Top 100 E-commerce Micro-Business Monetization Playbooks to Explode Profits to Double User Engagement and Session Duration
1. Dynamic Pricing Engine: Real-time Demand-Based Adjustments
Implement a real-time pricing engine that dynamically adjusts product prices based on current demand, inventory levels, competitor pricing, and even user behavior. This isn’t just about A/B testing static price points; it’s about a continuously optimizing system.
Consider a Python-based microservice using Redis for fast lookups of demand signals and inventory. The core logic can be a simple weighted average, but the sophistication comes from the input signals.
1.1. Data Ingestion and Signal Aggregation
We need to ingest signals from various sources. For this example, let’s assume we have:
- Inventory Service: A REST API endpoint
GET /api/inventory/{product_id}returning{"stock_level": 150}. - Demand Signals: Real-time event stream (e.g., Kafka topic `product_views`) with messages like
{"product_id": "XYZ789", "timestamp": "...", "event_type": "view"}. - Competitor Data: A scheduled scraper or API feed providing competitor prices.
The aggregation service will listen to the demand stream and periodically query the inventory and competitor data. Redis can store aggregated demand metrics (e.g., views per hour) and current stock.
1.2. Pricing Algorithm (Python Example)
This Python script acts as the pricing engine. It fetches data, applies a pricing formula, and updates the price in a product catalog database (or another service).
import redis
import requests
import time
import json
from datetime import datetime, timedelta
# --- Configuration ---
REDIS_HOST = 'localhost'
REDIS_PORT = 6379
INVENTORY_SERVICE_URL = 'http://inventory-service/api/inventory'
PRODUCT_CATALOG_URL = 'http://product-catalog/api/products' # For updating prices
# --- Pricing Parameters ---
BASE_PRICE_MULTIPLIER = 1.0 # Base price multiplier
DEMAND_SENSITIVITY = 0.05 # How much price changes with demand
INVENTORY_THRESHOLD_LOW = 50 # Stock level below which price increases
INVENTORY_THRESHOLD_HIGH = 200 # Stock level above which price decreases
INVENTORY_PRICE_ADJUSTMENT = 0.02 # Price adjustment factor for inventory
COMPETITOR_PRICE_ADJUSTMENT = 0.03 # Price adjustment factor for competitor price
# --- Redis Client ---
r = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=0, decode_responses=True)
def get_product_data(product_id):
# In a real system, this would fetch from a DB or another service
# For demo, we'll use a placeholder
return {"base_price": 100.00, "competitor_price": 98.00} # Example data
def get_inventory_level(product_id):
try:
response = requests.get(f"{INVENTORY_SERVICE_URL}/{product_id}")
response.raise_for_status()
return response.json().get("stock_level", 0)
except requests.exceptions.RequestException as e:
print(f"Error fetching inventory for {product_id}: {e}")
return None
def get_demand_score(product_id):
# Calculate demand score based on recent views (e.g., last hour)
now = datetime.now()
one_hour_ago = now - timedelta(hours=1)
# Assuming 'product_views' is a Redis sorted set keyed by timestamp
# Or a stream that we process to update counts in Redis
# For simplicity, let's assume we have a cached count in Redis
demand_count = r.get(f"demand:{product_id}:last_hour")
if demand_count:
return int(demand_count)
return 0
def update_product_price(product_id, new_price):
try:
response = requests.put(f"{PRODUCT_CATALOG_URL}/{product_id}/price", json={"price": round(new_price, 2)})
response.raise_for_status()
print(f"Updated price for {product_id} to {new_price:.2f}")
except requests.exceptions.RequestException as e:
print(f"Error updating price for {product_id}: {e}")
def calculate_new_price(product_id):
product_info = get_product_data(product_id)
