Capri Holdings Reports Sales Decline – Useful Datasets for Investment and Market Research

News Summary

According to WWD, Capri Holdings, the parent company of high-profile brands like Versace, Michael Kors, and Jimmy Choo, recently reported a notable decline in its second-quarter 2024 sales. CEO John Idol attributed the dip to weaker consumer demand, particularly in North America, and various economic pressures affecting luxury spending. The company’s revenue fell short of market expectations, leading to a revised outlook for the rest of the fiscal year. This sales performance impacts both the company’s stock and its broader market position in luxury retail. Read the full article here.

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Impacted Parties and Reasons

1. Investors (Private Equity, Investment Funds, Family Offices)
   • Why it Matters: Capri’s disappointing sales figures could lead to a decline in stock value, affecting institutional and retail investors who are sensitive to earnings misses. Investors may see a revised growth strategy or a shift in the brand’s market positioning, which could impact future valuations and investment interest in the luxury sector.
2. Industry Actors (Competitors, Suppliers, Employees, Advisors, Partners, Marketing Agencies)
   • Why it Matters: Competitors might reconsider their own pricing and discount strategies to capture market share, especially as economic headwinds affect consumer demand for luxury items. Suppliers could see reduced orders, while employees may face a changing work environment as Capri adapts to these market shifts. Advisors and marketing agencies may also need to adjust strategies for positioning and messaging in light of evolving consumer demand and economic constraints.

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Useful Datasets (General)

1. Market Size and Consumer Spending Trends
   • Tracks overall spending in the luxury market, which is crucial for understanding broader industry health and projecting future demand in the luxury segment.
2. Brand and Product Popularity Analysis
   • Analyzes consumer interest and loyalty across luxury brands, helping identify which brands are performing well in specific markets.
3. Competitor Pricing and Discount Strategies
   • Insights into competitor pricing can help gauge how Capri’s pricing aligns with other brands in the sector and identify where potential pricing or discount adjustments might attract consumers.
4. Economic Indicators Affecting Consumer Discretionary Spending
   • Datasets on inflation, employment rates, and consumer confidence are essential to assess how economic pressures influence luxury goods spending.

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Useful Datasets from Web Scraping on Data Boutique

1. E-Commerce Product Listings (Schema E0001)
   • Provides structured data on product listings, ideal for tracking retail prices and product availability across various brands. Schema details here.
2. Product Variant Price Analysis (Schema E0003)
   • Enables in-depth analysis by product variant, tracking differences in prices for specific product types or lines, which is valuable for segment-specific insights. Schema details here.
3. Inventory Levels and Stock Insights (Schema E-INVENTORY)
   • Allows tracking of inventory availability for Capri’s brands and competitors, useful for evaluating demand and stock issues that could affect sales. Schema details here.
4. Second-Hand Luxury and Discount Analysis (Schema FASHION-2ND-HAND)
   • Examines the resale market, offering insights into secondary market values and consumer interest in second-hand luxury items. Schema details here.

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Analysis That Can Be Done with Web Scraping Datasets

Python Example: Competitor Pricing and Discount Trend Analysis

Using Schema E0001, this example shows how to analyze pricing and discount trends across Capri’s luxury brands compared to competitors.

python
import pandas as pd

# Load product data for Capri and competitor brands
data = pd.read_csv("ecommerce_product_listings.csv")

# Calculate discount percentage
data['discount_percentage'] = (data['full_price'] - data['discounted_price']) / data['full_price'] * 100

# Filter for Capri brands and competitors
capri_data = data[data['brand'].isin(['Michael Kors', 'Versace', 'Jimmy Choo', 'Competitor1', 'Competitor2'])]

# Average discount per brand
discount_analysis = capri_data.groupby('brand')['discount_percentage'].mean().reset_index()

print(discount_analysis)

This code calculates the average discount for Capri’s brands and competitors, offering insights into comparative discount strategies and potential areas for adjustment.

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SQL Example: Stock Level Analysis to Predict Sales Opportunities

Using Schema E-INVENTORY, this SQL example examines stock levels for high-demand items, helping predict where Capri might face stock shortages that could affect sales.

sql
SELECT brand, product_code, SUM(quantity) AS total_stock, AVG(price) AS avg_price
FROM inventory_data
WHERE brand IN ('Michael Kors', 'Versace', 'Jimmy Choo')
GROUP BY brand, product_code
HAVING total_stock < 50
ORDER BY total_stock ASC;

This query identifies products with low stock levels, which can help Capri Holdings forecast restocking needs and prevent potential missed sales opportunities due to inventory shortages.