoint paths, output channel-level credits.
• Create a dashboard tile comparing Linear vs Last-click contributions by channel for the last 30/60/90 days.
• Run a budget reallocation simulation: shift 10% budget from an over-credited last-click channel to a mid-funnel channel and project results using linear shares.

Mini challenge

You have 4 conversions:

1. Organic → Blog → Email → Direct
2. Paid Search → Direct
3. Referral → Organic → Direct → Direct
4. Email → Email → Direct

Questions:

• What is Direct’s total credit?
• Which channel is second-highest by credit?

Learning path

1. Master Linear Attribution (this lesson): definitions, math, pitfalls.
2. Compare with Last-touch and First-touch to understand differences.
3. Learn Time-decay to incorporate recency.
4. Explore Data-driven/Markov models when you have sufficient data and tooling.

Next steps

• Finish the exercises below and review solutions.
• Take the Quick Test to confirm mastery. Test is available to everyone; only logged-in users get saved progress.
• Move to the next attribution subskill and compare outcomes.