How to Build Automated Cohort Analysis Reports

This automated cohort analysis reporting system groups customers and tracks retention, revenue, and engagement over time. Cohorts show you which acquisition strategies produce lasting value.

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 cohort analysis.

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

Compare cohorts across acquisition channels to find your best traffic sources.

Related Reading

• Building an Automated Feedback Analysis System - automated feedback analysis system guide
• The Scheduling System - scheduling system automated operations
• How to Set Up Automated Reporting for Clients - automated reporting clients guide