base_price = product_info["base_price"]
competitor_price = product_info["competitor_price"]
inventory_level = get_inventory_level(product_id)
demand_score = get_demand_score(product_id)
# --- Dynamic Pricing Logic ---
adjusted_price = base_price * BASE_PRICE_MULTIPLIER
# 1. Demand Adjustment
demand_factor = 1 + (demand_score * DEMAND_SENSITIVITY)
adjusted_price *= demand_factor
# 2. Inventory Adjustment
if inventory_level is not None:
if inventory_level < INVENTORY_THRESHOLD_LOW:
inventory_factor = 1 + (INVENTORY_PRICE_ADJUSTMENT * (INVENTORY_THRESHOLD_LOW - inventory_level) / INVENTORY_THRESHOLD_LOW)
adjusted_price *= inventory_factor
elif inventory_level > INVENTORY_THRESHOLD_HIGH:
inventory_factor = 1 - (INVENTORY_PRICE_ADJUSTMENT * (inventory_level - INVENTORY_THRESHOLD_HIGH) / (INVENTORY_THRESHOLD_HIGH * 2)) # Cap reduction
adjusted_price *= inventory_factor
# 3. Competitor Price Adjustment (optional, can be complex)
# Simple approach: try to stay within X% of competitor, but not below our cost/margin
# For this example, let's just nudge it slightly if it's too far off
if competitor_price and abs(adjusted_price - competitor_price) / competitor_price > 0.1: # If more than 10% difference
if adjusted_price > competitor_price:
adjusted_price = adjusted_price * (1 - COMPETITOR_PRICE_ADJUSTMENT)
else:
adjusted_price = adjusted_price * (1 + COMPETITOR_PRICE_ADJUSTMENT)
# Ensure price doesn't go below a minimum threshold (e.g., cost + margin)
MIN_PRICE = base_price * 0.8 # Example minimum
adjusted_price = max(adjusted_price, MIN_PRICE)
return adjusted_price
if __name__ == "__main__":
# In a real system, this would be triggered by events or a scheduler
# For demo, let's process a few hypothetical product IDs
product_ids_to_process = ["ABC123", "XYZ789", "LMN456"]
# Simulate some demand data in Redis for demo
r.set("demand:ABC123:last_hour", 500)
r.set("demand:XYZ789:last_hour", 1200)
r.set("demand:LMN456:last_hour", 300)
# Simulate inventory levels
# Assume inventory service is running and accessible via INVENTORY_SERVICE_URL
# For local testing, you might mock this or have a dummy service
for pid in product_ids_to_process:
new_price = calculate_new_price(pid)
update_product_price(pid, new_price)
time.sleep(0.1) # Small delay between updates
1.3. Deployment and Orchestration
This pricing engine should run as a separate microservice. Use Kubernetes or Docker Swarm for deployment. A cron job or a message queue consumer can trigger the price calculation for specific products or batches of products. For high-volume sites, consider a stream processing framework like Apache Flink or Spark Streaming to process demand signals in near real-time.
2. Gamified Loyalty Program: Points, Badges, and Tiered Rewards
Transform passive customers into active participants by gamifying the loyalty program. This goes beyond simple points-for-purchase. Introduce badges for specific actions, tiered membership levels with increasing benefits, and leaderboards to foster a sense of competition and achievement.
2.1. Core Components
- Points System: Award points for purchases, reviews, social shares, referrals, profile completion, etc.
- Badges: Digital achievements for milestones (e.g., “First Purchase,” “Top Reviewer,” “Referral Master,” “Loyalty Champion – 1 Year”).
- Tiers: Bronze, Silver, Gold, Platinum, each with escalating perks (e.g., free shipping, early access to sales, dedicated support).
- Leaderboards: Display top users based on points, activity, or specific challenges.
- Redemption Store: A catalog where users can redeem points for discounts, exclusive products, or experiences.
2.2. Database Schema (Conceptual SQL)
A relational database like PostgreSQL is suitable for managing user loyalty data.
