--- name: marketing-analytics-attribution description: Implement marketing analytics and attribution models including multi-touch attribution, marketing mix modeling, funnel analysis, cohort analysis, campaign ROI tracking, and marketing dashboard design. Use when setting up attribution models, analyzing marketing performance, building marketing dashboards, tracking campaign ROI, or conducting funnel analysis. Triggers on phrases like "marketing analytics", "attribution model", "multi-touch attribution", "marketing mix modeling", "MMM", "funnel analysis", "cohort analysis", "campaign ROI", "marketing dashboard", "conversion tracking", "UTM parameters", "marketing metrics", "MQL to SQL", "lead attribution", "revenue attribution", "channel performance", "marketing report". --- # Marketing Analytics & Attribution Implement marketing analytics and attribution models including multi-touch attribution, funnel analysis, cohort analysis, and campaign ROI tracking. ## Workflow ### 1. Attribution Models ``` ATTRIBUTION MODELS ═══════════════════════════════════════ Model How It Works Pros Cons ─────────────────────────────────────────────────────────────────────────────── Last-click 100% to last touch Simple, clear Ignores all upstream First-click 100% to first touch Values awareness Ignores nurturing Linear Equal to all touches Fair across journey No weight differentiation Time-decay More credit to recent Values final touches Arbitrary decay rate Position-based 40% first, 40% last, Balanced Arbitrary split 20% middle touches Data-driven Algorithmic (ML) Most accurate Requires volume, complex Custom Rule-based weights Flexible Manual maintenance RECOMMENDED APPROACH: ═══════════════════════════════════════ Primary: Data-driven (GA4, when enough data) Secondary: Position-based (40/20/40) — when data insufficient Supplemental: Marketing mix modeling (MMM) — for paid media optimization ATTRIBUTION RESULTS BY MODEL: ═══════════════════════════════════════ Channel Last-click First-click Linear Position-based Data-driven ──────────────────────────────────────────────────────────────────────────────────────── Organic search 15% 45% 30% 35% 32% Paid search 35% 10% 20% 22% 24% Social (organic) 5% 20% 12% 14% 13% Email 10% 5% 15% 16% 15% Paid social 15% 8% 12% 10% 11% Content/SEO 5% 12% 11% 10% 12% Referral 5% 10% 8% 5% 8% Direct 10% 0% 5% 5% 7% ``` ### 2. Marketing Funnel Analysis ``` MARKETING FUNNEL ═══════════════════════════════════════ Funnel Stages: ═══════════════════════════════════════ Stage Volume Conversion Drop-off Metric ──────────────────────────────────────────────────────────────────────── Website visits 150,000 — — Traffic Engaged sessions 75,000 50% 50% Engagement rate Leads (MQL) 12,000 16% 84% Lead rate SQLs 3,600 30% 70% SQL rate Opportunities 1,800 50% 50% Opp rate Proposals 900 50% 50% Proposal rate Closed-won 360 40% 60% Win rate Overall conversion: 150,000 → 360 (0.24%) MQL to SQL: 30% (target: ≥35%) ⚠️ SQL to Opportunity: 50% (target: ≥55%) ⚠️ Opportunity to Won: 20% (target: ≥25%) ⚠️ Revenue per visitor: $18 (360 deals × $15K / 150,000 visits) Target revenue per visitor: $25 CONVERSION OPTIMIZATION PRIORITIES: ═══════════════════════════════════════ Stage Issue Action Impact ──────────────────────────────────────────────────────────────────────────────── Engaged → MQL Low form completion Optimize lead magnet +2K MQLs MQL → SQL Slow lead response Reduce to <5 min +600 SQLs SQL → Opportunity Poor discovery calls Improve qualification +360 Opps Opportunity → Won Long sales cycle Accelerate with proof +180 Won ``` ### 3. Cohort Analysis ``` COHORT ANALYSIS ═══════════════════════════════════════ Customer Retention (by signup month): ═══════════════════════════════════════ Cohort M0 M1 M2 M3 M6 M12 Churn Rate ──────────────────────────────────────────────────────────────────────────── Jan 2024 100% 88% 82% 78% 72% 65% 35%/year Feb 2024 100% 89% 84% 80% 75% — — Mar 2024 100% 90% 85% 82% — — — Apr 2024 