Hotel customer dataset with 31 variables describing a total of 83,590 instances (customers). It comprehends three full years of customer behavioral data. In addition to personal and behavioral information, the dataset also contains demographic and geographical information. This dataset contributes to reducing the lack of real-world business data that can be used for educational and research purposes. The dataset can be used in data mining, machine learning, and other analytical field problems in the scope of data science. Due to its unit of analysis, it is a dataset especially suitable for building customer segmentation models, including clustering and RFM (Recency, Frequency, and Monetary value) models, but also be used in classification and regression problems.

Global Predictive Analytics Market size worth at USD 16.19 Billion in 2023 and projected to USD 113.8 Billion by 2032, with a CAGR of around 24.19% between 2024-2032.

Dataset with 72000 pins from 117 users in Pinterest. Each pin contains a short raw text and an image. The images are processed using a pretrained Convolutional Neural Network and transformed into a vector of 4096 features.

This dataset was used in the paper "User Identification in Pinterest Through the Refinement of a Cascade Fusion of Text and Images" to idenfity specific users given their comments. The paper is publishe in the Research in Computing Science Journal, as part of the LKE 2017 conference. The dataset includes the splits used in the paper.

There are nine files. text_test, text_train and text_val, contain the raw text of each pin in the corresponding split of the data. imag_test, imag_train and imag_val contain the image features of each pin in the corresponding split of the data. train_user and val_test_users contain the index of the user of each pin (between 0 and 116). There is a correspondance one-to-one among the test, train and validation files for images, text and users. There are 400 pins per user in the train set, and 100 pins per user in the validation and test sets each one.

If you have questions regarding the data, write to: jc dot gomez at ugto dot mx

The technological development in the new economic era has brought challenges to enterprises. Enterprises need to use massive and effective consumption information to provide customers with high-quality customized services. Big data technology has strong mining ability. The relevant theories of computer data mining technology are summarized to optimize the marketing strategy of enterprises. The application of data mining in precision marketing services is analyzed. Extreme Gradient Boosting (XGBoost) has shown strong advantages in machine learning algorithms. In order to help enterprises to analyze customer data quickly and accurately, the characteristics of XGBoost feedback are used to reverse the main factors that can affect customer activation cards, and effective analysis is carried out for these factors. The data obtained from the analysis points out the direction of effective marketing for potential customers to be activated. Finally, the performance of XGBoost is compared with the other three methods. The characteristics that affect the top 7 prediction results are tested for differences. The results show that: (1) the accuracy and recall rate of the proposed model are higher than other algorithms, and the performance is the best. (2) The significance p values of the features included in the test are all less than 0.001. The data shows that there is a very significant difference between the proposed features and the results of activation or not. The contributions of this paper are mainly reflected in two aspects. 1. Four precision marketing strategies based on big data mining are designed to provide scientific support for enterprise decision-making. 2. The improvement of the connection rate and stickiness between enterprises and customers has played a huge driving role in overall customer marketing.

Business Analytics Market was valued at USD 84.42 Billion in 2024 and is projected to reach USD 176.14 Billion by 2031, growing at a CAGR of 9.63% from 2024 to 2031.

Global Business Analytics Market Drivers

The market drivers for the Business Analytics Market can be influenced by various factors. These may include:

