Ways to customize this agent

1. Connecting data from your marketing attribution system
2. Layering on campaign data from other ad channels
3. Cross-referencing this output with product usage data to find correlations with engagement in your product (e.g. Campaign A has weak performance but is generating a 20% paid conversion rate in the product’s free trial)

Steps and instructions

How to create the report in Google Ads

1. Go to Google Ads Reports

1. Sign into your Google Ads account.
   
   
2. In the top menu bar, click Reports → Reports.
   
   
3. Click the blue + Custom button → choose Table.

2. Configure the Report

You’ll now build a table with rows and metrics.

Rows (Dimensions)

Drag the following into the Rows area:

• Search keyword
  
  
• Campaign
  
  
• Campaign state
  
  
• Campaign type (Search, Display, etc.)
  
  
• Ad group
  
  
• Final URL (Ad final URL)
• Date
  
  

Columns (Metrics)

Drag the following into the Columns area:

• Clicks
  
  
• Impressions
  
  
• CTR
  
  
• Average CPC
  
  
• Cost
  
  
• Impr. (Abs. Top) %
  
  
• Impr. (Top) %
  
  
• Conversions
  
  
• View-through conversions
  
  
• Cost/conversion
  
  
• Conversion rate

3. Apply Filters (optional)

• Add a Date Range (top right) — for example, last 30 days (make sure both reports’ time ranges match).
  
  
• If you only want Search campaigns, add a filter:
  Campaign type = Search

4. Save & Schedule Monthly Exports

1. In the top right, click Save → name it Keyword Performance
   
   
2. To export monthly: click the Schedule icon → choose Google Sheets → Weekly or Monthly → Your Email as the Recipient.

‼️ Note: Make sure that the Download schedule for this report matches your scheduled trigger for the AI workflow (ideally the report is downloaded a day before the workflow triggers).

How to create to report in Google Analytics

‼️ Note: This type of report cannot be automatically generated on a schedule. A human will need to create it once, save it, and then manually download it once this workflow has been triggered monthly.

1. Open Google Analytics Explorations

1. Go to your GA4 property
   
   
2. In the left-hand menu, click Explore (just under Reports).
   
   
3. Click the + (blank exploration) button.
   

2. Configure the Exploration

Name the exploration

• In the top left, rename it something like:
  “Session performance by campaign”

Technique

• Choose Free form (default).

Date range

• In the date selector, set your desired range (e.g., last 30 days)(make sure both reports’ time ranges match).

3. Add Segments, Dimensions, and Metrics

Segments

• By default, you’ll see All Users segment. That’s fine, keep it.
  
  

Dimensions

Click the + next to “Dimensions” and import these:

• Session campaign
  
  
• Session manual term
  
  
• Landing page + query string
  
  
• Date + hour (YYYYMMDDHH)
  
  

✅ Once selected, click Import.

Metrics

Click the + next to “Metrics” and import these:

• Sessions
  
  
• Total users
  
  
• Engaged sessions
  
  
• Key events
  
  
• Average engagement time
  
  
• Active users
  
  
• Event count
  
  
• Bounce rate
  
  

✅ Then click Import.

4. Build the Table

In the Tab Settings (right panel):

• Rows:
  
  
  
  1. Drag in Session campaign
     
     
  2. Drag in Session manual term
     
     
  3. Drag in Landing page + query string
     
     
  4. Drag in Date + hour (YYYYMMDDHH)
     
     
• Values:
  
  
  
  1. Sessions
     
     
  2. Total users
     
     
  3. Engaged sessions
     
     
  4. Key events
     
     
  5. Average engagement time
     
     
  6. Active users
     
     
  7. Event count
     
     
  8. Bounce rate

5. Review and Adjust

• You’ll now see a table with Session campaign → Manual term → Landing page → Date/hour as nested rows, and all your metrics as columns.
  
  
• Totals will appear at the top.

6. Export the Data

• In the top right, click the download icon.
  
  
• Choose Export to Google Sheets.
• Reply “Done” (or anything) in the Slack notification received earlier to tell the workflow to continue.

Prompt: 

Analyze the performance correlation between Google Ads campaigns and actual website engagement by cross-referencing Google Ads keyword performance data (attached as "Keyword Performance") with Google Analytics session data (attached as "Free Form 1"). Identify which campaigns drive not just conversions, but quality traffic that generates meaningful site engagement.

#### Data Sources

Dataset A: Google Ads Keyword Performance Report

Columns: Search keyword, Campaign, Ad group, Clicks, Cost, Conversions, Ad final URL, etc.

