In February 2025, one gallon of diesel cost an average of 3.68 U.S. dollars in the United States. That was an increase compared to two months prior, which was the lowest price in the past 24-month period. Impact of crude prices on motor fuel consumer prices Diesel prices are primarily determined by the cost of crude oil. In fact, crude oil regularly accounts for around 50 percent of end consumer prices of diesel. As such, supply restrictions or weak demand outlooks influence prices at the pump. The fall in diesel prices noted in the latter half of 2024 is a reflection of lower crude prices. Diesel and gasoline price development The usage of distillate fuel oil began in the 1930s, but until further development in the 1960s, diesel vehicles were mostly applied to commercial use only. In the U.S., diesel-powered cars remain a fairly small portion of the automobile market and diesel consumption is far lower than gasoline consumption. In general, gasoline also tends to be more widely available than diesel fuel and usually sells for a lower retail price. However, diesel engines have better fuel economy than gasoline engines, and, as such, tend to be used for large commercial vehicles.

According to Cognitive Market Research, the global Artificial Intelligence in Retail market size is USD 4951.2 million in 2023and will expand at a compound annual growth rate (CAGR) of 39.50% from 2023 to 2030.

Enhanced customer personalization to provide viable market output
Demand for online remains higher in Artificial Intelligence in the Retail market.
The machine learning and deep learning category held the highest Artificial Intelligence in Retail market revenue share in 2023.
North American Artificial Intelligence In Retail will continue to lead, whereas the Asia-Pacific Artificial Intelligence In Retail market will experience the most substantial growth until 2030.

Enhanced Customer Personalization to Provide Viable Market Output

A primary driver of Artificial Intelligence in the Retail market is the pursuit of enhanced customer personalization. A.I. algorithms analyze vast datasets of customer behaviors, preferences, and purchase history to deliver highly personalized shopping experiences. Retailers leverage this insight to offer tailored product recommendations, targeted marketing campaigns, and personalized promotions. The drive for superior customer personalization not only enhances customer satisfaction but also increases engagement and boosts sales. This focus on individualized interactions through A.I. applications is a key driver shaping the dynamic landscape of A.I. in the retail market.

January 2023 - Microsoft and digital start-up AiFi worked together to offer Smart Store Analytics. It is a cloud-based tracking solution that helps merchants with operational and shopper insights for intelligent, cashierless stores.

Source-techcrunch.com/2023/01/10/aifi-microsoft-smart-store-analytics/

Improved Operational Efficiency to Propel Market Growth

Another pivotal driver is the quest for improved operational efficiency within the retail sector. A.I. technologies streamline various aspects of retail operations, from inventory management and demand forecasting to supply chain optimization and cashier-less checkout systems. By automating routine tasks and leveraging predictive analytics, retailers can enhance efficiency, reduce costs, and minimize errors. The pursuit of improved operational efficiency is a key motivator for retailers to invest in AI solutions, enabling them to stay competitive, adapt to dynamic market conditions, and meet the evolving demands of modern consumers in the highly competitive artificial intelligence (AI) retail market.

January 2023 - The EY Retail Intelligence solution, which is based on Microsoft Cloud, was introduced by the Fintech business EY to give customers a safe and efficient shopping experience. In order to deliver insightful information, this solution makes use of Microsoft Cloud for Retail and its technologies, which include image recognition, analytics, and artificial intelligence (A.I.).

Source-www.ey.com/en_gl/news/2023/01/ey-announces-launch-of-retail-solution-that-builds-on-the-microsoft-cloud-to-help-achieve-seamless-consumer-shopping-experiences

Market Dynamics of the Artificial Intelligence in the Retail market

Data Security Concerns to Restrict Market Growth

A prominent restraint in Artificial Intelligence in the Retail market is the pervasive concern over data security. As retailers increasingly rely on A.I. to process vast amounts of customer data for personalized experiences, there is a growing apprehension regarding the protection of sensitive information. The potential for data breaches and cyberattacks poses a significant challenge, as retailers must navigate the delicate balance between utilizing customer data for AI-driven initiatives and safeguarding it against potential security threats. Addressing these concerns is crucial to building and maintaining consumer trust in A.I. applications within the retail sector.

Impact of COVID–19 on the Artificial Intelligence in the Retail market

The COVID-19 pandemic significantly influenced artificial intelligence in the retail market, accelerating the adoption of A.I. technologies across the industry. With lockdowns, social distancing measures, and a surge in online shopping, retailers turned to A.I. to navigate the challenges posed by the pandemic. AI-powered solutions played a crucial role in optimizing supply chain management, predicting shifts in consumer behavior, and enhancing e-commerce experiences. Retailers lever...

