# Linear Attribution: The Neutral Baseline Model

Linear attribution gives equal credit to every touchpoint in the customer journey. Learn when linear is the right choice, how to implement it, and why it's the best starting point for multi-touch attribution.

- Canonical: https://mbuzz.co/articles/linear-attribution
- Published: 2026-05-01
- Last updated: 2026-07-23
- Author: Holly Mehakovic, mbuzz (https://mbuzz.co)

---

> **TL;DR:** Linear attribution divides conversion credit equally among all touchpoints in the customer journey. If there are 5 touchpoints, each gets 20% credit. It's the most neutral multi-touch model—no built-in bias toward first, last, or any specific position. Use linear as your baseline when moving from single-touch to multi-touch, or when you genuinely believe all touchpoints contribute equally.


## What Linear Attribution Measures

Linear attribution answers: **"How should I credit all the marketing touchpoints that contributed to this conversion?"**

Its answer: give everyone equal credit.

*[Chart omitted from the text version: Reusable customer-journey visual for attribution articles.
    Locals:
      title:    String header (uppercase tracking)
      steps:    Array of step hashes with keys:
                  day:     "Day 1" / "Day -30" / "Same day" — small label above
                  label:   "Facebook Ad" — main pill text
                  role:    :inactive | :credited | :conversion (controls colour)
                  percent: optional small label under pill (e.g. "100%", "50%")
                  sublabel: optional italic text under percent
      caption:  String — italic caption under the row]*


Four touchpoints, 25% each. Ten touchpoints, 10% each. The math is straightforward, and that's the point.

### Why Linear Matters

Linear attribution makes no assumptions about which touchpoints are more important. Compare this to:

| Model | Assumption | Bias |
|-------|------------|------|
| **First-touch** | Introduction matters most | Over-credits awareness |
| **Last-touch** | Closing matters most | Over-credits conversion |
| **Time-decay** | Recent touches matter more | Over-credits recency |
| **Position-based** | First and last matter most | Under-credits middle |
| **Linear** | All touches matter equally | None (but maybe naive) |

Linear's "bias" is assuming equal importance—which may or may not match reality. But it's the only model that doesn't bake in a predetermined hierarchy.

## When Linear Attribution Is the Right Choice

### 1. Your First Multi-Touch Model

If you're moving from last-touch or first-touch to multi-touch, start with linear:

| Channel | Last-touch | Linear | Change |
|---|---|---|---|
| Email | 45% | 22% | **−23pp** |
| Paid Search | 30% | 25% | **−5pp** |
| Paid Social | 5% | 28% | **+23pp** |
| Organic | 20% | 25% | **+5pp** |

The shift reveals what last-touch was hiding: Paid Social introduces far more customers than it closes. Linear exposes the full journey.

### 2. Long Consideration Journeys

For products with extended research phases—B2B software, high-ticket items, considered purchases—every touchpoint genuinely contributes:

- Initial ad creates awareness
- Content builds understanding
- Webinar demonstrates value
- Case study provides proof
- Email answers objections
- Demo enables evaluation

Giving 100% to any single touch ignores the others' contribution. Linear acknowledges the entire journey.

### 3. When You're Unsure What Matters Most

If you don't have strong hypotheses about which touchpoints drive conversion, linear is the honest choice. It says: "We don't know the relative importance, so we'll treat them equally until we learn more."

This is intellectually honest. Better to be neutrally wrong than confidently biased.

### 4. Baseline for Comparison

Run linear alongside other models to understand how each model's biases affect your data:

| Channel | Linear | Time-Decay | Position-Based | Insight |
|---------|--------|------------|----------------|---------|
| Paid Social | 28% | 15% | 32% | Time-decay undervalues early touches |
| Email | 22% | 35% | 18% | Time-decay over-credits recency |
| Organic | 25% | 20% | 22% | Consistent across models |
| Paid Search | 25% | 30% | 28% | Slight closer role |

Linear becomes your neutral reference point.