Growing Adoption of Big Data Analytics: In order to extract meaningful insights from their data, organizations are progressively using big data analytics in response to the exponential expansion of data. Making educated decisions through data analysis is facilitated by business analytics.
Growing Need for Data-driven Decision Making: In order to obtain a competitive edge, businesses are realizing the significance of data-driven decision making. The methods and instruments for data analysis and significant insights extraction for improved decision-making are offered by business analytics.
Growing Need for Predictive and Prescriptive Analytics: Predictive and prescriptive analytics are becoming more and more in demand as a means of projecting future trends and results. Businesses can use business analytics to prescribe activities to achieve desired outcomes and forecast future outcomes based on previous data.
Growing Emphasis on Customer Analytics: As e-commerce and digital marketing gain traction, companies are putting more of an emphasis on comprehending the behavior and preferences of their customers. In order to increase consumer engagement and personalize marketing efforts, business analytics is used to analyze customer data.
Emergence of Advanced Technologies: The use of advanced analytics solutions is being propelled by developments in fields like artificial intelligence (AI), machine learning (ML), and natural language processing (NLP). Businesses may now analyze data more effectively and gain deeper insights thanks to these technologies.
Operational Efficiency and Cost Optimization Are Necessary: Companies are always under pressure to increase operational efficiency and reduce costs. Business analytics promotes market expansion by assisting in the identification of opportunities for process and cost-cutting enhancements.
Compliance and Regulatory Requirements: The use of business analytics solutions for risk management and compliance reporting is being fueled by the growing regulatory requirements in a number of industries, including healthcare, banking, and retail.

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

What is Big data?

Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.

Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

Citation Request: This dataset is public available for research. The details are described in [Moro et al., 2011]. Please include this citation if you plan to use this database:

[Moro et al., 2011] S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM'2011, pp. 117-121, Guimarães, Portugal, October, 2011. EUROSIS.

Available at: [pdf] http://hdl.handle.net/1822/14838 [bib] http://www3.dsi.uminho.pt/pcortez/bib/2011-esm-1.txt

1. Title: Bank Marketing

2. Sources Created by: Paulo Cortez (Univ. Minho) and Sérgio Moro (ISCTE-IUL) @ 2012

3. Past Usage:

   The full dataset was described and analyzed in:

   S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM'2011, pp. 117-121, Guimarães, Portugal, October, 2011. EUROSIS.

4. Relevant Information:

   The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be (or not) subscribed.

   There are two datasets: 1) bank-full.csv with all examples, ordered by date (from May 2008 to November 2010). 2) bank.csv with 10% of the examples (4521), randomly selected from bank-full.csv. The smallest dataset is provided to test more computationally demanding machine learning algorithms (e.g. SVM).

   The classification goal is to predict if the client will subscribe a term deposit (variable y).

5. Number of Instances: 45211 for bank-full.csv (4521 for bank.csv)

6. Number of Attributes: 16 + output attribute.

7. Attribute information:

   For more information, read [Moro et al., 2011].

   Input variables:

   bank client data:

   1 - age (numeric) 2 - job : type of job (categorical: "admin.","unknown","unemployed","management","housemaid","entrepreneur","student", "blue-collar","self-employed","retired","technician","services") 3 - marital : marital status (categorical: "married","divorced","single"; note: "divorced" means divorced or widowed) 4 - education (categorical: "unknown","secondary","primary","tertiary") 5 - default: has credit in default? (binary: "yes","no") 6 - balance: average yearly balance, in euros (numeric) 7 - housing: has housing loan? (binary: "yes","no") 8 - loan: has personal loan? (binary: "yes","no")

   related with the last contact of the current campaign:

   9 - contact: contact communication type (categorical: "unknown","telephone","cellular") 10 - day: last contact day of the month (numeric) 11 - month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec") 12 - duration: last contact duration, in seconds (numeric)

   other attributes:

   13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted) 15 - previous: number of contacts performed before this campaign and for this client (numeric) 16 - poutcome: outcome of the previous marketing campaign (categorical: "unknown","other","failure","success")

   Output variable (desired target): 17 - y - has the client subscribed a term deposit? (binary: "yes","no")

8. Missing Attribute Values: None

The global big data and business analytics (BDA) market was valued at 168.8 billion U.S. dollars in 2018 and is forecast to grow to 215.7 billion U.S. dollars by 2021. In 2021, more than half of BDA spending will go towards services. IT services is projected to make up around 85 billion U.S. dollars, and business services will account for the remainder. Big data High volume, high velocity and high variety: one or more of these characteristics is used to define big data, the kind of data sets that are too large or too complex for traditional data processing applications. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets. For example, connected IoT devices are projected to generate 79.4 ZBs of data in 2025. Business analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate business insights. The size of the business intelligence and analytics software application market is forecast to reach around 16.5 billion U.S. dollars in 2022. Growth in this market is driven by a focus on digital transformation, a demand for data visualization dashboards, and an increased adoption of cloud.

