Linear vs Time-Decay vs Position-Based Attribution Models Explained

Three Models, Three Different Budget Recommendations

Every multi-touch attribution model answers the same question: when multiple marketing touchpoints contribute to a conversion, how do you divide the credit?

The answer shapes every budget decision you make. Give too much credit to the wrong touchpoints and you'll over-invest in channels that aren't pulling their weight. Give too little credit to channels that are quietly driving conversions and you'll starve your best performers.

Linear, time-decay, and position-based are the three most widely used rule-based multi-touch attribution models. Each distributes credit differently, and each tells a different story about your marketing performance.

Here's exactly how they work, when each makes sense, and when each will mislead you.

Linear Attribution: Equal Credit for Every Touchpoint

How It Works

Linear attribution divides conversion credit equally across every touchpoint in the customer journey.

If a customer interacts with 5 touchpoints before converting, each touchpoint gets 20% of the credit. Ten touchpoints? Each gets 10%.

Example: A customer's journey looks like this:

1. Facebook ad click (Day 1)
2. Blog post visit via organic search (Day 4)
3. Email open and click (Day 7)
4. Google Shopping ad click (Day 10)
5. Direct visit and purchase (Day 12)

Each touchpoint receives 20% of the $100 conversion value -- $20 each.

When Linear Attribution Works

Long, complex B2B sales cycles. When a deal takes 6 months and involves webinars, white papers, demos, and sales calls, it's genuinely hard to say which touchpoint mattered most. Linear gives every stage credit and prevents you from over-indexing on the closing interaction.

Channel ecosystem analysis. When you want a broad view of which channels participate in conversions -- not which ones "close" or "open" -- linear shows you the full picture.

Early-stage measurement programs. If you're moving from single-touch to multi-touch for the first time, linear is the simplest model to implement and explain to stakeholders.

Where Linear Attribution Misleads

It treats all touchpoints as equally important. A 2-second visit to your homepage from an accidental click gets the same credit as a 15-minute demo session. In reality, some interactions are far more influential than others.

It dilutes the signal for high-impact touchpoints. If a customer had 8 touchpoints but the Facebook prospecting ad and the final email were the ones that actually changed their behavior, linear buries that signal under 6 other touchpoints.

Journey length bias. Customers with longer journeys (more touchpoints) dilute credit more, making each individual touchpoint look less valuable. This can penalize channels that appear frequently in long journeys.

Time-Decay Attribution: Recency Gets More Credit

How It Works

Time-decay attribution assigns more credit to touchpoints that occur closer to the conversion. The typical implementation uses a half-life model -- each touchpoint gets proportionally more credit than the one before it.

Google's implementation uses a 7-day half-life by default. A touchpoint 7 days before conversion gets half the credit of a touchpoint on the day of conversion. A touchpoint 14 days before gets one quarter.

Example: Same 5-touchpoint journey:

1. Facebook ad click (Day 1) -- 5% credit
2. Blog visit (Day 4) -- 10% credit
3. Email click (Day 7) -- 15% credit
4. Google Shopping click (Day 10) -- 25% credit
5. Direct visit and purchase (Day 12) -- 45% credit

The Facebook prospecting ad that started the entire journey gets just 5% of the credit.

When Time-Decay Works

Short, high-frequency sales cycles. For ecommerce products with 3-7 day purchase cycles, the most recent interactions genuinely are the most influential. A customer who clicked a retargeting ad yesterday and bought today was likely influenced by that ad more than the organic visit last week.

Promotional and seasonal campaigns. During sales events (Black Friday, product launches), the urgency-creating touchpoints near the purchase are disproportionately influential. Time-decay reflects this.

Sales-heavy pipelines. In B2B where the sales team closes deals, time-decay appropriately weights the sales interactions that happen late in the funnel.

Where Time-Decay Misleads

It systematically undervalues awareness and prospecting. The touchpoints that brought someone into your ecosystem in the first place get the least credit. Over time, this leads to cutting top-of-funnel spend -- which seems fine until your pipeline dries up 30-60 days later.

Long consideration purchases. For products with 30-60 day purchase cycles, a 7-day half-life means the first touchpoint gets almost zero credit even if it was the critical moment of discovery.

