As of October 16, 2024, CaitlynMinimalist was the Etsy seller with the most sales on the platform in the previous year. The store, which sells jewelry, recorded over 680,000 purchases by Etsy buyers in 12 months. Esty’s Overview Etsy, an online marketplace, generates revenue from three primary segments: marketplace revenues, this includes fees for sales transactions and listings of products; seller service revenues; and other revenues which includes third-party payment processor fees. The annual revenue of Etsy has steadily increased over the past years, reaching over *** billion U.S. dollars, which in part is due to a steady increase of investment into their advertising. Usage of Etsy When ranking the leading websites by share of visits in the United States, esty.com was fourth, outranked by amazon.com, ebay.com, and walmart.com. Still, over the past years, the number of active Etsy sellers has increased, reaching over * million in 2023. That year, Etsy's active buyers also grew, reaching over ** million, a new high for the company.

In 2020, homewares and home furnishings were the top sales category on Etsy.com, generating *** billion U.S. dollars in gross merchandise sales. Personal jewelry and accessories ranked second, with a GMS of *** billion dollars, followed in third place by craft supplies, for *** billion.

ETSY reported $651.2M in Sales Revenues for its fiscal quarter ending in March of 2025. Data for ETSY - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last July in 2025.

According to a 2025 analysis, beads were among the most common items sold on Etsy. The marketplace reported over eight million listings featuring that item, as well as nearly 5.3 million listings for pendants. Beads also recorded the highest average click-through rate (CTR) among the selected items, at 102 percent.

In 2024, about *** million sellers sold goods through the Etsy Inc's platforms, a decrease from the **** million active sellers in the previous year. By comparison, there were around ** million active Etsy buyers worldwide. Etsy sellersEtsy has sellers from *** countries, most of them being based in the United States. With so many users across Etsy Inc's marketplaces, it is easy to question what motivates Etsy sellers to run their shops. In the latest recorded year, ** percent of Etsy sellers reported that they considered their Etsy shop as a business. Another ** percent of Etsy sellers aspired to increase their sales in the future. From the seller’s side of things, Etsy platforms are also women-dominated, with the majority of Etsy sellers identifying as women as of 2021. Etsy marketplace salesThe online marketplace has three core sections: handmade, vintage and supplies. During the coronavirus pandemic, the most popular categories among handmade Etsy sellers worldwide were home & living, art & collectibles and jewelry. In 2021, Etsy’s annual gross merchandise sales volume (GMV) amounted to nearly **** billion U.S. dollars, up from **** million in product sales in 2020.

ETSY reported $459.11M in Gross Profit on Sales for its fiscal quarter ending in March of 2025. Data for ETSY - Gross Profit On Sales including historical, tables and charts were last updated by Trading Economics this last July in 2025.

In 2024, 46 percent of Etsy's gross merchandise sales were generated through international buyers from outside of the United States, up from the international GMS volume of 45 percent from the previous year.

ETSY reported $192.06M in Cost of Sales for its fiscal quarter ending in March of 2025. Data for ETSY - Cost Of Sales including historical, tables and charts were last updated by Trading Economics this last July in 2025.

ETSY reported $269.23M in Selling and Administration Expenses for its fiscal quarter ending in March of 2025. Data for ETSY - Selling And Administration Expenses including historical, tables and charts were last updated by Trading Economics this last July in 2025.

During the first quarter of 2025, the C2C e-commerce company Etsy Inc. generated over 651 million U.S. dollars in revenue, up from 646 million U.S. dollars in the same quarter of the previous year.

In 2024, Etsy, an e-commerce website company specialized in craft and vintage items, generated revenues worth *** billion U.S. dollars, up from the *** billion generated in the previous year. Most products on Etsy are sold by independent sellers. Etsy's rise to the top Etsy launched in 2005 and went public in 2015 after a decade of operations, and is now one of the leading online marketplaces globally.The company generates revenue from three primary segments: marketplace revenues including fees for sales transactions and listings, seller service revenues, and other revenues including third-party payment processor fees. In recent years, Etsy’s marketplace has established itself as the most profitable segment, surpassing *** billion dollars for the first time in 2020. Mobile shopping Etsy has successfully capitalized on the increasing use of mobile shopping. The Etsy mobile app regularly reaches millions of downloads on a monthly basis. Downloads for the Etsy buyer app peaked in December of 2022, reaching over *** million in that month alone. With such high numbers of downloads worldwide, it comes as no surprise that mobile accounts for roughly ********** of Etsy’s gross merchandise volume. The fiscal year end of the company is December 31st.

ETSY reported $6.43B in Market Capitalization this July of 2025, considering the latest stock price and the number of outstanding shares.Data for ETSY - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Etsy is predicted to experience moderate growth in the coming year, driven by continued expansion in its core marketplace and growth in its newer categories such as home goods and apparel. Risks to this prediction include increased competition from both existing and new platforms, as well as the potential for economic headwinds that could impact consumer spending.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

ETSY Emerging E-Commerce Leader?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Etsy Eyes Growth Opportunities? (ETSY)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Etsy: A Hold for the Next 3 Months, But a Strong Buy for the Long Term

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

ETSY's Earnings: A Sign of E-commerce Strength?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Etsy's Artistic Adventure: Will It Paint a Profitable Picture? (ETSY)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

ETSY Stock Forecast: A Buy For The Next 4 Weeks

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

In 2022, the e-commerce platform Etsy generated 74.5 percent of its annual revenue through its marketplace segment. The remaining 25.5 percent was generated through the company's services business.