Operational Data Store Market Outlook

According to our latest research, the global operational data store (ODS) market size reached USD 5.2 billion in 2024 and is expected to grow at a remarkable CAGR of 13.8% during the forecast period, reaching USD 16.1 billion by 2033. The robust growth of the ODS market is driven by the increasing demand for real-time data integration and analytics across diverse industries, as organizations strive to enhance decision-making and operational efficiency in a rapidly digitizing world.

One of the primary growth factors fueling the operational data store market is the exponential increase in data volumes generated by modern enterprises. As organizations embrace digital transformation, the need to integrate disparate data sources into a unified, accessible format has become paramount. ODS solutions provide a critical layer for aggregating operational data from transactional systems, enabling businesses to streamline their analytics and reporting processes. The proliferation of IoT devices, mobile applications, and cloud-based platforms further accelerates data generation, making operational data stores an essential component for real-time data processing and decision support. Enterprises are increasingly recognizing the strategic value of ODS in improving operational agility, customer experience, and regulatory compliance.

Another significant driver for the operational data store market is the rising adoption of advanced analytics and business intelligence (BI) tools. Organizations are seeking ways to gain actionable insights from their operational data to optimize business processes and stay competitive. ODS systems act as an intermediary between transactional databases and data warehouses, ensuring that the most current data is available for analytics and reporting. The integration of artificial intelligence (AI) and machine learning (ML) with ODS platforms is empowering businesses to uncover patterns, predict trends, and automate decision-making processes. This synergy between ODS and advanced analytics is expected to propel market growth further, as companies across sectors invest in digital infrastructure to support data-driven strategies.

Cloud adoption is another pivotal factor shaping the operational data store market landscape. With the increasing shift toward cloud-based solutions, organizations benefit from scalable, cost-effective, and flexible ODS deployments. Cloud-based ODS platforms offer seamless integration with other cloud services, facilitating real-time data access and collaboration across geographically dispersed teams. The ease of deployment, reduced infrastructure costs, and enhanced security features of cloud ODS solutions are particularly attractive to small and medium enterprises (SMEs) as well as large organizations. The cloud trend is expected to continue dominating the market, with vendors focusing on offering robust, secure, and highly available operational data store services to meet evolving business needs.

From a regional perspective, North America remains the largest market for operational data stores, accounting for over 38% of the global revenue in 2024. The region’s dominance is attributed to the presence of leading technology vendors, early adoption of digital transformation initiatives, and a high concentration of data-driven enterprises. Europe and Asia Pacific are also experiencing substantial growth, driven by increasing IT investments, expanding industrial sectors, and growing awareness of the benefits of real-time data integration. The Asia Pacific region, in particular, is witnessing the fastest growth, with a projected CAGR of 15.2% during the forecast period, as businesses in emerging economies accelerate their digital journeys.

Component Analysis

The operational data store market is segmented by component into software, hardware, and services, each playing a distinct role in the overall ecosystem. The software segment currently dominates the market, accounting for the largest share of revenue in 2024. This dominance is attributed to the critical role that ODS software plays in data integration, transformation, and management. Modern ODS software solutions are designed to handle complex, heterogeneous data environments, offering robust features such as real-time data synchronization, data cleansing, and support for multiple data formats. Vendors are continuously innovating to provide user-friendly interface

NewSQL Database Market Outlook

According to our latest research, the global NewSQL Database market size reached USD 2.3 billion in 2024, driven by the rapidly evolving demands for high-performance, scalable, and ACID-compliant database solutions across diverse industries. The market is expected to grow at a robust CAGR of 24.6% from 2025 to 2033, propelling the market value to approximately USD 17.5 billion by 2033. This impressive growth is fueled by the increasing need for real-time analytics, digital transformation initiatives, and the proliferation of cloud-native applications, as enterprises seek to overcome the limitations of traditional relational and NoSQL databases while maintaining transactional integrity and scalability.

One of the primary growth factors for the NewSQL Database market is the surge in digital transformation projects and the exponential growth of data volumes across sectors such as BFSI, healthcare, retail, and telecommunications. Organizations are increasingly seeking database solutions that can handle high-throughput workloads, provide strong consistency, and support mission-critical applications. NewSQL databases, with their unique blend of scalability, performance, and ACID compliance, are ideally positioned to address these requirements. The rise of IoT, big data analytics, and real-time transaction processing in industries has further accelerated the adoption of NewSQL databases, as traditional RDBMS and NoSQL solutions often struggle to meet the demands for speed, reliability, and data consistency in modern enterprise environments.

