Top 10 Micro-SaaS Ideas for Developers with Minimal Startup Costs for Independent Web Developers and Indie Hackers

1. Automated E-commerce Reporting & Analytics Dashboard

Many small e-commerce businesses struggle with fragmented data. A Micro-SaaS that aggregates sales, marketing, and customer data from platforms like Shopify, WooCommerce, Google Analytics, and Facebook Ads into a single, intuitive dashboard can be invaluable. Focus on actionable insights, not just raw data.

Technical Stack:

• Backend: Python (Flask/Django) or Node.js (Express) for API integrations and data processing.
• Database: PostgreSQL for structured data, potentially Redis for caching.
• Frontend: React or Vue.js for a dynamic UI. Charting libraries like Chart.js or Highcharts.
• Integrations: OAuth 2.0 for secure API access to e-commerce platforms and ad networks.
• Deployment: Docker containers on AWS (ECS/EKS) or DigitalOcean.

Example API Integration (Shopify – conceptual):

You’ll need to handle OAuth for authentication. Once authenticated, you can fetch data via REST or GraphQL APIs.

import requests
import json

# Assume you have an access token from OAuth
ACCESS_TOKEN = "your_shopify_access_token"
SHOP_DOMAIN = "your-shop-name.myshopify.com"

def get_recent_orders():
    url = f"https://{SHOP_DOMAIN}/admin/api/2023-10/orders.json"
    headers = {
        "X-Shopify-Access-Token": ACCESS_TOKEN,
        "Content-Type": "application/json"
    }
    params = {
        "status": "any",
        "limit": 10,
        "fields": "id,created_at,total_price,customer"
    }
    try:
        response = requests.get(url, headers=headers, params=params)
        response.raise_for_status() # Raise an exception for bad status codes
        return response.json()
    except requests.exceptions.RequestException as e:
        print(f"Error fetching orders: {e}")
        return None

if __name__ == "__main__":
    orders = get_recent_orders()
    if orders:
        print(json.dumps(orders, indent=2))

2. AI-Powered Product Description Generator

E-commerce sellers need compelling product descriptions. An AI tool that takes product features, target audience, and tone as input and generates multiple description variations can save significant time and improve conversion rates. Leverage pre-trained LLMs.

Technical Stack:

• Backend: Python with libraries like `openai` or `transformers`.
• API: FastAPI or Flask for serving the AI model.
• Frontend: Simple HTML/CSS/JavaScript or a framework like Svelte for a clean input form.
• AI Model: OpenAI API (GPT-3.5/4) or self-hosted models via Hugging Face.
• Deployment: Serverless functions (AWS Lambda, Google Cloud Functions) for cost-efficiency, or a small VM.

Example Prompt Engineering (Python):

import openai

# Ensure you have your OpenAI API key set as an environment variable
# export OPENAI_API_KEY='your-api-key'
openai.api_key = os.getenv("OPENAI_API_KEY")

def generate_product_description(product_name, features, target_audience, tone="friendly and informative"):
    prompt = f"""
    Generate a compelling product description for an e-commerce website.

    Product Name: {product_name}
    Key Features:
    - {chr(10).join(features)}
    Target Audience: {target_audience}
    Tone: {tone}

    Description:
    """
    try:
        response = openai.Completion.create(
            engine="text-davinci-003", # Or a newer chat model like gpt-3.5-turbo
            prompt=prompt,
            max_tokens=150,
            n=3, # Generate 3 variations
            stop=None,
            temperature=0.7,
        )
        descriptions = [choice.text.strip() for choice in response.choices]
        return descriptions
    except Exception as e:
        print(f"Error generating description: {e}")
        return []

if __name__ == "__main__":
    product = "Ergonomic Office Chair"
    features = [
        "Adjustable lumbar support",
        "Breathable mesh back",
        "Pneumatic seat height adjustment",
        "360-degree swivel"
    ]
    audience = "Remote workers and office professionals seeking comfort"
    
    generated_descriptions = generate_product_description(product, features, audience)
    
    if generated_descriptions:
        print(f"--- Descriptions for {product} ---")
        for i, desc in enumerate(generated_descriptions):
            print(f"{i+1}. {desc}\n")
    else:
        print("Failed to generate descriptions.")

3. Automated Social Media Content Scheduler for E-commerce

E-commerce businesses need consistent social media presence. A tool that allows users to upload product images/videos, write captions, and schedule posts across platforms like Instagram, Facebook, and Twitter, with smart timing suggestions, is highly valuable.

