Analytics Reporting

This skill enables an AI agent to generate detailed marketing analytics reports that go beyond raw numbers to deliver actionable insights. The agent collects data across traffic, engagement, conversion, and revenue metrics, applies attribution models to understand channel contribution, performs funnel and cohort analysis, and produces executive-ready reports with clear recommendations. The output helps marketing teams make data-driven decisions about budget allocation, campaign optimization, and strategy shifts.

Workflow

1. Define reporting scope and KPIs. Clarify the report type (monthly overview, campaign-specific, channel deep-dive) and time period. Establish the primary KPIs to track: traffic metrics (sessions, unique visitors, pageviews), engagement metrics (bounce rate, time on page, pages per session), conversion metrics (conversion rate, leads generated, cost per acquisition), and revenue metrics (customer lifetime value, return on ad spend, marketing-attributed revenue).

2. Collect data from all sources. Pull data from web analytics (Google Analytics, Plausible), search console (impressions, clicks, average position), advertising platforms (Google Ads, Meta Ads, LinkedIn Ads), email marketing (Mailchimp, SendGrid), CRM (HubSpot, Salesforce), and social media analytics (native platform insights). Normalize date ranges and metric definitions across sources to ensure comparability.

3. Analyze trends and identify patterns. Compare current period metrics against previous period and year-over-year baselines. Calculate growth rates, identify statistically significant changes, and flag anomalies (traffic spikes from viral content, drops from algorithm updates or site outages). Segment data by channel, device, geography, and user cohort to uncover hidden patterns.

4. Apply attribution modeling. Move beyond last-click attribution to understand the full customer journey. Apply multi-touch models — linear (equal credit), time-decay (more credit to recent touchpoints), or data-driven (algorithmic) — to evaluate how each channel contributes to conversions. This prevents over-investing in bottom-funnel channels while starving the awareness channels that feed the pipeline.

5. Perform funnel and cohort analysis. Map the conversion funnel from first visit to purchase or signup. Calculate drop-off rates at each stage: landing page → lead form → MQL → SQL → customer. Identify the highest-friction stages and recommend tests to improve them. Run cohort analysis to understand retention — do users acquired from organic search retain better than those from paid ads after 30, 60, and 90 days?

6. Generate the report with visualizations and recommendations. Structure the report with an executive summary, channel-by-channel breakdown, top-performing content, funnel analysis, and a prioritized recommendation section. Include tables, trend charts, and comparison visualizations. End every section with a "So what?" — the specific action the team should take based on the data.

Usage