Another significant driver of market growth is the widespread adoption of cloud computing and the increasing popularity of hybrid and multi-cloud strategies. Enterprises are leveraging cloud-based NewSQL solutions to achieve greater flexibility, cost efficiency, and operational agility. Cloud deployment models enable organizations to scale their database infrastructure dynamically in response to fluctuating workloads, while hybrid and multi-cloud configurations provide enhanced resilience and data sovereignty. The seamless integration capabilities of NewSQL databases with popular cloud platforms, combined with their ability to deliver real-time analytics and transactional consistency, are making them the preferred choice for forward-thinking organizations looking to modernize their data architecture.

Furthermore, the growing focus on customer experience, regulatory compliance, and competitive differentiation is driving investments in advanced database technologies. Industries such as BFSI and healthcare require robust data management solutions to ensure security, privacy, and compliance with stringent regulations. NewSQL databases offer advanced security features, strong encryption, and fine-grained access controls, which are essential for protecting sensitive data and meeting regulatory mandates. The ability of NewSQL platforms to support complex queries, automate scaling, and deliver consistent performance under heavy loads is also contributing to their rising adoption in sectors where data integrity and uptime are mission-critical.

From a regional perspective, North America currently dominates the NewSQL Database market due to the presence of leading technology providers, early adoption of advanced IT solutions, and significant investments in cloud infrastructure. However, the Asia Pacific region is expected to witness the highest growth rate over the forecast period, driven by rapid digitalization, expanding e-commerce, and government initiatives promoting smart infrastructure. Europe is also emerging as a key market, with enterprises increasingly prioritizing data sovereignty and compliance with GDPR regulations. The competitive landscape is intensifying as both established vendors and innovative startups introduce cutting-edge NewSQL solutions tailored to industry-specific needs and regional regulatory requirements.

In addition to the cloud and hybrid strategies, the integration of Spatial Database technology is becoming increasingly relevant in the NewSQL landscape. Spatial Databases are designed to store and query data related to objects in space, making them invaluable for industries that rely on geospatial data, such as logistics, urban planning, and environmental monitoring. The ability to handle complex spatial queries and integrate seamlessly

HTAP Database Market Outlook

According to our latest research, the global HTAP database market size reached USD 2.1 billion in 2024, underpinned by rapidly increasing demand for real-time analytics and seamless data processing across industries. The market is projected to expand at a robust CAGR of 18.2% from 2025 to 2033, reaching an estimated value of USD 10.8 billion by 2033. This remarkable growth is primarily driven by the convergence of transactional and analytical workloads, enabling organizations to derive actionable insights from operational data without latency. As per our latest research, the adoption of HTAP (Hybrid Transactional/Analytical Processing) databases is accelerating as enterprises seek to modernize their data architectures and maintain a competitive edge in a data-driven economy.

One of the foremost growth factors for the HTAP database market is the increasing need for real-time decision-making capabilities across various sectors. In today’s fast-paced business environment, organizations are generating massive volumes of data from multiple sources, including IoT devices, digital transactions, and customer interactions. Traditional database systems, which separate transactional and analytical processing, often introduce latency and operational inefficiencies. HTAP databases overcome these limitations by enabling simultaneous transactional and analytical workloads on the same dataset. This empowers businesses to respond to market changes instantly, detect fraud in real-time, personalize customer experiences, and optimize supply chains dynamically. The growing emphasis on digital transformation and the proliferation of data-intensive applications are expected to further fuel the demand for HTAP solutions globally.

Another pivotal driver of the HTAP database market is the rapid evolution of cloud computing and the widespread adoption of hybrid and multi-cloud strategies. Modern enterprises are increasingly leveraging cloud infrastructure to scale their operations, reduce costs, and enhance agility. HTAP databases, with their ability to process both OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) workloads, are particularly well-suited for cloud environments. Cloud-based HTAP solutions offer flexibility, scalability, and seamless integration with other cloud-native services, making them highly attractive for organizations pursuing digital innovation. Additionally, advancements in in-memory computing, distributed architectures, and AI-driven analytics are enhancing the performance and capabilities of HTAP databases, driving their adoption across diverse industry verticals.

