Dataset Description: Smartphone Sales Transactions

This dataset contains information about smartphone sales transactions. Each row represents a unique transaction and includes detailed data points such as the date of the transaction, product details, customer demographics, payment methods, and customer ratings. The dataset can be useful for sales analysis, customer behavior study, and market trend prediction.

Columns:

Transaction ID – Unique identifier for each transaction.

Day – Day of the month when the transaction occurred.

Month – Month of the transaction.

Year – Year of the transaction.

Day Name – Name of the day (e.g., Saturday).

Brand – Smartphone brand sold (e.g., Xiaomi, Vivo).

Units Sold – Number of smartphone units sold in the transaction.

Price Per Unit – Selling price per unit (in local currency).

Customer Name – Name of the customer.

Customer Age – Age of the customer.

City – City where the transaction took place.

Payment Method – Method used for payment (e.g., UPI, Credit Card).

Customer Ratings – Rating given by the customer (1–5 scale).

Mobile Model – Specific model of the smartphone sold.

Research dataset and analysis for Payment Solutions including statistics, forecasts, and market insights

Non-card-linked wallets are forecast to be the most used type of wallet in India in 2028, but their growth is not as fast as card-linked wallets. This is according to hybrid research released in 2024, which - depending on the country - either used database modeling or data acquired via a consumer survey. Indeed, wallets were the most used payment method in the country when shopping online. India ranks as the country with the highest penetration of mobile wallets in the world.

Context

There is a lack of public available datasets on financial services and specially in the emerging mobile money transactions domain. Financial datasets are important to many researchers and in particular to us performing research in the domain of fraud detection. Part of the problem is the intrinsically private nature of financial transactions, that leads to no publicly available datasets.

We present a synthetic dataset generated using the simulator called PaySim as an approach to such a problem. PaySim uses aggregated data from the private dataset to generate a synthetic dataset that resembles the normal operation of transactions and injects malicious behaviour to later evaluate the performance of fraud detection methods.

Content

PaySim simulates mobile money transactions based on a sample of real transactions extracted from one month of financial logs from a mobile money service implemented in an African country. The original logs were provided by a multinational company, who is the provider of the mobile financial service which is currently running in more than 14 countries all around the world.

This synthetic dataset is scaled down 1/4 of the original dataset and it is created just for Kaggle.

NOTE: Transactions which are detected as fraud are cancelled, so for fraud detection these columns (oldbalanceOrg, newbalanceOrig, oldbalanceDest, newbalanceDest ) must not be used.

Headers

This is a sample of 1 row with headers explanation:

1,PAYMENT,1060.31,C429214117,1089.0,28.69,M1591654462,0.0,0.0,0,0

step - maps a unit of time in the real world. In this case 1 step is 1 hour of time. Total steps 744 (30 days simulation).

type - CASH-IN, CASH-OUT, DEBIT, PAYMENT and TRANSFER.

amount - amount of the transaction in local currency.

nameOrig - customer who started the transaction

oldbalanceOrg - initial balance before the transaction

newbalanceOrig - new balance after the transaction.

nameDest - customer who is the recipient of the transaction

oldbalanceDest - initial balance recipient before the transaction. Note that there is not information for customers that start with M (Merchants).

newbalanceDest - new balance recipient after the transaction. Note that there is not information for customers that start with M (Merchants).

isFraud - This is the transactions made by the fraudulent agents inside the simulation. In this specific dataset the fraudulent behavior of the agents aims to profit by taking control or customers accounts and try to empty the funds by transferring to another account and then cashing out of the system.

isFlaggedFraud - The business model aims to control massive transfers from one account to another and flags illegal attempts. An illegal attempt in this dataset is an attempt to transfer more than 200.000 in a single transaction.

Past Research

There are 5 similar files that contain the run of 5 different scenarios. These files are better explained at my PhD thesis chapter 7 (PhD Thesis Available here http://urn.kb.se/resolve?urn=urn:nbn:se:bth-12932.

We ran PaySim several times using random seeds for 744 steps, representing each hour of one month of real time, which matches the original logs. Each run took around 45 minutes on an i7 intel processor with 16GB of RAM. The final result of a run contains approximately 24 million of financial records divided into the 5 types of categories: CASH-IN, CASH-OUT, DEBIT, PAYMENT and TRANSFER.

Acknowledgements

This work is part of the research project ”Scalable resource-efficient systems for big data analytics” funded by the Knowledge Foundation (grant: 20140032) in Sweden.

Please refer to this dataset using the following citations:

PaySim first paper of the simulator:

E. A. Lopez-Rojas , A. Elmir, and S. Axelsson. "PaySim: A financial mobile money simulator for fraud detection". In: The 28th European Modeling and Simulation Symposium-EMSS, Larnaca, Cyprus. 2016

Overview This dataset contains 50,000 fictional e-commerce transaction records, making it ideal for data analysis, visualization, and machine learning experiments. It includes user demographics, product categories, purchase amounts, payment methods, and transaction dates to help understand consumer behavior and sales trends.