United States EIA Forecast: Electricity Price: Retail: Residential Sector data was reported at 13.027 0.01 USD/kWh in Dec 2019. This records a decrease from the previous number of 13.488 0.01 USD/kWh for Nov 2019. United States EIA Forecast: Electricity Price: Retail: Residential Sector data is updated monthly, averaging 13.050 0.01 USD/kWh from Mar 2016 (Median) to Dec 2019, with 46 observations. The data reached an all-time high of 13.716 0.01 USD/kWh in Sep 2019 and a record low of 12.002 0.01 USD/kWh in Jan 2017. United States EIA Forecast: Electricity Price: Retail: Residential Sector data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s USA – Table US.P003: Energy Price: Forecast: Energy Information Administration.

Between 2020 and 2022, prices in retail real estate in Italian cities are expected to decrease. However, the annual percentage decrease will lessen with time. Among major Italian cities, the largest price decrease is foreseen to take place in Rome, Turin, and Catania. On the other hand, in Milan the price change between 2020 and 2022 is forecasted to be the lowest. Milan is also the only city where prices are expected to grow in 2022, even though only slightly.

United States FRB Cleveland Forecast: Inflation Nowcast: Core Consumer Price Index (CPI): YoY data was reported at 2.123 % in Dec 2018. This records a decrease from the previous number of 2.199 % for Nov 2018. United States FRB Cleveland Forecast: Inflation Nowcast: Core Consumer Price Index (CPI): YoY data is updated monthly, averaging 1.861 % from Aug 2013 (Median) to Dec 2018, with 65 observations. The data reached an all-time high of 2.322 % in Aug 2018 and a record low of 1.583 % in Jan 2015. United States FRB Cleveland Forecast: Inflation Nowcast: Core Consumer Price Index (CPI): YoY data remains active status in CEIC and is reported by Federal Reserve Bank of Cleveland. The data is categorized under Global Database’s United States – Table US.I003: Consumer Price Index: Urban: Forecast: Federal Reserve Bank of Cleveland.

U.S. gasoline prices increased across all major grades in February 2025. Regular gasoline prices rose to an average of 3.12 U.S. dollars per gallon. In the period of consideration, gasoline prices reached their highest level in June 2022. Differences in fuel grades Fuel grades at U.S. gas stations are differentiated by octane level. Higher grade fuels have higher octane levels, meaning that the fuel can be compressed more in the engine. This enables high-performance engines to create more power. Fuel may also vary from state to state and pump to pump. Some cities also have regulations on gasoline in order to improve air quality. Bioethanol is added to gasoline in some cases to meet the renewable fuel standard. Gasoline-run engines are able to run on blends with a bioethanol percentage of up to 25 percent. Gasoline prices reach historic high Primarily a result of the Russia-Ukraine war and inflation, the annual retail price of gasoline reached a new historic high in 2022, climbing to nearly four U.S. dollars per gallon. By 2023, annual prices had decreased again slightly, reaching 2013 levels.

United States EIA Forecast: Retail Price incl Tax: On-highway Diesel Fuel data was reported at 314.511 0.01 USD/gal in Dec 2019. This records an increase from the previous number of 311.224 0.01 USD/gal for Nov 2019. United States EIA Forecast: Retail Price incl Tax: On-highway Diesel Fuel data is updated monthly, averaging 296.847 0.01 USD/gal from Mar 2016 (Median) to Dec 2019, with 46 observations. The data reached an all-time high of 318.433 0.01 USD/gal in Sep 2018 and a record low of 208.165 0.01 USD/gal in Mar 2016. United States EIA Forecast: Retail Price incl Tax: On-highway Diesel Fuel data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s USA – Table US.P003: Energy Price: Forecast: Energy Information Administration.

The report covers Retail Analytics Software Market Companies, and the market is segmented by Solution (Software and Service), Deployment (Cloud and On-premise), Function (Customer Management, In-store Operation, Supply Chain Management, Marketing, and Merchandising, Others), and Geography (North America, Europe, Asia-Pacific, Latin America, Middle East and Africa). The market size and forecasts are provided in terms of value (USD) for all the above segments.

