Conversion Tracking & Attribution Windows | Click-Through vs View-Through Impact

Enhanced Prompt: Analyzing Click-Through and View-Through Attribution Windows in Google Ads

Objective:
To conduct a comprehensive analysis of the impact of click-through (CT) and view-through (VT) attribution windows on conversion tracking and attribution in Google Ads. The goal is to optimize attribution strategies by understanding how different window sizes (micro, macro, and mega) influence reported conversions, conversion rate optimization (CRO), and overall marketing effectiveness.

Deliverables:

1. A detailed report analyzing the impact of CT and VT windows on conversions and attribution accuracy.
2. Recommendations for selecting the optimal attribution window based on industry best practices and business objectives.
3. A comparison of key performance indicators (KPIs), including conversion rate, cost per conversion, return on ad spend (ROAS), and return on ad impressions (ROI).
4. A actionable strategy for improving conversion tracking accuracy by leveraging best practices in attribution modeling.
5. A conclusion summarizing the findings and providing insights for future attribution strategy.

Industry Best Practices and Standards:

• Utilize well-established attribution models such as first-click, last-click, linear, and time-decay models.
• Reference Google Supported Comparator Models (GSMa) for CT and VT attribution windows (SMaaller window sizes for CT, larger window sizes for VT).
• Stay updated on Google's proposed attribution changes and their implications for conversion tracking.
• Align attribution window selection with business objectives and user journey stages.

Quality Expectations:

• KPIs should be measured with accuracy and consistency, ensuring comparability across attribution windows.
• Attribution models should be validated against actual conversions to assess accuracy and performance.
• Provide detailed data visualizations, including conversion heatmaps, bounce rates, and average window sizes.
• Include sensitivity testing to ensure attribution models are robust to changes in data distribution.
• Deliver recommendations based on data trends and business acumen, avoiding overgeneralization.

Technical Considerations:

• Use data from Google Analytics and Attribution Report for validation.
• Consider the importance of multi-touch attribution in aligning campaigns across channe