Attribution

Attribution

Overview

Attribution answers the existential marketing question: which of our activities actually produce revenue? The core principle is that single-touch attribution lies. First-touch attribution over-credits awareness channels. Last-touch attribution over-credits bottom-of-funnel conversion channels. Both lead to systematically bad investment decisions — over-funding channels that appear in the credited position and starving channels that create the conditions for conversion.

The non-obvious rule: the "best" attribution model depends on your GTM motion. Product-led growth companies should weight product-qualified signals higher. Sales-led companies should weight sales engagement higher. Channel-led companies should weight partner influence. There is no universal attribution model — only the model that matches how your customers actually buy.

This skill produces: a Multi-Touch Attribution Model Report comparing 4-6 attribution models on revenue credit allocation, channel ROI calculations with cost and revenue attribution, a UTM governance framework with taxonomy and enforcement rules, a source-of-truth reporting structure, and optimization recommendations for budget reallocation.

When to Use

• User says "attribution" or "attribution model" → activate this skill
• User asks "which channel generates the most revenue" → use this skill
• User says "marketing ROI" or "campaign ROI" → attribution is required
• User mentions "UTM tracking" or "UTM hygiene" or "UTM parameters" → use UTM governance module
• User asks "how do I measure multi-touch attribution" → implement a model
• User says "source of truth reporting" or "marketing and sales disagree on numbers" → use this skill
• Trigger phrases: attribution modeling, multi-touch, first-touch, last-touch, channel attribution, campaign ROI, marketing ROI, UTM parameters, tracking governance

Do NOT use for:

• Campaign-level analytics (open rates, reply rates, meeting rates) → use campaign-analytics
• Pipeline management and forecasting → use pipeline-management
• A/B testing methodology → use a-b-testing
• CRM data architecture → use crm-integration
• Real-time channel monitoring and alerting → use proactive-alerts

Authoritative Foundations

This skill draws from the following established methodologies:

• Bizible (Marketo/Marketo Measure) — Multi-Touch Attribution — The most widely deployed B2B multi-touch attribution system. Bizible's model maps every marketing and sales touch across the customer journey, weighting touches based on position (FT, LC, even, U-shaped, W-shaped, custom). Acquired by Adobe/Marketo, it's the enterprise standard.

• Full Circle Insights (Salesforce-native) — Attribution built inside the CRM rather than appended to it. The implicit criticism of marketing-platform-first attribution is that it misses sales touches. Full Circle captures both marketing and sales activity within the CRM.

• Google Analytics — Attribution Models — The most accessible attribution implementation, now with data-driven attribution as the default in GA4. Google's shift from rules-based to machine-learning-based attribution reflects the industry trend toward algorithmic models.

• DreamData — B2B Attribution — Account-based attribution for companies where multiple stakeholders influence a deal. Critical distinction: person-level attribution credits the individual who converted; account-level attribution distributes credit across the buying group.

Prerequisites

• CRM data with opportunity records including: create date, close date, deal amount, primary campaign source (HubSpot, Salesforce, or Attio)
• Marketing platform data with touch history: email opens, clicks, form fills, content downloads, webinar attendance, ad clicks (Marketo, HubSpot, Pardot, or GA4)
• Sales activity data: calls, emails, meetings logged on opportunities (CRM activity history)
• UTM-tagged URLs across all marketing channels (website, ads, email, social, partner)
• Cost data by channel: total spend per channel per period for ROI calculation
• Recommended: 12+ months of touchpoint data for stable attribution modeling
• Recommended upstream skills: crm-integration (for CRM data architecture), campaign-analytics (for campaign-level metrics)

Step-by-Step Process

Phase 1: Intake

Gather required information from the user. Ask all questions at once. Do not proceed until all answers are received.

Required intake questions:

1. GTM motion: What is your primary GTM motion? (Product-led growth, sales-led, channel/partner-led, marketing-led, hybrid?)
2. Average sales cycle: How long from first touch to closed-won? (30 days, 90 days, 180 days, 12+ months?) This determines the lookback window.
3. Buying group size: How many people typically influence a purchase? (Single decision-maker, 3-5 person committee, 6+ enterprise buying group?) Determines person-level vs account-level attribution.
4. Channel mix: What channels are active? (Outbound email, inbound/content, paid ads, events, partners, referrals, social, direct, product-led?)
5. Data sources: Where are touchpoints tracked? (CRM, marketing automation, product analytics, ad platforms, webinar platform?)
6. Current attribution: What attribution model, if any, is currently in use? What metrics does marketing currently report? What does sales report?
7. UTM status: Are UTMs consistently applied? Is there a UTM taxonomy documented? Who enforces it?
8. Budget data: Can you provide spend data by channel for the analysis period?

