The capstone project provides an opportunity for businesses and learners to collaborate to identify and translate a real business problem into an analytics problem. The project also includes data collection & preparation, data modeling and analysis with the potential to include predictive modeling, machine learning implementation, and a solution deployment plan. Capstone project results/ recommendations will be communicated in a report document and a final presentation.

You should submit a high-level proposal/business problem statement including relevant data sets and definitions, a list of acceptable tools (if applicable), and expected deliverables. Business datasets could be provided based on a non-disclosure agreement or in an anonymized/synthetic data format that is relevant to your organization and business problem. The capstone course instructors will review the documents to confirm the scope and timing of the proposed problem and its alignment with the capstone course requirements.

Analytics solution may be applicable for (however they are not limited to) the following topics:

1. Demand for social services (healthcare, emergency services, infrastructure, etc.)
2. Customer acquisition and retention
3. Merchandising for trade areas (categories)
4. Quantifying Customer Lifetime Value
5. Determining media consumption (mass vs digital)
6. Reduction of client churn (lower abandonment)
7. Cross-sell and upsell opportunities
8. Develop high propensity target markets
9. Customer segmentation (behavioral or transactional)
10. New Product/Product line development
11. Market Basket Analysis to understand which items are often purchased together
12. Ranking markets by potential revenue
13. Consumer personification

To ensure students’ learning objectives are achieved, we recommend that the datasets are at least 20,000+ rows in size. Data need not be ‘clean’; it is advantageous to the students’ learning experience to require hygiene prior to analysis. Similarly, if more than one database is provided, which must be conjoined, students will be required to integrate them. This supports the learning experience and minimizes partner data preparation.