> **Note:**
> **Our recommendation:** Start with linear when adopting multi-touch attribution. It's transparent, unbiased, and provides a solid foundation. Graduate to more sophisticated models once you understand your baseline.

## When Linear Is the Wrong Choice

### 1. Single-Session Conversions

If most conversions happen in one session with one touchpoint, linear adds complexity without value:

*[Chart omitted from the text version: Reusable customer-journey visual for attribution articles.
    Locals:
      title:    String header (uppercase tracking)
      steps:    Array of step hashes with keys:
                  day:     "Day 1" / "Day -30" / "Same day" — small label above
                  label:   "Facebook Ad" — main pill text
                  role:    :inactive | :credited | :conversion (controls colour)
                  percent: optional small label under pill (e.g. "100%", "50%")
                  sublabel: optional italic text under percent
      caption:  String — italic caption under the row]*


For impulse purchases or simple conversions, the overhead of multi-touch isn't worth it.

### 2. When Touchpoints Clearly Differ in Impact

Some touchpoints genuinely matter more:

- A 30-minute demo vs a banner impression
- A personalized email vs a generic newsletter
- A sales call vs an automated drip

Linear treats these equally, which may misallocate credit. If you have strong evidence about touchpoint impact, use a weighted model.

### 3. High-Volume Data-Driven Scenarios

If you have 5,000+ monthly conversions, algorithmic models (Markov, Shapley) can learn actual touchpoint impact from data rather than assuming equal contribution.

> **Warning:**
> **The linear trap:** Don't mistake "neutral" for "correct." Linear is unbiased, but if your touchpoints genuinely have different impacts, linear will misattribute credit. Use it as a baseline, then validate with incrementality testing.

## How Linear Attribution Works

### The Math

Linear attribution divides credit equally:

```
Credit per touchpoint = 1 / Number of touchpoints
```

For a journey with n touchpoints:

| Touchpoints | Credit Each |
|-------------|-------------|
| 2 | 50% |
| 3 | 33.3% |
| 4 | 25% |
| 5 | 20% |
| 10 | 10% |

### Implementation

```ruby
class LinearAttribution
  def initialize(lookback_days: 30)
    @lookback_days = lookback_days
  end

  def attribute(conversion)
    touchpoints = conversion.user.touchpoints
      .where("occurred_at >= ?", conversion.occurred_at - @lookback_days.days)
      .where("occurred_at <= ?", conversion.occurred_at)
      .order(:occurred_at)

    return [] if touchpoints.empty?

    credit_per_touch = 1.0 / touchpoints.count

    touchpoints.map do |touchpoint|
      {
        channel: touchpoint.channel,
        source: touchpoint.source,
        medium: touchpoint.medium,
        campaign: touchpoint.campaign,
        credit: credit_per_touch,
        touchpoint_at: touchpoint.occurred_at
      }
    end
  end
end
```

### Aggregating to Channels

To get channel-level credit:

```sql
SELECT
  channel,
  SUM(credit) as total_credit,
  SUM(credit * conversion_value) as attributed_revenue,
  COUNT(DISTINCT conversion_id) as assisted_conversions
FROM attribution_results
WHERE model = 'linear'
GROUP BY channel
ORDER BY attributed_revenue DESC;
```

### Key Implementation Decisions

| Decision | Options | Recommendation |
|----------|---------|----------------|
| **Lookback window** | 7, 30, 60, 90 days | Match your sales cycle |
| **Include direct?** | Yes / No | Include—it's a real touchpoint |
| **Deduplicate touches?** | Session-level or event-level | Session-level usually |
| **Minimum touchpoints** | 1, 2, or more | 1 (single-touch gets 100%) |

## Linear vs Other Models

### Linear vs Last-Touch

| Aspect | Linear | Last-Touch |
|--------|--------|------------|
| **Credit distribution** | Equal to all | 100% to last |
| **Awareness channel credit** | Fair | Under-credited |
| **Closer channel credit** | Fair | Over-credited |
| **Budget allocation** | Balanced | Starves funnel |
| **Complexity** | Simple | Simpler |
| **Best for** | Full-journey view | CRO only |

