5.2.1 Product Marketing Analysis Using Data Boutique’s Data: Consumer Electronics

Updated by Andrea Squatrito

Product Marketing Analysis Using Data Boutique’s Data: Consumer Electronics

Product launches in the consumer electronics sector require strategic demand forecasting, competitive analysis, and price optimization. This article demonstrates how to leverage Data Boutique’s E0001 schema for product marketing insights in consumer electronics, focusing on demand prediction, competitor comparison, and launch pricing sensitivity.

Overview of E0001 Schema Fields for Consumer Electronics Marketing

The E0001 schema includes the following key fields for consumer electronics 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: 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. Demand Prediction for Product Launches

Predicting demand for new products is crucial for managing inventory and setting effective launch prices.

Use SQL to calculate average prices and discount patterns for similar products, serving as a benchmark for launching new products.

SELECT
              product_code,
              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
          WHERE
              brand IN ('BrandA', 'BrandB')
          GROUP BY
              product_code, brand, month
          ORDER BY
              month;

Step 2: Interpreting Price Patterns

  • Price Consistency: Products with steady prices may indicate strong demand or premium positioning.
  • Discount Trends: High discount frequency can indicate competitive pressure or demand variability.

2. Competitor Comparison for Product Positioning

Competitor analysis helps position new products effectively in the market.

Step 1: Comparing Competitor Prices and Discounts

Compare average prices and discounts across key competitors to identify market gaps.

SELECT
              brand,
              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_percent
          FROM
              E0001_data
          GROUP BY
              brand;

Step 2: Visualizing Competitive Positioning

Create visualizations to highlight competitor pricing strategies:

  • Line Chart: Monthly average prices for competitive comparison.
  • Discount Heatmap: Identifies frequent discounts by brand, highlighting price sensitivity.

3. Pricing Sensitivity Analysis for Launch Optimization

Understanding price sensitivity informs adjustments to early-stage pricing.

Step 1: Measuring Price Elasticity

Calculate price changes over time to estimate customer sensitivity.

SELECT
              product_code,
              brand,
              competence_date,
              full_price_in_eur AS current_price,
              LAG(full_price_in_eur) OVER (PARTITION BY product_code ORDER BY competence_date) AS previous_price,
              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
              product_code, competence_date;

Step 2: Using Elasticity Insights

Products with high sensitivity may benefit from targeted discounts; those with stable demand can retain premium pricing.

4. Early Sentiment Analysis Based on Discount Behavior

Early discounts can indicate initial demand strength or reveal an effective promotional strategy.

Step 1: Tracking Discounts and Price Reductions

Calculate early discount frequency and depth to infer customer interest.

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;

Step 2: Creating Early Sentiment Indicators

Use a BI tool for sentiment insights:

  • Discount Frequency by Brand: Track early discount frequency by brand.
  • Average Discount Depth: Shows depth of discounts within the first three months.

Conclusion and Marketing Insights

Data Boutique’s E0001 schema equips marketers with the tools to predict demand, position products, and set optimal pricing strategies. These metrics provide actionable insights for a competitive product launch in consumer electronics, enhancing inventory planning and profitability.

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