Top 5 Custom Workflow and CRM Business Ideas for E-commerce Retailers that Will Dominate the Software Industry in 2026
1. AI-Powered Predictive Inventory Management & Replenishment System
Traditional inventory management relies on historical sales data and static reorder points. For 2026, the winning strategy involves a dynamic, AI-driven system that forecasts demand with granular accuracy, factoring in seasonality, marketing campaigns, competitor actions, and even external events like weather patterns or social media trends. This system should not only predict optimal stock levels but also automate replenishment orders, minimizing stockouts and overstock situations.
The core of this system would be a machine learning model, likely a time-series forecasting algorithm such as ARIMA, Prophet, or a more complex LSTM network. This model would ingest data from multiple sources:
- E-commerce platform sales data (SKU-level, timestamps, quantities, prices)
- Marketing campaign performance (ad spend, click-through rates, conversion rates)
- Website analytics (traffic sources, user behavior, conversion funnels)
- External data feeds (weather APIs, economic indicators, social media sentiment analysis)
- Supplier lead times and reliability metrics
Let’s outline a Python-based backend for this system. We’ll use libraries like pandas for data manipulation, scikit-learn or tensorflow/pytorch for ML models, and potentially statsmodels for traditional time-series analysis.
Data Ingestion and Preprocessing Pipeline
A robust data pipeline is crucial. This could be orchestrated using tools like Apache Airflow or Prefect. For demonstration, consider a simplified Python script that fetches data and prepares it for the model.
import pandas as pd
from datetime import datetime, timedelta
def fetch_sales_data(start_date, end_date):
# In a real scenario, this would query your e-commerce DB (e.g., PostgreSQL, MySQL)
# or an API (e.g., Shopify API, WooCommerce API).
# For this example, we'll simulate data.
dates = pd.date_range(start=start_date, end=end_date, freq='D')
data = {
'date': dates,
'sku': ['SKU001'] * len(dates),
'quantity_sold': [max(0, int(20 + 10 * (d.dayofweek - 3) + 5 * (d.month - 6) + pd.np.random.randn() * 5)) for d in dates]
}
return pd.DataFrame(data)
def fetch_marketing_data(start_date, end_date):
# Simulate marketing spend and its impact
dates = pd.date_range(start=start_date, end=end_date, freq='D')
data = {
'date': dates,
'campaign_spend': [100 + 50 * (d.dayofweek == 5) + 50 * (d.dayofweek == 6) + pd.np.random.randn() * 20 for d in dates],
'campaign_impact_factor': [1.0 + 0.1 * (d.dayofweek == 5) + 0.1 * (d.dayofweek == 6) + pd.np.random.randn() * 0.05 for d in dates]
}
return pd.DataFrame(data)
def preprocess_data(sales_df, marketing_df):
df = pd.merge(sales_df, marketing_df, on='date', how='left')
df['campaign_spend'] = df['campaign_spend'].fillna(0)
df['campaign_impact_factor'] = df['campaign_impact_factor'].fillna(1.0)
# Feature Engineering
df['day_of_week'] = df['date'].dt.dayofweek
df['month'] = df['date'].dt.month
df['year'] = df['date'].dt.year
df['week_of_year'] = df['date'].dt.isocalendar().week.astype(int)
# Lagged features (e.g., sales from previous day/week)
df['lag_1_day_sales'] = df['quantity_sold'].shift(1).fillna(0)
df['lag_7_day_sales'] = df['quantity_sold'].shift(7).fillna(0)
# Interaction features
df['spend_x_impact'] = df['campaign_spend'] * df['campaign_impact_factor']
df = df.set_index('date')
return df.dropna() # Remove rows with NaNs from shifts
# Example Usage
end_date = datetime.now()
start_date = end_date - timedelta(days=365 * 2) # Two years of data
sales_data = fetch_sales_data(start_date, end_date)
marketing_data = fetch_marketing_data(start_date, end_date)
processed_df = preprocess_data(sales_data, marketing_data)
print(processed_df.head())
Demand Forecasting Model (LSTM Example)
For advanced forecasting, a Long Short-Term Memory (LSTM) network can capture complex temporal dependencies. This requires libraries like tensorflow or pytorch.
