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Product-Market Fit Analysis Dataset
Data Science and Analytics
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About
Captures detailed customer subscription behaviour for a technology startup providing cloud expense management software. The primary purpose of the data is to facilitate a cohort analysis aimed at accurately measuring customer retention rates and assessing whether the business has achieved Product-Market Fit. Analysing these records is crucial for senior leadership to make informed strategic decisions, particularly concerning investment allocation between user acquisition (marketing) and product improvement.
Columns
The dataset contains 28 columns structured around customer identifiers, status, and monthly payment records:
- Cliente (Customer): A unique identifier for each customer record.
- Estado Cliente (Customer Status): Indicates the current state of the customer subscription, categorised as Active (continuing to pay for the service) or Churned (having ceased payments).
- Mes Registro (Registration Month): The specific month and year when the user first requested the service.
- Mes de Abandono (Churn Month): The specific month and year when the user cancelled their subscription. This column contains many missing values, which corresponds to currently active users.
- 1/2019 through 12/2020 (Monthly Payment Status): A series of binary columns representing each month within the analysis period. A value of '1' indicates the customer paid for the service in that month, while an empty cell signifies no payment was made.
Distribution
The information is provided in a tabular structure, contained within a single CSV file named
retencao-por-mes.csv. The file size is approximately 21.89 kilobytes. The structure includes 28 separate columns tracking customer activity over time. The data comprises 330 unique customer records, suitable for detailed behavioural analysis.Usage
This data is ideally suited for quantifying crucial SaaS business metrics. Key applications include:
- Performing detailed cohort analysis to identify trends in customer engagement and longevity.
- Calculating customer retention and churn percentages on both a monthly and quarterly basis.
- Identifying specific points in the customer lifecycle where significant customer leakage occurs.
- Serving as the evidentiary basis for evaluating the business’s Product-Market Fit (PMF).
- Supporting executive decision-making regarding scaling, budget priorities, and marketing spend optimization.
Coverage
The data encompasses the payment history and subscription records of users of a cloud-based expense management software. The temporal scope of the monthly payment tracking extends across a two-year period, beginning in January 2019 and concluding in December 2020. The records relate to internal customers of a startup; no specific geographic or demographic segmentation is explicitly detailed in the sampled data.
License
CC0: Public Domain
Who Can Use It
The dataset is valuable for individuals and teams focused on business performance and analytical strategy:
- Data Analysts and Data Scientists: For developing and executing cohort analyses and predictive churn models.
- Product Managers: For understanding feature adoption success and retention trends related to product functionality.
- Startup Founders and Executives: For validating growth assumptions, justifying funding rounds, and setting annual strategic priorities.
- Marketing and Finance Teams: For linking acquisition costs to lifetime value and determining appropriate budget allocation for user growth.
Dataset Name Suggestions
- SaaS Subscription Cohort Retention Data
- Product-Market Fit Analysis Dataset
- Expense Software Customer Churn Records
- Monthly SaaS Customer Retention Matrix
Attributes
Original Data Source:Product-Market Fit Analysis Dataset
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