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Apparel prices in US rising faster online than offline: Adobe

21 Apr '22
2 min read
Pic: Shutterstock
Pic: Shutterstock

The price of apparel in the United States is rising faster online than offline. In March 2022, prices for apparel increased 16.3 per cent year-over-year (YoY) and 0.3 per cent month-over-month (MoM), more than any other category, according to the online inflation data by Adobe. This has marked the 22nd consecutive month of YoY inflation online.

In this time period, apparel prices online also outpaced the Consumer Price Index (CPI). This reverses the longstanding pattern for the category, where seasonal discounts created predictable peaks and valleys in online prices, Adobe said in a press release.

Over the last 12 months, apparel has consistently outpaced the CPI, which captures prices that consumers pay for goods offline. In February, apparel prices rose 11.0 per cent in the DPI, compared to 3.1 per cent in the CPI (indexed to 2014).

“Consumers are feeling a greater hit to their pocketbooks, with consistently high levels of online inflation in categories such as groceries and pet products,” said Patrick Brown, vice president of growth marketing and insights, Adobe. “But while e-commerce prices have risen more than years past, durable demand shows that consumers are embracing more personalized experiences in the digital economy as well as the conveniences of online shopping, particularly for growing categories like groceries.”

The Adobe Digital Price Index (DPI) provides the most comprehensive view into how much consumers pay for goods online. Powered by Adobe Analytics, it analyzes one trillion visits to retail sites and over 100 million SKUs across 18 product categories. In March, 14 of the 18 categories tracked by the DPI saw YoY price increases, with apparel rising the most.

The DPI is modeled after the CPI, published by the US Bureau of Labour Statistics, and uses the Fisher Price Index to track online prices. The Fisher Price Index uses quantities of matched products purchased in the current period (month) and a previous period (previous month) to calculate the price changes by category. Adobe’s analysis is weighted by the real quantities of the products purchased in the two adjacent months.

Adobe uses a combination of Adobe Sensei, Adobe’s AI and machine learning framework, and manual effort to segment the products into the categories defined by the CPI manual. The methodology was first developed alongside renowned economists Austan Goolsbee and Pete Klenow.

Fibre2Fashion News Desk (RR)

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