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
# Assuming processed_df is available from the previous step
# We need to scale the data for the neural network
scaler = MinMaxScaler(feature_range=(0, 1))
# Select features and target
features = ['quantity_sold', 'campaign_spend', 'day_of_week', 'month', 'year', 'lag_1_day_sales', 'lag_7_day_sales', 'spend_x_impact']
target = 'quantity_sold'
scaled_data = scaler.fit_transform(processed_df[features + [target]])
# Prepare data for LSTM: create sequences
def create_sequences(data, sequence_length):
X, y = [], []
for i in range(len(data) - sequence_length):
X.append(data[i:(i + sequence_length), :-1]) # All features except target for input
y.append(data[i + sequence_length, -1]) # Target value
return np.array(X), np.array(y)
sequence_length = 30 # Use the last 30 days to predict the next day
X, y = create_sequences(scaled_data, sequence_length)
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, shuffle=False)
# Build the LSTM model
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))
model.add(Dropout(0.2))
model.add(LSTM(units=50, return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(units=25))
model.add(Dense(units=1)) # Output layer for predicting the target
model.compile(optimizer='adam', loss='mean_squared_error')
# Train the model
history = model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.1, verbose=1)
# Evaluate the model (on test set)
loss = model.evaluate(X_test, y_test, verbose=0)
print(f"Test Loss: {loss}")
# Function to predict future demand
def predict_demand(model, last_sequence, scaler, sequence_length, num_days_to_predict):
predictions = []
current_sequence = last_sequence.copy()
for _ in range(num_days_to_predict):
# Predict the next value
predicted_scaled = model.predict(current_sequence)
predictions.append(scaler.inverse_transform(np.concatenate([np.zeros((1, len(features))), predicted_scaled], axis=1))[:, -1][0]) # Inverse transform only the target
# Prepare the next sequence: remove the first element, add the prediction
# This is a simplified approach; in reality, you'd need to generate future features (e.g., campaign spend)
# For simplicity, we'll assume future features are constant or predictable.
# Here, we'll just use the predicted quantity and dummy future features.
next_input_features = np.zeros((1, len(features)))
next_input_features[0, -1] = predicted_scaled[0, 0] # Placeholder for scaled prediction
# In a real system, you'd generate day_of_week, month, year, campaign_spend etc. for future dates.
# For this example, we'll just use the last known features and the predicted quantity.
# This is a significant simplification.
# A more robust approach would involve predicting features too or using external forecasts.
# For demonstration, let's just shift and append a dummy feature.
next_sequence_input = np.append(current_sequence[0, 1:, :], next_input_features, axis=0)
current_sequence = next_sequence_input.reshape(1, sequence_length, len(features))
return predictions
# Example: Predict next 7 days
last_sequence_scaled = scaled_data[-sequence_length:]
last_sequence_scaled = last_sequence_scaled.reshape(1, sequence_length, len(features))
# Note: The predict_demand function needs significant enhancement to handle future feature generation.
# For now, it's illustrative.
# predicted_quantities = predict_demand(model, last_sequence_scaled, scaler, sequence_length, 7)
# print("Predicted quantities for next 7 days:", predicted_quantities)
Automated Replenishment Logic
Once demand is predicted, the system needs to calculate optimal reorder points and quantities. This involves considering:
- Predicted demand for the lead time period.
- Current inventory levels.
- Safety stock levels (based on demand variability and desired service level).
- Supplier lead times.
- Minimum Order Quantities (MOQs) from suppliers.
- Economic Order Quantity (EOQ) principles, if applicable.
def calculate_reorder_point(predicted_demand_lead_time, safety_stock):
return predicted_demand_lead_time + safety_stock
def calculate_reorder_quantity(current_inventory, reorder_point, supplier_moq, max_stock_level=None):
if current_inventory < reorder_point:
# Basic EOQ calculation (simplified)
# EOQ = sqrt((2 * D * S) / H)
# D = Annual Demand, S = Ordering Cost, H = Holding Cost per unit per year
# This requires more data than we have here. We'll use a simpler logic.
# Determine quantity needed to reach max_stock_level or a reasonable amount
needed = max_stock_level - current_inventory if max_stock_level else reorder_point * 2 # Arbitrary multiplier
# Ensure MOQ is met
reorder_qty = max(supplier_moq, needed)
return reorder_qty
return 0 # No reorder needed
# Example application
sku_data = {
'SKU001': {
'current_stock': 150,
'safety_stock': 50,
'supplier_moq': 100,
'max_stock_level': 500
}
}
# Assume predicted_demand_for_lead_time is calculated from the forecasting model's output
# For example, if lead time is 7 days and average predicted demand is 30/day:
predicted_demand_for_lead_time = 30 * 7 # Example value
for sku, data in sku_data.items():
reorder_point = calculate_reorder_point(predicted_demand_for_lead_time, data['safety_stock'])
if data['current_stock'] < reorder_point:
reorder_quantity = calculate_reorder_quantity(
data['current_stock'],
reorder_point,
data['supplier_moq'],
data['max_stock_level']
)
if reorder_quantity > 0:
print(f"SKU {sku}: Reorder {reorder_quantity} units. Current stock: {data['current_stock']}, Reorder Point: {reorder_point}")
# Trigger automated purchase order creation via API integration with ERP/supplier system
else:
print(f"SKU {sku}: No reorder needed. Current stock: {data['current_stock']}, Reorder Point: {reorder_point}")
2. Hyper-Personalized Customer Journey Orchestration Engine
Beyond basic segmentation, the future lies in orchestrating individual customer journeys in real-time. This involves a sophisticated CRM that tracks every touchpoint, analyzes behavior, and triggers personalized communications and offers across multiple channels (email, SMS, push notifications, website content, ads) dynamically. The goal is to guide each customer from awareness to advocacy, maximizing lifetime value.
Data Model for Customer Journey Tracking
A robust data model is essential. We’ll need tables for customers, events, campaigns, and the state of each customer’s journey.
-- Customers Table
CREATE TABLE customers (
customer_id UUID PRIMARY KEY,
email VARCHAR(255) UNIQUE NOT NULL,
first_name VARCHAR(100),
last_name VARCHAR(100),
created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
-- Add fields for segmentation, preferences, etc.
lifetime_value DECIMAL(10, 2) DEFAULT 0.00,
last_purchase_date DATE
);
-- Events Table (e.g., page view, add to cart, purchase, email open, click)
CREATE TABLE customer_events (
event_id BIGSERIAL PRIMARY KEY,
customer_id UUID REFERENCES customers(customer_id),
event_type VARCHAR(100) NOT NULL, -- e.g., 'page_view', 'add_to_cart', 'purchase', 'email_open'
event_timestamp TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
event_data JSONB -- Store event-specific details (e.g., product_id, URL, campaign_id)
);
-- Campaigns Table
CREATE TABLE campaigns (
campaign_id UUID PRIMARY KEY,
campaign_name VARCHAR(255) NOT NULL,
campaign_type VARCHAR(100) NOT NULL, -- e.g., 'email', 'sms', 'push', 'ad'
created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP
);
-- Customer Journey State Table
CREATE TABLE customer_journeys (
journey_id UUID PRIMARY KEY,
customer_id UUID REFERENCES customers(customer_id),
current_stage VARCHAR(100) NOT NULL, -- e.g., 'awareness', 'consideration', 'purchase', 'retention', 'advocacy'
last_stage_update TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
active_campaign_id UUID REFERENCES campaigns(campaign_id) NULL, -- Track current active campaign
journey_data JSONB -- Store journey-specific state or progress
);
-- Indexing for performance
CREATE INDEX idx_customer_events_customer_id ON customer_events(customer_id);
CREATE INDEX idx_customer_events_timestamp ON customer_events(event_timestamp);
CREATE INDEX idx_customer_journeys_customer_id ON customer_journeys(customer_id);
Real-time Event Processing and Journey Update
A message queue (like Kafka or RabbitMQ) and a stream processing engine (like Apache Flink or Spark Streaming) are ideal for handling high volumes of customer events. For simpler setups, a background job processor (e.g., Celery with Redis/RabbitMQ) can work.