Dataset B: Google Analytics (GA4) Session Report

Columns: Session campaign, Session manual term, Landing page, Sessions, Engaged sessions, Average engagement time, Key events, etc.

#### Correlation Analysis Framework

1. UTM Parameter Correlation

Primary Matching Logic:

Extract UTM parameters from Google Ads "Ad final URL" column

Match utm_campaign with GA4 "Session campaign"

Match utm_term with GA4 "Session manual term"

Cross-reference campaign names between datasets

#### Analysis Points:

Which Google Ads campaigns have corresponding GA4 data?

What percentage of ad clicks translate to tracked GA4 sessions?

Identify campaigns with high click volume but low session tracking

2. Time-Based Correlation Analysis

Temporal Matching:

Compare Google Ads click patterns by date with GA4 session patterns

Identify days with high ad spend but low site engagement

Analyze click-to-session conversion rates over time

Flag unusual discrepancies between clicks and sessions

Look For:

Days with high Google Ads activity but low GA4 sessions (potential tracking issues)

Correlation between ad spend timing and engagement quality

Seasonal patterns in campaign effectiveness

3. Landing Page Performance Analysis

URL Matching Strategy:

Match Google Ads "Ad final URL" with GA4 "Landing page"

Strip UTM parameters to identify base landing pages

Analyze which landing pages convert ad traffic to engaged sessions

Performance Indicators:

Landing pages with high ad traffic but poor engagement metrics

Pages that excel at converting paid traffic to engaged users

Bounce rate patterns by landing page from paid traffic

4. Campaign Quality Assessment

High-Performing Campaigns (Identify campaigns that:

Generate high click volume AND high engaged session rates

Have reasonable cost-per-engaged-session ratios

Drive sessions with above-average engagement time

Convert to meaningful key events/goals

Underperforming Campaigns (Flag campaigns that:

Have high ad spend but low corresponding GA4 activity

Generate clicks but poor engagement metrics (high bounce rate, low session duration)

Show disconnect between conversion tracking and actual site behavior

Cost significantly more per engaged session than average

5. Keyword-Level Insights

Quality Traffic Keywords:

Keywords driving engaged sessions with reasonable cost

Search terms that correlate with high-value site behavior

Keywords with strong click-to-engaged-session conversion rates

Problematic Keywords:

High-cost keywords with poor engagement correlation

Keywords that generate clicks but immediate bounces

Search terms with conversion tracking but no corresponding GA4 activity

6. Traffic Pattern Analysis

Overall Correlation Health:

What percentage of Google Ads clicks show up as GA4 sessions?

Average engagement time for paid traffic vs. organic

Key event conversion rates for paid vs. other traffic sources

Optimization Opportunities:

Campaigns ready for budget increases (high engagement, reasonable cost)

Keywords/campaigns needing negative keyword additions

Landing page optimization priorities based on paid traffic performance

#### Analysis Output Requirements

Executive Summary (Concise)

Provide a clear assessment of overall Google Ads to GA4 correlation quality and campaign performance ranking.

#### Detailed Findings

Top 3 Best Performing Campaigns: High engagement + reasonable cost

Top 3 Underperforming Campaigns: Poor engagement relative to spend

Data Quality Issues: Missing correlations, tracking gaps, discrepancies

Budget Reallocation Opportunities: Specific spend shift recommendations

#### Actionable Recommendations

Specific campaigns to increase/decrease budgets

Keywords to add as negatives

Landing pages needing optimization

Tracking implementation improvements needed

#### Supporting Data

Correlation success rates by campaign

Cost-per-engaged-session calculations

Click-to-session conversion rates

Engagement quality scores by traffic source

#### Data Processing Instructions

Handle Missing Data Gracefully: When exact UTM matching fails, use campaign name similarity matching

Account for Tracking Delays: Allow ±1 day buffer for time-based correlations

Calculate Derived Metrics: Create engagement efficiency scores combining cost, clicks, sessions, and engagement time

Flag Data Quality Issues: Identify campaigns with suspiciously low correlation rates

Provide Confidence Scores: Rate each correlation as High/Medium/Low confidence based on data completeness

#### Critical Analysis Questions

Which campaigns show strong ad performance but weak site engagement?

Are there high-engagement campaigns that deserve more budget allocation?

What percentage of ad spend correlates to meaningful site activity?

Which keywords drive quality traffic beyond just conversions?

Are there tracking gaps that need immediate attention?

What landing page optimizations would improve paid traffic conversion?

Format the analysis as an executive report with clear recommendations, supporting data, and specific next steps for campaign optimization. Make it concise and easily readable. Assume the reader to be a marketign lead with context on the campaigns.