Columns Transaction_ID – Unique identifier for each transaction User_Name – Randomly generated user name Age – Age of the user (18 to 70) Country – Country where the transaction took place (randomly chosen from 10 countries) Product_Category – Category of the purchased item (e.g., Electronics, Clothing, Books) Purchase_Amount – Total amount spent on the transaction (randomly generated between $5 and $1000) Payment_Method – Method used for payment (e.g., Credit Card, PayPal, UPI) Transaction_Date – Date of the purchase (randomly selected within the past two years)

Use Cases Sales and trend analysis – Identify which product categories are most popular Customer segmentation – Analyze spending behavior based on age and country Fraud detection – Detect unusual purchase patterns Machine learning projects – Train models for recommendation systems or revenue predictions

This dataset is synthetic and does not contain real user data. It can be used for research, experimentation, and educational purposes.

Techsalerator’s Business Technographic Data for Laos: Unlocking Insights into Laos' Technology Landscape

Techsalerator’s Business Technographic Data for Laos provides a detailed and comprehensive dataset essential for businesses, market analysts, and technology vendors seeking to understand and engage with companies operating within Laos. This dataset offers in-depth insights into the technological landscape, capturing and organizing data related to technology stacks, digital tools, and IT infrastructure used by businesses in the country.

Please reach out to us at info@techsalerator.com or visit Techsalerator Contact.

Top 5 Most Utilized Data Fields

• Company Name: This field lists the names of companies in Laos, allowing technology vendors to target potential clients and enabling analysts to assess technology adoption trends within specific businesses.

• Technology Stack: This field outlines the technologies and software solutions a company uses, such as enterprise resource planning (ERP) systems, customer management software, and cloud services. Understanding a company's technology stack is essential to evaluating its digital maturity and operational needs.

• Deployment Status: This field indicates whether the technology is currently deployed, planned for future deployment, or under evaluation. Vendors can leverage this information to assess technology adoption and identify opportunities among companies in Laos.

• Industry Sector: This field specifies the industry in which the company operates, such as agriculture, mining, or retail. Knowledge of the industry helps vendors tailor their products to sector-specific demands and emerging trends in Laos.

• Geographic Location: This field identifies the company's headquarters or primary operations within Laos. Geographic information is crucial for regional analysis and understanding localized technology adoption patterns across the country.

Top 5 Technology Trends in Laos

• Agricultural Technology: As agriculture is a key sector in Laos, businesses are increasingly adopting digital tools like smart farming technologies, irrigation systems, and crop monitoring software to enhance productivity and sustainability.

• Renewable Energy Technologies: Laos is harnessing its natural resources, particularly hydropower, to meet growing energy demands. There is increasing interest in solar power and other renewable energy solutions to diversify the energy mix.

• E-commerce and Digital Payments: The rapid rise of e-commerce is transforming Laos, with businesses embracing digital payment gateways, online marketplaces, and mobile banking services to reach a broader consumer base.

• Telecommunications and Connectivity: With growing internet penetration, telecommunications providers in Laos are expanding their infrastructure, introducing high-speed internet services, and deploying 4G and 5G technologies.

• Cloud Computing: Cloud-based solutions are becoming popular in Laos, particularly among businesses seeking cost-effective IT infrastructure to support operations in education, finance, and healthcare sectors.

Top 5 Companies with Notable Technographic Data in Laos

• BCEL Bank (Banque Pour Le Commerce Extérieur Lao): A leader in digital banking in Laos, BCEL is enhancing its offerings with online banking services, mobile apps, and robust cybersecurity solutions to meet growing consumer demands.

• Lao Telecom: As one of the largest telecom providers in Laos, Lao Telecom is expanding its digital infrastructure by investing in high-speed internet, 4G/5G networks, and data centers to support the country’s connectivity needs.

• Électricité du Laos (EDL): The primary electricity provider in Laos, EDL is investing in renewable energy projects such as hydropower and solar to meet the country’s sustainable energy goals and reduce dependency on traditional energy sources.

• Unitel Laos: A key player in the telecommunications space, Unitel is advancing mobile and internet services across the country, playing a critical role in improving digital connectivity for businesses and individuals.

• Lao Brewery Co. Ltd: One of the largest beverage companies in the country, Lao Brewery is adopting advanced manufacturing technologies and supply chain management systems to optimize production and distribution.

Accessing Techsalerator’s Business Technographic Data

For those interested in accessing Techsalerator’s Business Technographic Data for Laos, please contact info@techsalerator.com with your specific needs. Techsalerator offers customized quotes based on the required number of data fields and records, with datasets available for delivery within 24 hours. Ongoing access options can also be arranged upon request.

Included Data Fields

• Company Name
• Technology Stack
• Deployment Status
• Industry...

