Datasets for Investment and Market Research: Analyzing Farfetch Missed Sales Estimates

The News in Summary
According to a recent article on Business of Fashion, Farfetch, a luxury fashion e-commerce platform, has reported disappointing retail sales, falling short of analyst estimates. This miss has been attributed to multiple factors, including macroeconomic pressures, shifting consumer behaviors, and intensified competition in the online luxury segment. This news underscores potential risks and shifts within the e-commerce and luxury retail sectors, impacting investors and industry stakeholders alike.

Impacted Stakeholders and Why It Matters

1. Investors (Private Equity, Investment Funds, Family Offices)
   Investors closely monitor retail sales performance as it often reflects consumer demand, market positioning, and operational efficacy. Farfetch’s underperformance signals potential financial adjustments, possible impacts on stock prices, and longer-term outlooks on the luxury e-commerce market. Investors need reliable data to adjust forecasts and align their portfolios accordingly.
2. Industry Actors (Competitors, Suppliers, Employees, Advisors, Marketing Agencies)
   • Competitors: Other luxury e-commerce platforms, such as Net-a-Porter, MatchesFashion, and MyTheresa, can analyze this news for insights into changing consumer preferences or weaknesses in Farfetch’s strategy.
   • Suppliers and Brand Partners: Luxury brands that rely on Farfetch for digital reach may need to re-evaluate their collaborations. This could lead to shifts in inventory allocation and promotional strategies.
   • Employees: Sales performance impacts job security, especially in sales, marketing, and operational roles, where growth metrics are direct indicators of departmental health.
   • Marketing Agencies: Agencies involved in digital campaigns for luxury goods may pivot their focus, recalibrating strategies to align with potential market volatility.

Useful Datasets for Market and Investment Research
The following types of datasets could be valuable for gaining insights into Farfetch’s performance and market conditions:

1. Macroeconomic Indicators
   Data on consumer spending, inflation rates, and luxury market indices can help assess the broader economic factors affecting luxury consumption. These are available from sources like OECD, World Bank, and Federal Reserve Economic Data (FRED).
2. E-commerce Market Performance
   General statistics on e-commerce sales by sector and geography from entities such as eMarketer or Statista can reveal broader industry trends and Farfetch’s performance relative to its peers.
3. Competitor Financials and Market Share Data
   Platforms like Bloomberg, Thomson Reuters, and Yahoo Finance provide financial data and earnings summaries for comparable companies. Understanding competitor trends can give context to Farfetch’s sales performance.
4. Consumer Sentiment and Purchase Intent
   Surveys from providers like Nielsen or Ipsos and consumer sentiment indexes can reveal market attitudes toward luxury purchases and how economic downturns impact spending.
5. Website Traffic and User Engagement
   Sources like SimilarWeb, Comscore, or Google Trends offer insights into website traffic, search trends, and social media engagement, helping assess online brand visibility and consumer interest.

Useful Datasets from Web Scraping (Available on Data Boutique)
Below are specific web scraping datasets available from Data Boutique that provide critical, real-time insights into market conditions:

1. Product Listings and Price Tracking
   Schema: Product, with fields for item name, price, discount percentage, and stock status.
   This dataset tracks prices for luxury items, indicating changes in pricing strategies, seasonal discounts, or market dynamics across competitors.
2. Inventory Status and Availability
   Schema: Inventory, with fields for item ID, stock status, and location.
   Monitoring stock levels on Farfetch and other platforms can offer clues about demand, especially for high-end items in limited supply.
3. Customer Reviews and Ratings
   Schema: Customer Feedback, with fields for review text, rating, and product ID.
   Analyzing reviews provides insights into consumer satisfaction, product quality perceptions, and specific aspects of the luxury shopping experience.
4. Sales and Promotion Data
   Schema: Promotion and Sales, with fields for product ID, discount type, duration, and channel.
   This dataset reveals promotional patterns, helping analysts correlate marketing strategies with sales figures and demand fluctuations.

Analyses That Can Be Done with Web Scraping Datasets

Leveraging these web scraping datasets, analysts can derive actionable insights using both Python and SQL. Here are some sample analyses and code snippets:

• Price Trend Analysis Across Competitors
  Goal: Identify price trends over time to compare Farfetch’s pricing with competitors for similar products.

<code>
import pandas as pd 
import matplotlib.pyplot as plt 

# Load price data (example CSV) 
data = pd.read_csv('competitor_prices.csv') data['date'] = pd.to_datetime(data['date']) 

# Filter by product and competitor 
farfetch_data = data[data['competitor'] == 'Farfetch'] competitor_data = data[data['competitor'] != 'Farfetch'] 

# Plotting price trends 
plt.plot(farfetch_data['date'], farfetch_data['price'], label='Farfetch') plt.plot(competitor_data['date'], competitor_data['price'], label='Competitors', linestyle='--') plt.xlabel('Date') plt.ylabel('Price') plt.title('Price Trend Comparison') plt.legend() plt.show()

</code>

• Discount and Promotion Effectiveness
  Goal: Calculate the impact of discounts on sales, comparing with pre- and post-promotion stock levels to assess promotional effectiveness.

SQL Query Example
SELECT 
 product_id, 
 AVG(price_before) - AVG(price_after) AS avg_discount,  AVG(stock_before) - AVG(stock_after) AS avg_stock_reduction 
FROM 
 promotions_data 
WHERE promotion_period = 'Q4' 
GROUP BY product_id
;

• Sentiment Analysis on Customer Reviews
  Goal: Analyze customer sentiment to determine if there are recurring issues that impact the shopping experience or product quality.

from textblob import TextBlob 

# Load review data 
reviews = pd.read_csv('reviews.csv') reviews['sentiment'] = reviews['review_text'].apply(lambda x: TextBlob(x).sentiment.polarity) 

# Plot sentiment distribution 
reviews['sentiment'].hist() 
plt.xlabel('Sentiment Polarity') 
plt.ylabel('Frequency') 
plt.title('Sentiment Analysis of Customer Reviews') 
plt.show()

• Stock Availability and Demand Correlation
  Goal: Identify high-demand items by analyzing stock levels and replenishment frequency.

SELECT 
product_id, 
COUNT(*) AS replenishment_count, 
AVG(days_out_of_stock) AS avg_stockout_days 
FROM inventory_data 
WHERE product_category = 'Luxury' 
GROUP BY 
product_id 
ORDER BY avg_stockout_days DESC;

Conclusion
For investors, competitors, and other industry players, understanding the nuances of Farfetch’s underperformance requires more than the news headline. Access to web scraping datasets, especially those that capture real-time information on pricing, inventory, and consumer sentiment, empowers stakeholders to contextualize the sales shortfall and adjust their strategies accordingly. Data Boutique offers essential datasets for performing these analyses, allowing companies and investors to make data-driven decisions in an ever-evolving retail landscape.