Technical Stack:

• Backend: Node.js (Express) or PHP (Laravel/Symfony) for managing user accounts, content, and scheduling.
• Database: MySQL or PostgreSQL.
• Frontend: Vue.js or React for the scheduling interface.
• Social Media APIs: Facebook Graph API, Twitter API v2.
• Job Scheduling: `node-cron` (Node.js) or `cron` jobs (Linux) for triggering posts.
• Image/Video Handling: Cloudinary or AWS S3 for storage and processing.
• Deployment: Heroku, DigitalOcean, or AWS EC2.

Example Scheduling Logic (Node.js with `node-cron`):

const cron = require('node-cron');
const axios = require('axios'); // For making API requests to social media platforms

// Assume 'schedule' is an object from your database:
// { id: 1, user_id: 101, platform: 'twitter', content: 'Check out our new arrivals! #ecommerce', scheduled_time: '2023-10-27T10:00:00Z', posted_at: null }

function schedulePost(schedule) {
    // Parse the cron string from the scheduled_time
    // Example: '2023-10-27T10:00:00Z' -> '0 10 27 10 *' (minute, hour, dayOfMonth, month, dayOfWeek)
    const date = new Date(schedule.scheduled_time);
    const cronExpression = `${date.getMinutes()} ${date.getHours()} ${date.getDate()} ${date.getMonth() + 1} *`;

    cron.schedule(cronExpression, async () => {
        console.log(`Attempting to post to ${schedule.platform} for schedule ID ${schedule.id}`);
        try {
            let response;
            if (schedule.platform === 'twitter') {
                // Replace with actual Twitter API v2 call
                response = await axios.post('https://api.twitter.com/2/tweets', { text: schedule.content }, {
                    headers: { 'Authorization': `Bearer YOUR_TWITTER_BEARER_TOKEN` }
                });
                console.log('Tweet posted successfully:', response.data);
            } else if (schedule.platform === 'facebook') {
                // Replace with actual Facebook Graph API call
                response = await axios.post(`https://graph.facebook.com/v18.0/YOUR_PAGE_ID/feed`, null, {
                    params: {
                        message: schedule.content,
                        access_token: 'YOUR_PAGE_ACCESS_TOKEN'
                    }
                });
                console.log('Facebook post created:', response.data);
            }
            // Update schedule.posted_at in your database here
            // await updateScheduleStatus(schedule.id, response.data.id); 
        } catch (error) {
            console.error(`Error posting to ${schedule.platform} for schedule ID ${schedule.id}:`, error.response ? error.response.data : error.message);
            // Handle error, maybe retry or mark as failed
        }
    }, {
        scheduled: true,
        timezone: "UTC" // Important for accurate scheduling
    });
    console.log(`Scheduled post for ${schedule.id} to run at ${schedule.scheduled_time}`);
}

// Example usage:
// const mySchedule = { id: 1, user_id: 101, platform: 'twitter', content: 'New blog post live!', scheduled_time: '2023-10-28T15:30:00Z', posted_at: null };
// schedulePost(mySchedule);

4. Customer Review Aggregator & Management Tool

Managing reviews across Google, Yelp, Facebook, and niche platforms is time-consuming. A tool that pulls all reviews into one place, allows for easy filtering, and facilitates quick responses can be a lifesaver for businesses focused on reputation management.

Technical Stack:

• Backend: Ruby on Rails or Python (Django) for rapid development and API integrations.
• Database: PostgreSQL.
• Frontend: Stimulus (Rails) or Alpine.js for dynamic updates without a full SPA framework, keeping it lightweight.
• APIs: Google My Business API, Yelp Fusion API, Facebook Graph API. (Note: Direct scraping is fragile and often against ToS).
• Webhooks: For real-time notifications of new reviews.
• Deployment: Render, Heroku, or a VPS.