This synthetic dataset represents E-commerce sales transactions containing 34,500 records across 17 features. It has been carefully designed to simulate realistic online shopping data and can be used for multiple data science and machine learning tasks, including:

🔹 Sales Analysis – revenue trends, profit margins, regional performance, category-wise sales 🔹 Customer Segmentation – analyzing customer demographics, purchase behavior, loyalty scores 🔹 Churn Prediction – identifying customers likely to stop purchasing 🔹 Product Performance – tracking returns, pricing impact, and demand across categories

📊 Columns Overview

• order_id → Unique identifier for each order
• customer_id → Unique identifier for each customer
• product_id → Unique identifier for each product
• category → Product category (Electronics, Fashion, Home, Beauty, Sports, Toys, Grocery)
• price → Unit price of the product
• discount → Discount applied (%)
• quantity → Number of items purchased
• payment_method → Payment type (Credit Card, Debit Card, UPI, PayPal, COD, Wallet)
• order_date → Date of purchase
• delivery_time_days → Days taken to deliver the order
• region → Geographic region of the customer
• returned → Whether the product was returned (Yes/No)
• total_amount → Final bill amount after discounts
• shipping_cost → Delivery charges
• profit_margin → Profit earned from the order
• customer_age → Age of the customer (18–70)
• customer_gender → Gender of the customer (Male/Female/Other)

✅ Use Cases

• Exploratory Data Analysis (EDA)
• Time-series sales forecasting
• Customer segmentation (RFM analysis, clustering)
• Churn prediction (classification)
• Return prediction
• Price and discount analysis

👉 This dataset is ideal for machine learning practice, analytics projects, and Kaggle competitions related to sales, marketing, and customer behavior.

Time Series Database for Financial Services Market Outlook

As per our latest research, the global Time Series Database for Financial Services market size in 2024 reached USD 1.85 billion, demonstrating robust growth driven by the increasing adoption of real-time analytics and data-driven decision-making in the financial sector. The market is expected to expand at a CAGR of 13.2% from 2025 to 2033, reaching a forecasted value of USD 5.44 billion by 2033. The primary growth factor for this market is the escalating volume of financial transactions and the growing need for high-frequency data analysis, which is crucial for risk management, fraud detection, and algorithmic trading across global financial institutions.

One of the most significant growth drivers for the Time Series Database for Financial Services market is the exponential rise in digital transactions and the proliferation of fintech solutions. Financial institutions are increasingly leveraging time series databases to process and analyze vast streams of transactional data in real time. This capability is essential for supporting complex applications such as algorithmic trading, which relies on millisecond-level data precision to execute trades and manage portfolios efficiently. The surge in mobile banking, online payments, and digital wallets has further amplified the demand for scalable and high-performance databases that can handle the velocity, volume, and variety of financial data generated every second. As financial services become more digitized, the need for robust data infrastructure continues to intensify, propelling the market forward.

Another critical factor fueling market growth is the regulatory environment and the increasing emphasis on compliance and risk management. Financial institutions are under mounting pressure to comply with stringent regulations imposed by global authorities, which necessitate comprehensive data tracking, auditing, and reporting capabilities. Time series databases offer an efficient way to store and retrieve historical data, making it easier for banks, investment firms, and insurance companies to demonstrate compliance and quickly respond to regulatory inquiries. Moreover, the integration of advanced analytics and artificial intelligence with time series databases enables organizations to detect anomalies, predict risks, and automate compliance workflows, thereby reducing operational costs and mitigating potential penalties.

Technological advancements and the rise of cloud computing are also pivotal in shaping the growth trajectory of the Time Series Database for Financial Services market. Cloud-based deployment models have democratized access to high-performance databases, enabling even small and medium-sized enterprises to leverage sophisticated data management capabilities without significant upfront investments. The scalability, flexibility, and cost-efficiency offered by cloud solutions are attracting a diverse range of financial service providers, from traditional banks to innovative fintech startups. Furthermore, the integration of time series databases with big data platforms and machine learning tools is unlocking new opportunities for real-time analytics, personalized financial services, and predictive modeling, all of which contribute to the sustained expansion of the market.

From a regional perspective, North America continues to dominate the global Time Series Database for Financial Services market, accounting for the largest revenue share in 2024. This leadership position is attributed to the presence of major financial hubs, advanced IT infrastructure, and early adoption of cutting-edge technologies by leading banks and investment firms. However, the Asia Pacific region is emerging as the fastest-growing market, driven by rapid digital transformation, increasing investments in fintech, and the rising adoption of cloud-based solutions in countries such as China, India, and Singapore. Europe is also witnessing substantial growth, supported by stringent regulatory frameworks and the increasing focus on data-driven financial services. Latin America and the Middle East & Africa are gradually catching up, with financial institutions in these regions investing in modern database solutions to enhance operational efficiency and customer experience.

In the evolving landscape of financial services, <a href="https://growthmarketreports.com/report/managed-temporal-services-market" target="_blank&

Website visitation is nice, but sales and revenue are better. Grips tracks e-commerce-based sales across 5,000+ product categories, 30k retailers, and brands, enabling you to understand market size, share, opportunities, and threats.

Use Cases

Domain e-commerce performance Harness the power of data-driven analysis to evaluate critical metrics such as revenue, average order value (AOV), conversion rate, channels, and product assortment for an extensive selection of 30,000 leading e-commerce retailers, enabling you to make strategic decisions and stay ahead in the dynamic online marketplace.

Product Category e-commerce performance Unlock the potential of your business with our game-changing Share of Wallet analysis. Gain valuable insights into the market size and growth of over 5000+ product categories, as well as your retailer or brand's market share within each category.

Brand e-commerce performance Gain deep insights into the market size, share, and revenue growth of 30,000 top e-commerce brands in the digital ecosystem, exploring key metrics such as units sold, average price, and more. Empower your business with comprehensive data to make informed decisions and capitalize on lucrative opportunities in the ever-evolving online marketplace.