Overview: This dataset was collected and curated to support research on predicting real estate prices using machine learning algorithms, specifically Support Vector Regression (SVR) and Gradient Boosting Machine (GBM). The dataset includes comprehensive information on residential properties, enabling the development and evaluation of predictive models for accurate and transparent real estate appraisals.Data Source: The data was sourced from Department of Lands and Survey real estate listings.Features: The dataset contains the following key attributes for each property:Area (in square meters): The total living area of the property.Floor Number: The floor on which the property is located.Location: Geographic coordinates or city/region where the property is situated.Type of Apartment: The classification of the property, such as studio, one-bedroom, two-bedroom, etc.Number of Bathrooms: The total number of bathrooms in the property.Number of Bedrooms: The total number of bedrooms in the property.Property Age (in years): The number of years since the property was constructed.Property Condition: A categorical variable indicating the condition of the property (e.g., new, good, fair, needs renovation).Proximity to Amenities: The distance to nearby amenities such as schools, hospitals, shopping centers, and public transportation.Market Price (target variable): The actual sale price or listed price of the property.Data Preprocessing:Normalization: Numeric features such as area and proximity to amenities were normalized to ensure consistency and improve model performance.Categorical Encoding: Categorical features like property condition and type of apartment were encoded using one-hot encoding or label encoding, depending on the specific model requirements.Missing Values: Missing data points were handled using appropriate imputation techniques or by excluding records with significant missing information.Usage: This dataset was utilized to train and test machine learning models, aiming to predict the market price of residential properties based on the provided attributes. The models developed using this dataset demonstrated improved accuracy and transparency over traditional appraisal methods.Dataset Availability: The dataset is available for public use under the [CC BY 4.0]. Users are encouraged to cite the related publication when using the data in their research or applications.Citation: If you use this dataset in your research, please cite the following publication:[Real Estate Decision-Making: Precision in Price Prediction through Advanced Machine Learning Algorithms].

Global Retail Analytics Market Size By Component (Software, Service), By Deployment Model (On-premise, Cloud), By Application (Supply Chain Management, Merchandizing Intelligence), By Geographic Scope And Forecast

Macau SAR (China) % of Retailers: Forecast: Decrease: ≦5%: Goods in Department Stores data was reported at 1.333 % in Mar 2020. This records an increase from the previous number of 0.000 % for Dec 2019. Macau SAR (China) % of Retailers: Forecast: Decrease: ≦5%: Goods in Department Stores data is updated quarterly, averaging 1.667 % from Jun 2016 (Median) to Mar 2020, with 16 observations. The data reached an all-time high of 10.839 % in Dec 2017 and a record low of 0.000 % in Dec 2019. Macau SAR (China) % of Retailers: Forecast: Decrease: ≦5%: Goods in Department Stores data remains active status in CEIC and is reported by Statistics and Census Service. The data is categorized under Global Database’s Macau SAR (China) – Table MO.H002: Retailers' Forecast of Year-on-year Change in Retail Price. [COVID-19-IMPACT]

Global Artificial Intelligence in the Retail market is expected to hit USD 20.05 billion in 2026 and will grow to CAGR by 39% between 2020 and 2026. Digitalization in retail is much more than just linking objects. It's about turning data into observations that guide decisions that produce better market results.

The graph shows the forecast of average marijuana prices in Alaska from 2016 to 2020. In 2020, the average price of one gram of marijuana in Alaska is forecasted to amount to eight U.S. dollars.

The revenue in the e-commerce market in the United States was forecast to continuously increase between 2024 and 2029 by in total 657.8 billion U.S. dollars (+53.79 percent). After the tenth consecutive increasing year, the indicator is estimated to reach 1.9 trillion U.S. dollars and therefore a new peak in 2029. Notably, the revenue of the e-commerce market was continuously increasing over the past years.Find other key market indicators concerning the average revenue per user (ARPU) and number of users. The Statista Market Insights cover a broad range of additional markets.

Macau SAR (China) % of Retailers: Forecast: Decrease: Motor Vehicles data was reported at 83.184 % in Mar 2020. This records an increase from the previous number of 28.349 % for Dec 2019. Macau SAR (China) % of Retailers: Forecast: Decrease: Motor Vehicles data is updated quarterly, averaging 21.450 % from Mar 2006 (Median) to Mar 2020, with 57 observations. The data reached an all-time high of 83.184 % in Mar 2020 and a record low of 0.633 % in Dec 2009. Macau SAR (China) % of Retailers: Forecast: Decrease: Motor Vehicles data remains active status in CEIC and is reported by Statistics and Census Service. The data is categorized under Global Database’s Macau SAR (China) – Table MO.H002: Retailers' Forecast of Year-on-year Change in Retail Price. [COVID-19-IMPACT]

According to Cognitive Market Research, the global retail sector market size will be USD 29584.5 million in 2024. It will rise at a compound annual growth rate (CAGR) of 5.9% between 2024 and 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 11833.8 million in 2024 and will climb at a compound annual growth rate (CAGR) of 4.1% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 8875.4 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 6804.4 million in 2024 and will climb at a compound annual growth rate (CAGR) of 7.9% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 1479.2 million in 2024 and will climb at a compound annual growth rate (CAGR) of 5.3% from 2024 to 2031.
Middle East & Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 591.7 million in 2024 and will climb at a compound annual growth rate (CAGR) of 5.6% from 2024 to 2031.
The independent retailer segment is the fastest-growing ownership category of the retail sector industry.