Example Google My Business API Interaction (Conceptual Python):

import googleapiclient.discovery
from google.oauth2 import service_account

# Load credentials from a service account key file
# Ensure the service account has access to the Google My Business API
SERVICE_ACCOUNT_FILE = 'path/to/your/service_account.json'
SCOPES = ['https://www.googleapis.com/auth/business.manage']

def get_google_reviews(account_id, location_name):
    try:
        credentials = service_account.Credentials.from_service_account_file(
            SERVICE_ACCOUNT_FILE, scopes=SCOPES)
        
        service = googleapiclient.discovery.build('mybusiness', 'v4', credentials=credentials, cache_discovery=False)

        # Construct the location name string
        location_name_full = f"accounts/{account_id}/locations/{location_name}"

        # Fetch reviews
        request = service.accounts().locations().reviews().list(parent=location_name_full)
        response = request.execute()
        
        return response.get('reviews', [])

    except Exception as e:
        print(f"Error fetching Google reviews: {e}")
        return []

if __name__ == "__main__":
    # Replace with your actual account ID and location name/ID
    ACCOUNT_ID = "1234567890" 
    LOCATION_ID = "9876543210" 
    
    reviews = get_google_reviews(ACCOUNT_ID, LOCATION_ID)
    
    if reviews:
        print("--- Google Reviews ---")
        for review in reviews:
            print(f"Rating: {review['starRating']}")
            print(f"Author: {review['authorName']}")
            print(f"Timestamp: {review['updateTime']}")
            print(f"Content: {review['content']}\n")
    else:
        print("No reviews found or an error occurred.")

5. E-commerce SEO Audit & Improvement Tool

Many small e-commerce sites lack proper SEO. A tool that crawls a site, identifies common SEO issues (broken links, missing meta descriptions, slow pages, keyword density problems), and provides actionable recommendations can be a strong value proposition.

Technical Stack:

• Backend: Python with libraries like `Scrapy` or `BeautifulSoup` for crawling, `requests` for HTTP checks, and potentially `Ahrefs`/`Semrush` API for backlink data.
• Analysis: Custom algorithms for identifying issues.
• Frontend: A simple dashboard using Flask/Django templates or a lightweight framework.
• Reporting: PDF generation for detailed reports.
• Deployment: A dedicated server or a robust cloud instance (e.g., AWS EC2) due to crawling resource intensity.

Example Site Crawler Snippet (Python with `requests` and `BeautifulSoup`):

import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse

def analyze_page_seo(url):
    issues = []
    try:
        response = requests.get(url, timeout=10)
        response.raise_for_status()
        soup = BeautifulSoup(response.text, 'html.parser')

        # 1. Check Title Tag
        title_tag = soup.find('title')
        if not title_tag or not title_tag.string:
            issues.append({'type': 'Missing Title Tag', 'url': url})
        elif len(title_tag.string) < 30 or len(title_tag.string) > 60:
            issues.append({'type': 'Title Tag Length Issue', 'url': url, 'details': f"Length: {len(title_tag.string)} chars"})

        # 2. Check Meta Description
        meta_desc = soup.find('meta', attrs={'name': 'description'})
        if not meta_desc or not meta_desc.get('content'):
            issues.append({'type': 'Missing Meta Description', 'url': url})
        elif len(meta_desc['content']) < 70 or len(meta_desc['content']) > 160:
             issues.append({'type': 'Meta Description Length Issue', 'url': url, 'details': f"Length: {len(meta_desc['content'])} chars"})

        # 3. Check H1 Tags
        h1_tags = soup.find_all('h1')
        if len(h1_tags) != 1:
            issues.append({'type': 'H1 Tag Issue', 'url': url, 'details': f"Found {len(h1_tags)} H1 tags (expected 1)"})

        # 4. Check for broken internal links (simplified)
        base_domain = urlparse(url).netloc
        for link in soup.find_all('a', href=True):
            href = link.get('href')
            absolute_url = urljoin(url, href)
            parsed_url = urlparse(absolute_url)

            # Check only internal links
            if parsed_url.netloc == base_domain:
                try:
                    link_response = requests.head(absolute_url, timeout=5, allow_redirects=True)
                    if link_response.status_code >= 400:
                        issues.append({'type': 'Broken Internal Link', 'url': url, 'details': f"Link to: {absolute_url} (Status: {link_response.status_code})"})
                except requests.exceptions.RequestException:
                    issues.append({'type': 'Unreachable Internal Link', 'url': url, 'details': f"Link to: {absolute_url}"})
        