Data Methodology

We have a unique mix of sources from where we gather digital signals.

• Raw data collection - we have developed several productivity tools, including Retailer Benchmarking, which collectively create the world’s largest transactional dataset - public data captured from millions of sites and partnerships with top data providers.

• Data processing - cleaning and formatting, classification of products, sites and more preparation for the modelling phase.

• Data modeling: from the billions of digital signals we extrapolate in detail how global e-commerce sites and products are performing.

7-day free trial available Sign up for free at: https://gripsintelligence.com/

Payment Fraud Detection AI Market Outlook

According to our latest research, the global Payment Fraud Detection AI market size reached USD 9.8 billion in 2024, demonstrating robust momentum driven by rapid digital transformation and increasing sophistication of fraudulent activities. The market is projected to expand at a CAGR of 19.2% from 2025 to 2033, reaching a forecasted value of USD 46.1 billion by 2033. This remarkable growth is primarily fueled by the urgent need for advanced, real-time fraud detection solutions as organizations face escalating threats in online transactions and digital payments.

One of the most significant growth factors propelling the Payment Fraud Detection AI market is the exponential rise in online transactions and digital payment channels, particularly in the wake of the global shift toward cashless economies. As consumers and businesses increasingly embrace e-commerce, mobile banking, and contactless payments, the volume and complexity of digital transactions have surged. This expansion has inadvertently created a fertile ground for sophisticated cybercriminals, compelling financial institutions, retailers, and payment processors to invest heavily in AI-powered fraud detection technologies. These solutions leverage machine learning and advanced analytics to identify anomalous patterns, adapt to evolving fraud tactics, and provide real-time alerts, thereby minimizing financial losses and enhancing consumer trust.

Another pivotal driver is the regulatory landscape, which is becoming increasingly stringent regarding data security and consumer protection. Governments and regulatory bodies across the globe are enforcing stricter compliance standards, such as the General Data Protection Regulation (GDPR) in Europe and the Payment Card Industry Data Security Standard (PCI DSS) worldwide. These regulations mandate robust fraud prevention mechanisms, pushing organizations to adopt state-of-the-art AI-driven detection systems. The ability of AI algorithms to process vast datasets, recognize subtle fraud indicators, and automate risk assessment processes positions them as indispensable tools in achieving regulatory compliance while maintaining operational efficiency.

Additionally, the evolution of artificial intelligence itself is accelerating adoption rates. Modern AI models, particularly those utilizing deep learning and neural networks, are capable of handling complex, high-volume datasets typical of payment ecosystems. These technologies not only enhance detection accuracy but also reduce false positives, which have historically been a challenge for traditional rule-based systems. The integration of AI with other emerging technologies, such as blockchain and behavioral biometrics, further amplifies the effectiveness of fraud prevention strategies, enabling a proactive rather than reactive approach to payment security.

From a regional perspective, North America continues to dominate the Payment Fraud Detection AI market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The region's leadership is attributed to its advanced digital infrastructure, high penetration of digital payment platforms, and a mature regulatory environment. However, Asia Pacific is witnessing the fastest growth, driven by rapid digitalization in emerging economies, increasing e-commerce activities, and heightened awareness of cybersecurity threats. Latin America and the Middle East & Africa are also experiencing steady adoption, albeit at a relatively nascent stage, as financial inclusion initiatives and mobile payment adoption gather pace.

Component Analysis

The Payment Fraud Detection AI market by component is segmented into Software and Services, both of which play critical roles in the deployment and operation of advanced fraud detection systems. Software solutions form the backbone of this market, encompassing a wide array of products such as fraud analytics platforms, anomaly detection engines, and real-time risk assessment tools. These software offerings are designed to seamlessly integrate with existing payment infrastructures, leveraging machine learning algorithms to monitor transactions, detect suspicious activities, and automate response mechanisms. The continuous evolution of AI software, particularly the adoption of deep learning and natural language processing, is enabling organizations to stay ahead of increasingly sophisticated fra

The smartphone penetration in the United States was forecast to continuously increase between 2024 and 2029 by in total 1.3 percentage points. The penetration is estimated to amount to 97 percent in 2029. Notably, the smartphone penetration of was continuously increasing over the past years.The penetration rate refers to the share of the total population.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the smartphone penetration in countries like Mexico and Canada.

Events Tickets Market Outlook

The global events tickets market size was valued at approximately USD 68.5 billion in 2023 and is projected to reach USD 110.3 billion by 2032, growing at a CAGR of 5.4% during the forecast period. This significant growth is driven by the increasing popularity of live events, advancements in digital ticketing platforms, and the rising disposable incomes of consumers worldwide.

The burgeoning growth of the events tickets market is primarily fueled by the relentless rise in live entertainment and sports events, which have become a vital part of social and cultural life. The proliferation of music festivals, concerts, theatrical performances, and sporting events has created a robust demand for event tickets. Additionally, the growing trend of experiential spending, where consumers prioritize spending on experiences over material goods, further propels the market. Technological advancements, particularly in mobile ticketing and blockchain technology, enhance the convenience and security of purchasing tickets, thus driving market growth.