Phase 2: UTM Governance and Hygiene Audit

UTM parameters are the foundation of attribution accuracy. Bad UTMs = bad attribution.

1. UTM taxonomy design — enforce the standard 5 parameters:

• utm_source: The platform/site sending traffic (google, linkedin, hubspot, marketo, outreach). Must be a controlled value from an approved list.
• utm_medium: The marketing medium (cpc, organic_social, email, display, referral, partner, event). Must be a controlled value.
• utm_campaign: The specific campaign name. Use a consistent naming convention: [quarter]-[channel]-[audience]-[offer]-[variant]. Example: q1-2024-email-enterprise-compliance-toolkit-v2.
• utm_content: What was clicked (cta-text, banner-blue, subject-line-test-a). Used for A/B testing within campaigns.
• utm_term: Search keywords (only for paid search). Optional.

1. UTM hygiene audit — scan all URLs for common errors:

• Missing utm_source (most common error — without it, traffic is "direct")
• Inconsistent source naming: "linkedin," "LinkedIn," "linkedin.com," "LinkedIn Ads" are 4 different sources.
• Using utm_medium=cpc for non-paid traffic
• Spaces in UTM values (should use hyphens or underscores, never spaces)
• Case sensitivity: UTM values are case-sensitive. "Google" ≠ "google."
• Campaign parameter used as catch-all (same utm_campaign across multiple channels)
• Missing utm_campaign (leaves traffic unassigned to any campaign for ROI calculation)
• URL shorteners stripping UTM parameters (bit.ly, ow.ly strip by default — configure to preserve)
• Internal links tagged with UTM parameters (overwrites real attribution data)

1. UTM governance rules — document and enforce:

• All outbound links from marketing must have at minimum: utmsource, utmmedium, utm_campaign.
• Source values must come from pre-approved list (requires request to add new source).
• Medium values must come from 10 standard categories.
• Campaign naming convention is mandatory and validated at URL creation.
• Marketing automation platforms must auto-append UTMs (configure at the platform level).
• Sales-provided links should use a different tracking parameter (or UTM with sales-specific source) to distinguish sales vs marketing influence.

1. UTM data quality score — calculate for the analysis period:

• % of revenue-attributed opportunities with complete UTM data: target >90%
• % of website sessions with utm_source: target >80%
• % of campaign spend assigned to specific campaigns via UTM: target >95%
• If scores are below targets, attribution modeling will be unreliable. Fix UTM hygiene first.

Phase 3: Touchpoint Data Assembly

Build the unified touchpoint timeline:

1. Define the touchpoint taxonomy — categorize every interaction:

• Marketing touches: Email click, content download, webinar attendance, ad click, website visit, form fill, social engagement, event booth visit.
• Sales touches: Sales email, sales call, meeting held, demo delivered, proposal sent, LinkedIn connection/inmail.
• Product touches: Signup, activation event, invite team member, upgrade feature usage, PQL trigger.
• Partner touches: Partner referral, co-marketing event, marketplace listing click.

1. Build touchpoint timelines for each opportunity:

• For every closed-won and closed-lost opportunity, assemble all touchpoints from first interaction to close date.
• Each touchpoint records: timestamp, channel, touch type, campaign association, and whether it was marketing or sales initiated.
• For account-level attribution: roll up all contacts under the account and create a merged timeline.

1. Define the attribution window (lookback period):

• Standard: 90 days for SMB (shorter sales cycles), 180 days for mid-market, 365 days for enterprise.
• Custom window: if your average sales cycle is 120 days, use 120-day lookback.
• Touches outside the window are excluded from attribution but recorded for awareness contribution analysis.

1. Handle anonymous-to-known matching:

• Website visits before form fill are anonymous. Use cookie-based matching (Google Analytics User ID, HubSpot cookie) if available.
• If no matching possible, note the gap in data and acknowledge that early-stage awareness touches may be undercounted.

Phase 4: Multi-Touch Attribution Model Construction

Build and compare multiple attribution models:

Model 1: First-Touch Attribution

• 100% of credit to the very first touch.
• Best for: understanding which channels create awareness and initial interest.
• Worst for: understanding which channels close deals.
• Bias: over-credits top-of-funnel channels (content, social, ads).

Model 2: Last-Touch Attribution

• 100% of credit to the touch immediately before opportunity creation or deal close.
• Best for: understanding which channels drive conversion.
• Worst for: understanding the full buyer journey.
• Bias: over-credits bottom-of-funnel channels (direct, branded search, sales outreach).

