5.1.2 Fundamental Investment Analysis Using Data Boutique’s Data: Luxury Goods

Updated by Andrea Squatrito

Fundamental Investment Analysis Using Data Boutique’s Data: Luxury Goods

Luxury goods companies often maintain higher price points and tend to have a loyal customer base, making their pricing strategies, regional demand trends, and pricing power critical indicators of market strength. This guide outlines how to use Data Boutique’s E0001 schema to analyze these factors for luxury goods, helping analysts evaluate a brand’s resilience, assess regional demand, and understand the impact of currency fluctuations on purchasing behavior.

Overview of E0001 Schema Fields for Luxury Goods

The E0001 schema includes the following relevant fields for luxury goods analysis:

  • Website name: The retailer or eCommerce website where the product is listed.
  • Competence date: Date of data capture.
  • Country Code: Country where the product is listed.
  • Currency Code: Local currency for product pricing.
  • Brand: Brand name of the luxury product.
  • Category 1, Category 2, Category 3: Categorization for hierarchical product organization.
  • Product code: Unique identifier for each product.
  • Product title: Name of the product.
  • Full price in local currency: Product’s original price in the local currency.
  • Discounted price in local currency: Discounted price if applicable, otherwise matches the full price.
  • Full price in EUR: Original price converted to EUR.
  • Discounted price in EUR: Discounted price in EUR.
  • Flag discounted: Indicates if a discount is applied (1 = discounted, 0 = not discounted).

1. Pricing Strategy and Demand Resilience

Purpose

Track price changes over time to assess a luxury brand’s pricing resilience. Brands with consistent price increases often reflect strong brand equity, allowing them to withstand economic fluctuations and inflation.

Step 1: Tracking Price Changes by Brand

Use SQL to calculate the percentage change in prices for each product over time. This analysis helps gauge the brand’s resilience by observing price stability or increases, even amid economic pressures.

SQL Example

SELECT
    brand,
    product_code,
    competence_date,
    full_price_in_eur AS current_price_eur,
    LAG(full_price_in_eur) OVER (PARTITION BY product_code ORDER BY competence_date) AS previous_price_eur,
    ROUND((full_price_in_eur - LAG(full_price_in_eur) OVER (PARTITION BY product_code ORDER BY competence_date)) / 
          LAG(full_price_in_eur) OVER (PARTITION BY product_code ORDER BY competence_date) * 100, 2) AS price_change_percent
FROM
    E0001_data

ORDER BY
    brand, product_code, competence_date;

Step 2: Identifying Price Consistency and Premium Status

Aggregate the data to analyze average price change over time by brand, helping to highlight brands that consistently maintain or increase prices, indicating high customer loyalty and pricing power.

2. Regional Demand Shifts and Currency Impacts

Purpose

Track regional pricing and demand patterns to analyze how luxury brands adjust pricing across geographies, especially in response to currency fluctuations and regional purchasing power.

Step 1: Comparing Prices Across Regions

Use SQL to compare prices for the same product across different countries. By examining price variations, analysts can assess whether pricing strategies are currency-driven or reflect regional demand differences.

SQL Example

SELECT
    product_code,
    brand,
    country_code,
    currency_code,
    full_price_in_eur AS price_eur,
    full_price_in_local_currency AS price_local,
    competence_date
FROM
    E0001_data

ORDER BY
    product_code, brand, country_code, competence_date;

Step 2: Calculating Price Parity and Currency Sensitivity

Calculate price parity between EUR and local currencies to analyze if brands adjust pricing with currency fluctuations. This helps determine currency sensitivity and any consistent pricing adjustments across regions.

Example Calculation in BI Tool or Excel

  1. Create a Price Parity Metric: Calculate the difference between prices in EUR and local currency to monitor pricing adjustments across regions.
  2. Analyze Parity Over Time: Visualize changes over time to see if brands adjust pricing based on currency shifts or maintain consistent pricing.

Step 3: Regional Stock Outs and Restocking Patterns

Track product availability by region to identify demand shifts. For example, frequent restocking or quick sellouts in certain regions can indicate high demand.

3. Discount Analysis and Promotional Strategies

Purpose

In luxury goods, discounts can indicate demand softness. Analyzing the frequency and depth of discounts provides insights into whether luxury brands maintain exclusivity or adjust prices based on demand.

Step 1: Calculating Discount Frequency and Depth by Brand

Use flag_discounted to identify the frequency and depth of discounts, which may indicate lower demand for certain products or economic-driven demand elasticity.

SQL Example

SELECT
    brand,
    product_code,
    COUNT(CASE WHEN flag_discounted = 1 THEN 1 END) AS discount_frequency,
    AVG((full_price_in_eur - discounted_price_in_eur) / full_price_in_eur * 100) AS avg_discount_percentage
FROM
    E0001_data

GROUP BY
    brand, product_code;

Step 2: Visualizing Discount Depth and Frequency in BI Tools

  • Discount Frequency by Brand: Chart the frequency of discounts across different brands in a BI tool like Looker or Power BI to identify which brands discount more frequently.
  • Average Discount Depth: Compare the discount depth by brand and region to determine if discounts are region-specific or applied universally.

4. Seasonality and Product Lifecycle Analysis

Purpose

Luxury goods are often impacted by seasonal demand (e.g., holiday sales, fashion season releases). Analyzing seasonality in pricing and availability helps identify high-demand periods and predict revenue trends.

Step 1: Identifying Seasonal Pricing Patterns

Use date-based aggregations in SQL to calculate the average monthly or quarterly price and discount frequency, which can reveal seasonal trends.

SQL Example

SELECT
    brand,
    category_1,
    DATE_TRUNC('month', competence_date) AS month,
    AVG(full_price_in_eur) AS avg_price_eur,
    AVG(flag_discounted) * 100 AS discount_frequency
FROM
    E0001_data
GROUP BY
    brand, category_1, month
ORDER BY
    month;

Step 2: Visualizing Seasonality in BI Tools

  • Monthly Price Trends: Create a line chart to visualize average monthly prices and highlight peak seasons for luxury goods.
  • Discount Seasonality: Use a bar chart to track discount frequency by month, identifying any seasonal discounting patterns that could signal demand fluctuations.

Conclusion and Investment Insights

Using Data Boutique’s E0001 schema for luxury goods analysis provides valuable insights into a brand’s pricing resilience, regional demand shifts, and pricing strategies. By understanding price stability, discount patterns, and seasonality, investment analysts can gauge luxury brands’ economic resilience and assess potential risks or opportunities tied to consumer demand and pricing power.

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