According to a March 2020 survey of U.S. retailers and their expectations regarding the impact of the coronavirus on their business, a total of ** percent of respondents stated that they expected production delays and nearly as many responding retailers were worried about the strength of consumer confidence and the impact it might have on revenue. A fifth of U.S. retailers expected increases in e-commerce sales due to social isolating practices and more people choosing to making purchases online instead of risking infection through in-store shopping.

This timeline shows a forecast for the Philippines' retail sales growth from 2008 to 2018, by volume. It is forecasted that the Philippines' retail sales volume will grow by *** percent in 2017.

Economy in the Republic of the Philippines

The Republic of the Philippines is a tropical country in the western Pacific Ocean comprised of ***** islands, which are more broadly classified into three groups: Luyon, Visayas, and Mindanao. The nation is the twelfth most populated country worldwide, with some ** million more Filipino citizens working abroad. A recent economic boom has allowed the country to buck the moniker “sick man of Asia”. In the recent past, the country suffered an economic slowdown emanating from a sluggish world economy, which resulted in a decrease in demand for electronics, one of the country’s main exports.
Filipinos criticized the current national government recently for their delay in promised infrastructure spending. Several projects have now been approved, and with increasing global economic health the Philippines stand to experience significant growth over the next few years. A tropical island rich in natural resources, the Philippines has traditionally been an agriculturally-focused nation.

Venezuela Wholesale & Retail Trade: Sales Volume Index data was reported at 242.462 1997=100 in Sep 2013. This records an increase from the previous number of 240.706 1997=100 for Aug 2013. Venezuela Wholesale & Retail Trade: Sales Volume Index data is updated monthly, averaging 183.419 1997=100 from Jan 2004 (Median) to Sep 2013, with 117 observations. The data reached an all-time high of 296.915 1997=100 in Dec 2008 and a record low of 65.650 1997=100 in Feb 2004. Venezuela Wholesale & Retail Trade: Sales Volume Index data remains active status in CEIC and is reported by Central Bank of Venezuela. The data is categorized under Global Database’s Venezuela – Table VE.H001: Sales Value and Volume Index: 1997=100. Data lag exhibited in the series is caused by the delay of data releases from the Central Bank of Venezuela.

Venezuela Wholesale & Retail Trade: Sales Value Index data was reported at 5,247.164 1997=100 in Sep 2013. This records an increase from the previous number of 5,107.087 1997=100 for Aug 2013. Venezuela Wholesale & Retail Trade: Sales Value Index data is updated monthly, averaging 1,449.488 1997=100 from Jan 2004 (Median) to Sep 2013, with 117 observations. The data reached an all-time high of 5,247.164 1997=100 in Sep 2013 and a record low of 244.770 1997=100 in Jan 2004. Venezuela Wholesale & Retail Trade: Sales Value Index data remains active status in CEIC and is reported by Central Bank of Venezuela. The data is categorized under Global Database’s Venezuela – Table VE.H001: Sales Value and Volume Index: 1997=100. Data lag exhibited in the series is caused by the delay of data releases from the Central Bank of Venezuela.

Basketview Signal CPG Receipt & POS Consumer Data: A Fusion of Shopper Behavior Details

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. Basketview Signal CPG is aggregated CPG-tagged consumer purchase data that delivers the fastest daily data with 3-day lag, beating leading competitors by up to 20 days. Designed to meet the needs of financial services teams, enjoy a deeper level of market measurement of CPG product purchases and customer behavior with 5 years of data history combined with the ability to integrate with other CE datasets for more holistic analysis. Basketview Signal CPG offers CPG-tagged data on the world’s leading brands combined with CE’s proprietary attribution to product brand and parent company, unlocking insights into retail sales of both established and emerging brands by channel, geography, and more.

Consumer Edge’s Basketview Signal CPG with receipt and POS data offers insights into tracked retail channels including: • Mass • Grocery • Drug • Club • Dollar • Independent Convenience & Gas • Specialty Pet

Benefits • Visibility into CPG Sales: Understand online and offline wholesale sales for leading consumer packaged goods brands and companies, plus specialty channels • Understand Category Performance: Analyze performance by category and brand to inform investment theses. • Fine-Tune Competitive Analysis: Explore CPG retail performance compared to the category or top competitors with SKU-level data mapped to brand, company, and ticker. • See How Retail Pricing Drives Volume: Analyze how pricing affects product brand selection among CPG customers, calculate elasticity, and measure inflation of baskets. • Cut by Channel and Geography: See how trends differ across individual retail channels and subindustries as well as all 50 US states. • Daily Delivery and Granularity: Near real-time updates 365 days a year, with the ability to drill down into individual days to see the impact of promotions, holidays, and weather. Easy aggregation into company fiscal periods either from the day level or via CE aggregation. • Easier Comparisons: Data for different package sizes is equivalized into volumes (e.g., OUNCES) for better estimation of true demand.