Another significant growth factor is the increasing integration of advanced technologies such as artificial intelligence and machine learning into ticketing platforms. These technologies optimize customer experiences by providing personalized recommendations and dynamic pricing models. Furthermore, the implementation of augmented reality (AR) and virtual reality (VR) in events offers immersive experiences, thus attracting a broader audience and boosting ticket sales. The widespread adoption of mobile payments and digital wallets also facilitates seamless transactions, contributing to market expansion.

The shift of ticket sales from traditional offline methods to online platforms has revolutionized the events tickets market. Online ticketing platforms offer several advantages, including ease of access, a wide range of options, and secure payment gateways, which enhance user satisfaction. The convenience of purchasing tickets from anywhere at any time, coupled with the ability to compare prices and read reviews, has led to a substantial increase in online ticket sales. Moreover, social media marketing and influencer endorsements play a pivotal role in promoting events and driving ticket sales, particularly among younger demographics.

Live Entertainment Platforms have become a cornerstone in the events tickets market, transforming the way audiences engage with performances. These platforms provide a seamless interface for users to discover and access a wide array of live events, from concerts and theater productions to sports and festivals. By leveraging advanced technologies, live entertainment platforms offer personalized recommendations and real-time updates, enhancing the overall user experience. The integration of social media features allows users to share their experiences and connect with fellow enthusiasts, further amplifying the reach and popularity of events. As consumer preferences shift towards digital solutions, live entertainment platforms are poised to play a pivotal role in driving ticket sales and expanding market reach.

Regionally, North America holds a substantial share of the events tickets market, attributed to the high number of live events, robust digital infrastructure, and the presence of major market players. Europe follows closely, driven by a rich cultural heritage and a high disposable income. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by rapid urbanization, increasing internet penetration, and a burgeoning middle class with a growing appetite for entertainment. Latin America and the Middle East & Africa regions are also anticipated to experience significant growth, supported by a rising number of events and improving economic conditions.

Type Analysis

The events tickets market is segmented by type into sports, concerts, theater, festivals, and others. Each segment caters to a unique audience and contributes differently to the overall market dynamics. Sports events dominate the market, driven by the global popularity of various sports such as football, basketball, and cricket. Major sports leagues and events like the FIFA World Cup, the Olympics, and the Super Bowl attract millions of spectators, both in-person and online, creating a substantial demand for tickets. Sponsorships, media rights, and merchandise sales further amplify the revenue generated from sports events.

Comprehensive Amazon India sales dataset featuring 15,000 synthetic e-commerce transactions from 2025. This cleaned and validated dataset captures real-world shopping patterns including customer behavior, product preferences, payment methods, delivery metrics, and regional sales distribution across Indian states.

Key Features: - 15,000 orders across multiple product categories (Electronics, Clothing, Home & Kitchen, Beauty) - Daily OHLCV-style transactional data from January to December 2025 - Complete customer journey: Order placement, payment, delivery, and review - Geographic coverage across major Indian states - Payment method diversity: Credit Card, Debit Card, UPI, Cash on Delivery - Delivery status tracking: Delivered, Pending, Returned - Customer review ratings and sentiment analysis

Dataset Columns (14): Order_ID, Date, Customer_ID, Product_Category, Product_Name, Quantity, Unit_Price_INR, Total_Sales_INR, Payment_Method, Delivery_Status, Review_Rating, Review_Text, State, Country

Use Cases: - E-commerce sales analysis and forecasting - Customer behavior and segmentation studies - Payment method preference analysis - Regional market trends and geographic insights - Delivery optimization and logistics planning - Product performance and category analysis - Customer satisfaction and review analysis - SQL practice and business intelligence training

Data Quality: - Cleaned and validated for analysis - No missing values in critical fields - Consistent data types and formatting - Ready for immediate SQL/Python analysis

Perfect for data analysts, SQL learners, business intelligence projects, and e-commerce analytics practice!

The population share with mobile internet access in Canada was forecast to continuously increase between 2024 and 2029 by in total 1.5 percentage points. After the fifteenth consecutive increasing year, the mobile internet penetration is estimated to reach 92.51 percent and therefore a new peak in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like United States and Mexico.

The population share with mobile internet access in India was forecast to continuously increase between 2024 and 2029 by in total 25 percentage points. After the fifteenth consecutive increasing year, the mobile internet penetration is estimated to reach 73.62 percent and therefore a new peak in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Bangladesh and Sri Lanka.

Techsalerator’s Business Technographic Data for Vietnam: Unlocking Insights into Vietnam's Technology Landscape

Techsalerator’s Business Technographic Data for Vietnam provides a detailed and comprehensive dataset essential for businesses, market analysts, and technology vendors seeking to understand and engage with companies operating within Vietnam. This dataset offers in-depth insights into the technological landscape, capturing and organizing data related to technology stacks, digital tools, and IT infrastructure used by businesses in the country.

Please reach out to us at info@techsalerator.com or visit Techsalerator Contact.

Top 5 Most Utilized Data Fields

• Company Name: This field lists the names of companies in Vietnam, enabling technology vendors to target potential clients and allowing analysts to assess technology adoption trends within specific businesses.