Business Intelligence Software Market size was valued at USD 23.44 Billion in 2024 and is projected to reach USD 46.77 Billion by 2031, growing at a CAGR of 9.02% during the forecast period 2024-2031.

Key drivers of the Business Intelligence (BI) software market include the increasing demand for data-driven decision-making and real-time analytics. Businesses are looking to leverage large datasets for better insights, which fuels the need for advanced BI tools. Additionally, the adoption of cloud-based solutions and the integration of AI and machine learning into BI software enhance data visualization, prediction, and automation capabilities, making BI software indispensable.

Moreover, the rise in mobile BI solutions, enabling access to data on the go, and the growing need for regulatory compliance across industries further drive demand. Companies are also focusing on enhancing operational efficiency, reducing costs, and gaining a competitive edge through comprehensive data analysis, which bolsters the adoption of BI tools globally.

Salient Features of Dentists Email Addresses

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4. 15 million verification messages sent every month to validate email addresses
5. Connect with top Dentists across the US, Canada, UK, Europe, EMEA, Australia, APAC and many more countries.
6. egularly updated and cleansed databases to keep it free of duplicate and inaccurate data

How Can Our Dentists Data Help You to Market to Dentists?

We provide a variety of methods for marketing your dental appliances or products to the top-rated dentists in the United States. Take a glance at some of the available channels:

• Email blast • Marketing viability • Test campaigns • Direct mail • Sales leads • Drift campaigns • ABM campaigns • Product launches • B2B marketing

Data Sources

The contact details of your targeted healthcare professionals are compiled from highly credible resources like: • Websites • Medical seminars • Medical records • Trade shows • Medical conferences

What’s in for you? Over choosing us, here are a few advantages we authenticate- • Locate, target, and prospect leads from 170+ countries • Design and execute ABM and multi-channel campaigns • Seamless and smooth pre-and post-sale customer service • Connect with old leads and build a fruitful customer relationship • Analyze the market for product development and sales campaigns • Boost sales and ROI with increased customer acquisition and retention

Our security compliance

We use of globally recognized data laws like –

GDPR, CCPA, ACMA, EDPS, CAN-SPAM and ANTI CAN-SPAM to ensure the privacy and security of our database. We engage certified auditors to validate our security and privacy by providing us with certificates to represent our security compliance.

Our USPs- what makes us your ideal choice?

At DataCaptive™, we strive consistently to improve our services and cater to the needs of businesses around the world while keeping up with industry trends.

• Elaborate data mining from credible sources • 7-tier verification, including manual quality check • Strict adherence to global and local data policies • Guaranteed 95% accuracy or cash-back • Free sample database available on request

Guaranteed benefits of our Dentists email database!

85% email deliverability and 95% accuracy on other data fields

We understand the importance of data accuracy and employ every avenue to keep our database fresh and updated. We execute a multi-step QC process backed by our Patented AI and Machine learning tools to prevent anomalies in consistency and data precision. This cycle repeats every 45 days. Although maintaining 100% accuracy is quite impractical, since data such as email, physical addresses, and phone numbers are subjected to change, we guarantee 85% email deliverability and 95% accuracy on other data points.

100% replacement in case of hard bounces

Every data point is meticulously verified and then re-verified to ensure you get the best. Data Accuracy is paramount in successfully penetrating a new market or working within a familiar one. We are committed to precision. However, in an unlikely event where hard bounces or inaccuracies exceed the guaranteed percentage, we offer replacement with immediate effect. If need be, we even offer credits and/or refunds for inaccurate contacts.

