When asked about "Most returned online purchases by category", most U.S. respondents pick ********** as an answer. ** percent did so in our online survey in 2025. Looking to gain valuable insights about customers of online shops across the globe? Check out our reports about consumers of online shops worldwide. These reports offer the readers a comprehensive overview of customers of eCommerce brands: who they are; what they like; what they think; and how to reach them.

When asked about "Most returned online purchases by category", most Indian respondents pick ********** as an answer. ** percent did so in our online survey in 2025. Looking to gain valuable insights about customers of online shops across the globe? Check out our reports about consumers of online shops worldwide. These reports offer the readers a comprehensive overview of customers of eCommerce brands: who they are; what they like; what they think; and how to reach them.

This statistic depicts the results of a survey regarding the most returned online purchases by consumers in the United States in 2021. According to the survey, clothing was the most frequently returned online purchase; 88 percent of U.S. consumers reported returning these items.

Amazon Market Outlook

The global Amazon market is poised for significant growth over the forecast period, with the market size expected to increase from $260 billion in 2023 to nearly $700 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.5%. This robust expansion is driven by the increasing penetration of e-commerce, diversified product offerings, and innovative service models that Amazon continues to adopt and expand upon.

One of the primary growth factors for the Amazon market is the rapid shift of consumers towards online shopping. The convenience of shopping from home, coupled with a vast array of products and competitive pricing, has made e-commerce platforms like Amazon highly attractive. The COVID-19 pandemic further accelerated this trend, as lockdowns and social distancing measures pushed more consumers to shop online. This behavioral shift is expected to persist post-pandemic, solidifying Amazon's market position.

Another significant driver is Amazon's extensive investment in logistics and supply chain infrastructure. The company has heavily invested in technologies and facilities that enhance delivery speed and efficiency, such as automated warehouses, drone delivery systems, and an expanding network of fulfillment centers worldwide. These advancements have enabled Amazon to offer expedited delivery options, such as same-day or next-day delivery, which are critical factors in maintaining customer satisfaction and loyalty.

Furthermore, Amazon's diversification into various service offerings has substantially contributed to its growth. Services like Amazon Prime, Amazon Web Services (AWS), and Amazon Fresh have not only increased its revenue streams but also deepened customer engagement and loyalty. Amazon Prime, for instance, provides subscribers with benefits such as free shipping, access to exclusive content on Prime Video, and other perks, fostering a more engaged and loyal customer base. AWS, on the other hand, has established itself as a leader in the cloud computing industry, driving significant revenue and supporting the growth of other Amazon services.

Regionally, North America remains Amazon's largest market, accounting for a substantial share of its revenue. However, notable growth is expected in emerging markets within the Asia Pacific and Latin America regions. These regions are experiencing rapid internet penetration and a burgeoning middle class with increasing disposable incomes, making them ripe for e-commerce expansion. Amazon has been focusing on localizing its strategies to cater to the unique preferences and needs of these markets to capitalize on this potential.

Product Category Analysis

The electronics segment constitutes a significant portion of Amazon's product category, driven by a high demand for gadgets, home appliances, and other electronic items. Amazon's competitive pricing, extensive product range, and user-friendly return policies make it a preferred platform for purchasing electronics. Additionally, the seamless integration of customer reviews and ratings helps consumers make informed decisions, further boosting sales within this category. With continuous technological advancements and a steady stream of new product launches, the electronics segment is expected to maintain its growth trajectory.

Books were Amazon's original product category, and despite expanding into numerous other areas, books remain a core component of its offerings. The introduction of Kindle and e-books revolutionized the way consumers read, providing a boost to this segment. Amazon's extensive library of books, ranging from bestsellers to niche genres, caters to a wide audience. The convenience of purchasing and downloading books instantly, coupled with competitive pricing, continues to attract readers globally. The book segment is expected to see steady growth, supported by an increasing number of readers opting for e-books and audiobooks.

The clothing segment has seen substantial growth, driven by a diverse range of apparel and accessories for all ages and genders. Amazon's fashion segment includes both well-known brands and independent labels, providing a wide array of choices for consumers. The introduction of features like "Try Before You Buy" and personalized recommendations based on browsing history have enhanced the shopping experience, leading to higher conversion rates. As consumers become more comfortable with purchasing clothing online, this segment is expected to grow significantly.

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.