It confuses correlation with causation. The fact that a touchpoint happened close to conversion doesn't mean it caused the conversion. Branded search happens close to purchase because the customer already decided to buy -- it's correlation, not necessarily influence.

Position-Based (U-Shaped) Attribution: Prioritizing First and Last

How It Works

Position-based attribution gives the most credit to the first touchpoint (first click) and the last touchpoint (last click), typically 40% each. The remaining 20% is distributed evenly among middle touchpoints.

This is sometimes called U-shaped attribution because the credit distribution, when plotted, forms a U shape -- high at the beginning and end, low in the middle.

Example: Same journey:

1. Facebook ad click (Day 1) -- 40% ($40)
2. Blog visit (Day 4) -- 6.67% ($6.67)
3. Email click (Day 7) -- 6.67% ($6.67)
4. Google Shopping click (Day 10) -- 6.67% ($6.67)
5. Direct visit and purchase (Day 12) -- 40% ($40)

When Position-Based Works

Balanced growth strategies. If you value both acquiring new customers (first touch) and converting them (last touch), this model reflects both priorities.

DTC ecommerce. For brands spending across prospecting and retargeting, position-based attribution gives meaningful credit to the prospecting campaigns that create demand and the retargeting campaigns that capture it.

Mid-maturity measurement programs. Position-based is a strong upgrade from single-touch models without requiring the data volume needed for algorithmic attribution.

Where Position-Based Misleads

The 40/20/40 split is arbitrary. There's no evidence that the first and last touchpoints are always the most important. In some journeys, a mid-funnel case study or demo is the inflection point.

Mid-funnel gets consistently undervalued. Email nurture sequences, content marketing, and organic social often live in the middle of the journey. Position-based gives them crumbs. Over time, this can lead to underinvestment in mid-funnel programs.

Two-touchpoint journeys. When a customer only has two interactions, each gets 50% -- effectively becoming a linear model. This is fine, but it means the model's distinctive feature only applies to journeys with 3+ touchpoints.

Choosing the Right Model for Your Business

There's no universally correct model. The right choice depends on three factors:

1. Your Sales Cycle Length

| Sales Cycle | Recommended Starting Model | |------------|---------------------------| | 1-7 days | Time-decay | | 7-30 days | Position-based | | 30-90+ days | Linear or position-based |

2. Your Channel Mix

If you spend heavily on prospecting and retargeting with little mid-funnel, position-based works well. If you have a complex nurture sequence across many channels, linear may be more appropriate.

3. Your Decision Framework

Ask yourself: what budget decisions do I need to make? If you're deciding between prospecting and retargeting budgets, position-based helps. If you're evaluating every channel's contribution to justify a diversified media mix, linear provides that view.

The Model Matters Less Than the Data Quality

Here's a truth that gets lost in attribution model debates: the difference between a well-implemented linear model and a well-implemented position-based model is much smaller than the difference between accurate data and inaccurate data.

If your tracking misses 30% of conversions because of ad blockers and cookie restrictions, no model can compensate. Fix the data collection first -- implement server-side tracking, build first-party identity resolution, recover the missing conversions -- then optimize the model.

A simple model on clean data will outperform a sophisticated model on dirty data every time.

Frequently Asked Questions

Which attribution model does Google Analytics 4 use by default?

Google Analytics 4 uses data-driven attribution as its default model, which uses machine learning rather than predefined rules. However, GA4's data-driven model requires sufficient conversion volume to train, and it only sees touchpoints within the Google ecosystem (Google Ads, organic search) plus traffic it can directly observe. You can still apply last-click, first-click, linear, time-decay, or position-based models in GA4's model comparison tool for analysis purposes.

Can I use different attribution models for different campaigns?

You can analyze campaigns under different models, but you should use one model as your primary decision-making framework. Using time-decay for retargeting campaigns and first-click for prospecting campaigns technically gives each campaign its most flattering view, which defeats the purpose of attribution. Pick one model for consistent reporting and use the others as supplementary lenses when you need a different perspective.

How often should I change my attribution model?

Rarely. Changing models mid-quarter makes it impossible to compare performance over time. Choose a model, commit to it for at least 6 months, and evaluate whether it's supporting good decisions. The most common reason to switch is a fundamental change in your business model or sales cycle -- for example, moving from a short-cycle ecommerce model to a longer subscription model. Don't change models just because the current one makes a campaign look bad.

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