Model 3: Linear Attribution

• Equal credit distributed across all touches.
• Example: 10 touches = 10% credit each.
• Best for: balanced view when all touches are considered equally valuable.
• Worst for: companies where certain touches (demo, trial) are objectively more influential.
• Bias: over-credits high-frequency, low-value touches (email opens) and under-credits high-effort, high-value touches (in-person meetings).

Model 4: Time-Decay Attribution

• Credit increases exponentially as touches get closer to conversion.
• Example: 7-day half-life means a touch 14 days before close gets 4x the credit of a touch 28 days before close.
• Best for: long sales cycles where recency strongly correlates with influence.
• Worst for: short sales cycles or when early-stage education is critical.
• Bias: similar to last-touch but less extreme. Still under-credits awareness.

Model 5: U-Shaped (Position-Based) Attribution

• 40% to first touch, 40% to lead conversion touch (or last touch), 20% distributed evenly across middle touches.
• Best for: B2B where both awareness creation and conversion are critical.
• Worst for: transactional sales where middle touches are important (evaluation, demo).
• The 40/40/20 split is common but arbitrary. Test 30/30/40 or 35/35/30 splits.

Model 6: W-Shaped Attribution

• 30% to first touch, 30% to lead creation touch, 30% to opportunity creation touch, 10% distributed across remaining touches.
• Best for: complex enterprise B2B with distinct buying stages (awareness → consideration → decision).
• Requires clean data on when each milestone occurred.

Model 7 (Advanced): Custom Weighted / Algorithmic Attribution

• Use machine learning (Shapley values, Markov chains) to determine touch weights from historical data.
• Best for: companies with 500+ closed-won deals and reliable touch data.
• Available in: Google Analytics 4 (data-driven attribution), Bizible, DreamData, CaliberMind.
• If implementing custom: use logistic regression with conversion as the dependent variable and touch channel counts as independent variables to estimate channel coefficients.

Phase 5: Model Comparison and Selection

Compare attribution models side by side:

1. Revenue attribution by channel across all models:

• Create a matrix: channels as rows, models as columns, cell = % of total revenue attributed.
• Calculate variance: for each channel, what's the range between the most and least generous model?
• Channels with wide variance are sensitive to model choice — important to get right.
• Channels with narrow variance are robust — model choice doesn't matter much.

1. Model selection criteria:

• GTM motion fit: Sales-led → W-shaped or U-shaped (both marketing and sales get credit). Product-led → time-decay (product activation signals should weight highest). Marketing-led → linear or U-shaped (full funnel view).
• Data completeness: If you can't reliably identify first touch (cookie gaps, long history), don't use first-touch or U-shaped. If you can't identify lead creation moment, don't use W-shaped.
• Stakeholder alignment: The model both marketing and sales agree on is better than the "correct" model only marketing trusts. Attribution is as much political as analytical.
• Simplicity: A linear model that everyone understands and acts on beats a custom algorithmic model that nobody trusts or can explain.

1. Recommend a primary model with justification. Also report 1-2 comparison models so stakeholders see the sensitivity.

Phase 6: Channel ROI Calculation

Calculate return on investment by channel:

1. Channel cost assembly — collect all costs per channel:

• People cost: fully loaded salary for team members dedicated to the channel
• Media spend: ad spend, sponsorship, content promotion
• Tool cost: channel-specific tools (Apollo for outbound, SEMrush for SEO, webinar platform)
• Content cost: content created specifically for the channel
• Agency/vendor cost: external support for the channel
• Event cost: travel, booth, sponsorship, swag for event channel

1. Channel revenue attribution — using the selected primary model:

• For each channel, sum attributed revenue from closed-won deals in the analysis period.
• Separate new customer revenue from expansion revenue (different channels may drive each).

1. Channel ROI calculation:

`` Channel ROI = (Attributed Revenue - Channel Cost) ÷ Channel Cost `` Express as ratio (3:1) or percentage (300%).

1. Channel CAC calculation:

`` Channel CAC = Total Channel Cost ÷ New Customers Attributed to Channel `` Compare to company-wide CAC. Channels with >2x company-wide CAC are inefficient.

1. Channel efficiency metrics:

• Cost per opportunity: channel cost ÷ opportunities attributed
• Cost per meeting: channel cost ÷ meetings attributed (if meetings are trackable)
• Pipeline efficiency: attributed pipeline value ÷ channel cost
• Time to payback: average months to recover channel cost from attributed revenue

1. Assisted vs unassisted conversion (critical distinction):

• Assisted conversions: channel appeared in the touch history but wasn't the primary-attributed channel. These channels "assist" other channels in closing.
• **Unassisted

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Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: LeadMagic
• Source: LeadMagic/gtm-skills
• License: MIT
• Homepage: https://github.com/LeadMagic/gtm-skills

Install and usage instructions live in the source repository linked above.