This dataset analyzes the direct effect of positive and negative word-of-mouth, peer influence, alternative attractiveness, and trust in the incumbent provider on users' intentions to switch. It also examines the mediating role of alternative attractiveness in the relationship between positive WOM, and switching intentions; the mediating role of trust in incumbent service in the relationship between negative WOM and switching intentions.

This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context: Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration: The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information: The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase. Date: The date and time when the transaction occurred. It records the timestamp of each purchase. Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity. Product: A list of products purchased in the transaction. It includes the names of the products bought. Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought. Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction. Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment. City: The city where the purchase took place. It indicates the location of the transaction. Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc. Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction. Customer_Category: A category representing the customer's background or age group. Season: The season in which the purchase occurred, such as spring, summer, fall, or winter. Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items." Use Cases: Market Basket Analysis: Discover associations between products and uncover buying patterns. Customer Segmentation: Group customers based on purchasing behavior. Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions. Retail Analytics: Analyze store performance and customer trends. Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

Original Data Source: Retail Transactions Dataset

This dataset contains information on Samsung mobile sales, 5G network adoption, and market factors. It includes details such as regional 5G coverage, subscriber growth, and marketing influence.

Success.ai’s Ecommerce Merchant Data and B2B Contact Data for Global E-commerce Professionals provides a comprehensive and highly accurate database from over 170 million verified profiles. Specifically tailored for the e-commerce sector, this dataset features work emails, direct phone numbers, and enriched professional profiles to connect businesses with the leaders and decision-makers shaping the global e-commerce landscape. Continuously updated with advanced AI validation, this resource is ideal for enhancing marketing campaigns, sales initiatives, recruitment efforts, and market research.

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• Data Highlights 170M+ Verified Global Profiles 50M Direct Phone Numbers 700M Total Professional Profiles Worldwide 70M Verified Company Profiles

• Use Cases

1. Enhanced Marketing: Empower your e-commerce marketing strategies with precise email and phone contact details.
2. Sales Growth: Equip your sales team to connect with top-level executives and decision-makers.
3. Recruitment Excellence: Source global e-commerce talent efficiently with verified professional profiles.
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Analysis of ‘Mobile Phones Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/artempozdniakov/ukrainian-market-mobile-phones-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

The dataset set contains data about the mobile phones which were released in past 4 years and which can be bought in Ukraine. Dataset contains the model name, brand name and operating system of the phone and it's popularity. It also has it's financial characteristics like lowest/highest/best price and sellers amount. And some of the characteristics like screen/battery size, memory amount and release date. This data can be useful for improving your machine learning, analysis and vizualization, missing data filling skills. I'm waiting for your notebooks! :) Good luck!

--- Original source retains full ownership of the source dataset ---

This dataset offers a comprehensive overview of the iPhone's journey in the global smartphone market from 2010 to 2024 . It includes:

📊 Number of iPhone Users: Total users worldwide and within the USA. 📈 Sales Figures: Yearly iPhone sales data. 🏆 Market Share: Comparison of iOS and Android market shares across years. This dataset is perfect for:

Market forecasting and trend analysis. Competitive landscape studies between iOS and Android. Consumer behavior research in the tech industry. Whether you're a data scientist, market analyst, or tech enthusiast, this dataset provides valuable insights to support your research and projects.

Analysis of ‘Average Revenue per User (ARPU) in the Retail Mobile Market’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/naujdkauikiwx0yftdz86q on 07 January 2022.

--- Dataset description provided by original source is as follows ---

Total retail mobile revenues divided by number of active SIM cards

Original source

Electronic communications market indicators collected by Commission services, through National Regulatory Authorities, for the Communications Committee (COCOM) - January and July reports.:

http://ec.europa.eu/digital-agenda/about-fast-and-ultra-fast-internet-access

Parent dataset

This dataset is part of of another dataset:

http://digital-agenda-data.eu/datasets/digital_agenda_scoreboard_key_indicators

--- Original source retains full ownership of the source dataset ---

This dataset is about book series. It has 1 row and is filtered where the books is Digital disciplines : attaining market leadership via the cloud, big data, social, mobile, and the internet of things. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Analysis of ‘Participation & Sales by Month for Fresh 4 Less Farm Stands & Mobile Markets’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ece900b9-ce1e-498d-8d87-214b64e587f6 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset displays the number of customers and sales each month for farm stands and mobile markets in Austin Public Health's Fresh 4 Less program.

--- Original source retains full ownership of the source dataset ---

TechCorner Mobile Purchase & Engagement Data (2024-2025)

Context

TechCorner Mobile Sales & Customer Insights is a real-world dataset capturing 10 months of mobile phone sales transactions from a retail shop in Bangladesh. This dataset was designed to analyze customer location, buying behavior, and the impact of Facebook marketing efforts.

The primary goal was to identify whether customers are from the local area (Rangamati Sadar, Inside Rangamati) or completely outside Rangamati. Since TechCorner operates a Facebook page, the dataset also includes insights into whether Facebook marketing is effectively reaching potential buyers.

