How to Use Seasonality Analysis in Shopify: Step-by-Step Tutorial

Export Historical Order Data

The foundation of accurate seasonality analysis is comprehensive historical data. You need at least one full year of order data, though two or more years will produce significantly more reliable patterns.

Option A: Export via Shopify Admin

1. Log into your Shopify admin panel
2. Navigate to Orders in the left sidebar
3. Click the Export button in the top right
4. Select All orders (not just current page)
5. Choose CSV for Excel, Numbers, or other spreadsheet programs
6. Select your date range (minimum 12 months recommended)
7. Click Export orders

Option B: Use Shopify API

For larger stores or automated analysis, use the Shopify Admin API:

import shopify
from datetime import datetime, timedelta

# Configure API credentials
shop_url = "your-store.myshopify.com"
api_version = "2024-01"
access_token = "your_access_token"

session = shopify.Session(shop_url, api_version, access_token)
shopify.ShopifyResource.activate_session(session)

# Fetch orders from the last 2 years
end_date = datetime.now()
start_date = end_date - timedelta(days=730)

orders = []
page_info = None

while True:
    if page_info:
        new_orders = shopify.Order.find(
            limit=250,
            created_at_min=start_date.isoformat(),
            created_at_max=end_date.isoformat(),
            page_info=page_info
        )
    else:
        new_orders = shopify.Order.find(
            limit=250,
            created_at_min=start_date.isoformat(),
            created_at_max=end_date.isoformat()
        )

    orders.extend(new_orders)

    if not new_orders.has_next_page():
        break
    page_info = new_orders.next_page_info

# Extract relevant fields
import csv

with open('shopify_orders.csv', 'w', newline='') as file:
    writer = csv.writer(file)
    writer.writerow(['Order ID', 'Created At', 'Total Price', 'Currency', 'Items Count'])

    for order in orders:
        writer.writerow([
            order.id,
            order.created_at,
            order.total_price,
            order.currency,
            len(order.line_items)
        ])

shopify.ShopifyResource.clear_session()

Connect Your Data to MCP Analytics

Now that you have your order data, it's time to upload it to the seasonality analysis tool. MCP Analytics provides a specialized Shopify Orders Seasonality Analysis service designed specifically for this purpose.

Direct Upload Method:

1. Navigate to the Seasonality Analysis Tool
2. Click Upload Data or Choose File
3. Select your exported CSV file
4. Confirm the column mapping:
   • Date column → "Created At" or "Order Date"
   • Revenue column → "Total Price" or "Order Total"
   • Order ID column → "Order ID" or "Order Number"
5. Click Upload and Analyze

API Integration Method:

For recurring analysis, integrate directly with the MCP Analytics API:

import requests
import json

# MCP Analytics API endpoint
api_url = "https://api.mcpanalytics.ai/v1/analysis/seasonality"
api_key = "your_mcp_api_key"

# Prepare your data
with open('shopify_orders.csv', 'rb') as file:
    files = {'file': file}

    headers = {
        'Authorization': f'Bearer {api_key}'
    }

    params = {
        'analysis_type': 'shopify_orders_seasonality',
        'date_column': 'created_at',
        'value_column': 'total_price',
        'frequency': 'daily',
        'decomposition_method': 'additive'
    }

    response = requests.post(
        api_url,
        files=files,
        headers=headers,
        data=params
    )

    if response.status_code == 200:
        results = response.json()
        print("Analysis completed successfully!")
        print(f"Analysis ID: {results['analysis_id']}")
    else:
        print(f"Error: {response.status_code}")
        print(response.text)

{
  "analysis_id": "sa_1234567890abcdef",
  "status": "completed",
  "data_points": 731,
  "date_range": {
    "start": "2023-01-01",
    "end": "2024-12-31"
  },
  "metrics": {
    "total_orders": 15847,
    "total_revenue": 1248935.50,
    "average_order_value": 78.83
  }
}

Set Your Analysis Preferences

Proper configuration ensures your seasonality analysis produces meaningful insights tailored to your business needs.

Key Parameters to Configure:

1. Time Frequency

Choose how granular your analysis should be:

• Daily: Best for detecting day-of-week patterns (e.g., weekend vs. weekday sales)
• Weekly: Smooths out daily noise while showing intra-month patterns
• Monthly: Ideal for identifying seasonal trends (summer vs. winter, holiday seasons)

2. Decomposition Method

Select how seasonal components are separated from the trend:

• Additive: Use when seasonal variations are roughly constant over time (e.g., sales increase by ~$10,000 every December)
• Multiplicative: Use when seasonal variations grow proportionally with your trend (e.g., December sales are 2x the monthly average, and this multiplier stays consistent as you grow)

3. Metrics to Analyze

Select which metrics to examine:

• Total Revenue: Shows dollar value patterns
• Order Count: Shows customer transaction frequency
• Average Order Value: Shows customer spending behavior
• Units Sold: Shows product volume patterns

