We are building a strategic marketing plan for a children's book e-commerce platform. The core objective is to identify which ZIP codes present the most significant opportunities based on socio-economic and demographic factors.
- Which ZIP codes are most likely to yield the highest engagement and sales for children's books sold online?
- Secure data on household income, family composition, and literacy rates within ZIP codes.
- Conduct a deep dive into the data, examining each variable to understand the distribution within and across ZIP codes.
- Ensure the reliability of our data by comparing it to known benchmarks and explaining any discrepancies.
- Craft an MPI to score and rank ZIP codes, focusing on variables like average income, number of children, and bookstore proximity.
- Collect data from credible sources to form a robust dataset for analysis.
- Analyze key variables in isolation (e.g., population vs. ZIP, income vs. ZIP).
- Validate the data by comparing it to expected distributions and rationalizing outliers.
- Use the MPI to evaluate and rank each ZIP code according to our target market criteria.
- Integrate an SIR model to simulate market penetration and customer lifecycle in the future phases of our project.
(Include instructions for setting up the project, running the tests, and deployment as previously outlined)
- Python - The programming language used.
- Pandas - Library for data manipulation and analysis.
- NumPy - Library for numerical operations.
- Matplotlib/Seaborn - Libraries for data visualization.
(Continue with Contributing, Versioning, Authors, License, Acknowledgments, Project Status as previously outlined)
- Data collection complete, covering socio-economic and demographic variables across numerous ZIP codes.
- Exploratory data analysis is ongoing, with a focus on visualizing and understanding data trends.
- MPI is under development to rank ZIP codes effectively.
- Alejandro Diaz - Initial work - DiaA6383
- Credit to data providers, supportive community members, and advisors.