**Verdict:** Linear is better for budget decisions. Last-touch remains useful for conversion optimization specifically.

### Linear vs First-Touch

| Aspect | Linear | First-Touch |
|--------|--------|-------------|
| **Credit distribution** | Equal to all | 100% to first |
| **Awareness channel credit** | Fair | Over-credited |
| **Closer channel credit** | Fair | Under-credited |
| **Use case** | Budget allocation | Pipeline sourcing |

**Verdict:** Use both. First-touch answers "where do leads come from?" Linear answers "what's the full contribution?"

### Linear vs Time-Decay

| Aspect | Linear | Time-Decay |
|--------|--------|------------|
| **Credit distribution** | Equal to all | More to recent |
| **Assumption** | All touches equal | Recency matters |
| **Early-funnel credit** | Fair | Under-credited |
| **Late-funnel credit** | Fair | Over-credited |
| **Best for** | Long cycles | Short cycles, e-commerce |

**Verdict:** Time-decay makes sense when recent touches genuinely matter more (e.commerce, urgency products). Linear is more neutral for considered purchases.

### Linear vs Position-Based

| Aspect | Linear | Position-Based |
|--------|--------|----------------|
| **Credit distribution** | Equal to all | 40/20/40 (U-shaped) |
| **First-touch credit** | Equal share | 40% |
| **Last-touch credit** | Equal share | 40% |
| **Middle touches** | Equal share | 20% split |
| **Best for** | Unknown importance | Known first/last value |

**Verdict:** Position-based is better when you have strong reason to believe introduction and conversion are the key moments. Linear is better when the middle of the journey matters.

## Common Linear Attribution Mistakes

### Mistake 1: Treating Linear as "Truth"

Linear is not the correct answer—it's a neutral starting point. All attribution models are simplifications.

**Fix:** Use linear as a baseline, then validate with incrementality tests.

### Mistake 2: Too Many Low-Quality Touchpoints

If you include every page view, email open, and ad impression, you dilute credit across noise:

<div class="not-prose my-6 bg-amber-50 border-l-4 border-amber-400 px-5 py-4 rounded-r-md">
  <p class="text-xs font-bold text-amber-700 tracking-[0.15em] mb-2">JOURNEY WITH 50 TOUCHPOINTS</p>
  <p class="text-sm text-slate-700 leading-snug">Each touchpoint gets 2% credit. The 30-minute demo gets 2%. A random banner impression also gets 2%. Same credit, wildly different impact &mdash; this is when linear stops being useful.</p>
</div>

**Fix:** Define what counts as a touchpoint. Usually: clicks, meaningful engagements, not passive impressions.

### Mistake 3: Not Accounting for Journey Length

Conversions with 2 touchpoints vs 10 touchpoints get the same total credit (100%), but distributed very differently:

| Metric | 2-Touch Journey | 10-Touch Journey |
|--------|-----------------|------------------|
| Total credit | 100% | 100% |
| Credit per touch | 50% | 10% |
| Interpretation | High-impact touches | Many small contributions |

**Fix:** Segment analysis by journey length. Compare channels within similar journey lengths.

### Mistake 4: Ignoring Conversion Value

Linear credits all touchpoints equally, but if conversion values vary, weight by value:

```ruby
# Credit weighted by conversion value
attributed_value = credit_per_touch * conversion.value

# Channel gets sum of attributed values
channel_revenue = attributions.sum { |a| a[:credit] * a[:conversion_value] }
```

## Linear Attribution and GA4

Google Analytics 4 removed linear attribution in 2023. Your options:

### 1. Use a Third-Party Tool

Attribution platforms like mbuzz support linear and other models out of the box.