• Technology Stack: This field outlines the technologies and software solutions a company uses, such as accounting systems, customer management software, and cloud services. Understanding a company's technology stack is key to evaluating its digital maturity and operational needs.

• Deployment Status: This field indicates whether the technology is currently deployed, planned for future deployment, or under evaluation. Vendors can use this information to assess the level of technology adoption and interest among companies in Vietnam.

• Industry Sector: This field specifies the industry in which the company operates, such as manufacturing, retail, or finance. Knowing the industry helps vendors tailor their products to sector-specific demands and emerging trends in Vietnam.

• Geographic Location: This field identifies the company's headquarters or primary operations within Vietnam. Geographic information aids in regional analysis and understanding localized technology adoption patterns across the country.

Top 5 Technology Trends in Vietnam

• E-commerce Expansion: With a rapidly growing digital consumer base, Vietnamese companies are increasingly investing in e-commerce platforms, digital marketing, and online payment systems to capture a larger market share and enhance customer experience.

• Fintech Innovations: Vietnam’s fintech sector is experiencing significant growth, with businesses adopting advanced financial technologies such as mobile payment solutions, digital wallets, and blockchain to improve financial transactions and services.

• Smart Manufacturing: The manufacturing sector in Vietnam is embracing Industry 4.0 technologies, including automation, IoT, and AI-driven analytics, to enhance productivity, efficiency, and competitiveness in the global market.

• Cloud Computing and SaaS: Cloud-based solutions and Software-as-a-Service (SaaS) offerings are gaining traction, providing Vietnamese businesses with scalable and flexible IT infrastructure that supports remote work and digital transformation initiatives.

• Cybersecurity Enhancements: As digital activities increase, so does the need for robust cybersecurity measures. Companies in Vietnam are investing in advanced security solutions, including threat detection systems and data protection tools, to safeguard their operations and customer data.

Top 5 Companies with Notable Technographic Data in Vietnam

• Vietcombank: A leading financial institution, Vietcombank is implementing cutting-edge digital banking solutions, including mobile banking apps and secure online transaction systems, to enhance customer service and operational efficiency.

• Vingroup: As a major conglomerate, Vingroup leverages advanced technologies across its diverse business segments, including real estate, retail, and healthcare, integrating smart technologies and digital platforms into its operations.

• FPT Corporation: A major IT services and software development company, FPT is at the forefront of digital transformation in Vietnam, offering solutions in cloud computing, AI, and cybersecurity to both domestic and international clients.

• Masan Group: A leading consumer goods and retail company, Masan Group is adopting digital tools and e-commerce platforms to optimize its supply chain, enhance customer engagement, and drive business growth.

• VNPT: Vietnam’s largest telecommunications provider, VNPT is expanding its network infrastructure and investing in advanced technologies such as 5G and IoT to improve connectivity and support the digital economy.

Accessing Techsalerator’s Business Technographic Data

For those interested in accessing Techsalerator’s Business Technographic Data for Vietnam, please contact info@techsalerator.com with your specific needs. Techsalerator offers customized quotes based on the required number of data fields and records, with datasets available for delivery within 24 hours. Ongoing access ...

This dataset provides a comprehensive collection of consumer behavior data that can be used for various market research and statistical analyses. It includes information on purchasing patterns, demographics, product preferences, customer satisfaction, and more, making it ideal for market segmentation, predictive modeling, and understanding customer decision-making processes.

The dataset is designed to help researchers, data scientists, and marketers gain insights into consumer purchasing behavior across a wide range of categories. By analyzing this dataset, users can identify key trends, segment customers, and make data-driven decisions to improve product offerings, marketing strategies, and customer engagement.

Key Features: Customer Demographics: Understand age, income, gender, and education level for better segmentation and targeted marketing. Purchase Behavior: Includes purchase amount, frequency, category, and channel preferences to assess spending patterns. Customer Loyalty: Features like brand loyalty, engagement with ads, and loyalty program membership provide insights into long-term customer retention. Product Feedback: Customer ratings and satisfaction levels allow for analysis of product quality and customer sentiment. Decision-Making: Time spent on product research, time to decision, and purchase intent reflect how customers make purchasing decisions. Influences on Purchase: Factors such as social media influence, discount sensitivity, and return rates are included to analyze how external factors affect purchasing behavior.

