Marketing Analytics & Attribution: Measure What Matters

Marketing attribution reveals which touchpoints drive conversions, enabling data-driven budget allocation. Companies with advanced attribution modeling see 15-20% improvement in marketing ROI by shifting spend to high-performing channels.

Attribution Models

Single-Touch Attribution

First-Touch Attribution:

• Credits first interaction
• Use case: Brand awareness campaigns
• Pro: Simple to implement
• Con: Ignores nurturing impact

Last-Touch Attribution:

• Credits final interaction before conversion
• Use case: Direct response campaigns
• Pro: Easy to understand
• Con: Undervalues top-of-funnel

Multi-Touch Attribution

Linear Attribution: Equal credit to all touchpoints

5 touchpoints = 20% credit each

Time-Decay Attribution: More credit to recent interactions

Day 1: 10% | Day 7: 15% | Day 14: 25% | Day 21: 50%

U-Shaped (Position-Based): 40% first touch, 40% last touch, 20% middle

First: 40% | Middle touches: 20% | Last: 40%

W-Shaped Attribution: 30% first, 30% lead conversion, 30% opportunity creation, 10% middle

First: 30% | Lead: 30% | Opportunity: 30% | Middle: 10%

Data-Driven Attribution: Machine learning assigns credit based on historical data

• Most accurate
• Requires significant data volume
• Available in Google Analytics 4, Adobe Analytics

Key Marketing Metrics

Acquisition Metrics

Traffic Metrics:

• Total visitors
• Unique visitors
• Traffic by source/channel
• New vs returning visitors
• Geographic distribution

Lead Generation:

• Total leads
• Marketing Qualified Leads (MQLs)
• Sales Qualified Leads (SQLs)
• Lead-to-MQL conversion rate
• MQL-to-SQL conversion rate

Cost Metrics:

• Cost Per Click (CPC)
• Cost Per Lead (CPL)
• Cost Per Acquisition (CPA)
• Customer Acquisition Cost (CAC)

Engagement Metrics

Website:

• Pages per session
• Average session duration
• Bounce rate
• Scroll depth
• Form completion rate

Content:

• Page views
• Time on page
• Social shares
• Comments
• Downloads

Email:

• Open rate: 15-25 percent
• Click-through rate: 2-5 percent
• Conversion rate: 1-3 percent
• Unsubscribe rate: less than 0.5 percent

Revenue Metrics

Pipeline Impact:

• Marketing-sourced revenue
• Marketing-influenced revenue
• Average deal size
• Win rate
• Sales cycle length

Customer Value:

• Customer Lifetime Value (CLV)
• Average Revenue Per User (ARPU)
• Net Revenue Retention (NRR)
• Churn rate
• Expansion revenue

ROI Metrics:

• Return on Marketing Investment (ROMI)
• Marketing % of Customer Acquisition Cost
• LTV:CAC ratio (target 3:1)
• Payback period

Analytics Setup

Google Analytics 4

Essential Configuration:

• Cross-domain tracking
• Enhanced measurement enabled
• Custom events setup
• Conversion tracking
• Audience segmentation

Key Reports:

• Acquisition overview
• User behavior flow
• Landing page performance
• Conversion paths
• Ecommerce transactions

Marketing Dashboard

Executive Dashboard (Weekly):

• Total leads generated
• MQLs created
• Pipeline influenced ($)
• CAC trending
• Top performing channels

Channel Performance (Daily):

• Traffic by source
• Conversion rates
• Cost per conversion
• ROI by channel
• Campaign performance

Content Performance (Monthly):

• Top pages by traffic
• Top converting content
• Content engagement metrics
• SEO rankings
• Social performance

Attribution Implementation

Data Collection

Tracking Requirements:

• UTM parameters on all campaigns
• Conversion pixel installation
• CRM integration
• Call tracking
• Form tracking

UTM Parameter Structure:

utm_source: Where traffic comes from (google, linkedin)
utm_medium: Marketing medium (cpc, email, social)
utm_campaign: Campaign name (spring_sale_2025)
utm_content: Ad variation (blue_cta, red_cta)
utm_term: Paid keywords (ai_software)

Example:
https://techbant.com/demo?utm_source=linkedin&utm_medium=cpc&utm_campaign=abm_q1&utm_content=cto_targeting

CRM Integration

Required Data Flow:

1. Marketing platform → CRM (leads)
2. CRM → Marketing platform (conversion data)
3. Bidirectional contact sync
4. Campaign member tracking
5. Opportunity association

Salesforce Attribution Fields:

• First Touch Campaign
• Last Touch Campaign
• Campaign Influence (multi-touch)
• Opportunity source
• Lead source detail

Channel Performance Analysis

Paid Advertising

Google Ads Metrics:

• Impressions & reach
• Click-through rate
• Quality score
• Conversion rate
• Cost per conversion
• ROAS (Return on Ad Spend)

LinkedIn Ads Metrics:

• Engagement rate
• Lead form completions
• Cost per lead
• Account engagement (ABM)
• Demographic performance

Facebook/Instagram:

• Relevance score
• Cost per result
• Landing page views
• Frequency
• Attribution window

Organic Channels

SEO Performance:

• Organic traffic
• Keyword rankings
• Click-through rate (SERP)
• Organic conversions
• Pages indexed
• Domain authority

Content Marketing:

• Page views
• Engagement rate
• Lead captures
• Time on page
• Social shares
• Backlinks earned

Email Marketing:

• List growth rate
• Email deliverability
• Engagement rate
• Revenue per email
• List churn rate

Social Media

LinkedIn Analytics:

• Follower growth
• Engagement rate
• Content impressions
• Click-through rate
• Lead gen forms

Twitter/X Metrics:

• Tweet impressions
• Engagement rate
• Link clicks
• Mentions
• Follower growth

Predictive Analytics

Lead Scoring

Behavioral Signals (0-100 points):

• Email opens: +3
• Link clicks: +5
• Page visits: +10
• Content downloads: +15
• Pricing page: +25
• Demo request: +50

Firmographic Scoring:

• Ideal company size: +20
• Target industry: +15
• Decision-maker title: +15
• Budget fit: +10

Predictive Lead Score: Machine learning predicts conversion likelihood based on:

• Historical conversion patterns
• Engagement velocity
• Similar customer profiles
• Intent signals
• Competitive research activity

Forecasting

Pipeline Forecasting:

Expected Revenue =
Σ (Opportunity Value × Win Probability)

Stage win rates:
Discovery: 10%
Demo: 25%
Proposal: 50%
Negotiation: 75%

Marketing Contribution Forecast:

Q2 Pipeline Goal: $5M
Historical marketing contribution: 40%
Marketing-sourced target: $2M

Required monthly MQLs:
$2M goal ÷ $50K ACV ÷ 25% win rate ÷ 3 months = 53 MQLs/month

Reporting Best Practices

Executive Reporting

Monthly Marketing Report:

1. Executive summary (wins/challenges)
2. Key metrics vs goals
3. Pipeline impact
4. Top campaigns
5. Spend overview
6. Next month plan

Metrics That Matter to Executives:

• Marketing-sourced revenue
• Pipeline influenced
• Cost per acquisition
• Marketing ROI
• Forecast accuracy

Team Reporting

Campaign Post-Mortem:

• Campaign goals
• Actual results
• Key learnings
• What worked
• What didn’t
• Recommendations

Channel Performance Review:

• Traffic trends
• Conversion rates
• Cost efficiency
• Attribution impact
• Optimization opportunities

Tools & Technology

Analytics Platforms:

• Google Analytics 4
• Adobe Analytics
• Mixpanel
• Amplitude
• Heap

Attribution Tools:

• HubSpot Attribution
• Salesforce Marketing Cloud
• Bizible (Adobe)
• Ruler Analytics
• Wicked Reports

Dashboard Tools:

• Tableau
• Looker
• Power BI
• Google Data Studio
• Klipfolio

Marketing Data Warehouses:

• Snowflake
• Google BigQuery
• Amazon Redshift
• Databricks

Getting Started

Week 1: Audit

• Review current tracking
• Identify data gaps
• Document tech stack
• Map customer journey
• Define key metrics

Week 2: Implementation

• Fix tracking issues
• Implement UTM strategy
• Set up conversion tracking
• Configure attribution model
• Build initial dashboards

Week 3: Analysis

• Baseline performance
• Channel comparison
• Attribution analysis
• Opportunity identification
• Stakeholder review

Week 4: Optimization

• Shift budget to winners
• Kill underperformers
• Test improvements
• Document learnings
• Plan next iteration

Conclusion

Marketing analytics and attribution transform marketing from a cost center to a revenue driver. Accurate measurement enables data-driven decisions, optimal budget allocation, and continuous improvement.

Start with proper tracking foundation, choose appropriate attribution model for your business, and build dashboards that drive action. Iterate based on data, not assumptions.

Next Steps:

1. Audit current analytics setup
2. Implement comprehensive tracking
3. Choose attribution model
4. Build performance dashboards
5. Establish reporting cadence