Other promised benefits

• Contacts are for the perpetual usage • The database comprises consent-based opt-in contacts only • The list is free of duplicate contacts and generic emails • Round-the-clock customer service assistance • 360-degree database solutions

In 2021, the global social media analytics market was valued at roughly seven billion U.S. dollars. It was expected to grow to 8.5 billion in 2022 and surpass 26 billion dollars in 2028. Social media analytics tools are used, among others, to manage customer experience, as well as marketing management, and to gain competitive intelligence.

Data Set Information:

The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed.

There are four datasets: 1) bank-additional-full.csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014] 2) bank-additional.csv with 10% of the examples (4119), randomly selected from 1), and 20 inputs. 3) bank-full.csv with all examples and 17 inputs, ordered by date (older version of this dataset with less inputs). 4) bank.csv with 10% of the examples and 17 inputs, randomly selected from 3 (older version of this dataset with less inputs). The smallest datasets are provided to test more computationally demanding machine learning algorithms (e.g., SVM).

The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).

Attribute Information:

Input variables:

bank client data:

1 - age (numeric) 2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown') 3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed) 4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown') 5 - default: has credit in default? (categorical: 'no','yes','unknown') 6 - housing: has housing loan? (categorical: 'no','yes','unknown') 7 - loan: has personal loan? (categorical: 'no','yes','unknown')

related with the last contact of the current campaign:

8 - contact: contact communication type (categorical: 'cellular','telephone') 9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec') 10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri') 11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.

other attributes:

12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 14 - previous: number of contacts performed before this campaign and for this client (numeric) 15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')

social and economic context attributes

16 - emp.var.rate: employment variation rate - quarterly indicator (numeric) 17 - cons.price.idx: consumer price index - monthly indicator (numeric) 18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) 19 - euribor3m: euribor 3 month rate - daily indicator (numeric) 20 - nr.employed: number of employees - quarterly indicator (numeric)

Output variable (desired target): 21 - y - has the client subscribed a term deposit? (binary: 'yes','no')

Relevant Papers:

S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014

S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM'2011, pp. 117-121, Guimaraes, Portugal, October, 2011. EUROSIS. [bank.zip]

Snapshot img

Big Data Market Size 2024-2028

The big data market size is forecast to increase by USD 508.73 billion at a CAGR of 21.46% between 2023 and 2028.

The market is experiencing significant growth due to the growth in data generation from various sources, including IoT platforms and digital transformation services. This data deluge presents opportunities for businesses to leverage advanced analytics tools for applications such as fraud detection and prevention, workforce analytics, and business intelligence. However, the increasing adoption of big data implementation also brings challenges, including the need for data security and privacy measures. Quantum computing and blockchain technology are emerging trends In the big data landscape, offering potential solutions to complex data processing and security issues. In healthcare analytics, data protection regulations are driving the need for secure data management and sharing.
Additionally, supply chain optimization is another area where big data can bring significant value, enabling real-time monitoring and predictive analytics. Overall, the market is poised for continued growth, driven by the need to extract valuable insights from the vast amounts of data being generated.

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

Request Free Sample

The market is experiencing growth as businesses increasingly leverage information from vast datasets to drive strategic decision-making, enhance customer experiences, and improve operational efficiency. The digital revolution has led to an exponential increase in data creation, fueling demand for advanced analytics capabilities, real-time processing, and data protection and privacy solutions. Hardware and software companies offer on-premise and cloud-based systems to accommodate various industry needs, including customer analytics in retail and e-commerce, supply chain analytics in manufacturing, marketing analytics, pricing analytics, spatial analytics, workforce analytics, risk and credit analytics, transportation analytics, healthcare, energy and utilities, and IT and telecom. Big data applications span numerous sectors, enabling organizations to gain valuable insights from their data to optimize operations, mitigate risks, and innovate new products and services.

How is this Big Data Industry segmented and which is the largest segment?

