5.2.3 Product Marketing Analysis Using Data Boutique’s Data: Luxury Goods

Updated by Andrea Squatrito

Product Marketing Analysis Using Data Boutique’s Data: Luxury Goods

Luxury goods marketing relies heavily on brand loyalty, exclusivity, and premium pricing. This article shows how to leverage Data Boutique’s E0001 schema for insights into pricing resilience, regional demand variations, and brand loyalty in the luxury goods industry. These metrics support targeted marketing and pricing strategies that reinforce brand positioning.

Overview of E0001 Schema Fields for Luxury Goods Marketing

The E0001 schema includes key data points for analyzing luxury goods:

  • Website name: eCommerce platform listing the product.
  • Competence date: Date of data capture.
  • Country Code and Currency Code: Country and currency of listing.
  • Brand: Product’s brand.
  • Category 1, Category 2, Category 3: Hierarchical categorization.
  • Product code: Unique identifier for each product.
  • Product title: Product 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 indicator for discounts (1 = discounted, 0 = not discounted).

1. Assessing Brand Loyalty Through Price Stability

Purpose

Price stability in luxury goods often reflects strong brand loyalty. Tracking consistent pricing or limited discounts helps gauge a brand’s ability to maintain premium positioning.

Step 1: Tracking Price Consistency Across Regions

Use SQL to calculate the frequency and depth of discounts for each luxury brand across regions, revealing price resilience.

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: Analyzing Price Consistency

  • Low Discount Frequency: Consistently high prices with limited discounting indicate strong loyalty and support premium brand positioning.
  • Price Variance Across Regions: Compare average prices by country to assess brand loyalty on a regional basis.

2. Exclusive Pricing and Regional Demand Shifts

Purpose

For luxury goods, regional demand shifts can influence localized pricing strategies. Tracking price parity across countries helps determine if pricing adjustments reflect currency fluctuations or demand.

Step 1: Monitoring Price Parity and Cross-Region Differences

Calculate price differences between regions for luxury goods to identify currency-driven pricing adjustments or strong regional demand.

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: Creating a Regional Demand Map in BI Tools

Visualizations:

  • Price Comparison by Region: Chart average prices across countries to identify regions with premium pricing.
  • Demand Map: Use BI tools to map discount frequency by region, indicating high demand areas.

3. Consumer Price Sensitivity and Promotional Strategy

Purpose

Luxury brands rarely discount but may use targeted promotions to respond to regional demand fluctuations. Monitoring discounting behaviors provides insight into consumer price sensitivity and optimal promotion strategies.

Step 1: Analyzing Discount Frequency and Depth by Region

Use SQL to calculate the frequency and depth of discounts, helping to identify consumer price sensitivity in specific regions.

SELECT
              brand,
              country_code,
              COUNT(CASE WHEN flag_discounted = 1 THEN 1 END) AS discount_count,
              AVG((full_price_in_eur - discounted_price_in_eur) / full_price_in_eur * 100) AS avg_discount_percentage
          FROM
              E0001_data
          GROUP BY
              brand, country_code;

Step 2: Interpreting Price Sensitivity in High-Demand Regions

  • High-Discount Regions: Regions with higher discount counts may indicate lower brand loyalty or higher price sensitivity.
  • Premium Regions: Regions with low discount frequency likely maintain strong demand for luxury products without price adjustments.

4. Seasonal Demand and Pricing Strategy

Purpose

Seasonality impacts luxury goods, especially during high shopping seasons like holidays. Tracking seasonal demand patterns can optimize the timing of campaigns and ensure alignment with consumer demand.

Step 1: Identifying Seasonal Pricing Patterns

Calculate average monthly prices to determine if seasonal price adjustments occur, such as for end-of-year shopping.

SELECT
              brand,
              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, month
          ORDER BY
              month;

Step 2: Visualizing Seasonal Demand Patterns in BI Tools

In BI tools:

  • Monthly Pricing Trends: Line chart of average monthly prices to reveal seasonal patterns.
  • Discount Frequency by Month: Bar chart showing discounting behavior during peak shopping periods.

Conclusion and Marketing Insights

Using Data Boutique’s E0001 schema for luxury goods marketing provides valuable insights into brand loyalty, pricing resilience, and regional demand shifts. Tracking these metrics enables marketers to craft region-specific pricing strategies, time seasonal campaigns, and maintain a premium brand positioning in competitive luxury markets.

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