Use Case: Pet Specialty Forecasting

Problem A public investor wants to refine their tools for company modeling on key investments in the pet specialty industry.

Solution The firm leveraged Consumer Edge Basketview Signal CPG data to: • Gain visibility into CPG sales for pet brands across mass, grocery, drug, club, dollar, convenience, and pet specialty channels – including both offline and e-commerce • Better understand overall performance of pet specialty categories and brands • Explore retail performance for key pet brands compared to the category with SKU-level data aggregated to brand, company, and ticker • See how retail pricing influences pet brand selection

Metrics Include: • Spend • Items • Volume • Transactions • Price Per Volume

Inquire about a Basketview subscription to perform more complex, near real-time analyses on public tickers and private brands as well as for industries beyond CPG like: • How changes in distribution and channels are affecting a brand’s overall sales • Understand consumer price elasticity • Analyze emerging trends in inflation

Consumer Edge offers a variety of datasets covering the US, Europe (UK, Austria, France, Germany, Italy, Spain), and across the globe, with subscription options serving a wide range of business needs.

Consumer Edge is the Leader in Data-Driven Insights Focused on the Global Consumer

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.

Mallinckrodt in Debt Over Opioid Crisis, Seeks Payment Delay

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.

American Vanguard (AVD) Stock: Dividend Delay, Direction Doubts?

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 2023, around ** percent of food retailers in Germany had experienced delivery problems. Roughly ** percent of businesses in the automotive trade faced these challenges as well. Delivery problems could be due to delays or supply shortages.

Real-Time Fashion Trend Forecast Market Outlook

According to our latest research, the global real-time fashion trend forecast market size reached USD 1.62 billion in 2024, driven by the rapid digital transformation across the fashion industry and the growing adoption of advanced analytics. The market is expected to expand at a robust CAGR of 18.3% from 2025 to 2033, reaching a projected value of USD 7.54 billion by 2033. This significant growth is attributed primarily to the increasing demand for data-driven decision-making among fashion brands, retailers, and e-commerce platforms, as well as the integration of technologies such as artificial intelligence (AI), machine learning (ML), and big data analytics for precise trend forecasting.

A major growth factor propelling the real-time fashion trend forecast market is the heightened need for agility and responsiveness in the fashion industry. The traditional model of trend identification, which relied heavily on manual observation and delayed reporting, is no longer sufficient to meet the demands of today’s fast-paced market. With the proliferation of social media platforms and the constant influx of consumer-generated content, fashion brands are under pressure to anticipate and respond to emerging trends almost instantaneously. Real-time fashion trend forecasting solutions, powered by advanced analytics, enable stakeholders to monitor shifting consumer preferences, viral fashion moments, and influencer activities in real time. This capability not only reduces the risk of inventory mismanagement but also empowers brands to launch collections that resonate with current consumer sentiment, thus driving higher conversion rates and improved profitability.

Another crucial driver is the widespread adoption of AI and machine learning technologies within the fashion sector. These technologies facilitate the processing of massive volumes of unstructured data from diverse sources, including social media feeds, runway events, retail sales, and online browsing patterns. By leveraging AI-powered algorithms, fashion companies can detect subtle patterns, predict future trends, and generate actionable insights with unparalleled accuracy and speed. This shift towards intelligent automation is particularly beneficial for fashion brands and retailers seeking to enhance their competitive edge in an increasingly saturated market. Furthermore, the integration of big data analytics and social media analytics has enabled a more granular understanding of micro-trends and niche consumer segments, allowing for highly targeted marketing and product development strategies.

The evolving retail landscape, characterized by the convergence of physical and digital channels, is also contributing significantly to market growth. E-commerce platforms, in particular, are leveraging real-time trend forecasting tools to optimize their product assortments, personalize shopping experiences, and execute dynamic pricing strategies. The ability to quickly identify and capitalize on emerging trends has become a key differentiator for online retailers, especially in an era where consumer preferences can shift overnight. Additionally, collaborations between technology providers and fashion houses are fostering innovation in trend forecasting methodologies, further accelerating market expansion. As fashion brands increasingly prioritize sustainability and ethical practices, real-time trend forecasting also aids in minimizing overproduction and reducing environmental impact by aligning supply with actual demand.