100% 91% 87% — — — — May 2024 100% 90% 86% — — — — Trend: Retention improving (M1: 88% → 91%) Revenue by Cohort: ═══════════════════════════════════════ Cohort MTR (M1) MTR (M3) MTR (M6) ARPU LTV LTV:CAC ─────────────────────────────────────────────────────────────────────────────── Jan 2024 $150 $280 $520 $1,800 $5,400 3.2x Feb 2024 $155 $300 — $1,850 — — Mar 2024 $160 $310 — $1,900 — — Trend: MTR increasing ($150 → $160 M1) LTV:CAC: 3.2x (target: ≥3x) ✓ LTV CALCULATION: ═══════════════════════════════════════ ARPU (Annual Revenue Per User): $1,800 Gross margin: 80% Monthly churn rate: 2.5% LTV = ARPU × Gross margin × (1 / Monthly churn) LTV = $1,800 × 0.80 × (1 / 0.025) = $57,600 × 0.025 = $5,760 CAC (Customer Acquisition Cost): $1,800 LTV:CAC = $5,760 / $1,800 = 3.2x ✓ Payback period: 6 months (target: ≤12 months) ✓ ``` ### 4. Campaign ROI Tracking ``` CAMPAIGN ROI TRACKING ═══════════════════════════════════════ Campaign Performance (Q4 2024): ═══════════════════════════════════════ Campaign Spend Leads SQLs Revenue ROAS CPA ──────────────────────────────────────────────────────────────────────────────── Google Ads — Search $20,000 180 54 $108,000 5.4x $111 Google Ads — PMax $10,000 120 36 $72,000 7.2x $83 LinkedIn Ads $15,000 90 36 $54,000 3.6x $167 Facebook/Instagram $5,000 65 15 $22,500 4.5x $77 Content/SEO $0* 450 90 $135,000 ∞ $0 Email marketing $2,000 85 42 $63,000 31.5x $24 Webinars $3,000 45 22 $33,000 11.0x $67 Referral program $1,000 30 18 $27,000 27.0x $33 *Content cost included in marketing overhead Total: $56,000 → 1,065 leads → 313 SQLs → $514,500 revenue Overall ROAS: 9.2x | Avg CPA: $53 UTM TRACKING STANDARD: ═══════════════════════════════════════ UTM Parameters: → utm_source: Platform (google, linkedin, facebook, email, webinar) → utm_medium: Channel (cpc, cpm, email, organic, referral) → utm_campaign: Campaign name (q4-product-launch) → utm_content: Ad variant (headline-a, headline-b) → utm_term: Keyword (project-management-software) Naming Convention: → {quarter}-{campaign-type}-{campaign-name} → Example: q4-content-gate-gatekeeper ``` ### 5. Marketing Dashboard ``` MARKETING DASHBOARD ═══════════════════════════════════════ Executive View: ═══════════════════════════════════════ Revenue Leads MQLs SQLs CAC LTV:CAC ──────────────────────────────────────────────────────────────────────────────── $514,500 1,065 580 313 $53 3.2x Traffic by Channel (Monthly): ═══════════════════════════════════════ Channel Sessions Conv. Rate Leads Cost/Lead ──────────────────────────────────────────────────────────────────────── Organic search 45,000 3.2% 1,440 $0 Direct 25,000 4.5% 1,125 $0 Paid search 18,000 5.0% 900 $111 Social (organic) 15,000 2.1% 315 $0 Email 12,000 7.1% 850 $24 Paid social 8,000 3.8% 304 $167 Referral 5,000 6.0% 300 $33 Other 22,000 2.5% 550 — Total: 150,000 sessions → 5,884 leads → Avg conversion: 3.9% ``` ## Edge Cases - **B2B long cycles**: 6-12 month attribution windows - **Multi-channel**: Cross-device, cross-platform tracking - **Privacy**: Cookieless tracking, server-side, first-party data - **Enterprise**: Complex funnel, multiple touchpoints - **Attribution gaps**: Offline conversions, sales calls ## Integration Points - **Analytics**: GA4, Adobe Analytics, Mixpanel, Amplitude - **Attribution**: Bizible, Rockyard, Impact, Triple Whale - **BI**: Tableau, Looker, Power BI, Databox - **CRM**: Salesforce, HubSpot - **Advertising**: Google Ads, Meta, LinkedIn, TikTok - **Marketing automation**: HubSpot, Marketo, Pardot ## Output ### Marketing Analytics Status ``` MARKETING ANALYTICS — Q4 2024 ═══════════════════════════════════════ Total revenue attributed: $514,500 Overall ROAS: 9.2x CAC: $53 (target: ≤$60) ✓ LTV:CAC: 3.2x (target: ≥3x) ✓ Conversion rate: 3.9% (website → lead) MQL → SQL: 30% (target: ≥35%) ⚠️ Payback period: 6 months (target: ≤12) ✓ Top channel: Content/SEO ($135K revenue, $0 CPA) Next priority: Improve MQL→SQL rate (30% → 35%), optimize LinkedIn CPA ```