The big data industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  On-premises
  Cloud-based
  Hybrid


Type

  Services
  Software


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  South America



  Middle East and Africa

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period. On-premises big data software solutions involve the installation of hardware and software by the end-user, granting them complete control over the system. Despite the high upfront costs, on-premises solutions offer advantages such as full ownership and operational efficiency. In contrast, cloud-based solutions require recurring monthly payments and involve data storage on companies' servers, increasing security concerns. Advanced analytics, real-time processing, and integrated analytics are key features driving the market. Data creation from digital transformation, customer experiences, and various industries like retail, healthcare, and finance, fuel the demand for scalable infrastructure and user-friendly interfaces. Technologies such as quantum computing, blockchain, AI-driven analytics platforms, and automation are transforming business intelligence solutions.

Ensuring data protection and privacy, accessibility, and seamless data transactions are crucial in this data-driven era. Key technologies include distributed computing, visualization tools, and social media. Target audiences range from decision-makers to various industries, including transportation, energy, and consumer engagement.

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

The On-premises segment was valued at USD 86.53 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 47% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market size of various regions, Request Free Sample

The market in North America is experiencing significant growth due to digital transformation initiatives by enterprises in sectors such as healthcare, retail

Snapshot img

Retail Analytics Market Size 2024-2028

The retail analytics market size is forecast to increase by USD 21.6 billion at a CAGR of 28.1% between 2023 and 2028.

The market is experiencing significant growth due to the increasing volume and complexity of data generated by retail businesses. This data provides valuable insights into consumer behavior, inventory management, and operational efficiency. Another key trend is the increasing adoption of artificial intelligence and inventory robots in the retail sector to analyze customer preferences, optimize pricing, and personalize marketing efforts. 
However, privacy and security concerns of customers remain a challenge, as retailers must ensure that customer data is protected while still providing personalized experiences. To address this, retailers are investing in advanced security measures and transparent data handling practices. Overall, the market is poised for continued growth as retailers seek to gain a competitive edge through data-driven insights.

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

Request Free Sample

The market is experiencing significant growth due to the increasing adoption of big data, data mining, and artificial intelligence (AI) technologies to gain business insights from customer behavior patterns and preferences in both physical retail stores and virtual stores, including e-commerce platforms. 
Retailers are leveraging data analytics to optimize product inventory management, shelf space allocation, and assortment planning, minimizing out-of-stock situations and reducing product obsolescence. AI-driven solutions enable micro-level analysis of multi-channel order performance, supply chain processes, and shelf space management. Blockchain technology is also gaining traction in the retail sector for secure data sharing and enhanced transparency in supply chain movements and inventory levels.
Procurement levels, marketing decisions, product recommendations, pricing strategy, and promotional campaigns are among the various areas where retail analytics plays a crucial role in driving operational efficiency and enhancing customer satisfaction.

How is this Retail Analytics Industry segmented and which is the largest segment?

The retail analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  In-store operation
  Customer management
  Supply chain management
  Marketing and merchandizing
  Others


Component

  Software
  Services


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    India


  Middle East and Africa



  South America

By Application Insights

The in-store operation segment is estimated to witness significant growth during the forecast period.

The market encompasses the utilization of big data, data mining, and business insights to optimize retail operations across various channels. This includes physical retail stores, virtual stores, e-commerce platforms, mobile apps, and online grocery shopping. Retailers employ customer-level analytics for understanding customer behavior patterns, sales performance, and preferences. Advanced technologies such as artificial intelligence (AI), machine learning, and self-learning computer algorithms are used to analyze data and provide real-time assistance. Retailers focus on inventory management, including product inventory management, shelf space allocation, micro-level analysis, assortment planning, and out-of-stock situations. They also address obsolescence, multi-channel order performance, and supply chain processes.