Columns Overview: Customer_ID: Unique identifier for each customer. Age: Customer's age (integer). Gender: Customer's gender (categorical: Male, Female, Non-binary, Other). Income_Level: Customer's income level (categorical: Low, Middle, High). Marital_Status: Customer's marital status (categorical: Single, Married, Divorced, Widowed). Education_Level: Highest level of education completed (categorical: High School, Bachelor's, Master's, Doctorate). Occupation: Customer's occupation (categorical: Various job titles). Location: Customer's location (city, region, or country). Purchase_Category: Category of purchased products (e.g., Electronics, Clothing, Groceries). Purchase_Amount: Amount spent during the purchase (decimal). Frequency_of_Purchase: Number of purchases made per month (integer). Purchase_Channel: The purchase method (categorical: Online, In-Store, Mixed). Brand_Loyalty: Loyalty to brands (1-5 scale). Product_Rating: Rating given by the customer to a purchased product (1-5 scale). Time_Spent_on_Product_Research: Time spent researching a product (integer, hours or minutes). Social_Media_Influence: Influence of social media on purchasing decision (categorical: High, Medium, Low, None). Discount_Sensitivity: Sensitivity to discounts (categorical: Very Sensitive, Somewhat Sensitive, Not Sensitive). Return_Rate: Percentage of products returned (decimal). Customer_Satisfaction: Overall satisfaction with the purchase (1-10 scale). Engagement_with_Ads: Engagement level with advertisements (categorical: High, Medium, Low, None). Device_Used_for_Shopping: Device used for shopping (categorical: Smartphone, Desktop, Tablet). Payment_Method: Method of payment used for the purchase (categorical: Credit Card, Debit Card, PayPal, Cash, Other). Time_of_Purchase: Timestamp of when the purchase was made (date/time). Discount_Used: Whether the customer used a discount (Boolean: True/False). Customer_Loyalty_Program_Member: Whether the customer is part of a loyalty program (Boolean: True/False). Purchase_Intent: The intent behind the purchase (categorical: Impulsive, Planned, Need-based, Wants-based). Shipping_Preference: Shipping preference (categorical: Standard, Express, No Preference). Payment_Frequency: Frequency of payment (categorical: One-time, Subscription, Installments). Time_to_Decision: Time taken from consideration to actual purchase (in days).

Use Cases: Market Segmentation: Segment customers based on demographics, preferences, and behavior. Predictive Analytics: Use data to predict customer spending habits, loyalty, and product preferences. Customer Profiling: Build detailed profiles of different consumer segments based on purchase behavior, social media influence, and decision-making patterns. Retail and E-commerce Insights: Analyze purchase channels, payment methods, and shipping preferences to optimize marketing and sales strategies.

Target Audience: Data scientists and analysts looking for consumer behavior data. Marketers interested in improving customer segmentation and targeting. Researchers are exploring factors influencing consumer decisions and preferences. Companies aiming to improve customer experience and increase sales through data-driven decisions.

This dataset is available in CSV format for easy integration into data analysis tools and platforms such as Python, R, and Excel.

A league table of the 120 cryptocurrencies with the highest market cap reveals how diverse each crypto is and potentially how much risk is involved when investing in one. Bitcoin (BTC), for instance, had a so-called "high cap" - a market cap worth more than 10 billion U.S. dollars - indicating this crypto project has a certain track record or, at the very least, is considered a major player in the cryptocurrency space. This is different in Decentralize Finance (DeFi), where Bitcoin is only a relatively new player. A concentrated market The number of existing cryptocurrencies is several thousands, even if most have a limited significance. Indeed, Bitcoin and Ethereum account for nearly 75 percent of the entire crypto market capitalization. As crypto is relatively easy to create, the range of projects varies significantly - from improving payments to solving real-world issues, but also meme coins and more speculative investments. Crypto is not considered a payment method While often talked about as an investment vehicle, cryptocurrencies have not yet established a clear use case in day-to-day life. Central bankers found that usefulness of crypto in domestic payments or remittances to be negligible. A forecast for the world's main online payment methods took a similar stance: It predicts that cryptocurrency would only take up 0.2 percent of total transaction value by 2027.

Techsalerator’s Business Funding Data for Ghana

Techsalerator’s Business Funding Data for Ghana offers a comprehensive and insightful collection of information vital for businesses, investors, and financial analysts. This dataset provides an in-depth analysis of the funding activities of companies across various sectors in Ghana, capturing and categorizing data related to their funding rounds, investment sources, and financial milestones.

If you need the full dataset, reach out to us at info@techsalerator.com or https://www.techsalerator.com/contact-us.

Techsalerator’s Business Funding Data for Ghana

Techsalerator’s Business Funding Data for Ghana provides a detailed and insightful overview of critical information for businesses, investors, and financial analysts. This dataset delivers a thorough examination of funding activities across various sectors in Ghana, detailing data related to funding rounds, investment sources, and key financial milestones.

Top 5 Key Data Fields

Company Name: Identifies the company receiving funding. This information helps investors identify potential opportunities and allows analysts to monitor funding trends within specific industries.

Funding Amount: Shows the total amount of funding a company has received. Understanding these amounts reveals insights into the financial health and growth potential of businesses and the scale of investment activities.

Funding Round: Indicates the stage of funding, such as seed, Series A, Series B, or later stages. This helps investors assess a business’s maturity and growth trajectory.

Investor Name: Provides details about the investors or investment firms involved. Knowing the investors helps gauge the credibility of the funding source and their strategic interests.

Investment Date: Records when the funding was completed. The timing of investments can reflect market trends, investor confidence, and potential impacts on a company’s future.

Top 5 Funding Trends in Ghana

Fintech and Mobile Payments: Ghana's fintech sector is booming, with increasing investments in mobile payment solutions, online banking services, and digital financial platforms that enhance financial inclusion.

Agriculture and Agritech: As a key driver of Ghana’s economy, agriculture is receiving funding to improve sustainability, increase productivity, and integrate modern technologies such as precision farming and data analytics.

Energy and Renewable Resources: Investments are being directed towards renewable energy projects like solar and wind power, contributing to Ghana’s goal of increasing energy access and reducing reliance on fossil fuels.

