In the United States, ** percent of online orders made during Amazon Prime Day 2024 were worth up to ** U.S. dollars, on average. In that edition of the popular online sale event, ** percent of U.S. orders were worth up to *** euros.

In 2023, Amazon Logistics delivered around 5.86 billion packages in the United States (U.S.). This is an increase of around one billion packages compared with the previous year.

This dataset offers a granular look at the daily Open, High, Low, Close, and Volume (OHLCV) data for some of the Amazon.com Inc. stock (AMZN) Updated every trading day, it aims to serve as a resource for researchers, traders, and finance enthusiasts.

Key Features: - OHLCV: The foundational data points for any financial analysis. - Adjusted Close: This considers dividends and splits, offering a true reflection of value over time. - Change %: Offers a day-to-day percentage change, capturing market momentum. - 20 Day Average Volume: A rolling metric to gauge trading liquidity and interest over roughly a month.

Split Adjusted: Given its history of stock splits, a separate dataset with split-adjusted data is available, ensuring accuracy in historical comparisons.

Image source: https://wallpapers.com/wallpapers/amazon-digital-city-art-npcp6jc782ixp9zs.html

Asset Price Dataset Description

This dataset is a comprehensive collection of historical financial data on a specific asset, covering a wide range of information related to daily prices, trading volume and technical indicators. It is designed to provide a detailed, multi-faceted view of asset performance over time, enabling in-depth analysis and the application of various financial strategies.

Information on the columns of the dataset

1. Date: The specific date of the entry.
2. Opening: The opening price of the asset at the beginning of the day.
3. High: The highest price reached by the asset during the day.
4. Low: The lowest price reached by the asset during the day.
5. Closing: The price of the asset at the end of the day.
6. Adjusted Closing: The closing price adjusted for dividends and stock splits.
7. Volume: The number of shares traded during the day.
8. Amplitude: The difference between the highest and lowest price of the day (High - Low).
9. MA7: Moving average of the closing price of the last 7 days.
10. MA14: Moving average of the closing price of the last 14 days.
11. MA30: Moving average of the closing price over the last 30 days.
12. Daily Return: The percentage change in the closing price in relation to the previous day.
13. ATR (Average True Range): Moving average of the True Range (TR) for a given period, used to measure volatility.
14. RSI (Relative Strength Index): Relative Strength Index, a momentum indicator that measures the speed and change of price movements.
15. Annual growth percentage: Percentage of annual growth.
16. Percentage of daily growth: Percentage of daily growth.
17. Absolute Daily Growth: Daily absolute growth, the absolute difference in the closing price compared to the previous day.
18. Day: The day of the week.
19. Month: The month of the year.
20. TR (True Range): The biggest difference between:
21. The maximum price of the day minus the minimum price of the day.
22. The maximum price of the day minus the closing price of the previous day.
23. The minimum price of the day minus the closing price of the previous day.

Applicability

1. Trend Analysis:
   • Through historical data, it is possible to identify short and long-term price trends, helping analysts and investors make informed decisions about buying and selling assets.
2. Development of Negotiation Strategies:
   • The data can be used to develop and test automated trading strategies, including the use of moving averages, relative strength indexes (RSI), and other technical indicators.
3. Volatility Study:
   • With metrics such as Average True Range (ATR), the dataset allows measuring asset volatility over time, essential for risk management strategies and understanding asset stability.
4. Performance Assessment:
   • The detailed history of opening, closing, high and low prices, as well as trading volume, allows an accurate assessment of the asset's performance in different periods.
5. Modeling and Forecasting:
   • The data can be used to build predictive models using machine learning and statistical analysis techniques, providing predictions about future price movements.
6. Education and Research:
   • For students and researchers, the dataset offers a rich source of real data to study financial markets, test hypotheses and perform simulations.

Importance

1. Informed Decision Making:
   • Access to detailed historical data allows investors and analysts to make evidence-based decisions, reducing uncertainty and risk associated with financial markets.
2. Backtesting:
   • It is possible to apply trading strategies to historical data to verify their effectiveness before implementing them in the real market, a crucial process for developing robust trading systems.
3. Comparative Performance Analysis:
   • With consistent data, you can compare the asset's performance over different periods or with other assets, providing a clear perspective on its relative performance.
4. Pattern Identification:
   • The dataset allows the identification of patterns and anomalies in price movements, which can be explored to develop trading strategies or to better understand the factors that influence the market.
5. Risk Management:
   • Analyzing volatility and price behavior over time helps in building risk management strategies, essential for preserving capital and optimizing returns.

This dataset is a valuable tool for anyone involved in financial markets, from individual investors to market analysts and academic researchers, providing the necessary foundation for detailed analysis and informed financial decisions.

The online revenue of amazon.ae amounted to US$769.8m in 2024. Discover eCommerce insights, including sales development, shopping cart size, and many more.

In 2024, Amazon Prime Day sales reached a massive $14.2 billion in comparison to $12.9 billion in 2023. This was a huge $1.3 billion increase.