# Example configuration for the analysis
config = {
    "frequency": "monthly",
    "decomposition_method": "multiplicative",
    "metrics": ["total_revenue", "order_count", "average_order_value"],
    "seasonal_periods": 12,  # 12 months for annual seasonality
    "confidence_interval": 0.95,
    "detect_anomalies": True,
    "forecast_periods": 6  # Optional: forecast next 6 periods
}

Interpret Your Seasonality Results

Once the analysis completes, you'll see several visualizations and data tables. Here's how to interpret each component:

1. Trend Component

This shows the long-term direction of your sales, stripped of seasonal fluctuations. A rising trend indicates overall business growth, while a declining trend suggests you need to address fundamental issues.

2. Seasonal Component

This is the heart of your analysis—the repeating patterns that occur at regular intervals. Look for:

• Peak Months: Months with values above zero (or above 1.0 for multiplicative) indicate higher-than-average sales
• Off-Peak Months: Months with values below zero (or below 1.0) indicate lower-than-average sales
• Magnitude: The size of the variation tells you how dramatic the seasonal swing is

Month          Revenue Index    Order Count Index
January        0.85 (-15%)      0.88 (-12%)
February       0.82 (-18%)      0.85 (-15%)
March          0.95 (-5%)       0.96 (-4%)
April          1.03 (+3%)       1.02 (+2%)
May            1.08 (+8%)       1.07 (+7%)
June           1.12 (+12%)      1.10 (+10%)
July           1.15 (+15%)      1.13 (+13%)
August         1.09 (+9%)       1.08 (+8%)
September      0.98 (-2%)       0.99 (-1%)
October        1.05 (+5%)       1.04 (+4%)
November       1.32 (+32%)      1.28 (+28%)
December       1.56 (+56%)      1.48 (+48%)

What This Tells You:

In this example, December shows a 56% increase over the average month, while January drops 15% below average. This is a classic holiday shopping pattern with a post-holiday slump.

3. Day-of-Week Patterns (If Analyzing Daily Data)

Look for weekly cycles that can inform operational decisions:

Day of Week    Revenue Index    Peak Hours
Monday         0.92 (-8%)       2pm-4pm
Tuesday        0.95 (-5%)       11am-1pm, 3pm-5pm
Wednesday      1.02 (+2%)       12pm-3pm
Thursday       1.08 (+8%)       1pm-4pm
Friday         1.15 (+15%)      12pm-5pm
Saturday       1.10 (+10%)      10am-3pm
Sunday         0.78 (-22%)      1pm-4pm

Actionable Insight: Friday shows peak sales, making it ideal for launching promotions or new products. Sunday's low performance suggests this isn't the best day for email campaigns.

Turn Analysis Into Action

The final step is translating your seasonality insights into concrete business actions.

Export Your Results:

1. Click Download Report in the analysis dashboard
2. Choose your format:
   • PDF: For presentations to stakeholders
   • CSV: For further analysis in Excel or Google Sheets
   • JSON: For integration with other tools or custom dashboards
3. Optionally schedule automated reports (weekly/monthly)

Key Applications of Your Seasonality Data:

1. Inventory Planning

Use your seasonal indices to adjust inventory levels 2-3 months before peak periods:

# Example: Calculate required inventory for peak season
base_monthly_inventory = 5000  # units
november_index = 1.32  # 32% above average
december_index = 1.56  # 56% above average

november_target = base_monthly_inventory * november_index
december_target = base_monthly_inventory * december_index

print(f"November inventory target: {november_target:.0f} units")
print(f"December inventory target: {december_target:.0f} units")

# Output:
# November inventory target: 6600 units
# December inventory target: 7800 units

2. Marketing Budget Allocation

Shift ad spend to align with high-conversion periods:

# Distribute annual marketing budget based on seasonal patterns
annual_budget = 120000  # $120,000
monthly_base = annual_budget / 12  # $10,000

# Apply seasonal adjustments
seasonal_budgets = {
    'January': monthly_base * 0.85,
    'February': monthly_base * 0.82,
    'March': monthly_base * 0.95,
    'April': monthly_base * 1.03,
    'May': monthly_base * 1.08,
    'June': monthly_base * 1.12,
    'July': monthly_base * 1.15,
    'August': monthly_base * 1.09,
    'September': monthly_base * 0.98,
    'October': monthly_base * 1.05,
    'November': monthly_base * 1.32,
    'December': monthly_base * 1.56
}

for month, budget in seasonal_budgets.items():
    print(f"{month}: ${budget:,.2f}")

3. Staffing Optimization

Schedule more customer support and fulfillment staff during peak periods and reduce during slow periods to control costs.

4. Promotional Calendar

Avoid scheduling major promotions during naturally high-demand periods (you'd be discounting sales you'd get anyway). Instead, use promotions to boost off-peak months.