Traffic Drop Diagnosis

Traffic Drop Diagnosis

Systematically diagnose the root cause of a traffic decline.

Steps

1. Call list_properties to confirm the exact site_url.
2. Call traffic_drops to identify the time range and magnitude of the drop. Note the start date and affected segments (queries, pages, device types).
3. Call check_alerts to check for manual actions, security issues, or GSC notifications around the drop date.
4. Call analytics_anomalies to detect statistical anomalies and corroborate the drop timing with data.
5. Call seo_lost_queries to find the specific queries that lost clicks or impressions. Note the top 20 by click loss.
6. Call compare_search_periods for the 28 days before vs. the 28 days after the drop, using dimensions=query and limit=50. Flag any query with more than 30% click decline.
7. Cross-reference the drop timing against known Google algorithm update dates. Ask the user for the approximate drop date if not yet provided.

Output format

Present a structured diagnosis in four sections:

Drop summary: date range, magnitude (clicks and impressions % change), overall health status from step 2.

Root cause candidates (ranked by likelihood based on the data):

1. Manual action or penalty (from check_alerts)
2. Algorithm update impact (correlated with timing)
3. Specific query losses (from seo_lost_queries)
4. Technical issues (crawl or index problems)

Most affected queries: table with Query | Clicks before | Clicks after | % change

Next steps: specific actions ordered by priority, one per root cause candidate identified.