The trade of wild meat in urban markets has become a controversial topic because despite the economic returns it can generate for local communities, it can cause a dramatic increase in harvest rates of game species. The trade of wild meat could be a very accessible and low-cost method to monitor the regional game populations. Nevertheless, the wild meat trade is difficult to monitor because this is an illegal activity and vendors often distrust researchers. In this study, we used two long-term monitoring datasets collected in one of the most important and largest open markets in wildlife in the Amazon, in Iquitos (Peru), to estimate the minimum effort required to obtain reliable information on the amount and trends of wild meat trade. Two 12-month surveys were conducted in the Belén Market between September 2006 and August 2007 (2,443 interviews in 182 sampling days), and between September 2017 and August 2018 (2,081 interviews in 138 sampling days). The data submited in page "interviews year-along" includes the price and the amount of total wild meat, and volume (in kg) of meat of Tayassu pecari -white-lipped peccary-, Cuniculus paca -paca-, Pecari tajacu -collared pecari-, and Mazama sp. -brocket deer- sold in each interview day. In October 2018, at the end of the survey of 2017-2018, we conducted an interview directly to the eleven most frequent wild meat sellers in order to obtain their personal perception on the average price and daily amount of wild meat sold year-along. This information is included in the page "Single questionary".

In 2024, Amazon was the online marketplace with the highest gross merchandise value in the United States, amounting to approximately *** billion U.S. dollars. It was followed by Walmart, with a GMV of roughly *** billion U.S. dollars. Amazon: the U.S.’s clear favorite While Amazon might not be the leading online marketplace worldwide, it is the front-runner in the United States. As of April 2023, Amazon dominated the list of leading online marketplaces in the U.S. based on number of monthly visits, with over *** billion website visitors. Regarding the market share of leading retail e-commerce companies, Amazon was also in the lead with over ** percent difference from the next competitor, Walmart. Even when it comes to holiday shopping, consumers favor Amazon. Nearly ** percent of consumers stated that they prefer the online marketplace for their holiday shopping needs over other websites. A prime day for shopping Consumer purchasing behavior often relies on shopping events that occur throughout the year. Amazon incurs the largest increase of sales during Black Friday, which is followed by Prime Day, an event exclusively for Amazons’ Prime members. Consumers often plan their shopping around this event, due to the deals offered to Prime members. In 2023, most shoppers planned on purchasing electronics, at almost ** percent of consumers. The most purchased items on Prime Day, however, fell into the category of home goods, with around ** percent of shoppers, and only around ** percent of shoppers purchased electronics. In general, the average value of Prime Day orders was worth up to ** U.S. dollars, with ** percent of orders.

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

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'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

Amazon net income for the twelve months ending March 31, 2025 was $65.944B, a 74.99% increase year-over-year. Amazon annual net income for 2024 was $59.248B, a 94.73% increase from 2023. Amazon annual net income for 2023 was $30.425B, a 1217.74% decline from 2022. Amazon annual net income for 2022 was $-2.722B, a 108.16% decline from 2021.

In 2022, big e-commerce sites providing Buy Now, Pay Later (BNPL) options could count on higher average online orders (AOV). A worldwide study showed that online stores with annual revenue between one and ten million U.S. dollars would generate online orders worth nearly 280 U.S. dollars when providing BNPL options, compared to under 250 U.S. dollars without offering BNPL payment. In addition to that, online stores providing BNPL options reported higher online traffic.

Buy Now, Pay Later on Amazon Prime Day

In the United States, BNPL plays an important role in sales events. The value of purchases made on Amazon on Prime Day increased between 2022 and 2023. In the latest year, shoppers spent 461 million U.S. dollars on the first Prime Day and 466 million U.S. dollars on day two using BNPL options. This represented nearly a 100 million U.S. dollar increase compared to the previous year.

Buy Now, Pay Later in online shopping

BNPL contributes to the success of several e-commerce formats in the United States. In a 2023 survey, 17 percent of shoppers considered BNPL options a leading feature driving sign-ups to retail subscription boxes. In 2022, an even higher number of U.S. shoppers appreciated BNPL services offered in live commerce events, with Gen X consumers being the most enthusiastic about it (62 percent).