Market Dynamics of Retail Sector Market

Key Drivers for Retail Sector Market

Increased Focus on Personalized User Experience to Facilitate Market Growth

The rapid growth of e-commerce has transformed the retail landscape. Consumers increasingly prefer the convenience of online shopping due to its accessibility, variety, and ease of comparison. This flexibility is particularly appealing to busy individuals and families. The proliferation of smartphones and improved internet access globally enables more people to engage in online shopping. This trend is especially prominent in emerging markets where digital access is expanding rapidly. Retailers are continuously investing in user-friendly websites, mobile apps, and personalized shopping experiences, utilizing AI and machine learning to tailor recommendations and promotions to individual preferences. For instance, on January 19, 2023, Tata Consultancy Services (TCS) announced TCS Customer Intelligence & Insights (CI&I) for Retail 3.0 to assist merchants in strengthening their client interactions. This provides hyper-personalized involvement at all stages of the customer journey. The platform delivers insights, forecasts, and recommended actions at key physical and digital touchpoints, resulting in increased marketing ROI and customer happiness.

Robust Adoption of Highly Advanced Technologies to Promote Market Developments

Emerging technological innovations are reshaping the retail sector by enhancing operational efficiency, improving customer experiences, and enabling personalized marketing strategies. Retailers are leveraging AI for inventory management, customer service, and personalized recommendations. AI-driven analytics help retailers understand consumer preferences and optimize their product offerings accordingly. The use of big data allows retailers to analyze consumer behavior, preferences, and purchasing patterns. This data-driven approach enables targeted marketing strategies and improves customer engagement. For instance, in January 2023, Microsoft and AiFi, a firm that helps businesses adopt modern shopping technology at a reasonable cost, announced their cloud service 'Smart Store Analytics'. Smart store analytics, which is part of Microsoft's Cloud for Retail product suite, provides shopper and operational data for retailers who use AiFi technology in their smart store fleets.

Restraint Factor for the Retail Sector Market

Growing Number of Retail Players Increases Price Wars to Limit Market Share

The growing number of retailers and e-commerce platforms is intensifying price competition within the retail sector. As more players enter the market, both brick-and-mortar stores and online platforms are vying for consumer attention by offering competitive pricing strategies. This increased competition leads to frequent discounting, promotional offers, and price wars, which can erode profit margins for retailers. Smaller businesses, in particular, face challenges in maintaining profitability as they compete with larger retailers who can leverage economies of scale to offer lower prices. Thus, the pressure to balance competitive pricing with sustainable marg...

The global retail market size reached USD 30,092.3 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 48,867.9 Billion by 2033, exhibiting a growth rate (CAGR) of 5.26% during 2025-2033. There are various factors that are driving the market, which include the rising focus on personalized user experience, technological innovations, and various collaborations and partnerships among key players to expand their market reach and increase user engagement.

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the global, regional, and country levels for 2025-2033. Our report has categorized the market based on product and distribution channel.

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.

Dow Jones Industrial Average Index Target Price Prediction

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 2020, global retail sales fell by 2.9 percent as a result of the COVID-19 pandemic, bouncing back in 2021 with a growth of 9.7 percent Global retail sales were projected to amount to around 27.3 trillion U.S. dollars by 2022, up from approximately 23.7 trillion U.S. dollars in 2020.

American retailers worldwide
As a result of globalization and various trade agreements between markets and countries, many retailers are capable of doing business on a global scale. Many of the world’s leading retailers are American companies. Walmart and Amazon are examples of such American retailers. The success of U.S. retailers can also be seen through their performance in online retail.

Retail in the U.S.
The domestic retail market in the United States is a lucrative market, in which many companies compete. Walmart, a retail chain offering low prices and a wide selection of products, is the leading retailer in the United States. Amazon, The Kroger Co., Costco, and Target are a selection of other leading U.S. retailers.

The online retail market is anticipated to grow at a significant CAGR during the forecast period 2020-2027. The market is segmented by product and by distribution channel.