5.1.5 Fundamental Investment Analysis Using Data Boutique’s Data: Apparel and Fast Fashion

Updated by Andrea Squatrito

Fundamental Investment Analysis Using Data Boutique’s Data: Apparel and Fast Fashion

The apparel and fast fashion industry is heavily influenced by seasonality, trend cycles, and pricing agility. This article outlines how to leverage Data Boutique’s E0001 schema to analyze seasonal demand, brand agility, and discounting strategies in the fast fashion market. These insights allow investment analysts to assess the competitive positioning and financial resilience of fast fashion brands.

Overview of E0001 Schema Fields for Apparel and Fast Fashion

Data Boutique’s E0001 schema includes the following relevant fields for apparel and fast fashion analysis:

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

1. Trend Tracking and Seasonal Demand Analysis

Purpose

Track demand patterns across different seasons to identify high-demand periods. Fast fashion relies heavily on seasonal trends, so understanding these demand cycles can help analysts forecast revenue trends and competitive positioning.

Use SQL to calculate average monthly prices, helping to identify peak demand periods or seasonal pricing changes.

SQL Example

SELECT
              product_code,
              brand,
              DATE_TRUNC('month', competence_date) AS month,
              AVG(full_price_in_eur) AS avg_price_eur
          FROM
              E0001_data
          GROUP BY
              product_code, brand, month
          ORDER BY
              month;

Step 2: Seasonal Discount Frequency Analysis

Frequent discounts during certain months (e.g., January sales, back-to-school, or holiday seasons) can reveal seasonal strategies that may impact revenue. Visualize this in a BI tool by tracking discount frequency across seasons.

2. Brand Agility and Inventory Management

Purpose

Analyze inventory turns and discount patterns to assess a brand’s agility in managing inventory. Fast fashion brands that effectively respond to trends often adjust prices quickly to avoid overstock, highlighting their adaptability.

Step 1: Measuring Inventory Turns and Stockouts

Track stockouts and restocking patterns to gauge a brand’s ability to meet demand. Frequent stockouts may indicate high demand, while rapid restocking can signal strong supply chain responsiveness.

SQL Example

SELECT
              product_code,
              brand,
              competence_date,
              COUNT(CASE WHEN full_price_in_eur IS NULL THEN 1 END) AS stockouts
          FROM
              E0001_data
          GROUP BY
              product_code, brand, competence_date;

Step 2: Inventory Management Visualization in BI Tools

In a BI tool like Looker or Power BI:

  • Stockout Heatmap: Display stockout frequency by month, highlighting periods of inventory strain.
  • Inventory Turnover Line Chart: Track inventory levels over time to assess restocking efficiency.

3. Price Sensitivity and Discounting Strategy

Purpose

Identify the depth and frequency of discounts for fast fashion brands, which often rely on discounting to attract price-sensitive customers. Tracking these strategies helps assess the potential revenue impact of promotions.

Step 1: Calculating Discount Depth and Frequency

Use SQL to calculate the average depth of discounts and frequency by brand, which can reveal aggressive discounting strategies.

SQL Example

SELECT
              product_code,
              brand,
              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
              product_code, brand;

In a BI tool:

  • Discount Frequency by Month: Display discount frequency by month to identify high-discount periods.
  • Average Discount Depth by Brand: Track average discount depth over time to see which brands rely heavily on promotions.

4. Product Lifecycle and Seasonal Sales Peaks

Purpose

Analyze product lifecycles to understand which items sell quickly or get discounted over time. This helps assess how well fast fashion brands manage inventory and align with demand trends.

Step 1: Tracking Product Price Reductions Over Time

By monitoring the price trajectory of items, analysts can identify products that align with demand versus those that are heavily discounted over time.

SQL Example

SELECT
              product_code,
              brand,
              competence_date,
              full_price_in_eur AS initial_price,
              discounted_price_in_eur AS current_price,
              ROUND((full_price_in_eur - discounted_price_in_eur) / full_price_in_eur * 100, 2) AS discount_percentage
          FROM
              E0001_data
          ORDER BY
              product_code, competence_date;

Visualizations in BI tools:

  • Line Chart of Price Reductions: Track price reductions over the product’s lifecycle.
  • Discount Duration Analysis: Show the duration of discounts to reveal how quickly brands discount slow-moving items.

Conclusion and Investment Insights

By leveraging Data Boutique’s E0001 schema for apparel and fast fashion, investment analysts can gain insights into seasonal demand, inventory agility, and discounting strategies. Monitoring these metrics provides a comprehensive understanding of a brand’s market responsiveness, customer appeal, and operational efficiency—critical factors in the fast-paced apparel sector.

How did we do?