### 2. Build in BigQuery

Export GA4 data to BigQuery and calculate linear attribution:

```sql
WITH touchpoints AS (
  SELECT
    user_pseudo_id,
    event_timestamp,
    traffic_source.source,
    traffic_source.medium
  FROM `project.dataset.events_*`
  WHERE event_name IN ('page_view', 'click', ...)
),
conversions AS (
  SELECT
    user_pseudo_id,
    event_timestamp as conversion_time,
    event_params.value.double_value as value
  FROM `project.dataset.events_*`
  WHERE event_name = 'purchase'
),
journeys AS (
  SELECT
    c.user_pseudo_id,
    c.conversion_time,
    c.value,
    t.source,
    t.medium,
    COUNT(*) OVER (PARTITION BY c.user_pseudo_id, c.conversion_time) as touchpoint_count
  FROM conversions c
  JOIN touchpoints t ON c.user_pseudo_id = t.user_pseudo_id
    AND t.event_timestamp < c.conversion_time
    AND t.event_timestamp > TIMESTAMP_SUB(c.conversion_time, INTERVAL 30 DAY)
)
SELECT
  source,
  medium,
  SUM(value / touchpoint_count) as linear_attributed_revenue
FROM journeys
GROUP BY source, medium
ORDER BY linear_attributed_revenue DESC;
```

### 3. Use Segment or CDPs

Customer data platforms often include attribution modeling as a feature.

## Validating Linear Attribution

### Compare to Other Models

If linear produces wildly different results from time-decay or position-based, investigate why:

| Channel | Linear | Time-Decay | Gap | Investigation |
|---------|--------|------------|-----|---------------|
| Paid Social | 30% | 12% | -18pp | Lots of early touches, few late |
| Email | 18% | 35% | +17pp | Mostly late-journey touches |

Large gaps suggest the channel has a specific role (introducer vs closer).

### Validate with Incrementality

Linear might over- or under-credit certain channels. Test with holdout experiments:

1. Pause a channel in test markets
2. Measure actual revenue impact
3. Compare to linear-attributed revenue
4. Calculate calibration factor

```
Linear attributed: $100K/month to Paid Social
Incrementality test: $80K actual lift
Calibration factor: 0.8x
```

Apply calibration to adjust linear attribution toward truth.

## Implementing Linear Attribution in mbuzz


mbuzz uses **AML (Attribution Modeling Language)** — a small Ruby DSL — to define how credit is distributed. Linear is the simplest model in the language.

### Basic linear

```ruby
within_window 30.days
  apply 1.0 / touchpoints.length to touchpoints
end
```

One unit of credit, split equally across every touchpoint in the 30-day lookback. Change `30.days` to lengthen or shorten the window.

### Linear with channel filtering

To exclude direct and branded organic from the credit pool, filter touchpoints first:

```ruby
within_window 30.days
  marketing = touchpoints.reject { |tp| %w[direct organic_brand].include?(tp.channel) }
  apply 1.0 / marketing.length to marketing
end
```

The full AML reference — including segment weights, conditional logic, and time-decay primitives — is at [docs/attribution-models](/docs/attribution-models).

## Tuning Linear Parameters for Your Business

Linear attribution has few parameters, but the ones it has matter. Here's how to tune them for different contexts.

### By Business Type

| Business Type | Lookback Window | Include Direct? |
|---------------|-----------------|-----------------|
| **Impulse e-commerce** | 7 days | Yes |
| **Considered purchase** | 30 days | Yes |
| **B2B SaaS** | 60-90 days | No (often brand) |
| **Enterprise B2B** | 90-180 days | No |
| **Subscription/DTC** | 30 days | Yes |

In AML, the lookback is the `within_window` duration. To exclude direct, filter it out of `touchpoints` before the `apply` call (see channel-filtering example above).

### Seasonal Adjustments

Buying behavior compresses during sale periods. Shorten the AML window from `30.days` to `14.days` for BFCM and the holiday peak; stretch it to `45.days` post-holiday to credit gift card redemptions back to the original campaigns. Run the seasonal variant alongside the standard one and compare the credit split.