-- Users table (assuming it exists)
-- CREATE TABLE users (user_id INT PRIMARY KEY, ...);
CREATE TABLE loyalty_tiers (
tier_id SERIAL PRIMARY KEY,
tier_name VARCHAR(50) NOT NULL UNIQUE,
min_points INT NOT NULL,
benefits TEXT -- e.g., "Free Shipping, 10% Discount"
);
CREATE TABLE user_loyalty (
user_id INT PRIMARY KEY REFERENCES users(user_id),
current_points BIGINT DEFAULT 0,
current_tier_id INT REFERENCES loyalty_tiers(tier_id),
last_activity TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
CREATE TABLE loyalty_transactions (
transaction_id BIGSERIAL PRIMARY KEY,
user_id INT NOT NULL REFERENCES users(user_id),
points_change INT NOT NULL, -- Positive for earning, negative for redemption
transaction_type VARCHAR(50) NOT NULL, -- e.g., 'purchase', 'review', 'redemption', 'bonus'
related_entity_id VARCHAR(100), -- e.g., order_id, product_id
timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
CREATE TABLE badges (
badge_id SERIAL PRIMARY KEY,
badge_name VARCHAR(100) NOT NULL UNIQUE,
badge_description TEXT NOT NULL,
icon_url VARCHAR(255)
);
CREATE TABLE user_badges (
user_badge_id BIGSERIAL PRIMARY KEY,
user_id INT NOT NULL REFERENCES users(user_id),
badge_id INT NOT NULL REFERENCES badges(badge_id),
earned_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
UNIQUE (user_id, badge_id) -- A user can only earn a badge once
);
CREATE TABLE redemption_items (
item_id SERIAL PRIMARY KEY,
item_name VARCHAR(100) NOT NULL,
points_cost INT NOT NULL,
description TEXT,
image_url VARCHAR(255),
stock_quantity INT DEFAULT -1 -- -1 for unlimited
);
CREATE TABLE user_redemptions (
redemption_id BIGSERIAL PRIMARY KEY,
user_id INT NOT NULL REFERENCES users(user_id),
item_id INT NOT NULL REFERENCES redemption_items(item_id),
redeemed_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
quantity INT DEFAULT 1,
status VARCHAR(50) DEFAULT 'pending' -- e.g., 'pending', 'fulfilled', 'cancelled'
);
2.3. Backend Logic (PHP Example – Laravel)
This example uses Eloquent ORM in Laravel to handle point accrual and tier updates.
<?php
namespace App\Services;
use App\Models\User;
use App\Models\LoyaltyTransaction;
use App\Models\Badge;
use App\Models\UserBadge;
use App\Models\RedemptionItem;
use App\Models\UserRedemption;
use App\Models\LoyaltyTier;
use Illuminate\Support\Facades\DB;
use Exception;
class LoyaltyService
{
// --- Point Accrual ---
public function addPointsForPurchase(User $user, int $orderId, float $orderTotal): void
{
$pointsToEarn = $this->calculatePointsForPurchase($orderTotal);
$this->recordTransaction($user, $pointsToEarn, 'purchase', $orderId);
$this->checkAndAwardBadges($user, 'purchase');
$this->updateUserTier($user);
}
public function addPointsForReview(User $user, int $productId): void
{
$pointsToEarn = config('loyalty.points.review', 50); // e.g., 50 points
$this->recordTransaction($user, $pointsToEarn, 'review', $productId);
$this->checkAndAwardBadges($user, 'review');
$this->updateUserTier($user);
}
public function addPointsForReferral(User $referrer, User $referredUser): void
{
$pointsToEarn = config('loyalty.points.referral', 200);
$this->recordTransaction($referrer, $pointsToEarn, 'referral', $referredUser->id);
$this->checkAndAwardBadges($referrer, 'referral');
$this->updateUserTier($referrer);
}
// --- Redemption ---
public function redeemItem(User $user, int $itemId, int $quantity = 1): ?UserRedemption
{
$item = RedemptionItem::findOrFail($itemId);
$totalCost = $item->points_cost * $quantity;
if ($user->loyalty_points < $totalCost) {
throw new Exception("Insufficient points.");
}
if ($item->stock_quantity !== -1 && $item->stock_quantity < $quantity) {
throw new Exception("Insufficient stock for this item.");
}
DB::transaction(function () use ($user, $item, $totalCost, $quantity, $itemId) {
$this->recordTransaction($user, -$totalCost, 'redemption', $itemId);
$redemption = new UserRedemption();
$redemption->user_id = $user->id;
$redemption->item_id = $itemId;
$redemption->quantity = $quantity;
$redemption->save();
if ($item->stock_quantity !== -1) {
$item->decrement('stock_quantity', $quantity);
}
$this->updateUserTier($user); // Tier might change after redemption
});
return $redemption ?? null;
}
// --- Badge & Tier Management ---
private function checkAndAwardBadges(User $user, string $triggerType): void
{
// Logic to check if user has met criteria for any new badges
// Example: if $triggerType == 'purchase' and $user->loyaltyTransactions->where('transaction_type', 'purchase')->count() == 1
// then award 'First Purchase' badge.
