Case Study: MatchesFashion Inventory Outflows
Web data for market insights
Case Study: MatchesFashion Inventory Outflows
In a previous discussion, we explored the utilization of web-based data to monitor Key Performance Indicators (KPIs) during significant market events, including the instance of the closure of MatchesFashion.com.
This detailed examination focuses on the strategic application of inventory level data, acquired through web scraping techniques, to enhance competitive and investment research within the retail sector.
Analytical Perspective: Understanding Market Dynamics
Our investigation centers on the impact of a major retailer, with annual net revenues surpassing 400 million USD, ceasing operations during a peak period of full-price sales. This situation often leads to market disruptions, as competitors may face challenges when inventory is released into the market at substantially reduced prices.
Research Hypothesis: Evaluating Inventory Redistribution
We aim to assess the reallocation of inventory in the wake of a retailer's closure. By comparing inventory data at a detailed SKU level from March 11th, 2024, with data from March 20th, we can observe changes in inventory levels across different brands.
Key Considerations
The analysis period spans nine days, a concise timeframe that, despite its brevity, offers valuable insights due to the recency of the event. Analysts are encouraged to extend this research to gain a broader perspective, as data accessibility is assured.
We assume no new inventory additions during this period, considering the retailer's declared cessation of business. Therefore, observed net changes are attributed solely to inventory outflows.
It's important to note that inventory reductions are not exclusively due to direct sales. Factors include stock clearance to discount outlets, returns to brands under concession agreements, and other non-sale channels.
Inventory outflows captured in this analysis may not align with consumer purchase data from credit card transactions, especially if inventory is reclaimed by brands, sold through B2B channels, or in regions outside the credit card data provider's scope.
The analysis values inventory outflows at retail prices post-discounts. However, actual sales may incorporate additional reductions, B2B transactions may occur at lower wholesale prices, and returns to brands might not be monetarily compensated.
This study does not provide geographical specifics. The data was sourced from the UK version of the retailer's website, which caters to multiple regions, leaving the final destination of sold items indeterminate.
Where to find the Data
The analysis was based entirely on Inventory data available on Data Boutique and updated daily. Any user, analyst, or researcher can access and download the latest data to continue this study.
Research Findings
Our focus was on the top 50 brands, ranked by the number of items reduced over the 9-day period. The analysis presents both the percentage change in units and their corresponding value at retail prices.
A greater decline in value compared to units suggests that higher-priced items were predominantly affected.
Overall, the findings reveal a 3% reduction in units and a 5% decrease in value among the top 50 brands within the nine-day timeframe, detailed further in the accompanying chart.
This case study exemplifies the potent application of web-scraped data for in-depth market analysis, offering valuable insights for retailers, brands, market analysts, and professionals in the luxury, fashion, and web-scraping domains.
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