analyze-web-traffic

name	analyze-web-traffic
compatibility	Requires Altertable MCP server
description	Analyzes web analytics traffic patterns and user behavior. Use when asked about pageviews, sessions, traffic sources, or website user behavior.
metadata	{"author":"altertable-ai","requires":"altertable-mcp"}

Analyzing Web Traffic

Quick Start

To analyze web traffic:

1. Query pageview and session data via the Altertable MCP server
2. Compare the current period against a previous period (WoW, MoM, or YoY)
3. Segment by traffic source, device, geography, or page
4. Surface anomalies, trends, and actionable findings

When to Use This Skill

• User asks about website traffic, pageviews, or sessions
• Investigating traffic spikes, drops, or trends
• Comparing traffic across time periods or segments
• Evaluating traffic source mix or user engagement

Analysis Workflow

Step 1: Determine Scope and Time Frame

Ask the user (or infer from context):

• Period: What date range to analyze (default to last 7 days if unspecified)
• Comparison: What to compare against (previous period of equal length)
• Focus: Overall traffic, a specific source, a specific page, or a segment

Step 2: Query Traffic Data

Use Altertable MCP tools to fetch web analytics data:

• get_catalog to confirm the Product Analytics tables and columns available in this environment
• query_lakehouse for custom web traffic analysis
• render_insight when the user wants a chart preview

Common Product Analytics tables include product_analytics.analytics.web_sessions and product_analytics.analytics.web_pageviews. Query them to compute:

• Volume: pageviews, sessions, unique visitors
• Engagement: bounce rate, pages per session, avg session duration
• Acquisition: traffic by source/medium, referrer breakdown

Always pull both the current period and the comparison period so you can compute deltas.

Step 3: Segment the Data

Break down by at least one dimension to find where changes originate:

• Traffic source: organic, paid, direct, social, referral, email
• Device type: desktop, mobile, tablet
• Geography: country, region
• Page or section: top pages, landing pages, exit pages

When a top-level metric moves, drill into segments to isolate which segment drove the change.

Step 4: Identify Patterns and Anomalies

Look for:

• Trends: sustained directional movement across multiple periods
• Anomalies: single-period spikes or drops that break the pattern
• Shifts in mix: a source growing as a share even if total traffic is flat

Quantify every observation with absolute numbers and percentage change.

Step 5: Summarize and Recommend

Present findings with:

• The metric, its value, and the delta vs. the comparison period
• Which segment is responsible for the change
• A hypothesis for why (site changes, seasonality, campaigns)
• A suggested next step or action when applicable

Time Period Comparison Guide

Comparison	When to Use
WoW (week-over-week)	Short-term monitoring, recent changes
MoM (month-over-month)	Growth tracking, campaign evaluation
YoY (year-over-year)	Seasonal businesses, long-term trends

Always compare equal-length periods. When comparing WoW, align on the same day of week. When comparing MoM, account for differing month lengths.

Segmentation Priorities

When the user does not specify a segment, default to this order:

1. Traffic source -- most common driver of traffic changes
2. Device type -- surfaces mobile vs. desktop divergence
3. Top pages -- pinpoints content driving volume shifts

Only add geography or other dimensions if the first pass does not explain the change.

Common Pitfalls

• Reporting totals without comparison: always include a delta to a prior period so the user can gauge significance
• Ignoring seasonality: a WoW drop on a holiday week is expected, not alarming -- flag it rather than over-interpreting
• Mixing up pageviews and sessions: these measure different things; present both when discussing volume
• Not drilling into segments: a flat total can hide offsetting gains and losses across sources or pages
• Presenting numbers without context: raw counts are meaningless without comparison, percentage change, or benchmarks
• Forgetting to check for tracking issues: sudden drops to zero or impossible spikes often indicate instrumentation problems, not real traffic changes

Reference Files

• Web events reference
• Session analysis
• Trend comparison