$badgesToAward = Badge::where('trigger_type', $triggerType)->get(); // Assuming badges have a trigger_type column
foreach ($badgesToAward as $badge) {
// Add complex logic here to check if user qualifies for $badge
// e.g., based on total points, number of reviews, referral count etc.
// For simplicity, let's assume a direct check based on trigger type and a simple condition
if ($triggerType === 'purchase' && $badge->badge_name === 'First Purchase' && $user->loyaltyTransactions->where('transaction_type', 'purchase')->count() === 1) {
$this->awardBadge($user, $badge);
}
// Add more badge conditions...
}
}
private function awardBadge(User $user, Badge $badge): void
{
// Check if user already has the badge
if (!UserBadge::where('user_id', $user->id)->where('badge_id', $badge->id)->exists()) {
$userBadge = new UserBadge();
$userBadge->user_id = $user->id;
$userBadge->badge_id = $badge->id;
$userBadge->save();
// Optionally trigger notification
broadcast(new BadgeAwarded($user, $badge));
}
}
public function updateUserTier(User $user): void
{
$totalPoints = $user->loyalty_points; // Assuming user model has a 'loyalty_points' attribute
$tiers = LoyaltyTier::orderBy('min_points', 'desc')->get();
$newTier = $tiers->firstWhere('min_points', '<=', $totalPoints);
if ($newTier && $user->current_tier_id !== $newTier->tier_id) {
$user->current_tier_id = $newTier->tier_id;
$user->save();
// Optionally trigger notification about tier upgrade
broadcast(new TierUpgraded($user, $newTier));
}
}
// --- Helper Methods ---
private function recordTransaction(User $user, int $points, string $type, ?int $relatedId = null): LoyaltyTransaction
{
$transaction = new LoyaltyTransaction();
$transaction->user_id = $user->id;
$transaction->points_change = $points;
$transaction->transaction_type = $type;
$transaction->related_entity_id = $relatedId;
$transaction->save();
// Update user's total points
$user->increment('loyalty_points', $points);
$user->last_activity = now();
$user->save();
return $transaction;
}
private function calculatePointsForPurchase(float $orderTotal): int
{
// Example: 1 point per dollar spent
return floor($orderTotal * config('loyalty.points.purchase_rate', 1));
}
}
2.4. Frontend Integration
Display user’s points, current tier, earned badges, and available redemption items prominently on the user dashboard and product pages. Use AJAX to update points in real-time after actions like leaving a review. Implement a dedicated “Rewards” or “Loyalty” section for detailed information and redemption.
3. Personalized Product Recommendations: AI-Driven Upsells & Cross-sells
Leverage machine learning to provide hyper-personalized product recommendations. This is crucial for increasing Average Order Value (AOV) and improving user engagement by showing relevant products at the right time.
3.1. Recommendation Engine Architecture
A common approach involves a hybrid model combining:
- Collaborative Filtering: “Users who bought X also bought Y.” Requires user-item interaction data (purchases, views, ratings).
- Content-Based Filtering: “If you liked product X (with attributes A, B, C), you might like product Y (also with attributes A, B).” Requires rich product metadata.
- Matrix Factorization (e.g., SVD): Decomposes the user-item interaction matrix into latent factors representing user preferences and item characteristics.
- Deep Learning Models (e.g., Recurrent Neural Networks – RNNs): Can capture sequential user behavior and session context.
3.2. Data Pipeline & Model Training (Python Example using Surprise & Pandas)
This example uses the `surprise` library for building and testing recommendation models. In production, you’d likely use more robust ML platforms (e.g., AWS SageMaker, Google AI Platform, MLflow).
import pandas as pd
from surprise import Dataset, Reader, SVD
from surprise.model_selection import train_test_split
from surprise import accuracy
import joblib # For saving the model
# --- 1. Load and Prepare Data ---
# Assume you have a CSV file: user_id, item_id, rating (e.g., 1 for view, 5 for purchase)
# Or a Pandas DataFrame loaded from your database.
# Example DataFrame structure:
# data = {'user_id': [1, 1, 2, 2, 3, 3, 3],
# 'item_id': ['A', 'B', 'A', 'C', 'B', 'C', 'D'],
# 'rating': [5, 3, 4, 5, 2, 4, 3]} # 5=purchase, 3=add-to-cart, 1=view
# Load data from CSV
try:
df = pd.read_csv('user_interactions.csv')
except FileNotFoundError:
print("Error: user_interactions.csv not found. Please create it.")