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

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

Moist tropical forests in Amazonia and elsewhere are subjected to increasingly severe drought episodes through the El Nino-Southern Oscillation (ENSO) and possibly through deforestation-driven reductions in rainfall. The effects of this trend on tropical forest canopy dynamics, emissions of greenhouse gases, and other ecological functions are potentially large but poorly understood. We established a throughfall exclusion experiment in an east-central Amazon forest (Tapajos National Forest, Brazil) to help understand these effects. After 1-year intercalibration period of two 1-ha forest plots, we installed plastic panels and wooden gutters in the understory of one of the plots, thereby excluding similar to 890 mm of throughfall during the exclusion period of 2000 (late January to early August) and similar to680 mm thus far in the exclusion period of 2001 (early January to late May). Average daily throughfall reaching the soil during the exclusion period in 2000 was 4.9 and 8.3 mm in the treatment and control plots and was 4.8 and 8.1 mm in 2001, respectively. During the first exclusion period, surface soil water content (0-2 m) declined by similar to100 mm, while deep soil water (2-11 m) was unaffected. During the second exclusion period, which began shortly after the dry season when soil water content was low, surface and deep soil water content declined by similar to140 and 160 mm, respectively. Although this depletion of soil water provoked no detectable increase in leaf drought stress (i.e., no reduction in predawn leaf water potential), photosynthetic capacity declined for some species, the canopy thinned (greater canopy openness and lower leaf area index) during the second exclusion period, stem radial growth of trees <15 m tall declined, and fine litterfall declined in the treatment plot, as did tree fruiting. Aboveground net primary productivity (NPP) (stemwood increment and fine litter production) declined by one fourth, from 15.1 to 11.4 Mg ha(-1) yr(-1), in the treatment plot and decreased slightly, from 11.9 to 11.5 Mg ha(-1) yr(-1), in the control plot. Stem respiration varied seasonally and was correlated with stem radial growth but showed no treatment response. The fastest response to the throughfall exclusion, and the surface soil moisture deficits that it provoked, was found in the soil itself. The treatment reduced N2O emissions and increased CH4 consumption relative to the control plot, presumably in response to the improved soil aeration that is associated with soil drying. Our hypothesis that NO emissions would increase following exclusion was not supported. The conductivity and alkalinity of water percolating through the litter layer and through the mineral soil to a depth of 200 cm was higher in the treatment plot, perhaps because of the lower volume of water that was moving through these soil layers in this plot. Decomposition of the litter showed no difference between plots. In sum, the small soil water reductions provoked during the first 2 years of partial throughfall exclusion were sufficient to lower aboveground NPP, including the stemwood increment that determines the amount of carbon stored in the forest. These results suggest that the net accumulation of carbon in mature Amazon forests indicated by recent permanent plot and eddy covariance studies may be very sensitive to small reductions in rainfall. The soil water reductions were also sufficient to increase soil emissions of N2O and to increase soil consumption of CH4-both radiatively important gases in the atmosphere.The possible reduction of tree reproductive activity points to potentially important effects of drought on the long-term species composition of Amazon forests.

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

In March 2024, Amazon.com had approximately 2.2 billion combined web visits, up from 2.1 billion visits in February. In the fourth quarter of 2024, Amazon’s net income amounted to approximately 20 billion U.S. dollars. Online retail in the United States Online retail in the United States is constantly growing. In the third quarter of 2023, e-commerce sales accounted for 15.6 percent of retail sales in the United States. During that quarter, U.S. retail e-commerce sales amounted to over 284 billion U.S. dollars. Amazon is the leading online store in the country, in terms of e-commerce net sales. Amazon.com generated around 130 billion U.S. dollars in online sales in 2022. Walmart ranked as the second-biggest online store, with revenues of 52 billion U.S. dollars. The king of Black Friday In 2023, Amazon ranked as U.S. shoppers' favorite place to go shopping during Black Friday, even surpassing in-store purchasing. Nearly six out of ten consumers chose Amazon as the number one place to go find the best Black Friday deals. Similar findings can be observed in the United Kingdom (UK), where Amazon is also ranked as the preferred Black Friday destination.

Amazon enjoyed staggering sales growth in United Kingdom over the past decade, taking net sales from roughly four billion to almost 33.6 billion U.S. dollars in 2023. That makes the UK the retail behemoth’s second biggest European market, sitting behind Germany where the company reported total net sales of about 37.6 billion U.S. dollars in 2023.

Amazon’s other UK presence Amazon runs 20 distribution services in the UK, where Amazon has its largest European logistics and fulfillment presence. Operating under the “Amazon UK Services” name, the retailer generated over two billion British pounds in 2018. This represented over 200 percent turnover growth since 2015.

Consumers have no problem shopping with Amazon

In proportion to the pace Amazon’s retail empire is expanding, worries are voiced within the industry about the monopoly held by the retailer, not to mention the privacy concerns revolving around Amazon’s own brand smart devices. Yet shoppers seem unfazed, as convenience and variety offered by the retailer convert more and more people into being Amazon shoppers. A recent survey conducted with UK shoppers found out that only a small share of consumers felt guilty about or actively chose not shopping with Amazon. In comparison, nearly one quarter of those surveyed said they “loved” shopping with Amazon.

According to the most recent data, U.S. viewers aged 15 years and older spent on average almost ***** hours watching TV per day in 2023. Adults aged 65 and above spent the most time watching television at over **** hours, whilst 15 to 19-year-olds watched TV for less than *** hours each day. The dynamic TV landscape The way people consume video entertainment platforms has significantly changed in the past decade, with a forecast suggesting that the time spent watching traditional TV in the U.S. will probably decline in the years ahead, while digital video will gain in popularity. Younger age groups in particular tend to cut the cord and subscribe to video streaming services, such as Netflix, Hulu, and Amazon Prime Video. TV advertising in a transition period Similarly, the TV advertising market made a development away from traditional linear TV towards online media. While the ad spending on traditional TV in the U.S. generally increased until the end of the 2010s, this value is projected to decline to below ** billion U.S. dollars in the next few years. By contrast, investments in connected TV advertising are expected to steadily grow, despite the amount being just over half of the traditional TV ad spend by 2025.