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SMS Transaction Dataset
Data Science and Analytics
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Free
About
This dataset contains SMS data pertaining to riders, designed for extracting valuable insights into their financial well-being. It can be used to understand various aspects of financial behaviour, such as how many users utilise multiple primary banks for transactions, the proportion of SMS messages containing financial transaction details, and the split between income and expense transactions. Additionally, it helps ascertain the percentage of users involved in EMIs and loans, the prevalence of salary receipts within the first seven days of the month, and the proportion of users maintaining a positive cash flow.
Columns
- phoneNumber: This column represents the mobile number associated with the rider.
- id: A unique identifier for each record.
- updateAt: Specifies the date and time when the message was updated.
- senderAddress: Contains the code identifying the sender of the SMS data.
- text: This column holds the actual content of the SMS message.
Distribution
The dataset comprises SMS text data. While specific total row or record counts are not available, the data distribution indicates a wide variety across key fields. For instance, the
phoneNumber and id fields show that 86% of entries fall under "Other", with two specific values accounting for 8% and 6% respectively. Similarly, updateAt shows 96% of messages updated on "Other" dates, with specific dates from May 2022 making up smaller percentages (2% each). Sender addresses (senderAddress) are highly varied, with 94% categorised as "Other", and specific senders like TX-SFXRDR and AD-SWIGGY each representing 3%. The text content is also diverse, with 99% falling into the "Other" category, and specific messages related to location or network issues accounting for 1% and 0% respectively. The typical format for this type of data file is CSV, with sample files updated separately on the platform.Usage
This dataset is ideal for deriving insights into the financial health of riders. It can be used for:
- Analysing transaction patterns, including the distinction between income and expense transactions.
- Identifying the proportion of users engaged in EMIs and loan repayments.
- Studying salary disbursement timing relative to the beginning of the month.
- Assessing the percentage of users with positive cash flow.
- Exploring the use of multiple primary banks by users.
- Applications in data analytics, natural language processing (NLP), and data visualisation.
Coverage
The dataset's region of coverage is global. The
updateAt column indicates that sampled data includes entries from May 2022. The demographic scope primarily focuses on "riders," implying individuals engaged in delivery or similar service-oriented roles.License
CC0
Who Can Use It
This dataset is suitable for data scientists, data analysts, and researchers interested in financial behaviour, mobile data analysis, or natural language processing. It is designed to be accessible for both beginner and intermediate-level users seeking to gain insights from text-based financial data. Potential users include financial institutions for credit scoring, fintech companies for product development, and academic researchers studying socio-economic trends in gig economy workers.
Dataset Name Suggestions
- Rider Financial Health SMS Data
- Mobile SMS Financial Insights
- SMS Transaction Data for Riders
- Financial Health Analytics from Rider SMS
- Rider Mobile Data for Financial Insights
Attributes
Original Data Source: SMS-Data
QUALITY