# Create dummy data if file not found for demonstration
data = {'user_id': [1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 5, 5],
'item_id': ['A', 'B', 'A', 'C', 'B', 'C', 'D', 'A', 'E', 'B', 'D', 'F'],
'rating': [5, 3, 4, 5, 2, 4, 3, 5, 4, 3, 5, 2]}
df = pd.DataFrame(data)
df.to_csv('user_interactions.csv', index=False)
print("Created dummy user_interactions.csv")
# Define the rating scale (e.g., 1-5)
reader = Reader(rating_scale=(1, 5))
# Load the dataset into Surprise's format
data = Dataset.load_from_df(df[['user_id', 'item_id', 'rating']], reader)
# --- 2. Split Data ---
trainset, testset = train_test_split(data, test_size=0.25)
# --- 3. Choose and Train Model ---
# Using Singular Value Decomposition (SVD) as an example
algo = SVD(n_factors=100, n_epochs=20, lr_all=0.005, reg_all=0.02)
print("Training model...")
algo.fit(trainset)
print("Model training complete.")
# --- 4. Evaluate Model (Optional but Recommended) ---
print("Evaluating model on test set...")
predictions = algo.test(testset)
rmse = accuracy.rmse(predictions)
mae = accuracy.mae(predictions)
print(f"RMSE: {rmse}")
print(f"MAE: {mae}")
# --- 5. Save the Trained Model ---
model_filename = 'recommendation_svd_model.joblib'
joblib.dump(algo, model_filename)
print(f"Model saved to {model_filename}")
# --- 6. Function to Get Recommendations for a User ---
def get_recommendations(user_id, n_recommendations=10):
# Load the model if not already loaded
try:
loaded_algo = joblib.load(model_filename)
except FileNotFoundError:
print(f"Error: Model file '{model_filename}' not found. Train the model first.")
return []
# Get a list of all unique item IDs
all_item_ids = df['item_id'].unique()
# Get items the user has already interacted with
user_interactions = df[df['user_id'] == user_id]['item_id'].tolist()
# Predict ratings for items the user hasn't interacted with
items_to_predict = [item_id for item_id in all_item_ids if item_id not in user_interactions]
if not items_to_predict:
print(f"User {user_id} has interacted with all available items.")
return []
user_predictions = []
for item_id in items_to_predict:
predicted_rating = loaded_algo.predict(user_id, item_id).est
user_predictions.append((item_id, predicted_rating))
# Sort predictions by estimated rating in descending order
user_predictions.sort(key=lambda x: x[1], reverse=True)
# Return top N recommendations
return user_predictions[:n_recommendations]
# --- Example Usage ---
if __name__ == "__main__":
target_user_id = 3
recommendations = get_recommendations(target_user_id, n_recommendations=5)
print(f"\nRecommendations for User ID {target_user_id}:")
if recommendations:
for item_id, predicted_rating in recommendations:
print(f"- Item: {item_id}, Predicted Rating: {predicted_rating:.2f}")
else:
print("No recommendations available.")
# Example for a user not in the training data (cold start problem)
# The model will still try to predict based on general patterns if possible,
# but performance will be poor. Hybrid approaches or fallback strategies are needed.
target_user_id_new = 99
recommendations_new = get_recommendations(target_user_id_new, n_recommendations=5)
print(f"\nRecommendations for New User ID {target_user_id_new}:")
if recommendations_new:
for item_id, predicted_rating in recommendations_new:
print(f"- Item: {item_id}, Predicted Rating: {predicted_rating:.2f}")
else:
print("No recommendations available (cold start).")
3.3. Serving Recommendations in Production
Deploy the trained model as a microservice (e.g., using Flask or FastAPI in Python). This service will expose an API endpoint like GET /recommendations?user_id={user_id}&count={n}. For real-time recommendations within a user session, consider using a fast in-memory data store (like Redis) to cache recent user activity and pre-computed recommendations.
Cold Start Problem: For new users or new products, collaborative filtering struggles. Use content-based filtering, popularity-based recommendations, or ask users for initial preferences during onboarding.