Additionally, the dataset helps in determining: ✔ How many customers are new vs. returning buyers ✔ If customers are followers of the shop’s Facebook page ✔ Whether a customer was recommended by an existing buyer

This dataset is valuable for:

Retail sales analysis to understand product demand fluctuations.

Marketing impact measurement (Facebook engagement vs. actual purchase behavior).

Customer segmentation (local vs. non-local buyers, social media influence, word-of-mouth impact).

Sales trend analysis based on preferred phone models and price ranges.

With a realistic, non-uniform distribution of daily sales and some intentional missing values, this dataset reflects actual retail business conditions rather than artificially smooth AI-generated data.

Marketing & Customer Queries

Does he/she Come from Facebook Page? → Whether the customer came from a Facebook page (Yes/No). Used to analyze Facebook marketing reach.

Does he/she Followed Our Page? → Whether the customer is already a follower of the shop’s Facebook page (Yes/No). Helps measure brand loyalty and organic engagement.

Did he/she buy any mobile before? → Whether the customer is a repeat buyer (Yes/No). Determines the percentage of returning customers.

Did he/she hear of our shop before? → Whether the customer knew about the shop before purchasing (Yes/No). Identifies the impact of referrals or previous marketing efforts.

Was this customer recommended by an old customer? → Whether an existing customer referred them to the shop (Yes/No). Helps evaluate the effectiveness of word-of-mouth marketing.

Acknowledgements

This dataset is derived from real-world mobile sales transactions recorded at TechCorner, a retail shop in Bangladesh. It accurately reflects customer purchasing behavior, pricing trends, and the effectiveness of Facebook marketing in driving sales. Special appreciation to TechCorner for providing comprehensive insights into daily sales patterns, customer demographics, and market dynamics.

This dataset can be used for:

📊 Predictive modeling of sales trends based on customer demographics and marketing channels. 📈 Marketing effectiveness analysis (impact of Facebook promotions vs. organic sales). 🔍 Clustering customers based on purchasing habits (new vs. returning buyers, Facebook users vs. walk-ins). 📌 Understanding demand for different smartphone brands in a local retail market. 🚀 Analyzing how word-of-mouth recommendations influence new customer acquisition.

💡 Can you build a model to predict if a customer is likely to return? 💬 How effective is Facebook in driving actual sales compared to walk-ins? 🔍 Can we cluster customers based on behavior and brand preferences?

BDEX identifies in-market automotive consumers via multiple real-time datasets including:

1) Online vehicle searches from popular car search web sites 2) Lease expirations 3) Geolocation data identifying consumers visiting a dealership location

We use all of the following to identify consumer behaviors, map them against our comprehensive identity graph and send you a feed of these in-market consumers in any output format you need including Mobile IDs (MAIDs), Hashed Emails (MD5, SHA256) or full postal including Name, Address, Phone and Email.

Success.ai’s Phone Number Data offers direct access to over 50 million verified phone numbers for professionals worldwide, extracted from our expansive collection of 170 million profiles. This robust dataset includes work emails and key decision-maker profiles, making it an essential resource for companies aiming to enhance their communication strategies and outreach efficiency. Whether you're launching targeted marketing campaigns, setting up sales calls, or conducting market research, our phone number data ensures you're connected to the right professionals at the right time.

Why Choose Success.ai’s Phone Number Data?

Direct Communication: Reach out directly to professionals with verified phone numbers and work emails, ensuring your message gets to the right person without delay. Global Coverage: Our data spans across continents, providing phone numbers for professionals in North America, Europe, APAC, and emerging markets. Continuously Updated: We regularly refresh our dataset to maintain accuracy and relevance, reflecting changes like promotions, company moves, or industry shifts. Comprehensive Data Points:

Verified Phone Numbers: Direct lines and mobile numbers of professionals across various industries. Work Emails: Reliable email addresses to complement phone communications. Professional Profiles: Decision-makers’ profiles including job titles, company details, and industry information. Flexible Delivery and Integration: Success.ai offers this dataset in various formats suitable for seamless integration into your CRM or sales platform. Whether you prefer API access for real-time data retrieval or static files for periodic updates, we tailor the delivery to meet your operational needs.

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Data Accuracy: Our data is verified for accuracy to ensure over 99% deliverability rates. Compliance: Fully compliant with GDPR and other international data protection regulations, allowing you to use the data with confidence globally. Customization and Support:

Tailored Data Solutions: Customize the data according to geographic, industry-specific, or job role filters to match your unique business needs. Dedicated Support: Our team is on hand to assist with data integration, usage, and any questions you may have. Start with Success.ai Today: Engage with Success.ai to leverage our Phone Number Data and connect with global professionals effectively. Schedule a consultation or request a sample through our dedicated client portal and begin transforming your outreach and communication strategies today.

Remember, with Success.ai, you don’t just buy data; you invest in a partnership that grows with your business needs, backed by our commitment to quality and affordability.