Healthcare and Healthtech: With the growing demand for improved healthcare services, investments are flowing into hospitals, telemedicine platforms, and health tech solutions aimed at providing better access to medical services and innovations.

Education and E-learning: Funding is being allocated to educational initiatives, e-learning platforms, and vocational training programs to support Ghana’s human capital development and enhance skills training for the workforce.

Top 5 Companies with Notable Funding Data in Ghana

MTN Ghana: The largest telecom company in Ghana, MTN has received significant funding for network expansion, digital services, and mobile payment solutions.

Farmerline: This agritech company has attracted funding to expand its platform that provides digital tools and resources for smallholder farmers, focusing on boosting productivity and sustainability.

CalBank: A leading financial institution, CalBank has secured investments to enhance its digital banking services, increase its reach, and support financial inclusion efforts across the country.

Zipline Ghana: Known for using drones to deliver medical supplies, Zipline has received funding to expand its operations, providing critical healthcare services to remote regions in Ghana.

mPharma: A health tech company focused on improving access to affordable medications, mPharma has garnered significant investment to grow its services and enhance healthcare infrastructure.

Accessing Techsalerator’s Business Funding Data

To obtain Techsalerator’s Business Funding Data for Ghana, contact info@techsalerator.com with your specific needs. Techsalerator will provide a customized quote based on the required data fields and records, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

Company Name
Funding Amount
Funding Round
Investor Name
Investment Date
Funding Type (Equity, Debt, Grants, etc.)
Sector Focus
Deal Structure
Investment Stage
Contact Information

For detailed insights into funding activities and financial trends in Ghana, Techsalerator’s dataset is an invaluable resource for investors, business analysts, and financial professionals seeking informed, strategic decisions.

Objectives:

• I leveraged advanced data visualization techniques to extract valuable insights from a comprehensive dataset. By visualizing sales patterns, customer behavior, and product trends, I identified key growth opportunities and provided actionable recommendations to optimize business strategies and enhance overall performance. you can find the GitHub repo here Link to GitHub Repository.

Data Description:

there are exactly 6 table and 1 is a fact table and the rest of them are dimension tables: Fact Table:

payment_key:
  Description: An identifier representing the payment transaction associated with the fact.
  Use Case: This key links to a payment dimension table, providing details about the payment method and related information.

customer_key:
  Description: An identifier representing the customer associated with the fact.
  Use Case: This key links to a customer dimension table, providing details about the customer, such as name, address, and other customer-specific information.

time_key:
  Description: An identifier representing the time dimension associated with the fact.
  Use Case: This key links to a time dimension table, providing details about the time of the transaction, such as date, day of the week, and month.

item_key:
  Description: An identifier representing the item or product associated with the fact.
  Use Case: This key links to an item dimension table, providing details about the product, such as category, sub-category, and product name.

store_key:
  Description: An identifier representing the store or location associated with the fact.
  Use Case: This key links to a store dimension table, providing details about the store, such as location, store name, and other store-specific information.

quantity:
  Description: The quantity of items sold or involved in the transaction.
  Use Case: Represents the amount or number of items associated with the transaction.

unit:
  Description: The unit or measurement associated with the quantity (e.g., pieces, kilograms).
  Use Case: Specifies the unit of measurement for the quantity.

unit_price:
  Description: The price per unit of the item.
  Use Case: Represents the cost or price associated with each unit of the item.

total_price:
  Description: The total price of the transaction, calculated as the product of quantity and unit price.
  Use Case: Represents the overall cost or revenue generated by the transaction.

Customer Table: customer_key:

Description: An identifier representing a unique customer.
Use Case: Serves as the primary key to link with the fact table, allowing for easy and efficient retrieval of customer-specific information.

name:

Description: The name of the customer.
Use Case: Captures the personal or business name of the customer for identification and reference purposes.

contact_no:

Description: The contact number associated with the customer.
Use Case: Stores the phone number or contact details for communication or outreach purposes.

nid:

Description: The National ID (NID) or a unique identification number for the customer.

Item Table: item_key:

Description: An identifier representing a unique item or product.
Use Case: Serves as the primary key to link with the fact table, enabling retrieval of detailed information about specific items in transactions.

item_name:

Description: The name or title of the item.
Use Case: Captures the descriptive name of the item, providing a recognizable label for the product.

desc:

Description: A description of the item.
Use Case: Contains additional details about the item, such as features, specifications, or any relevant information.

unit_price:

Description: The price per unit of the item.
Use Case: Represents the cost or price associated with each unit of the item.

man_country:

Description: The country where the item is manufactured.
Use Case: Captures the origin or manufacturing location of the item.

supplier:

Description: The supplier or vendor providing the item.
Use Case: Stores the name or identifier of the supplier, facilitating tracking of item sources.

unit:

Description: The unit of measurement associated with the item (e.g., pieces, kilograms).

Store Table: store_key:

Description: An identifier representing a unique store or location.
Use Case: Serves as the primary key to link with the fact table, allowing for easy retrieval of information about transactions associated with specific stores.

division:

Description: The administrative division or region where the store is located.
Use Case: Captures the broader geographical area in which...