Real-time data analysis is crucial for managing inventory levels, procurement levels, pricing strategies, and marketing decisions. Retailers use customer personas and data privacy regulations to ensure customer satisfaction and loyalty. Retail analytics plays a significant role in Industry 4.0, digital transformation, and real-time data analysis. It enables predictive learning algorithms, demand forecasting, and visualization for effective inventory management and pricing strategies. Large enterprises in finance, sales and marketing, and supply chain sectors benefit from retail analytics, along with retail chains and merchandising teams for strategy and planning, staff management, and pricing management.

Get a glance at the Retail Analytics Industry report of share of various segments Request Free Sample

The In-store operation segment was valued at USD 1.03 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 30% to the growth of the global market during the forecast period.

Technavio's analysts have el

Context

A real online retail transaction data set of two years.

Content

This Online Retail II data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011.The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers.

Column Descriptors

InvoiceNo: Invoice number. Nominal. A 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', it indicates a cancellation. StockCode: Product (item) code. Nominal. A 5-digit integral number uniquely assigned to each distinct product. Description: Product (item) name. Nominal. Quantity: The quantities of each product (item) per transaction. Numeric. InvoiceDate: Invice date and time. Numeric. The day and time when a transaction was generated. UnitPrice: Unit price. Numeric. Product price per unit in sterling (£). CustomerID: Customer number. Nominal. A 5-digit integral number uniquely assigned to each customer. Country: Country name. Nominal. The name of the country where a customer resides.

Acknowledgements

Here you can find references about data set: https://archive.ics.uci.edu/ml/datasets/Online+Retail+II and

Relevant Papers:

Chen, D. Sain, S.L., and Guo, K. (2012), Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197-208. doi: [Web Link]. Chen, D., Guo, K. and Ubakanma, G. (2015), Predicting customer profitability over time based on RFM time series, International Journal of Business Forecasting and Marketing Intelligence, Vol. 2, No. 1, pp.1-18. doi: [Web Link]. Chen, D., Guo, K., and Li, Bo (2019), Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study, 24th Iberoamerican Congress on Pattern Recognition (CIARP 2019), Havana, Cuba, 28-31 Oct, 2019. Laha Ale, Ning Zhang, Huici Wu, Dajiang Chen, and Tao Han, Online Proactive Caching in Mobile Edge Computing Using Bidirectional Deep Recurrent Neural Network, IEEE Internet of Things Journal, Vol. 6, Issue 3, pp. 5520-5530, 2019. Rina Singh, Jeffrey A. Graves, Douglas A. Talbert, William Eberle, Prefix and Suffix Sequential Pattern Mining, Industrial Conference on Data Mining 2018: Advances in Data Mining. Applications and Theoretical Aspects, pp. 309-324. 2018.

Inspiration

This is Data Set Characteristics: Multivariate, Sequential, Time-Series, Text

This archive includes Stata files used for the elaboration of:

Deliverable D5.3 - Data mining from Big Data analysis (Sections 3.2, 4.2 and 5)

and:

Arabadzhyan, A., Figini, P., Vici, L., 2021. Measuring destination image: a novel approach based on visual data mining. A methodological proposal and an application to European islands, Journal of Destination Marketing and Management, DOI: https://doi.org/10.1016/j.jdmm.2021.100611

Used-car market monitoring was started in 2018, 03 January, when Federal Administrative Court in Leipzig (Germany) allowed cities and communes in Germany to impose bans for diesel cars in order to reduce the level of nitric oxide in the air. This data-set represent 5th year of market monitoring, 2022. First year monitoring (2018) data-set is available together with data article: Skribans, V. Used-car market dataset for Latvia 2018, (2019) Data in Brief, 22, pp. 859-862. doi: 10.1016/j.dib.2018.12.075. Methodology of data mining is available in research paper: SKRIBANS, V. and HEGERTY, S.W., 2019. Data mining application for used-car market analysis, IMCIC 2019 - 10th International Multi-Conference on Complexity, Informatics and Cybernetics, Proceedings 2019, pp. 35-40. Second year monitoring (2019) data-set is available there: Skribans, Valerijs (2019), “Latvian Used-car Market Announcements Monitoring in 2019”, Mendeley Data, V1, doi: 10.17632/kyf948x63n.1 Third year year monitoring (2020) data-set is available there: Skribans, Valerijs; Gulbis, Aivars; Krastins, Aivars (2020), “Latvian Used-car Market Announcements Monitoring in 2020”, Mendeley Data, V1, doi: 10.17632/hg38nn6c45.1 . First quarter of the fourth year of market monitoring, 2021. set is available there: Skribans, Valerijs; Gulbis, Aivars; Cevers, Aldis; Rudzitis, Normunds; Krastins, Aivars (2021), “Latvian Used-car Market Announcements Monitoring in 2021 Q1”, Mendeley Data, V1, doi: 10.17632/rh9zh9ncnk.1

