5.2.2 Product Marketing Analysis Using Data Boutique’s Data: Apparel and Fashion

Updated by Andrea Squatrito

Product Marketing Analysis Using Data Boutique’s Data: Apparel and Fashion

The apparel and fashion industry is highly seasonal and trend-driven, where aligning campaigns with demand patterns and understanding consumer preferences are essential. This article covers how to use Data Boutique’s E0001 schema to gain insights for seasonal planning, campaign timing, and trend alignment in the fashion market.

Overview of E0001 Schema Fields for Apparel and Fashion Marketing

The E0001 schema provides several key fields for apparel and fashion analysis:

  • Website name: The eCommerce platform listing the product.
  • Competence date: Date of data capture.
  • Country Code and Currency Code: Country and currency for the listing.
  • Brand: Brand of the apparel item.
  • Category 1, Category 2, Category 3: Hierarchical categorization of the product.
  • Product code: Unique identifier for each product.
  • Product title: Product’s name or title.
  • Full price in local currency and Discounted price in local currency: Original and discounted prices.
  • Full price in EUR and Discounted price in EUR: Prices converted to EUR.
  • Flag discounted: Boolean indicating if a discount is applied (1 = discounted, 0 = not discounted).

1. Seasonal Campaign Planning and Demand Analysis

Purpose

Seasonal demand is critical in fashion. Identifying high-demand periods and aligning campaigns can optimize marketing spend and inventory planning.

Use SQL to calculate average monthly prices by brand and country to identify peak seasons and demand cycles for apparel items.

SELECT
              product_code,
              brand,
              country_code,
              DATE_TRUNC('month', competence_date) AS month,
              AVG(full_price_in_eur) AS avg_price_eur
          FROM
              E0001_data
          GROUP BY
              product_code, brand, country_code, month
          ORDER BY
              month;
  • High Average Prices: Brands that maintain higher average prices may indicate high seasonal demand.
  • Discount Patterns: Monitoring when brands initiate discounts can signal off-season periods or seasonal stock clearances.

2. Promotional Strategy Impact Analysis

Purpose

Understanding how discounts impact consumer demand is key for planning effective promotional campaigns.

Step 1: Analyzing Discount Frequency and Depth by Country

Track how frequently products are discounted and the average depth of discounts across brands and countries. This helps determine optimal timing and discount rates for future campaigns.

SELECT
              product_code,
              brand,
              country_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
              product_code, brand, country_code;

Step 2: Visualizing Promotional Impact in BI Tools

In a BI tool:

  • Discount Frequency by Country: Chart discount frequency to identify markets with higher promotional sensitivity.
  • Average Discount Depth: Track how deeply discounts are applied to assess promotion effectiveness across regions.

3. Trend Analysis and Alignment

Purpose

Fashion trends evolve rapidly, and tracking consumer preferences across regions helps brands stay relevant. This analysis estimates demand trends through product pricing and discounting behavior.

Step 1: Analyzing Product Price Consistency by Brand and Country

Track how consistently brands maintain prices across regions, as frequent discounting can signal lower demand alignment with trends.

SELECT
              brand,
              country_code,
              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, country_code, month
          ORDER BY
              month;

Step 2: Creating a Regional Trend Index in BI Tools

Create visualizations to reveal demand alignment with trends:

  • Monthly Average Prices: Track average monthly prices by region to reveal demand stability.
  • Discount Frequency Heatmap: Show discount frequency by region to highlight areas of high price sensitivity or trend misalignment.

4. Cross-Region Price Positioning and Consumer Sensitivity

Purpose

Analyzing cross-region pricing provides insight into demand elasticity and potential for localized pricing strategies.

Step 1: Comparing Prices and Discounts Across Regions

Calculate average prices and discount depths for similar items by brand across regions to identify opportunities for regional pricing adjustments.

SELECT
              product_code,
              brand,
              country_code,
              AVG(full_price_in_eur) AS avg_price_eur,
              AVG(discounted_price_in_eur) AS avg_discounted_price_eur,
              AVG(flag_discounted) * 100 AS discount_frequency
          FROM
              E0001_data
          GROUP BY
              product_code, brand, country_code;

Step 2: Identifying Price Sensitivity and Regional Adjustments

By comparing discount behavior across countries, brands can adjust pricing strategies to meet regional demand elasticity.

Conclusion and Marketing Insights

Using Data Boutique’s E0001 schema, marketers can gain valuable insights into seasonal demand, regional pricing sensitivity, and discount effectiveness. Tracking these metrics helps align campaigns with consumer demand, optimize pricing, and leverage trends for a successful product marketing strategy in the apparel and fashion industry.

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