How to Automate Google Analytics Reporting with AI

This system automates google analytics reporting with ai insights by pulling GA4 data and generating recommendations. Weekly reports with traffic trends and AI commentary.

What You Need Before Starting

• Python 3.8+
• Data source API access
• pandas and matplotlib installed
• SMTP for email delivery

Step 1: Connect Data Sources

Set up API connections for google analytics.

import requests
from datetime import datetime

def fetch_data(api_config):
    results = {}
    for source in api_config:
        response = requests.get(source["url"], headers=source.get("headers", {}))
        if response.status_code == 200:
            results[source["name"]] = response.json()
    results["fetched_at"] = datetime.now().isoformat()
    return results

Step 2: Process and Calculate

Transform raw data into the metrics you need.

import pandas as pd

def calculate_metrics(raw_data):
    df = pd.DataFrame(raw_data)
    metrics = {
        "total": df["value"].sum(),
        "average": df["value"].mean(),
        "trend": df["value"].pct_change().tail(7).mean(),
        "period": datetime.now().strftime("%Y-%m-%d"),
    }
    return metrics

Step 3: Generate the Report

Build the report using your template.

from jinja2 import Template

REPORT = Template("""
<h2>{{ title }} - {{ date }}</h2>
<table>
{% for metric, value in metrics.items() %}
<tr><td>{{ metric }}</td><td>{{ value }}</td></tr>
{% endfor %}
</table>
""")

def build_report(metrics, title):
    return REPORT.render(title=title, date=datetime.now().strftime("%Y-%m-%d"), metrics=metrics)

Step 4: Add AI Commentary

Use Claude to explain what the numbers mean.

import anthropic

def add_narrative(metrics):
    client = anthropic.Anthropic()
    message = client.messages.create(
        model="claude-sonnet-4-20250514", max_tokens=500,
        messages=[{"role": "user",
            "content": f"Write a 3-sentence analysis of these metrics. Be specific.\n{json.dumps(metrics)}"}])
    return message.content[0].text

Step 5: Schedule Delivery

Automate report generation and distribution.

import smtplib
from email.mime.text import MIMEText

def send_report(html_content, recipients, subject):
    msg = MIMEText(html_content, "html")
    msg["Subject"] = subject
    for recipient in recipients:
        msg["To"] = recipient
        with smtplib.SMTP("smtp.gmail.com", 587) as server:
            server.starttls()
            server.login("[email protected]", "app-password")
            server.send_message(msg)

What to Build Next

Add AI recommendations based on traffic patterns and conversion data.

Related Reading

• Setting Up Google Analytics Automated Reports - google analytics automated reports
• How to Set Up Automated Reporting for Clients - automated reporting clients guide
• Setting Up Automated Client Reporting Pipelines - automated client reporting pipeline