# Example using a hypothetical event consumer and journey update logic
import json
from datetime import datetime
# Assume these functions interact with your database
def get_customer_journey(customer_id):
# Fetch from customer_journeys table
pass
def update_customer_journey(customer_id, new_stage, active_campaign_id=None):
# Update customer_journeys table
pass
def get_next_campaign(customer_id, current_stage):
# Logic to determine the next best campaign based on stage, behavior, etc.
# This would involve complex rules or ML models.
# Example: If stage is 'consideration' and event is 'add_to_cart', trigger 'abandoned_cart_email'.
return {'campaign_id': 'some-uuid', 'campaign_type': 'email'}
def process_customer_event(event_payload):
event_data = json.loads(event_payload)
customer_id = event_data.get('customer_id')
event_type = event_data.get('event_type')
event_timestamp = datetime.fromisoformat(event_data.get('event_timestamp'))
if not customer_id:
return
journey = get_customer_journey(customer_id)
current_stage = journey['current_stage'] if journey else 'awareness'
# --- Journey Logic ---
new_stage = current_stage
next_campaign = None
if event_type == 'page_view' and 'product' in event_data.get('event_data', {}):
if current_stage == 'awareness':
new_stage = 'consideration'
next_campaign = get_next_campaign(customer_id, new_stage) # Suggest next step
elif event_type == 'add_to_cart':
if current_stage != 'purchase':
new_stage = 'consideration' # Or 'cart_filled'
next_campaign = get_next_campaign(customer_id, new_stage) # Trigger abandoned cart sequence if needed
elif event_type == 'purchase':
new_stage = 'purchase'
# Update LTV, last purchase date, etc.
update_customer_metrics(customer_id, event_data['order_total'])
next_campaign = get_next_campaign(customer_id, new_stage) # Move to retention/post-purchase
elif event_type == 'email_open' or event_type == 'email_click':
# Update engagement metrics, potentially move stage
pass
# Update journey state if changed
if new_stage != current_stage or (next_campaign and next_campaign['campaign_id'] != journey.get('active_campaign_id')):
update_customer_journey(customer_id, new_stage, next_campaign['campaign_id'] if next_campaign else None)
# Trigger campaign execution if a new campaign is identified
if next_campaign:
trigger_campaign(next_campaign['campaign_id'], customer_id)
# --- Campaign Triggering ---
def trigger_campaign(campaign_id, customer_id):
# Enqueue a job to send the campaign (email, SMS, etc.)
print(f"Triggering campaign {campaign_id} for customer {customer_id}")
pass
# Example of receiving an event (e.g., from Kafka consumer)
# event_payload = '{"customer_id": "...", "event_type": "add_to_cart", "event_timestamp": "...", "event_data": {"product_id": "...", "quantity": 1}}'
# process_customer_event(event_payload)
Personalization Engine Integration
The orchestration engine should feed into a personalization engine. This engine uses ML models (e.g., collaborative filtering, content-based filtering, deep learning recommenders) to generate personalized product recommendations, content suggestions, and even dynamic website layouts. The orchestration engine ensures these personalized elements are delivered at the right time and through the right channel.
# Example: Integrating with a recommendation service
def get_personalized_recommendations(customer_id, num_recommendations=5):
# Call an external recommendation API or internal ML model
# Example API call:
# response = requests.get(f"https://api.example.com/recommendations?customer_id={customer_id}&count={num_recommendations}")
# return response.json()['products']
return [f"product_id_{i}" for i in range(num_recommendations)] # Dummy recommendations
# In process_customer_event, after determining a stage or triggering a campaign:
# if new_stage == 'consideration':
# recommendations = get_personalized_recommendations(customer_id)
# # Use these recommendations in the next email/website content
# print(f"Recommendations for {customer_id}: {recommendations}")
3. Dynamic Pricing & Promotion Optimization Platform
Static pricing is a relic. Winning e-commerce businesses in 2026 will leverage platforms that dynamically adjust prices and promotions based on real-time market conditions, competitor pricing, inventory levels, customer segmentation, and demand elasticity. This requires sophisticated algorithms and seamless integration with the e-commerce platform.
Competitor Price Monitoring Module
Web scraping is the foundation. Tools like Scrapy (Python) or commercial services can be used to collect competitor pricing data. This data needs to be cleaned, structured, and stored efficiently.