This dataset is about book series. It has 1 row and is filtered where the books is eBusiness essentials : technology and network requirements for mobile and online markets. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Here are a few use cases for this project:

1. Traffic Surveillance: The "Mobil" model can be used by traffic surveillance systems to detect vehicle types, identify specific car models on the road or monitor the frequency of a particular make/model in specific areas for transportation studies.

2. Dealership Inventory Management: Car dealerships can use it to organise or count their inventory of vehicles. By simply scanning images of their stockyard, they can get counts on how many and what type of vehicles they have in real-time.

3. Insurance: Insurance companies can apply the computer vision model to assess the type and model of cars for which they are providing policies. This can also help in determining the value of a car insurance policy based on the model of the car.

4. Automotive Market Research: Market research agencies can use the "Mobil" model to gather data on the popularity or prevalence of different car models in different regions. This could be useful in understanding consumer behavior and market trends.

5. Smart Parking Solutions: The model can be utilized in smart parking solutions. It can help in classifying the cars in parking lots or garages for better management, fast retrieval, or for providing user-specific data like availability of spots for a particular car model, etc.

Analysis of ‘Mobile Price Classification’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/iabhishekofficial/mobile-price-classification on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Bob has started his own mobile company. He wants to give tough fight to big companies like Apple,Samsung etc.

He does not know how to estimate price of mobiles his company creates. In this competitive mobile phone market you cannot simply assume things. To solve this problem he collects sales data of mobile phones of various companies.

Bob wants to find out some relation between features of a mobile phone(eg:- RAM,Internal Memory etc) and its selling price. But he is not so good at Machine Learning. So he needs your help to solve this problem.

In this problem you do not have to predict actual price but a price range indicating how high the price is

--- Original source retains full ownership of the source dataset ---

This quarterly dataset for the UK fixed-line and mobile telecommunication markets contains data for aggregated call revenues, mobile phone and landline connections, call volumes, message volumes and subscriber numbers. The tables are published quarterly on the Ofcom website in pdf and csv formats.

NoSQL Database Market was valued at $9.38 Billion in 2023, and is projected to reach $USD 86.48 Billion by 2032, at a CAGR of 28% from 2023 to 2032.

Leverage the most reliable and compliant mobile device location/foot traffic dataset on the market. Veraset Movement (Mobile Device GPS / Foot Traffic Data) offers unparalleled insights into footfall traffic patterns across North America.

Covering the United States, Canada and Mexico, Veraset's Mobile Location Data draws on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement. Ideal for ad tech, planning, retail analysis, and transportation logistics, Veraset's Movement data helps in shaping strategy and making data-driven decisions.

Veraset’s North American Movement Panel: - United States: 768M Devices, 70B+ Pings - Canada: 55M+ Devices, 9B+ Pings - Mexico: 125M+ Devices, 14B+ Pings - MAU/Devices and Monthly Pings

Uses for Veraset's Mobile Location Data: - Advertising - Ad Placement, Attribution, and Segmentation - Audience Creation/Building - Dynamic Ad Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Credit Card Loyalty - Competitive Analysis - Risk assessment, Underwriting, and Policy Personalization - Enrichment of Existing Datasets - Trade Area Analysis - Predictive Analytics and Trend Forecasting

Leverage the most reliable and compliant global mobility and foot traffic dataset on the market. Veraset Movement (Mobile Device GPS Mobility Data) offers unparalleled real-time insights into footfall traffic patterns globally.

Covering 200+ countries, Veraset's Mobile Location Data draws on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement.

Ideal for ad tech, planning, retail analysis, and transportation logistics, Veraset's mobile location data helps in shaping strategy and making data-driven decisions.

Veraset Global Movement panel (mobile location) includes: - 1.8+ Billion Devices Monthly - 200 Billion Pings Monthly Device and Ping counts by Country are available upon request

Common Use Cases of Veraset's Mobile Location Data: - Advertising - Ad Placement, Attribution, and Segmentation - Audience Creation/Building - Dynamic Ad Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Credit Card Loyalty - Competitive Analysis - Risk assessment, Underwriting, and Policy Personalization - Enrichment of Existing Datasets - Trade Area Analysis - Predictive Analytics and Trend Forecasting

Please visit: https://www.veraset.com/docs/movement for more information and schemas

This study discloses a dataset of electric vehicles’ (EVs’) charging transactions at a scale for multi-faceted analysis from both EV charger and user perspectives. The data comprises of whole sessions that occurred during a charging operation company’s annual commercial operation period, specifically including identifiers and charger location categories. For data acquisition, machine-to-machine wireless communication system with proper retransmission for interruption is utilised. Entire dataset is newly collected and available with 72856 sessions from 2238 EV users and 2119 chargers. The dataset can be used in a variety of ways to the functioning of power systems and markets, including EV charging service businesses, charger installation siting, demand transaction market design, and long-term investment planning of EV-related infrastructure.