4. Subscription Box Model: Recurring Revenue Streams
Introduce subscription boxes tailored to specific customer segments or product categories. This provides predictable recurring revenue and fosters customer loyalty through curated experiences.
4.1. Key Considerations
- Curation: What products go into the box? How is it themed?
- Frequency: Monthly, quarterly, bi-monthly?
- Pricing Tiers: Basic, premium, limited edition?
- Logistics: Sourcing, packing, shipping, inventory management for box components.
- Customer Management: Handling subscriptions, cancellations, pauses, and customer feedback.
4.2. Subscription Management Backend (Node.js/Express Example)
Use a robust payment gateway with subscription capabilities (e.g., Stripe, Braintree). This example outlines a basic Express.js API for managing subscriptions.
const express = require('express');
const stripe = require('stripe')('sk_test_YOUR_SECRET_KEY'); // Replace with your actual secret key
const bodyParser = require('body-parser');
const app = express();
app.use(bodyParser.json());
// --- Subscription Product Setup (Done via Stripe Dashboard or API) ---
// Example: A 'monthly_box' product with a 'monthly_price' price ID.
// Price ID: price_12345abcde...
// --- API Endpoints ---
// Endpoint to create a new subscription checkout session
app.post('/create-checkout-session', async (req, res) => {
const { customer_email, price_id } = req.body; // price_id is the Stripe Price ID
if (!customer_email || !price_id) {
return res.status(400).send({ error: 'Missing required fields: customer_email, price_id' });
}
try {
const session = await stripe.checkout.sessions.create({
payment_method_types: ['card'],
line_items: [
{
price: price_id,
quantity: 1,
},
],
mode: 'subscription',
success_url: 'https://yourdomain.com/success?session_id={CHECKOUT_SESSION_ID}',
cancel_url: 'https://yourdomain.com/cancel',
customer_email: customer_email,
// You can also pass an existing Stripe customer ID if available:
// customer: 'cus_xxxxxxxxxxxxxx',
});
res.json({ id: session.id });
} catch (error) {
console.error('Stripe Checkout Session Error:', error);
res.status(500).send({ error: error.message });
}
});
// Endpoint to handle Stripe webhooks (for subscription events)
app.post('/webhook', bodyParser.raw({type: 'application/json'}), async (req, res) => {
const sig = req.headers['stripe-signature'];
const webhookSecret = 'whsec_YOUR_WEBHOOK_SECRET'; // Replace with your actual webhook secret
let event;
try {
event = stripe.webhooks.constructEvent(req.body, sig, webhookSecret);
} catch (err) {
console.log(`Webhook signature verification failed.`, err.message);
return res.sendStatus(400);
}
// Handle the event
switch (event.type) {
case 'customer.subscription.created':
// A customer's subscription was created.
// You might want to update your internal database to mark the user as subscribed.
const subscriptionCreated = event.data.object;
console.log('Subscription created:', subscriptionCreated.id);
// Example: update your user record in your DB
// updateUserSubscriptionStatus(subscriptionCreated.customer, true);
break;
case 'customer.subscription.updated':
// A customer's subscription was updated (e.g., plan changed, cancelled).
const subscriptionUpdated = event.data.object;
console.log('Subscription updated:', subscriptionUpdated.id, 'Status:', subscriptionUpdated.status);
// Handle status changes: active, past_due, canceled, incomplete, etc.
// updateUserSubscriptionStatus(subscriptionUpdated.customer, subscriptionUpdated.status === 'active');
break;
case 'customer.subscription.deleted':
// A customer's subscription was deleted.
const subscriptionDeleted = event.data.object;
console.log('Subscription deleted:', subscriptionDeleted.id);
// updateUserSubscriptionStatus(subscriptionDeleted.customer, false);
break;
// ... handle other event types as needed
default:
console.log(`Unhandled event type ${event.type}`);
}
// Return a 200 response to acknowledge receipt of the event
res.json({received: true});
});
// --- Helper function placeholder ---
// async function updateUserSubscriptionStatus(stripeCustomerId, isSubscribed) {
// // Logic to find user by Stripe customer ID and update their subscription status in your DB
// console.log(`Updating status for customer ${stripeCustomerId} to ${isSubscribed}`);
// }
const PORT = process.env.PORT || 3000;
app.listen(PORT, () => console.log(`Server running on port ${PORT}`));