traffic-structure-analyzer

What This Skill Does

The traffic-structure-analyzer is a specialized OpenClaw agent skill designed to provide surgical precision for cross-channel advertising performance. It moves beyond high-level dashboards to perform deep traffic decomposition, allowing users to understand the 'why' behind ROAS fluctuations, CPA spikes, and budget inefficiencies. By auditing Meta, Google, TikTok, YouTube, Amazon, and programmatic DSP data, this skill identifies exactly which segments of your funnel are driving growth and which are draining capital.

Installation

To install this skill, run the following command in your terminal: clawhub install openclaw/skills/skills/danyangliu-sandwichlab/traffic-structure-analyzer

Use Cases

1. Anomaly Diagnosis: Quickly identify if a sudden drop in revenue is caused by a specific campaign, a platform-wide algorithmic change, or a tracking implementation error.
2. Budget Reallocation: Determine where to shift spend by comparing the marginal efficiency of demand-capture channels (Google/Amazon) against demand-generation channels (Meta/TikTok).
3. Funnel Optimization: Analyze conversion rate trends across the funnel to prioritize whether you need to optimize creative assets or landing page experience.

Example Prompts

1. "Our ROAS on Meta has dropped 15% over the last 7 days despite stable spend. Can you decompose the traffic by campaign and audience to find the specific driver?"
2. "Compare our customer acquisition costs across Google Ads and TikTok Ads for the last 30 days and provide a recommendation on which channel to scale for Q4."
3. "Is the recent increase in CPA on our brand search campaign due to higher competition or a decline in conversion rate? Please analyze the last 14 days of data."

Tips & Limitations

• Metric Canonicalization: Always ensure you have a shared definition for 'conversion' and 'attribution window' before starting the analysis. If metrics conflict, use the built-in disambiguation flow.
• Confidence Awareness: This skill marks results as 'directional' if data sample sizes are insufficient. Do not base major budget pivots on directional data alone without A/B testing.
• Channel Specificity: The skill is aware of platform nuances; use it to prioritize creative testing on social platforms and intent-matching on search platforms. Avoid trying to force a 'one-size-fits-all' strategy across disparate ecosystems like DSP and Amazon.