        # Add more checks: image alt text, keyword density, page speed (via API), etc.

    except requests.exceptions.RequestException as e:
        issues.append({'type': 'Request Error', 'url': url, 'details': str(e)})
    except Exception as e:
        issues.append({'type': 'Parsing Error', 'url': url, 'details': str(e)})
        
    return issues

# To run this, you'd need a crawler function to discover URLs and then call analyze_page_seo for each.
# Example:
# site_url = "https://your-ecommerce-site.com"
# all_issues = []
# discovered_urls = crawl_site(site_url) # Implement a crawler
# for page_url in discovered_urls:
#     all_issues.extend(analyze_page_seo(page_url))
# print(all_issues)

6. Automated Inventory Sync & Low Stock Alerts

For businesses selling on multiple channels (e.g., own website, Amazon, eBay), keeping inventory synchronized is critical to avoid overselling. A Micro-SaaS that syncs inventory levels across platforms and sends alerts for low stock items is a high-value utility.

Technical Stack:

• Backend: Python (Celery for background tasks) or Go for performance.
• Database: PostgreSQL.
• APIs: Shopify, WooCommerce, Amazon MWS/SP-API, eBay API.
• Task Queue: Redis with Celery or RabbitMQ.
• Alerting: Email (SendGrid/Mailgun), SMS (Twilio).
• Deployment: Cloud VM (AWS EC2, GCP Compute Engine) with background workers.

Example Inventory Sync Logic (Conceptual Python with Celery):

from celery import Celery
import requests # For API calls
import os

# Configure Celery
# Assumes Redis is running on localhost:6379
app = Celery('inventory_sync', broker=os.environ.get('CELERY_BROKER_URL', 'redis://localhost:6379/0'))

# --- Placeholder API Client Functions ---
def get_shopify_inventory(product_variant_id):
    # Replace with actual Shopify API call
    print(f"Fetching Shopify inventory for {product_variant_id}...")
    # Dummy response
    return {"available": 50} 

def update_amazon_inventory(sku, quantity):
    # Replace with actual Amazon MWS/SP-API call
    print(f"Updating Amazon inventory for SKU {sku} to {quantity}...")
    return True # Success

def get_platform_inventory_levels(platform, product_identifier):
    # This function would abstract the API calls to different platforms
    if platform == 'shopify':
        return get_shopify_inventory(product_identifier)
    # Add other platforms (amazon, ebay, etc.)
    return {"available": 0}

# --- Celery Task ---
@app.task
def sync_inventory_task(product_sku, source_platform, source_identifier, target_platforms):
    """
    Syncs inventory for a given product SKU across multiple platforms.
    
    Args:
        product_sku (str): The unique SKU for the product.
        source_platform (str): The platform to get the 'master' inventory level from (e.g., 'shopify').
        source_identifier (str): The product/variant ID on the source platform.
        target_platforms (list): A list of tuples, e.g., [('amazon', 'FBA123'), ('ebay', 'EBAY456')]
    """
    print(f"Starting sync for SKU: {product_sku}")
    
    # 1. Get inventory from the source platform
    source_inventory = get_platform_inventory_levels(source_platform, source_identifier)
    master_quantity = source_inventory.get("available", 0)
    
    if master_quantity is None:
        print(f"Could not retrieve inventory from source platform {source_platform} for {source_identifier}")
        return {"status": "error", "message": "Failed to get source inventory"}

    print(f"Master quantity for {product_sku} on {source_platform}: {master_quantity}")

    # 2. Update inventory on target platforms
    results = []
    for platform, identifier in target_platforms:
        try:
            # In a real scenario, you'd check current inventory on target platform first
            # and only update if necessary to avoid API rate limits and unnecessary calls.
            # For simplicity, we're just updating directly here.
            
            success = False
            if platform == 'amazon':
                success = update_amazon_inventory(identifier, master_quantity)
            # Add logic for other platforms (eBay, etc.)
            
            if success:
                print(f"Successfully updated {platform} inventory for {identifier} to {master_quantity}")
                results.append({"platform": platform, "identifier": identifier, "status": "success"})
                
                # 3. Check for low stock alerts
                # Define your low stock threshold, e.g., 5 units
                LOW_STOCK_THRESHOLD = 5 
                if master_quantity <= LOW_STOCK_THRESHOLD:
                    # Trigger low stock alert (e.g., send email/SMS)
                    send_low_stock_alert.delay(product_sku, platform, identifier, master_quantity)
            else:
                print(f"Failed to update {platform} inventory for {identifier}")
                results.append({"platform": platform, "identifier": identifier, "status": "failed"})
                
        except Exception as e:
            print(f"Error updating {platform} ({identifier}): {e}")
            results.append({"platform": platform, "identifier": identifier, "status": "error", "message": str(e)})

    return {"status": "completed", "updates": results}

@app.task
def send_low_stock_alert(sku, platform, identifier, current_quantity):
    """Sends a low stock notification."""
    subject = f"Low Stock Alert: {sku} on {platform}"
    message = f"Product SKU: {sku}\nPlatform: {platform}\nIdentifier: {identifier}\nCurrent Quantity: {current_quantity}\n\nThreshold is {LOW_STOCK_THRESHOLD} or less."
    