Furthermore, regulatory compliance and data governance requirements are shaping the adoption landscape of the HTAP database market. Industries such as BFSI, healthcare, and retail are subject to stringent data privacy regulations, necessitating robust and secure data management solutions. HTAP databases facilitate real-time monitoring, auditing, and reporting, enabling organizations to meet compliance mandates efficiently. The ability to conduct real-time analytics on operational data also supports proactive risk management and fraud detection, which are critical for highly regulated sectors. As regulatory frameworks continue to evolve, the demand for compliant, high-performance database solutions like HTAP is expected to grow, fostering innovation in security features and data governance capabilities.

Regionally, North America currently dominates the HTAP database market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The United States, in particular, is at the forefront of adoption due to its advanced IT infrastructure, strong presence of technology innovators, and high investment in digital transformation initiatives. Europe is witnessing significant growth, driven by increasing demand for real-time analytics in financial services and manufacturing sectors. Meanwhile, the Asia Pacific region is emerging as a lucrative market, propelled by rapid digitalization, expanding e-commerce, and government initiatives promoting smart cities and Industry 4.0. As enterprises worldwide recognize the value of real-time insights, the HTAP database market is poised for sustained expansion across all major regions.

Component Analysis

The HTAP database market is segmented by component into software, hardware, and services, each playing a critical role in enabling hybrid transactional

HTAP Database Market Outlook

According to our latest research, the HTAP (Hybrid Transactional/Analytical Processing) Database market size reached USD 3.4 billion in 2024, reflecting robust adoption across diverse industry verticals. The market is forecasted to expand at a CAGR of 20.6% from 2025 to 2033, reaching an estimated USD 22.1 billion by 2033. This significant growth is propelled by the rising need for real-time data analytics and seamless integration of transactional and analytical workloads, which are becoming critical for business agility and informed decision-making in an increasingly digital world.

One of the principal growth factors driving the HTAP Database market is the exponential increase in the volume and velocity of data generated by enterprises. As organizations across sectors such as BFSI, healthcare, retail, and manufacturing embrace digital transformation, they require advanced database solutions capable of handling both transactional and analytical data processing simultaneously. The ability of HTAP databases to provide real-time insights while maintaining transactional integrity is revolutionizing business operations. Enterprises are leveraging these solutions to streamline operations, enhance customer experiences, and gain a competitive edge by making data-driven decisions at unprecedented speed. Additionally, the integration of AI and machine learning with HTAP databases is further amplifying their value proposition, enabling predictive analytics and automation for complex business processes.

Another crucial driver for the HTAP Database market is the widespread adoption of cloud computing and hybrid IT environments. Organizations are increasingly migrating their data infrastructures to the cloud to benefit from scalability, flexibility, and cost efficiencies. HTAP databases are uniquely positioned to thrive in these environments, as they can efficiently handle dynamic workloads and support distributed architectures. The ability to deploy HTAP solutions both on-premises and in the cloud is enabling businesses to modernize their data management frameworks without sacrificing performance or security. Furthermore, the rise of edge computing and IoT is generating new use cases for HTAP databases, particularly in industries that require real-time processing of large volumes of data from disparate sources.

Regulatory compliance and the growing emphasis on data security are also shaping the HTAP Database market landscape. As data privacy regulations such as GDPR and CCPA become more stringent, organizations are seeking database solutions that offer robust security features, granular access controls, and comprehensive audit trails. HTAP databases are evolving to meet these requirements, offering advanced encryption, data masking, and real-time monitoring capabilities. This is particularly important for sectors like BFSI and healthcare, where the protection of sensitive data is paramount. The convergence of regulatory compliance and technological innovation is fostering a favorable environment for the adoption of HTAP databases, as businesses strive to balance agility with security.

From a regional perspective, North America currently dominates the HTAP Database market, accounting for the largest revenue share in 2024, followed closely by Europe and the Asia Pacific. The presence of leading technology vendors, early adoption of advanced IT solutions, and a mature digital infrastructure contribute to the region’s leadership. Meanwhile, Asia Pacific is poised for the highest growth rate over the forecast period, driven by rapid digitalization, expanding cloud adoption, and increasing investments in big data analytics across emerging economies such as China and India. Latin America and the Middle East & Africa are also witnessing steady growth, supported by government initiatives and the proliferation of digital services.

