# Sales Attribution Model Guide

> First-touch, last-touch, and five multi-touch attribution models explained with formulas, pros/cons, and a decision tree for which one actually fits your sales motion.

Free guide for sales leaders. Topic: [Metrics, Analytics & RevOps](https://www.peerlab.ai/library/topic/metrics-analytics-revops/). Published by PeerLab in the [Sales Enablement Resource Library](https://www.peerlab.ai/library/). Source: https://www.peerlab.ai/library/sales-attribution-model-guide/

## How to use it

Run your own sales motion through the decision tree to land on a candidate model, then use the worked example's allocation logic as a template for recalculating a handful of your own recent closed deals before rolling the model out in reporting.

## What's inside

- First-touch and last-touch definitions with pros/cons
- Linear, time-decay, U-shaped, W-shaped, and full-path/custom multi-touch models with allocation formulas
- Algorithmic/data-driven attribution overview
- Full decision tree from cycle length through data maturity
- Worked 5-touch example showing how credit differs across all 5 major models side by side

## Why attribution model choice matters
Every attribution model tells a different story about which activity "deserves" credit for a closed deal, and that story determines where marketing and sales leadership invest budget next quarter. Pick the wrong model for your motion and you'll systematically over- or under-fund the channel that's actually driving revenue.

## The Models, Explained

### First-Touch Attribution
100% of credit goes to the very first recorded interaction (e.g., the first ad click, first form fill, first cold call).
- **Pros:** Simple, shows what generates initial awareness/demand.
- **Cons:** Ignores everything that happens during the (often long) B2B sales cycle, undervalues sales and nurture effort entirely.
- **Best for:** Short-cycle, single-touch-heavy motions (e.g., PLG signup flows).

### Last-Touch Attribution
100% of credit goes to the final interaction before conversion (e.g., the demo request, the last email before close).
- **Pros:** Simple, reflects what closed the deal in the moment.
- **Cons:** Ignores all the demand-generation work that built the pipeline in the first place, overvalues bottom-of-funnel activity.
- **Best for:** Very short cycles, or as a secondary sanity-check metric alongside a multi-touch model.

### Linear Attribution (Multi-Touch)
Credit is split evenly across every touch in the journey.
`Credit per touch = 100% ÷ number of touches`
- **Pros:** Acknowledges every touch mattered; simple to calculate.
- **Cons:** Treats a passive email open the same as a live demo, doesn't reflect that some touches matter more than others.
- **Best for:** Teams that want a fast, defensible multi-touch model without building a weighting scheme.

### Time-Decay Attribution
Credit increases the closer a touch is to the conversion, using a decay formula (commonly a 7-day half-life).
`Credit(touch) ∝ 2^(-days_before_conversion / half_life)`, normalized so all touches sum to 100%
- **Pros:** Reflects that recent touches usually have more influence on the final decision.
- **Cons:** Still undervalues the top-of-funnel touch that originally created the opportunity.
- **Best for:** Longer cycles where late-stage touches (proposal, negotiation calls) are genuinely more decisive.

### U-Shaped / Position-Based Attribution
Fixed weight to the first touch and the lead-conversion touch (the touch that turned a contact into a qualified lead), remainder split across the middle.
Common split: **40% first touch / 40% lead-conversion touch / 20% split across everything else**
- **Pros:** Credits both demand creation and qualification, the two moments most orgs care about most.
- **Cons:** Still undervalues middle-of-funnel nurture and, critically, undervalues everything that happens *after* the opportunity is created (i.e., sales' own work).
- **Best for:** Marketing-attribution reporting where MQL→SQL conversion is the key metric being managed.

### W-Shaped Attribution
Fixed weight to three moments: first touch, lead-conversion touch, and opportunity-creation touch, remainder split across the rest.
Common split: **30% / 30% / 30% across the three key moments / 10% split across everything else**
- **Pros:** The first model on this list that gives explicit credit to the opportunity-creation moment, meaningful for orgs with a real SQL→Opportunity handoff.
- **Cons:** More complex to calculate and explain; needs clean stage-timestamp data.
- **Best for:** Enterprise motions with a defined MQL → SQL → Opportunity funnel and multiple influencing touches at each stage.

### Full-Path / Custom Attribution
Extends W-shaped further to also weight post-opportunity touches (e.g., a champion-enablement call, a security review call), often built as a fully custom weighting scheme per company.
- **Pros:** Most complete picture for complex, multi-stakeholder enterprise sales.
- **Cons:** Requires mature data infrastructure and ongoing model maintenance.
- **Best for:** Enterprise/ABM motions with long cycles and many post-opportunity touches from multiple stakeholders.

### Algorithmic / Data-Driven Attribution
Uses statistical or ML modeling (e.g., Markov chains, Shapley value) on your own historical conversion data to assign credit based on actual observed influence, rather than a fixed rule.
- **Pros:** Most accurate in principle, lets the data decide what actually correlates with conversion.
- **Cons:** Needs a large volume of historical deal data and either in-house data science or a platform that runs it.
- **Best for:** High-volume, high-data-maturity organizations that have outgrown rule-based models.

## Decision Tree, Which Model Fits Your Motion

```
START
│
├─ Is your sales cycle under 30 days and largely self-serve/PLG?
│ YES → Use Last-Touch (or First-Touch as a secondary demand-gen metric)
│ NO ↓
│
├─ Do you need to credit marketing's role in creating AND qualifying the lead,
│ with a simple, explainable model?
│ YES → Use U-Shaped (40/40/20)
│ NO ↓
│
├─ Do you have a clean MQL → SQL → Opportunity handoff with timestamped stages,
│ and want to credit the opportunity-creation moment explicitly?
│ YES → Use W-Shaped (30/30/30/10)
│ NO ↓
│
├─ Is your motion enterprise/ABM with multiple stakeholders touching the deal
│ AFTER the opportunity is created (security review, exec sponsor calls, etc.)?
│ YES → Use Full-Path / Custom weighting
│ NO ↓
│
├─ Do you have high deal volume, clean historical data, and either in-house
│ data science or a platform that runs data-driven attribution?
│ YES → Use Algorithmic / Data-Driven Attribution
│ NO → Default to Linear or Time-Decay as the most defensible simple model
│ available without heavier infrastructure
```

## Worked Example

A deal with 5 recorded touches before close:
1. Paid ad click (Day 0)
2. Webinar registration → becomes an MQL (Day 12)
3. SDR outbound call → becomes an SQL (Day 20)
4. Opportunity created after discovery call (Day 35)
5. Final negotiation call (Day 58, deal closes Day 60)

| Model | Touch 1 (ad) | Touch 2 (webinar/MQL) | Touch 3 (SDR call/SQL) | Touch 4 (opp created) | Touch 5 (negotiation) |
|---|---|---|---|---|---|
| First-touch | 100% | 0% | 0% | 0% | 0% |
| Last-touch | 0% | 0% | 0% | 0% | 100% |
| Linear | 20% | 20% | 20% | 20% | 20% |
| U-shaped (40/40/20) | 40% | 40% | 6.7% | 6.7% | 6.7% |
| W-shaped (30/30/30/10) | 30% | 30% | 3.3% | 30% | 3.3% |

Notice how differently each model treats Touch 4 (opportunity creation): 20% under Linear, only 6.7% under U-shaped, but a full 30% under W-shaped. If your leadership team cares about crediting sales' opportunity-creation work specifically, U-shaped will systematically make that work invisible in your reporting.