Many advertisers face a common challenge when running TikTok campaigns: Pangle placements often underperform compared to native TikTok ad slots. The influx of new traffic through Pangle raises critical questions about how to effectively identify and filter out low-quality placements to improve overall return on investment.
This article examines TikTok's Pangle Brand Safety features and presents analytical strategies for optimizing placement performance through systematic testing and data-informed blocking techniques.
Understanding Pangle Brand Safety Features
The Pangle Brand Safety module offers three core functions for placement management:
- Publisher List: Displays publishers by region and time period. If empty, this may indicate either no recent campaigns or an insufficient time window selection. For optimal results, analyze accounts with substantial recent ad spend over extended periods.
- Ad Delivery Report: Shows impression distribution across apps, sortable by volume to identify top-performing placements.
- Block List: Enables bulk blocking of specific placements using Bundle IDs.
Data-Informed Placement Optimization Strategies
1. Performance-Based Placement Exclusion
This approach identifies underperforming placements with significant traffic volume using historical conversion data:
- Data Collection: Compare conversion metrics across two time periods (e.g., current vs. previous month) from TikTok's reporting dashboard.
- Placement Analysis: Sort placement data by impression volume in both periods. Identify new placements that appear in the top rankings during underperforming periods.
- Testing Protocol: Add suspect placements to the block list and monitor performance changes. Improved metrics confirm the placement's low quality.
Key Considerations:
- This strategy only applies to campaigns using Pangle placements. Ensure analysis excludes TikTok-only campaigns to prevent data contamination.
- The method provides indirect quality assessment through post-blocking observation, without guaranteeing superior replacement traffic.
2. Category-Specific Placement Testing
This advanced strategy evaluates placement categories through controlled experiments:
- Category Classification: Export publisher lists by target region and categorize by app type (games, utilities, social platforms, etc.).
- Targeted Campaigns: Create separate ad sets for each category, using block lists to restrict placements exclusively to the test category.
- Performance Evaluation: Compare conversion metrics across categories to identify optimal placement types for your product.
Implementation Notes:
- Requires substantial testing budgets. Consider parallel testing across multiple accounts for efficiency.
- Budget-conscious advertisers should prioritize the first strategy, gradually building block lists based on performance data.
Activating Block Lists
After creating block lists, advertisers must enable the Pangle Block List feature at the ad group level for the restrictions to take effect. Detailed activation instructions are available in TikTok's advertising documentation.
Strategic Insights
TikTok's Brand Safety tools empower advertisers to systematically improve Pangle placement performance. Whether through performance-based exclusions or category testing, data-driven approaches can significantly reduce inefficient ad spend. Both large-scale advertisers and smaller operations can adapt these methodologies to maximize placement value according to their budget and campaign objectives.