# Simplified Scrapy spider example (conceptual)
# In a real scenario, this would be a full Scrapy project with pipelines.
import scrapy
import json
from datetime import datetime
class CompetitorPriceSpider(scrapy.Spider):
name = 'competitor_prices'
# Define start_urls based on product pages of competitors
# start_urls = ['http://competitor.com/product/123']
def parse(self, response):
# Extract product name, price, availability from the response
product_name = response.css('h1.product-title::text').get().strip()
price_text = response.css('span.price::text').get()
price = float(price_text.replace('$', '').replace(',', '')) if price_text else None
availability = response.css('div.availability::text').get().strip()
yield {
'sku': response.meta.get('sku'), # Pass your internal SKU if available
'competitor_url': response.url,
'product_name': product_name,
'price': price,
'currency': 'USD', # Or extract dynamically
'is_available': 'In Stock' in availability,
'scraped_at': datetime.utcnow().isoformat()
}
# --- Data Storage ---
# This scraped data would be stored in a database (e.g., PostgreSQL, MongoDB)
# and potentially processed by a data pipeline (e.g., Airflow).
Dynamic Pricing Algorithm
The algorithm needs to balance profit maximization with sales volume and market share. It should consider:
- Competitor prices (lowest, average, MAP – Minimum Advertised Price).
- Your own inventory levels (high inventory might warrant lower prices).
- Demand elasticity (how sensitive sales are to price changes).
- Product lifecycle stage.
- Promotional calendar.
- Customer segmentation (e.g., offering discounts to loyal customers).
import pandas as pd
import numpy as np
from scipy.optimize import minimize
# Assume 'competitor_data' is a DataFrame with scraped prices
# Assume 'product_data' contains your internal product info (cost, current_price, inventory)
# Assume 'demand_elasticity_model' is a pre-trained model or lookup table
def get_competitor_benchmark(sku, competitor_data):
sku_data = competitor_data[competitor_data['sku'] == sku]
if sku_data.empty:
return {'min_price': None, 'avg_price': None}
return {
'min_price': sku_data['price'].min(),
'avg_price': sku_data['price'].mean()
}
def estimate_demand(price, sku, product_data, competitor_benchmark, demand_elasticity_model):
# This is a complex function. It could use a regression model trained on historical sales data.
# For simplicity, let's use a basic elasticity formula:
# Q = Q0 * (P / P0)^E
# Where Q0 is baseline quantity, P0 is baseline price, E is elasticity.
# A negative E means demand decreases as price increases.
base_quantity = product_data.loc[sku, 'base_sales_volume']
base_price = product_data.loc[sku, 'base_price']
elasticity = product_data.loc[sku, 'demand_elasticity'] # e.g., -1.5
if base_price == 0 or elasticity is None: return base_quantity # Cannot calculate
estimated_quantity = base_quantity * (price / base_price)**elasticity
return max(0, estimated_quantity) # Quantity cannot be negative
def calculate_profit(price, sku, product_data, competitor_benchmark, demand_elasticity_model):
estimated_quantity = estimate_demand(price, sku, product_data, competitor_benchmark, demand_elasticity_model)
cost_per_unit = product_data.loc[sku, 'cost']
profit_per_unit = price - cost_per_unit
total_profit = profit_per_unit * estimated_quantity
return -total_profit # We want to maximize profit, so minimize negative profit
def optimize_price(sku, product_data, competitor_data, demand_elasticity_model):
benchmark = get_competitor_benchmark(sku, competitor_data)
cost = product_data.loc[sku, 'cost']
current_price = product_data.loc[sku, 'current_price']
inventory = product_data.loc[sku, 'inventory']
min_advertised_price = product_data.loc[sku, 'map']
# Define bounds for price optimization
lower_bound = max(cost, min_advertised_price if min_advertised_price else cost * 1.1)
upper_bound = benchmark['min_price'] * 0.95 if benchmark['min_price'] else current_price * 1.5 # Don't price much higher than lowest competitor
# Initial guess
initial_price_guess = current_price
# Objective function to minimize (negative profit)
objective_func = lambda price: calculate_profit(price[0], sku, product_data, benchmark, demand_elasticity_model)
