How to Automate Ecommerce Review Collection

I build automate ecommerce review collection post-purchase systems that give store owners automated post-purchase review requests. This automation runs in the background so you can focus on growth instead of operational tasks.

What You Need

• Python 3.9+ or Node.js 18+
• Ecommerce API credentials
• A notification channel (Slack, email, or SMS)

Step 1: Connect to the Ecommerce API

import requests
import os
from dotenv import load_dotenv

load_dotenv()

API_KEY = os.getenv("ECOMMERCE_API_KEY")
API_URL = os.getenv('ECOMMERCE_API_URL')

headers = {
    'Authorization': f'Bearer {API_KEY}',
    "Content-Type": "application/json"
}

def fetch_data(endpoint, params=None):
    url = f'{API_URL}/{endpoint}'
    response = requests.get(url, headers=headers, params=params or {})
    return response.json()

Step 2: Build the Processing Logic

from datetime import datetime
import json

def process_items(items):
    results = []
    for item in items:
        processed = {
            "id": item.get("id"),
            "name": item.get("name", item.get("title", "")),
            "status": item.get("status", ""),
            "amount": item.get("total_price", item.get("amount", 0)),
            "processed_at": datetime.now().isoformat()
        }
        results.append(processed)
    return results

Step 3: Set Up Notifications

def send_notification(message):
    slack_token = os.getenv("SLACK_BOT_TOKEN")
    channel = os.getenv("ECOM_CHANNEL")

    requests.post("https://slack.com/api/chat.postMessage",
        headers={"Authorization": f"Bearer {slack_token}"},
        json={"channel": channel, "text": message})

def notify_batch(processed_items):
    for item in processed_items:
        msg = f"*{item['name']}* | Status: {item['status']} | ${item['amount']}"
        send_notification(msg)
    print(f"Sent {len(processed_items)} notifications")

Step 4: Add Webhook Handler

from flask import Flask, request

app = Flask(__name__)

@app.route("/webhook/ecommerce", methods=["POST"])
def handle_webhook():
    data = request.json
    processed = process_items([data])
    notify_batch(processed)
    return "OK", 200

Step 5: Run on Schedule

import sqlite3

def log_run(action, count):
    conn = sqlite3.connect("ecom_automation.db")
    conn.execute("""CREATE TABLE IF NOT EXISTS runs (
        action TEXT, count INTEGER, ran_at TEXT
    )""")
    conn.execute("INSERT INTO runs VALUES (?, ?, ?)",
        (action, count, datetime.now().isoformat()))
    conn.commit()

def main():
    data = fetch_data("orders", {"status": "any", "limit": 50})
    items = data.get("orders", data.get("data", []))
    processed = process_items(items)
    notify_batch(processed)
    log_run("ecommerce review collection", len(processed))

main()

What to Build Next

Add a dashboard in Google Sheets that shows ecommerce review collection trends over time. Connect it to the webhook handler so data flows in real time, not just on schedule.

Related Reading

• Setting Up Automated Data Collection - turning raw data into actionable business decisions
• Setting Up Automated Review Responses - practical guidance for building AI-powered business systems
• Setting Up Automated Social Proof Collection - practical guidance for building AI-powered business systems