Regionally, North America holds the largest share of the real-time fashion trend forecast market, accounting for over 38% of global revenue in 2024. The region’s dominance is attributed to the high concentration of leading fashion brands, advanced technological infrastructure, and a mature e-commerce ecosystem. Europe follows closely, driven by a strong presence of luxury fashion houses and progressive adoption of digital solutions. The Asia Pacific region is anticipated to witness the fastest growth during the forecast period, fueled by the rapid digitalization of retail, expanding middle-class population, and the influence of K-fashion and J-fashion trends. Latin America and the Middle East & Africa are also emerging as promising markets, supported by increasing smartphone penetration and the growing popularity of social commerce.

Component Analysis

The component segment o

Snapshot img

US Online Household Furniture Market Size 2025-2029

The US online household furniture market size is forecast to increase by USD 6.45 billion at a CAGR of 4.4% between 2024 and 2029.

The US online household furniture market is growing, driven by consumer demand for convenience in home shopping and advancements in mobile platforms that simplify purchases. This report delivers valuable insights through detailed market size data, growth forecasts, and analysis of key segments like bedroom furniture, which leads due to its frequent online sale/e-commerce shopping. It highlights a key trend in personalized product offerings, meeting diverse customer tastes, while addressing a challenge from supply chain disruptions, which can delay deliveries. With data on regional dynamics, vendor approaches, and buyer preferences, this report enables businesses to optimize operations, enhance client experiences, and stay competitive in a fast-paced global landscape by navigating customization and logistics hurdles.

What will be the Size of the Market During the Forecast Period?

Request Free Sample

In the digital world, online shopping has become a preferred choice for consumers seeking convenience and a wide range of options. Furniture shopping is no exception to this trend. The extensive furniture collections available online cater to diverse consumer preferences, from modern designs to rustic pieces made of traditional woods, metals, composites, and glass. The convenience of online furniture shopping allows consumers to browse and purchase from the comfort of their homes, eliminating the time-consuming excursion to brick-and-mortar stores. Smartphones and laptops have made it easier than ever to access these collections, enabling consumers to compare prices and features at their leisure.
In addition, online furniture retailers offer customisation options, enabling consumers to tailor their furniture to their personal tastes. This level of customisation is not always possible in traditional settings. Moreover, consumers can take advantage of discounts and sales, ensuring they get the best value for their money. The rise of online furniture shopping has disrupted the traditional furniture market. Consumers now have access to a vast array of furniture options, allowing them to make informed decisions based on their preferences and budgets. The convenience and flexibility offered by online shopping have made it an attractive alternative to the traditional shopping experience.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Living room furniture
  Bedroom furniture
  Storage furniture
  Others


Material

  Wood
  Metal
  Others


Type

  Non-ready to assemble
  Ready to assemble


Geography

  US

By Product Insights

The living room furniture segment is estimated to witness significant growth during the forecast period.The market holds substantial growth potential, particularly within the living room furniture segment. Consumers with elevated purchasing power are investing significantly in modern living room designs, featuring extensive furniture collections. These collections encompass a blend of traditional woods, metals, and rustic pieces. Multifunctional and multipurpose furniture, such as sofas and couches, are increasingly popular due to the trend of downsizing living spaces. Large families are adapting to smaller dwellings, necessitating seating solutions that offer versatility, such as chairs that can double as mini sofas or sectional sofas. This shift has driven the demand for online household furniture retailers, offering a wide range of modern designs and convenient shopping experiences.

Get a glance at the market report of share of various segments Request Free Sample

Market Dynamics

The online household furniture market in the US is driven by demand for ergonomic design, fabric durability, and cushion support to enhance user comfort. Frame strength, weight lightness, and eco-friendly materials ensure long-lasting and sustainable choices. Consumers prioritize delivery speed, assembly time, and packaging care for convenience. Space optimization, storage features, and design flexibility cater to modern living needs, while color retention, stain proofing, and surface protection improve longevity. With a focus on cost efficiency, comfort rating, maintenance ease, and noise reduction, the market continues to expand with versatile and high-quality offerings.

Firstly, the increasing preference for convenient online shopping experiences is driving sales in this sector. Secondly, the trend towards innovative and customized furniture designs is gaining tract