5.1.3 Fundamental Investment Analysis Using Data Boutique’s Data: Consumer Electronics

Updated by Andrea Squatrito

Fundamental Investment Analysis Using Data Boutique’s Data: Consumer Electronics

Consumer electronics companies often experience fluctuating demand tied to new product releases, seasonal promotions, and price sensitivity. This article covers how to use Data Boutique’s E0001 schema to track product lifecycle stages, analyze pricing power, and assess seasonal demand trends within the consumer electronics industry. These insights are valuable for investment analysts looking to predict revenue impact and assess brand competitiveness.

Overview of E0001 Schema Fields for Consumer Electronics

The E0001 schema provides the following key fields for analyzing consumer electronics:

  • Website name: The retailer or eCommerce website where the product is listed.
  • Competence date: The date of data capture.
  • Country Code: The country where the product is listed.
  • Currency Code: The currency in which the price is listed.
  • Brand: The brand of the consumer electronics product.
  • Category 1, Category 2, Category 3: Product categorization for hierarchical organization.
  • Product code: Unique identifier for each product.
  • Product title: Name or title of the product.
  • Full price in local currency: Original price before any 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. Product Release Cycles and Market Sentiment

Purpose

Track consumer electronics product release cycles and market sentiment to predict sales performance for newly launched products. Understanding early pricing patterns and consumer interest can provide valuable indicators for forecasting demand.

Step 1: Analyzing Price Volatility Post-Launch

Use SQL to identify pricing trends after a product’s release, focusing on price stability or volatility. Quick price drops may signal lower-than-expected demand, while stable prices could indicate strong initial sales.

SQL Example

SELECT
              product_code,
              brand,
              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
              product_code, competence_date;

Step 2: Assessing Consumer Interest with Discounts and Price Changes

Frequent discounting or price adjustments soon after a launch may indicate limited demand or overstock. By monitoring the flag discounted field and calculating price changes, analysts can gain insights into early market reception.

2. Supply Chain Health and Inventory Cycles

Purpose

Track stock availability patterns over time to assess supply chain health and inventory management. Frequent stockouts may indicate high demand, while consistent availability could suggest effective supply chain operations or lower demand.

Step 1: Measuring Stock Turns and Availability Patterns

Use SQL to observe stock availability by product, focusing on products with frequent restocking or sellouts.

SQL Example

SELECT
              product_code,
              brand,
              competence_date,
              flag_discounted,
              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: Analyzing Seasonal Inventory Patterns

Seasonal trends in inventory availability—like during holiday seasons or back-to-school periods—can signal expected demand surges. Use BI tools to visualize these patterns, identifying high-demand periods.

Purpose

Identify seasonal trends and promotional patterns to predict high-demand periods and gauge the potential impact on revenue.

Step 1: Calculating Discount Frequency and Average Discount Depth

Calculate how often products go on discount and the average discount percentage. This can reveal seasonal sales patterns and provide insight into consumer demand sensitivity.

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;

Step 2: Visualizing Seasonal Discount Patterns in BI Tools

In a BI tool like Looker or Power BI, create visualizations that track discount frequency and depth over time. This can reveal key seasonal promotional strategies, such as Black Friday or back-to-school promotions, which are critical periods for consumer electronics.

4. Competitive Pricing Analysis

Purpose

Analyze the competitive landscape by comparing prices across brands for similar products. Frequent price adjustments by a brand can indicate sensitivity to competitor pricing or demand fluctuations.

Step 1: Comparing Prices Across Brands

Use SQL to compare average prices between brands. This provides insight into each brand’s positioning in the market and may indicate aggressive pricing tactics or premium pricing strategies.

SQL Example

SELECT
              brand,
              product_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_percent
          FROM
              E0001_data
          GROUP BY
              brand, product_code;

Step 2: Identifying Competitive Positioning in BI Tools

  • Price Trends by Brand: Use a line chart to track price trends across brands, helping identify brands that consistently hold higher price points.
  • Discounting Strategy Heatmaps: Visualize discount frequency and depth by brand, showing which brands discount frequently versus those maintaining premium pricing.

Conclusion and Investment Insights

Using Data Boutique’s E0001 schema for consumer electronics, investment analysts can gain valuable insights into product cycles, brand demand, and seasonal trends. These metrics, such as price volatility, stock availability, and promotional frequency, are essential for understanding revenue performance and predicting quarterly earnings.

This article provides practical SQL examples and BI strategies, helping analysts make data-driven decisions in the fast-evolving consumer electronics market.

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