Brand performance data collected from AI search platforms for the prompt "where to buy microchips online".

Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features

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This statistic presents the results of a survey conducted in 2017 about the preferred locations for purchasing toothpaste among Japanese consumers. The survey revealed that the majority of respondents, over ** percent, bought toothpaste most frequently at drugstores in Japan.

Between 86 percent and 91 percent of homebuyers in the United States purchased their homes through a real estate agent or broker, depending on their age group, in 2024. Homebuyers in the age group of 79 to 99 were most likely to buy a house through an agent or broker. On the other hand, the oldest homebuyers were most likely to buy their new home from the previous owner directly.

According to a survey among real estate experts, approximately 60 percent of respondents recommended buying medical offices in 2025. Meanwhile, about 47 percent of respondents recommended selling central city and suburban offices.

Buy consumer data from us to find the target audience for b2c marketing. FrescoData offer the Highest Value for People and consumer marketing.

In 2023, 63 percent of Poles purchased food and beverages online on Allegro. Only three percent bought food and beverages on Spar, while 15 percent used other websites.

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 LON:LIKE Stock Buy or Sell? (Stock Forecast)

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

BNPL solutions enable consumers to purchase goods or services and pay for them in installments over a fixed period of time. Key product features include:

Interest-free or low-interest payment options Flexible repayment schedules Seamless checkout integration Mobile-friendly interfaces

Best Buy Vermögenswerte - Diese Werte, historische Daten, Prognosen, Statistiken, Diagramme und ökonomische Kalender - Jun 2025.Data for Best Buy | Vermögenswerte including historical, tables and charts were last updated by Trading Economics this last June in 2025.

The monthly share of consumers who planned to purchase consumer electronic articles in the following three months in Italy fluctuated from January 2021 to March 2024. As of February 2024, around 30 percent of respondents were planning to purchase consumer electronics, an increase compared to the previous month.

Official statistics on the number of sales of dwellings under the Right to Buy scheme. These statistics relate only to sales by local authorities under the Right to Buy scheme, excluding sales by registered providers under preserved Right to Buy.

United States CCI: Plans to Buy Within 6 Mos: sa: Home: Yes data was reported at 4.500 % in Apr 2025. This records a decrease from the previous number of 5.600 % for Mar 2025. United States CCI: Plans to Buy Within 6 Mos: sa: Home: Yes data is updated monthly, averaging 3.600 % from Feb 1967 (Median) to Apr 2025, with 637 observations. The data reached an all-time high of 7.700 % in Jul 2020 and a record low of 1.700 % in Dec 2009. United States CCI: Plans to Buy Within 6 Mos: sa: Home: Yes data remains active status in CEIC and is reported by The Conference Board. The data is categorized under Global Database’s United States – Table US.H054: Consumer Confidence Index: Buying Plans & Intended Vacations. [COVID-19-IMPACT]

Best Buy Ticaret Alacaklılar - Akım değerleri, tarihsel veriler, tahminler, istatistikler, grafikler ve ekonomik takvim - Jul 2025.Data for Best Buy | Ticaret Alacaklılar including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Dataset

This dataset was created by Joy Chakraborty

Released under Database: Open Database, Contents: Database Contents

Contents

Context

The problem that the retail industry often encounters in customer operations is that consumers often make only one purchase, and it is difficult to convert them into closely-connected loyal members. Therefore, it is hoped that consumers' willingness to buy back can be strengthened through marketing activities, so that the connection between merchants and members will be deeper and more stable. We will use the accurate marketing methods reached by data science to carry out accurate campaign launches, so that the marketing campaigns can achieve their goals more effectively and accurately.

Content

train.csv: the original training data set, including the ID of the member and the corresponding characteristics. It contains the prediction target (target), which corresponds to whether to repurchase in the translation. test.csv: the original test data set. transaction.csv: member transaction records, including member ID, merchant ID, transaction amount, item and other information.

Inspiration

Based on the historical data of some members, predict who in another group of members will buy back products. User data in the retail industry, including personal information, transaction records, etc., can help operations or marketing strategies

United States CSI: Home Buying Conditions: Bad Time: Can't Afford data was reported at 7.000 % in May 2018. This records an increase from the previous number of 5.000 % for Apr 2018. United States CSI: Home Buying Conditions: Bad Time: Can't Afford data is updated monthly, averaging 8.000 % from Feb 1978 (Median) to May 2018, with 467 observations. The data reached an all-time high of 19.000 % in Aug 2011 and a record low of 1.000 % in Jul 1999. United States CSI: Home Buying Conditions: Bad Time: Can't Afford data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H036: Consumer Sentiment Index: Home Buying and Selling Conditions. The question was: Generally speaking, do you think now is a good time or a bad time to buy a house? Responses to the query 'Why do you say so?'

United States CSI: Home Buying Conditions: Bad Time: Prices are High data was reported at 22.000 % in May 2018. This records an increase from the previous number of 18.000 % for Apr 2018. United States CSI: Home Buying Conditions: Bad Time: Prices are High data is updated monthly, averaging 10.000 % from Feb 1978 (Median) to May 2018, with 467 observations. The data reached an all-time high of 34.000 % in Aug 1978 and a record low of 2.000 % in Sep 2012. United States CSI: Home Buying Conditions: Bad Time: Prices are High data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H036: Consumer Sentiment Index: Home Buying and Selling Conditions. The question was: Generally speaking, do you think now is a good time or a bad time to buy a house? Responses to the query 'Why do you say so?'

GSA e-Buy, is an electronic Request for Quote (RFQ) / Request for Proposal (RFP) system designed to allow government buyers to request information, find sources, and prepare RFQs/RFPs, online, for millions of services and products offered through GSA's Multiple Award Schedule (MAS) and GSA Technology Contracts. Government buyers can use eBuy to obtain quotes or proposals for services, large quantity purchases, big ticket items, and purchases with complex requirements. Buyers may use e-Buy to evaluate and accept the quotation that represents the best value. Buyers may then make award to any contractor whose quotation was accepted. The e-Buy Award dataset are the award data collected by e-Buy for a given fiscal year.

Market Size and Growth: The global E-commerce Buy Now Pay Later (BNPL) market is projected to reach a market size of 674.52 million by 2033, growing at a CAGR of 14.41% from the base year of 2025. This growth is driven by factors such as the increasing millennial and Generation Z population, the popularity of online shopping, and the convenience of spreading out purchases into smaller, interest-free payments. Key Drivers, Trends, and Segments: Key drivers of the E-commerce BNPL market include the rising adoption of digital wallets and mobile payments, the growth of the online fashion and electronics segments, and the increasing demand for flexible payment options. Major trends include the emergence of embedded BNPL solutions, the personalization of BNPL services, and the development of regulations to safeguard consumer interests. Key market segments include payment methods (credit card, debit card, digital wallet), consumer types (millennials, Generation X), and purchase types (fashion, electronics). Key drivers for this market are: Growing consumer adoption rates, Expansion in emerging markets; Integration with mobile payment solutions; Enhanced customer loyalty and retention; Rising demand for flexible payment options.. Potential restraints include: rising consumer demand, increased merchant adoption; regulatory scrutiny; competition among providers; technological advancements.