Quick Start

Python
import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt

# Basic competitor tracking setup
competitors = {
    'Competitor_X': {'segment': 'thrillers', 'target_price': 3.99},
    'Competitor_Y': {'segment': 'romance', 'target_price': 2.99}
}

# Sample data structure
pricing_data = pd.DataFrame({
    'date': ['2024-01-01', '2024-02-01'],
    'competitor': ['Competitor_X', 'Competitor_X'],
    'segment': ['thrillers', 'thrillers'],
    'books_at_target_price': [15, 23],
    'total_books': [50, 52],
    'avg_price': [4.20, 3.95]
})

Workflow

Progress:

• Define competitor segments and price points to track
• Set up data collection schedule (monthly/weekly)
• Create standardized data format
• Collect pricing data consistently
• Analyze trends and patterns
• Generate actionable insights report

1. Segment Definition

   • Identify key genres (thrillers, romance, sci-fi)
   • Define price brackets (€2.99, €3.99, €4.99, etc.)
   • List main competitors to track

2. Data Collection Template

   Date: YYYY-MM-DD
   Competitor: [Name]
   Segment: [Genre]
   Books at €3.99: [Count]
   Books at €2.99: [Count]
   Total books in segment: [Count]
   Average price: [Amount]
   

3. Monthly Analysis

   • Calculate percentage at each price point
   • Track month-over-month changes
   • Identify seasonal patterns
   • Note promotional periods

4. Report Generation

   • Trend charts per competitor
   • Market share by price segment
   • Competitive positioning matrix
   • Pricing strategy recommendations

Examples

Example 1: Input: Competitor X has 25 thrillers at €3.99 out of 60 total thrillers in March Output: "Competitor X: 41.7% of thrillers priced at €3.99 (up from 35% in February)"

Example 2: Input: Monthly data showing seasonal thriller pricing Output: Trend analysis showing "Q4 sees 60% more titles at €2.99 (holiday promotion pattern)"

Best Practices

• Consistent timing: Collect data on same day each month
• Standard categories: Use consistent genre classifications
• Context tracking: Note major releases or market events
• Price tiers: Focus on 3-4 key price points per segment
• Automation: Use web scraping tools for large competitor sets
• Validation: Cross-check with multiple sources when possible

Common Pitfalls

• Don't mix promotional and regular pricing without noting it
• Avoid comparing different time periods without seasonal context
• Don't ignore new releases vs backlist pricing differences
• Avoid single-month conclusions; look for 3+ month trends
• Don't forget to track competitor catalog size changes