Component Analysis

The HTAP Database market by component is segmented into software, hardware, and services. The software segment holds the largest share, underpinned by the increasing dem

The small datasets for calculating the frequency of itemsets in transaction database contain Accidents, Chess, Connection, Mushroom, PUSBM, and Retail [32] transaction datasets. There are 500, 1000, 2000, and 5000 transactions per dataset. The small datasets for calculating the utility of itemsets in a transaction database contain Accidents, Chess, Connection, Mushroom, PUSBM, and Retail [32] transaction datasets. There are 500, 1000, 2000, and 5000 transactions per dataset. The large datasets for caluclating the frequency of itemsets in a transaction database contain Accidents, Connection, and PUSBM [32] datasets. There are 10000, 20000, 30000, and 50000 transactions per dataset. The large datasets for calculating the utility of itemsets in a transaction database contain Accidents, Connection, and PUSBM [32] transaction datasets. There are 10000, 20000, 30000, and 50000 transactions per dataset. (ZIP)

Visual Analytics Market size was valued at USD 5.76 Billion in 2024 and is projected to reach USD 12.35 Billion by 2032, growing at a CAGR of 11.03% from 2026 to 2032.Global Visual Analytics Market DriversGrowing Data Volume: The exponential increase in data volume, often referred to as Big Data, is the foundational driver for visual analytics. Businesses and individuals across all sectors from IoT sensors and social media to transactional databases are generating petabytes of structured and unstructured information daily. Traditional, text-based analytical methods are simply overwhelmed and inefficient at processing this deluge. Rising Need for Real-Time Insights: Organizations are facing increasing pressure to make swift, informed decisions in highly competitive and rapidly changing market conditions, fueling the demand for real-time visual insights. Delaying analysis by even a few hours can result in missed opportunities, operational failures, or inadequate risk response.

Market Basket Analysis

Market basket analysis with Apriori algorithm

The retailer wants to target customers with suggestions on itemset that a customer is most likely to purchase .I was given dataset contains data of a retailer; the transaction data provides data around all the transactions that have happened over a period of time. Retailer will use result to grove in his industry and provide for customer suggestions on itemset, we be able increase customer engagement and improve customer experience and identify customer behavior. I will solve this problem with use Association Rules type of unsupervised learning technique that checks for the dependency of one data item on another data item.

Introduction

Association Rule is most used when you are planning to build association in different objects in a set. It works when you are planning to find frequent patterns in a transaction database. It can tell you what items do customers frequently buy together and it allows retailer to identify relationships between the items.

An Example of Association Rules

Assume there are 100 customers, 10 of them bought Computer Mouth, 9 bought Mat for Mouse and 8 bought both of them. - bought Computer Mouth => bought Mat for Mouse - support = P(Mouth & Mat) = 8/100 = 0.08 - confidence = support/P(Mat for Mouse) = 0.08/0.09 = 0.89 - lift = confidence/P(Computer Mouth) = 0.89/0.10 = 8.9 This just simple example. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.

Strategy

• Data Import
• Data Understanding and Exploration
• Transformation of the data – so that is ready to be consumed by the association rules algorithm
• Running association rules
• Exploring the rules generated
• Filtering the generated rules
• Visualization of Rule

Dataset Description

• File name: Assignment-1_Data
• List name: retaildata
• File format: . xlsx
• Number of Row: 522065

• Number of Attributes: 7

  • BillNo: 6-digit number assigned to each transaction. Nominal.
  • Itemname: Product name. Nominal.
  • Quantity: The quantities of each product per transaction. Numeric.
  • Date: The day and time when each transaction was generated. Numeric.
  • Price: Product price. Numeric.
  • CustomerID: 5-digit number assigned to each customer. Nominal.
  • Country: Name of the country where each customer resides. Nominal.

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Libraries in R

First, we need to load required libraries. Shortly I describe all libraries.

• arules - Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules).
• arulesViz - Extends package 'arules' with various visualization. techniques for association rules and item-sets. The package also includes several interactive visualizations for rule exploration.
• tidyverse - The tidyverse is an opinionated collection of R packages designed for data science.
• readxl - Read Excel Files in R.
• plyr - Tools for Splitting, Applying and Combining Data.
• ggplot2 - A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
• knitr - Dynamic Report generation in R.
• magrittr- Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions.
• dplyr - A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
• tidyverse - This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step.

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Data Pre-processing

Next, we need to upload Assignment-1_Data. xlsx to R to read the dataset.Now we can see our data in R.

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After we will clear our data frame, will remove missing values.

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To apply Association Rule mining, we need to convert dataframe into transaction data to make all items that are bought together in one invoice will be in ...

The basket dataset contains a list of items available for purchase for customers. These items can be found in sets as well. For eg. milk and sugar.