# Constraints (e.g., inventory level considerations)
# If inventory is high, we might want to push sales even at lower margins.
# This can be incorporated into the objective function or bounds.
if inventory < 50: # Low inventory, prioritize margin
upper_bound = min(upper_bound, current_price * 1.1) # Don't drop price too much
elif inventory > 500: # High inventory, push sales
lower_bound = min(lower_bound, current_price * 0.9) # Allow lower prices
result = minimize(objective_func, [initial_price_guess], bounds=[(lower_bound, upper_bound)], method='L-BFGS-B')
if result.success:
optimal_price = result.x[0]
estimated_demand = estimate_demand(optimal_price, sku, product_data, benchmark, demand_elasticity_model)
estimated_profit = -result.fun
return optimal_price, estimated_demand, estimated_profit
else:
return current_price, estimate_demand(current_price, sku, product_data, benchmark, demand_elasticity_model), (current_price - cost) * estimate_demand(current_price, sku, product_data, benchmark, demand_elasticity_model)
# Example Usage:
# Assume product_data and competitor_data are pandas DataFrames
# Assume demand_elasticity_model is loaded or defined
# optimal_price, demand, profit = optimize_price('SKU001', product_data, competitor_data, demand_elasticity_model)
# print(f"Optimal price for SKU001: {optimal_price:.2f}, Estimated Demand: {demand:.0f}, Estimated Profit: {profit:.2f}")
# If optimal_price differs significantly from current_price, trigger an update via e-commerce API.
Promotion Engine
This module determines the optimal type, timing, and targeting of promotions (e.g., BOGO, percentage off, free shipping). It uses similar data inputs as the pricing engine but focuses on discount strategies. It can also integrate with the customer journey orchestration engine to deliver personalized promotions.
def determine_best_promotion(sku, product_data, competitor_data, customer_segment):
# Logic to decide if a promotion is needed, and what type.
# Factors: inventory, competitor actions, sales targets, customer segment sensitivity.
inventory = product_data.loc[sku, 'inventory']
current_price = product_data.loc[sku, 'current_price']
cost = product_data.loc[sku, 'cost']
competitor_min = get_competitor_benchmark(sku, competitor_data)['min_price']
promotion_type = None
discount_value = 0.0
if inventory > 1000 and (competitor_min is None or current_price < competitor_min):
# High inventory, no competitive pressure - maybe a targeted discount?
if customer_segment == 'new':
promotion_type = 'percentage_off'
discount_value = 0.10 # 10% off for new customers
elif customer_segment == 'lapsed':
promotion_type = 'free_shipping' # Incentive to return
discount_value = 0.0
elif competitor_min and current_price > competitor_min * 1.05:
# Competitor is cheaper, need to react
promotion_type = 'price_match' # Or slightly better
discount_value = current_price - competitor_min
elif inventory < 50:
# Low inventory, avoid promotions unless necessary for strategic reasons
pass
return promotion_type, discount_value
# Example Usage:
# promotion_type, discount = determine_best_promotion('SKU001', product_data, competitor_data, 'loyal')
# if promotion_type:
# print(f"SKU001: Offer {promotion_type} with value {discount}")
# # Apply promotion via e-commerce platform API
4. Intelligent Returns & Reverse Logistics Management
Returns are a significant cost center. An intelligent system can reduce return rates by providing better product information pre-purchase, guide customers through a self-service return process, automate return authorization, and optimize the disposition of returned items (restock, refurbish, liquidate, recycle). This also feeds back into product development and merchandising.
Self-Service Returns Portal & Automated Authorization
A customer-facing portal allows users to initiate returns, select reasons, and print labels. Backend logic automates approval based on predefined rules (e.g., within return window, item not final sale).
# Backend API endpoint for initiating a return
from flask import Flask, request, jsonify
from datetime import datetime, timedelta
app = Flask(__name__)
# Assume database functions: get_order_details, get_customer_info, create_return_request, get_product_info
# Assume external service: generate_shipping_label
RETURN_WINDOW_DAYS = 30
FINAL_SALE_CATEGORIES = ['clearance', 'custom_made']
@app.route('/returns/initiate', methods=['POST'])
def initiate_return():
data = request.get_json()
order_id = data.get('order_id')
item_sku = data.get('sku')
return_reason = data.get('reason')
customer_id = data.get('customer_id') # Assume authenticated
if not all([order