Marketing Campaign Analytics: A/B Testing & Attribution

Analyze marketing campaign performance using A/B testing, attribution modeling, and customer journey analytics to optimize marketing ROI.

## Business Context

A digital marketing team has run multiple campaigns across channels (Google Ads, Facebook, Email, Organic) over the past quarter. They need to understand which campaigns drove conversions and how to allocate next quarter's budget.

## Challenges

1. Multi-Touch Attribution
- Customers interact with multiple touchpoints
- Last-click attribution undervalues awareness campaigns
- Need to assign credit appropriately

2. A/B Testing Analysis
- Multiple tests ran simultaneously
- Need to determine statistical significance
- Account for seasonality and external factors

3. Channel Performance
- Compare ROI across channels
- Understand funnel progression
- Identify bottlenecks

4. Budget Optimization
- Recommend budget allocation
- Forecast expected conversions
- Consider diminishing returns

## Data Available

- Campaign data (impressions, clicks, spend)
- User-level journey data (touchpoints, timestamps)
- Conversion data (purchases, value, timestamps)
- A/B test assignments and results
- Channel costs and budgets

## Analytics Tasks

### Descriptive Analytics
- Campaign performance summary
- Channel-wise conversion rates
- Customer journey visualization

### Diagnostic Analytics
- Why did Campaign A outperform Campaign B?
- Which touchpoints are most influential?
- What's the optimal frequency?

### Predictive Analytics
- Forecast conversions for budget scenarios
- Predict customer LTV by acquisition channel
- Estimate diminishing returns curves

### Prescriptive Analytics
- Recommend budget allocation
- Suggest campaign optimizations
- Propose testing roadmap

## Deliverables

1. Marketing Dashboard
- Campaign performance overview
- Channel attribution breakdown
- A/B test results
- Funnel visualization

2. Attribution Model
- Compare models (first-click, last-click, linear, time-decay)
- Recommend best model for business
- Show credit allocation

3. Budget Recommendation
- Optimal allocation by channel
- Expected impact on conversions
- Sensitivity analysis

4. Testing Roadmap
- Priority tests for next quarter
- Sample size calculations
- Success metrics

## Success Criteria

- Clear attribution insights
- Statistically valid A/B test conclusions
- Actionable budget recommendations
- Professional dashboard design

Dataset Includes:
- 100,000+ user journeys
- 4 marketing channels
- 20+ campaigns
- 3 months of data
- A/B test results (5 tests)

Data Quality:
- User IDs anonymized
- Timestamps in UTC
- Currency: USD
- Some missing UTM parameters (~5%)

Tools:
- Python (pandas, statsmodels, scipy)
- R (for statistical tests)
- Google Analytics (optional, for comparison)
- Tableau/Looker for dashboards

Attribution Models to Implement:

1. Single-Touch
- First-click attribution
- Last-click attribution

2. Multi-Touch
- Linear attribution
- Time-decay attribution
- Position-based (U-shaped)
- Data-driven (Markov chains)

A/B Testing Framework:

1. Hypothesis Testing
- Define null and alternative hypotheses
- Choose significance level (α = 0.05)
- Calculate test statistic
- Determine p-value

2. Sample Size Calculation
- Power analysis
- Minimum detectable effect
- Duration estimation

3. Results Interpretation
- Statistical significance
- Practical significance
- Confidence intervals

Dashboard Components:
- Campaign performance table
- Attribution comparison chart
- Funnel visualization
- A/B test results cards
- Budget allocation simulator

Visualization Types:
- Sankey diagram (customer journeys)
- Bar charts (campaign comparison)
- Line charts (trends over time)
- Pie charts (attribution breakdown)