Explore the Big Data and Analytics Market trends! Covers key players, growth rate 14.6% CAGR, market size $226.31 Billion, and forecasts to 2034. Get insights now!

Context

This Online Retail II data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011.The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers.

Content

Attribute Information:

InvoiceNo: Invoice number. Nominal. A 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', it indicates a cancellation. StockCode: Product (item) code. Nominal. A 5-digit integral number uniquely assigned to each distinct product. Description: Product (item) name. Nominal. Quantity: The quantities of each product (item) per transaction. Numeric. InvoiceDate: Invice date and time. Numeric. The day and time when a transaction was generated. UnitPrice: Unit price. Numeric. Product price per unit in sterling (£). CustomerID: Customer number. Nominal. A 5-digit integral number uniquely assigned to each customer. Country: Country name. Nominal. The name of the country where a customer resides.

Acknowledgements

Chen, D. Sain, S.L., and Guo, K. (2012), Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197-208. doi: [Web Link]. Chen, D., Guo, K. and Ubakanma, G. (2015), Predicting customer profitability over time based on RFM time series, International Journal of Business Forecasting and Marketing Intelligence, Vol. 2, No. 1, pp.1-18. doi: [Web Link]. Chen, D., Guo, K., and Li, Bo (2019), Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study, 24th Iberoamerican Congress on Pattern Recognition (CIARP 2019), Havana, Cuba, 28-31 Oct, 2019. Laha Ale, Ning Zhang, Huici Wu, Dajiang Chen, and Tao Han, Online Proactive Caching in Mobile Edge Computing Using Bidirectional Deep Recurrent Neural Network, IEEE Internet of Things Journal, Vol. 6, Issue 3, pp. 5520-5530, 2019. Rina Singh, Jeffrey A. Graves, Douglas A. Talbert, William Eberle, Prefix and Suffix Sequential Pattern Mining, Industrial Conference on Data Mining 2018: Advances in Data Mining. Applications and Theoretical Aspects, pp. 309-324. 2018.

The global text mining system market is anticipated to experience substantial growth, with a market size of XXX million in 2025 and projected to reach XXX million by 2033, expanding at a CAGR of XX% during the forecast period of 2025-2033. Key drivers of this growth include the increasing volume of unstructured data, the need for businesses to extract insights from text data, and the advancements in natural language processing (NLP) technologies. The market is segmented by type (text classification tool, text clustering tool, named entity recognition tool, keyword extraction tool, sentiment analysis tools, topic modeling tools, text generation tools, text analysis platform) and application (intelligence analysis, public opinion monitoring, financial sector, medical insurance, marketing, education industry, human resource management). North America is the dominant region, followed by Europe and Asia-Pacific. Major players in the market include IBM, Microsoft, SAS, Google, Amazon, Oracle, SAP, Lexalytics, Altair Engineering, Knime, Aylien, MonkeyLearn, Basis Technology, Linguamatics, Shenzhen Tianyuan DIC Information Technology, Qualtrics, Mozenda, Semantic Web Company, BenchSci, Algolia, SPOTTER, Rossum, SciBite, KapCode, Brandwatch, Apache Lucene, and Derlte.