### Big Campaign / Launch Adjustments

For a launch attribution view, narrow the lookback and restrict touchpoints to the launch campaigns:

```ruby
within_window 14.days
  launch = touchpoints.select { |tp| tp.campaign.starts_with?("product-launch-2024") }
  apply 1.0 / launch.length to launch
end
```

### Parameter Tuning Cheatsheet

| Scenario | Parameter Change | Why |
|----------|------------------|-----|
| **Faster sales cycle** | Shorter lookback (7-14d) | Avoid crediting irrelevant old touches |
| **Slower sales cycle** | Longer lookback (60-90d) | Capture full journey |
| **Strong brand** | Exclude direct | Brand visits distort marketing credit |
| **New market/product** | Include direct | Every touch matters, brand is weak |
| **High-frequency touchpoints** | Session dedupe | Avoid over-crediting email opens etc. |
| **Low-frequency, high-value touches** | Event dedupe | Capture every interaction |
| **Seasonality spike** | Shorter lookback | Reflect compressed cycles |
| **Post-holiday normalization** | Longer lookback | Account for gift card redemptions |

## Summary

Linear attribution distributes credit equally across all touchpoints. It's simple, transparent, and unbiased—making it the ideal baseline for multi-touch attribution.

**Use linear when:**
- Moving from single-touch to multi-touch attribution
- You want a neutral, unbiased view of contribution
- Journey touchpoints are roughly equal in importance
- You need a baseline to compare other models against

**Don't rely solely on linear because:**
- Some touchpoints genuinely matter more than others
- It may dilute credit across low-impact touches
- It doesn't capture recency or position effects

**Best practice:** Start with linear, understand your baseline, then explore time-decay or position-based if you have strong hypotheses about touchpoint importance. Validate any model with incrementality tests.

## Further Reading

**On Attribution Models:**
- [First-Touch Attribution](/articles/first-touch-attribution) — Credits the introduction
- [Last-Touch Attribution](/articles/last-touch-attribution) — Credits the close
- [Time-Decay Attribution](/articles/time-decay-attribution) — Credits recency
- [How to Choose the Right Attribution Model](/articles/how-to-choose-attribution-model) — Decision framework

**On Validation:**
- [MTA vs MMM: What's the Difference?](/articles/mta-vs-mmm) — Where attribution fits in measurement
- [Triangulating MTA, MMM, and Incrementality](/articles/mta-mmm-incrementality-triangulation) — Validating with multiple methods

## Key takeaways

- Linear gives equal credit to all touchpoints—no positional bias
- Best starting point when moving from single-touch to multi-touch attribution
- Simple to explain and implement, fully transparent
- May undervalue particularly important touchpoints, but provides neutral baseline


## FAQ

**What is linear attribution?**

Linear attribution is a multi-touch model that divides conversion credit equally among all marketing touchpoints. If a customer had 4 touchpoints before converting, each gets 25% of the credit. It's called 'linear' because credit is distributed in a straight line—no weighting.

**When should I use linear attribution?**

Use linear when you're moving from single-touch to multi-touch, when you're unsure which touchpoints matter most, or when you want a neutral baseline to compare against other models. It's also good for long consideration journeys where every touch genuinely contributes.

**Is linear attribution better than last-touch?**

For budget allocation, yes—linear doesn't systematically over-credit closers like last-touch does. For conversion optimization specifically, last-touch may still be useful. Linear is better as a default for understanding full-journey contribution.

**Does Google Analytics 4 support linear attribution?**

No. GA4 removed linear attribution in 2023, keeping only last-click and data-driven. To use linear attribution, you need a third-party tool or build your own model in your data warehouse.

**What are the disadvantages of linear attribution?**

Linear assumes all touchpoints are equally important, which may not reflect reality. A compelling demo might matter more than a banner ad impression. For businesses with clearly differentiated touchpoint importance, position-based or data-driven models may be more accurate.