    # Replace with actual email sending logic (e.g., using SendGrid, Mailgun)
    print(f"--- Sending Low Stock Alert ---")
    print(f"To: [email protected]")
    print(f"Subject: {subject}")
    print(f"Body:\n{message}")
    print(f"-----------------------------")
    # Example: send_email_via_sendgrid(to='[email protected]', subject=subject, body=message)
    return True

# To run this:
# 1. Start Redis: redis-server
# 2. Start Celery worker: celery -A your_module_name worker -l info
# 3. Call the task: sync_inventory_task.delay('SKU123', 'shopify', 'variant_id_xyz', [('amazon', 'FBA123'), ('ebay', 'EBAY456')])

7. Automated Order Fulfillment Status Tracker

Customers want to know where their orders are. A tool that integrates with shipping carriers (FedEx, UPS, USPS) via APIs and provides a centralized, branded tracking page for customers, or updates internal systems, can improve customer satisfaction and reduce “where is my order?” (WISMO) support inquiries.

Technical Stack:

• Backend: Node.js (Express) or PHP (Laravel).
• Database: PostgreSQL or MongoDB.
• Frontend: Simple HTML/CSS/JS or a framework like Vue.js for the tracking page.
• Shipping APIs: Shippo, EasyPost, or direct carrier APIs (UPS, FedEx).
• Webhooks: To receive real-time shipping status updates.
• Deployment: Serverless (AWS Lambda + API Gateway) or a small containerized service.

Example Tracking Logic (Node.js with EasyPost):

const EasyPostClient = require('@easypost/api');
const express = require('express');
const bodyParser = require('body-parser');
const app = express();
app.use(bodyParser.json());

// Initialize EasyPost client with your API key
// Ensure your API key is stored securely (e.g., environment variable)
const easypost = new EasyPostClient(process.env.EASYPOST_API_KEY);

// Endpoint to retrieve tracking status for a given tracking code
app.get('/track/:tracking_code', async (req, res) => {
    const { tracking_code } = req.params;

    try {
        // EasyPost automatically detects the carrier based on the tracking code format
        const tracking = await easypost.tracking.retrieve(tracking_code);
        
        // You might want to store this tracking info in your DB associated with an order ID
        // await saveTrackingInfo(orderId, tracking_code, tracking);

        res.json({
            tracking_code: tracking.id,
            status: tracking.status,
            carrier: tracking.carrier,
            events: tracking.tracker.track_events, // Array of tracking events
            created_at: tracking.tracker.created_at
        });
    } catch (error) {
        console.error(`Error retrieving tracking for ${tracking_code}:`, error);
        res.status(500).json({ error: 'Failed to retrieve tracking information.' });
    }
});

// Endpoint to handle webhooks from EasyPost (e.g., when status changes)
app.post('/webhooks/easypost', async (req, res) => {
    const payload = req.body;
    console.log('Received EasyPost webhook:', payload.event);

    // Verify the webhook signature if security is critical
    // const signature = req.headers['x-easypost-signature'];
    // if (!EasyPostClient.verifySignature(payload, process.env.EASYPOST_WEBHOOK_SECRET, signature)) {
    //     return res.status(400).send('Invalid signature');
    // }

    if (payload.event === 'track.update') {
        const trackingData = payload.data;
        console.log(`Tracking update for ${trackingData.id}: Status is now ${trackingData.status}`);
        
        // Update your database with the new status
        // await updateOrderStatusFromWebhook(trackingData.id, trackingData.status); 
    }

    res.status(200).send('Webhook received');
});

// Start the server
const PORT = process.env.PORT || 3000;
app.listen(PORT, () => {
    console.log(`Tracking service running on port ${PORT}`);
});

// Example usage:
// 1. Create a shipment and get a tracking code (simplified)
// easypost.shipment.create({ ... }).then(shipment => {
//     const trackingCode = shipment.tracking_code;
//     console.log(`Tracking code: ${trackingCode}`);
//     // Now you can use GET /track/:trackingCode
// });

8. Automated Discount Code Generator & Manager

Businesses often run promotions with unique or time-limited discount codes. A tool that helps generate complex codes (e.g., `SUMMER2023-XYZ123`), manages their usage, and tracks their effectiveness can streamline marketing efforts.

