Marketing AI Skill

Marketing Analytics Attribution

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

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