Amazon Stock: A Hold for the Next Month, But With Potential for Upside

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

The online apparel and footwear market is experiencing robust growth, driven by the increasing adoption of e-commerce, the convenience of online shopping, and the rising popularity of online-only brands. The market, estimated at $500 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $1.5 trillion by 2033. This expansion is fueled by several key factors. Firstly, the younger generations' preference for online shopping and the seamless integration of social media into the purchasing process are significantly boosting market penetration. Secondly, the rapid advancement in technologies like augmented reality (AR) and virtual reality (VR) allow consumers to "try on" clothes and shoes virtually, eliminating a significant barrier to online apparel purchases. Furthermore, the increasing availability of affordable and faster shipping options further enhances the consumer experience. However, challenges remain, including concerns about product authenticity, sizing inconsistencies, and the high return rates associated with online apparel shopping. Companies are addressing these concerns by investing in improved customer service, implementing advanced sizing technologies, and offering flexible return policies. The competitive landscape is dominated by major players like Amazon, ASOS, Zalando, Boohoo Group, Nike, and Adidas, each leveraging its unique strengths and strategies to capture market share. Regional variations exist, with North America and Europe currently leading the market, but significant growth potential is seen in emerging economies in Asia and Latin America. The segmentation within the online apparel and footwear market is diverse, encompassing various product categories (e.g., sportswear, casual wear, formal wear, footwear), price points (luxury, mid-range, budget), and customer demographics. Companies are focusing on personalization and targeted marketing strategies to cater to specific segments. The market's future growth trajectory depends on factors such as economic conditions, consumer spending patterns, technological advancements, and evolving fashion trends. Successful players will need to adapt to changing consumer preferences, maintain robust supply chains, and effectively manage logistics and returns to sustain their competitive edge. Continued investment in innovative technologies and data-driven marketing will play a critical role in shaping the future of this dynamic market.

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.

Amazon.com Inc. (AMZN): The Future of Retail?

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.

Amazon Escapes $268 Million Tax Bill in Luxembourg

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

The Austrian e-commerce market, exhibiting a robust Compound Annual Growth Rate (CAGR) of 5.80%, presents a compelling investment opportunity. While the precise market size (XX) for 2025 is unavailable, a reasonable estimation can be derived using the provided 2017-2027 timeframe and CAGR. Assuming a 2017 market size in the range of €20 billion (a plausible figure given Austria's economic standing and regional e-commerce penetration), extrapolating this forward using a 5.80% CAGR suggests a 2025 market size exceeding €30 billion. This growth is fuelled by several key drivers: increasing internet and smartphone penetration, a burgeoning young and digitally savvy population, and the rising convenience and accessibility of online shopping. The market is segmented by various product categories, with Beauty & Personal Care, Consumer Electronics, Fashion & Apparel, and Food & Beverage leading the charge. These sectors are seeing significant growth due to targeted marketing strategies, enhanced online product presentations, and the expansion of delivery services catering to consumers' changing needs and expectations. However, challenges remain, including the need to address concerns around data security and privacy, the high cost of logistics and delivery, and the need for better customer service and efficient return processes. Prominent players like Amazon, Zalando, and local retailers like XXXLutz are actively competing in this dynamic landscape, driving innovation and shaping the overall market experience. The forecast period of 2025-2033 indicates sustained growth, with market size projections exceeding €40 billion by 2033, assuming a consistent CAGR. This continued growth will be driven by the ongoing digital transformation of Austrian society and the expansion of e-commerce across niche markets and previously underserved segments. The increasing adoption of mobile commerce (m-commerce) and the rise of social commerce platforms will further propel market expansion. Despite potential economic fluctuations, the long-term outlook for the Austrian e-commerce market remains positive, presenting opportunities for both established players and new entrants. Key strategies for success will involve leveraging sophisticated data analytics, offering personalized shopping experiences, and adapting to evolving consumer preferences. Recent developments include: April 2022 - Shop Apotheke announced a strategic acquisition of FIRST A, a quick commerce player in the German pharmacy market. The acquisition will help the firm to enter the growing q-commerce market. It will also customer-centric platform strategy and strengthens its position as a one-stop shop in the pharmacy space., April 2022 - Customers in Germany, Austria, and Switzerland will be able to use the "Pay Later 30" payment options attributed to a partnership between Klarna and the European eCommerce startup About You. Customers in the Netherlands, Denmark, Sweden, Belgium, Finland, and Norway have already been able to use Klarna payment services. Customers may pay for purchases 30 days after they make them with Klarna's "Pay Later 30" option, which the business claims is enough time to order, receive, try on, and return a piece of apparel. As a result, customers are no longer obligated to buy something before seeing if they like it or if it fits.. Key drivers for this market are: Growth of the market during the COVID-19 Pandemic, Penetration of Internet and Smartphone Usage. Potential restraints include: Growth of the market during the COVID-19 Pandemic, Penetration of Internet and Smartphone Usage. Notable trends are: Significant Growth in E-Commerce is Expected due to digital transformation.

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.

WHAT IS A COURTESY ADJUSTMENT ON AMAZON?

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.

Amazon's Future: Bright, But the Stock May Be Overvalued

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.

Is JD (JD) E-Commerce the Next Amazon?

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