Technical Stack:

• Backend: PHP (Laravel) or Python (Django).
• Database: PostgreSQL.
• Code Generation: Use libraries like `random` and `string` in Python, or `Str::random()` in Laravel.
• E-commerce Platform Integration: APIs for Shopify, WooCommerce, etc., to create and manage coupon codes.
• Frontend: Simple form for defining code parameters (prefix, length, expiry, discount type).
• Deployment: Standard web hosting or PaaS (Heroku, Render).

Example Code Generation & Shopify API (Python):

import requests
import string
import random
import json
from datetime import datetime, timedelta

# Assume Shopify API credentials are set as environment variables
SHOPIFY_STORE_DOMAIN = os.environ.get("SHOPIFY_STORE_DOMAIN") # e.g., "your-store.myshopify.com"
SHOPIFY_API_KEY = os.environ.get("SHOPIFY_API_KEY")
SHOPIFY_API_PASSWORD = os.environ.get("SHOPIFY_API_PASSWORD")
SHOPIFY_API_VERSION = "2023-10"

def generate_discount_code(prefix="SALE", length=8):
    """Generates a random alphanumeric code."""
    characters = string.ascii_uppercase + string.digits
    random_part = ''.join(random.choice(characters) for _ in range(length))
    return f"{prefix}-{random_part}"

def create_shopify_discount(code, amount, type='percentage', starts_at=None, ends_at=None, usage_limit=None):
    """Creates a discount code in Shopify."""
    url = f"https://{SHOPIFY_STORE_DOMAIN}/admin/api/{SHOPIFY_API_VERSION}/discount_codes.json"
    auth = (SHOPIFY_API_KEY, SHOPIFY_API_PASSWORD)
    headers = {"Content-Type": "application/json"}

    discount_data = {
        "code": code,
        "amount": amount,
        "type": type, # e.g., 'percentage', 'fixed_amount'
    }

    if starts_at:
        discount_data["starts_at"] = starts_at.isoformat() + "Z"
    if ends_at:
        discount_data["ends_at"] = ends_at.isoformat() + "Z"
    if usage_limit:
        discount_data["usage_limit"] = usage_limit
        
    # For simplicity, this example creates a general discount code.
    # Real-world scenarios might involve creating Price Rules first, then attaching codes.
    # The Shopify API for discount codes has evolved. Check current documentation.
    # This example uses the older /discount_codes.json endpoint which might be deprecated.
    # A more robust solution uses the /price_rules.json endpoint.

    payload = json.dumps({"discount_code": discount_data})

    try:
        response = requests.post(url, headers=headers, data=payload, auth=auth)
        response.raise_for_status()
        return response.json()
    except requests.exceptions.RequestException as e:
        print(f"Error creating Shopify discount: {e}")
        print(f"Response: {response.text if response else 'No response'}")
        return None

if __name__ == "__main__":
    # Example: Create a 15% off code valid for 7 days
    generated_code = generate_discount_code("SUMMER15", 6)
    
    now = datetime.utcnow()
    end_date = now + timedelta(days=7)
    
    print(f"Generated code: {generated_code}")
    
    # Note: The direct /discount_codes.json endpoint might be deprecated. 
    # Refer to Shopify's latest API docs for creating discounts via Price Rules.
    # This is a conceptual example.
    # result = create_shopify_discount(
    #     code=generated_code, 
    #     amount=15, 
    #     type='percentage', 
    #     starts_at=now, 
    #     ends_at=end_date,
    #     usage_limit=100 
    # )
    
    # if result:
    #     print("Shopify discount created successfully:")
    #     print(json.dumps(result, indent=2))
    # else:
    #     print("Failed to create Shopify discount.")
    print("Note: Shopify API for discount codes has changed. Please consult latest documentation for Price Rules.")

9. Automated Competitor Price Monitoring

Knowing competitor pricing is crucial for market positioning. A tool that monitors competitor product pages and alerts