The analysis being done is to ascertain for the retailers which item or sets of items are purchased. Sometimes it so happens that the purchase of an item by the customer leads the customer to purchase another item as well. It is a sort of an association of items. This is called "Association Rule Mining".

It shows which items appear together in a transaction or relation. It’s majorly used by retailers, grocery stores, an online marketplace that has a large transactional database.

We wouldn’t want to calculate all associations between every possible combination of products. Instead, we would want to select only potentially “relevant” rules from the set of all possible rules. Therefore, we use the measures support, confidence and lift to reduce the number of relationships we need to analyze.

Support says how popular an item is, as measured in the proportion of transactions in which an item set appears.

Confidence says how likely item Y is purchased when item X is purchased, Thus it is measured by the proportion of transaction with item X in which item Y also appears (Support/Antecedent (LHS)).

Lift says how likely item Y is purchased when item X is purchased while controlling for how popular item Y is. (Confidence/Consequent (RHS))

Electronic Card Transactions from 2017-2020

Exploring Retail Spending Trends

By data.govt.nz [source]

About this dataset

This dataset contains valuable information about electronic card transactions from 2017-2020. The data is sourced from a trustable source and was last updated on 2020-08-10. It paints a comprehensive picture of electronic payments made during this period and includes important variables such as the series reference, period, data value, suppresed status, units magnitude, subject, group and five titles (series_title). This data is useful for exploring timely trends in electronic card spending activity over the 4 year period between 2017 to 2020 making it an invaluable asset for researchers looking to better understand purchasing habits in New Zealand. This dataset offers analysts unprecedented insight into consumer behavior allowing them to develop strategies aimed at increasing transaction volumes or customer loyalty initiatives

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How to use the dataset

• This dataset contains information about electronic card transactions from 2017-2020. It includes data on the series reference, period, data value, suppressed status, units of measurement, magnitude, subject and group. Using this dataset can provide you with insightful information that can help you improve your business operations and make better decisions.
• For example, you could use the data values to identify patterns and trends in sales over time. You could also use the period data to determine whether certain months have more sales than others. Finally, you could utilize the suppressed status to understand which transactions should or should not be included when making conclusions from your dataset.
• In order to use this dataset effectively it would be helpful for you to be familiar with database software such as Microsoft Access or SQLite database manager which is free and open source software for managing a variety of databases. Once familiar with database software of your choice it is important to understand how different fields interact together in order create meaningful results from queries such as finding out what kind of expenditure is done during different times of year or discovering what kind of products contribute most revenue during certain periods etc.. It is also sensible for one become knowledgeable in some basic coding languages like Python - that have modules designed specifically for statistical analysis - so they are well equipped when it comes down crunching these kinds complex datasets into tangible insights!

Research Ideas

• Visualizing changes in spending habits of consumers over time by analyzing the Data_value, UNITS and Magnitude columns.
• Analyzing the correlation between areas with higher spending volumes and socioeconomic factors such as income level or population density by leveraging Subject and Group details.
• Predicting future trends in electronic card transactions through machine learning algorithms that use historical data from the dataset to make predictions on changing patterns of consumer spending behaviors over time based on all columns in the dataset eg Series_title_1, STATUS etc.

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: electronic-card-transactions-feb-2018-csv-tables.csv | Column name | Description | |:---------------------|:--------------------------------------------------------------------------------| | Series_reference | Unique identifier for each series. (String) | | Period | Date of the transaction. (Date) | | Data_value | Value of the transaction. (Integer) | | Suppressed | Boolean value indicating whether the data has been suppressed or not. (Boolean) | | STATUS | Status of the transaction. (String) | | UNITS | Units of the transaction. (String) | |...

Dataset: Online Shopping Dataset;

CustomerID

Description: Unique identifier for each customer. Data Type: Numeric;

Gender:

Description: Gender of the customer (e.g., Male, Female). Data Type: Categorical;

Location:

Description: Location or address information of the customer. Data Type: Text;

Tenure_Months:

Description: Number of months the customer has been associated with the platform. Data Type: Numeric;

Transaction_ID:

Description: Unique identifier for each transaction. Data Type: Numeric;

Transaction_Date:

Description: Date of the transaction. Data Type: Date;

Product_SKU:

Description: Stock Keeping Unit (SKU) identifier for the product. Data Type: Text;

Product_Description:

Description: Description of the product. Data Type: Text;

Product_Category:

Description: Category to which the product belongs. Data Type: Categorical;

Quantity:

Description: Quantity of the product purchased in the transaction. Data Type: Numeric;

Avg_Price:

Description: Average price of the product. Data Type: Numeric;

Delivery_Charges:

Description: Charges associated with the delivery of the product. Data Type: Numeric;

Coupon_Status:

Description: Status of the coupon associated with the transaction. Data Type: Categorical;

GST:

Description: Goods and Services Tax associated with the transaction. Data Type: Numeric;

Date:

Description: Date of the transaction (potentially redundant with Transaction_Date). Data Type: Date;

Offline_Spend:

Description: Amount spent offline by the customer. Data Type: Numeric;

Online_Spend:

Description: Amount spent online by the customer. Data Type: Numeric;

Month:

Description: Month of the transaction. Data Type: Categorical;

Coupon_Code:

Description: Code associated with a coupon, if applicable. Data Type: Text;

Discount_pct:

Description: Percentage of discount applied to the transaction. Data Type: Numeric;

Context

The Customer Shopping Preferences Dataset offers valuable insights into consumer behavior and purchasing patterns. Understanding customer preferences and trends is critical for businesses to tailor their products, marketing strategies, and overall customer experience. This dataset captures a wide range of customer attributes including age, gender, purchase history, preferred payment methods, frequency of purchases, and more. Analyzing this data can help businesses make informed decisions, optimize product offerings, and enhance customer satisfaction. The dataset stands as a valuable resource for businesses aiming to align their strategies with customer needs and preferences. It's important to note that this dataset is a Synthetic Dataset Created for Beginners to learn more about Data Analysis and Machine Learning.

Content

This dataset encompasses various features related to customer shopping preferences, gathering essential information for businesses seeking to enhance their understanding of their customer base. The features include customer age, gender, purchase amount, preferred payment methods, frequency of purchases, and feedback ratings. Additionally, data on the type of items purchased, shopping frequency, preferred shopping seasons, and interactions with promotional offers is included. With a collection of 3900 records, this dataset serves as a foundation for businesses looking to apply data-driven insights for better decision-making and customer-centric strategies.

Dataset Glossary (Column-wise)

• Customer ID - Unique identifier for each customer
• Age - Age of the customer
• Gender - Gender of the customer (Male/Female)
• Item Purchased - The item purchased by the customer
• Category - Category of the item purchased
• Purchase Amount (USD) - The amount of the purchase in USD
• Location - Location where the purchase was made
• Size - Size of the purchased item
• Color - Color of the purchased item
• Season - Season during which the purchase was made
• Review Rating - Rating given by the customer for the purchased item
• Subscription Status - Indicates if the customer has a subscription (Yes/No)
• Shipping Type - Type of shipping chosen by the customer
• Discount Applied - Indicates if a discount was applied to the purchase (Yes/No)
• Promo Code Used - Indicates if a promo code was used for the purchase (Yes/No)
• Previous Purchases - The total count of transactions concluded by the customer at the store, excluding the ongoing transaction
• Payment Method - Customer's most preferred payment method
• Frequency of Purchases - Frequency at which the customer makes purchases (e.g., Weekly, Fortnightly, Monthly)

Structure of the Dataset

https://i.imgur.com/6UEqejq.png" alt="">

Acknowledgement

This dataset is a synthetic creation generated using ChatGPT to simulate a realistic customer shopping experience. Its purpose is to provide a platform for beginners and data enthusiasts, allowing them to create, enjoy, practice, and learn from a dataset that mirrors real-world customer shopping behavior. The aim is to foster learning and experimentation in a simulated environment, encouraging a deeper understanding of data analysis and interpretation in the context of consumer preferences and retail scenarios.

Cover Photo by: Freepik

Thumbnail by: Clothing icons created by Flat Icons - Flaticon

Context

Typically e-commerce datasets are proprietary and consequently hard to find among publicly available data. However, The UCI Machine Learning Repository has made this dataset containing actual transactions from 2010 and 2011. The dataset is maintained on their site, where it can be found by the title "Online Retail".

Content

"This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers."

Acknowledgements

Per the UCI Machine Learning Repository, this data was made available by Dr Daqing Chen, Director: Public Analytics group. chend '@' lsbu.ac.uk, School of Engineering, London South Bank University, London SE1 0AA, UK.

Image from stocksnap.io.

Inspiration

Analyses for this dataset could include time series, clustering, classification and more.