The project provides an opportunity for businesses and students to identify and translate a real business problem into an analytics problem(s). The projects will allow the students to apply the acquired data analytics skills to the area of business intelligence (BI). The projects can be short and based on the information provided the students will apply their learnings to address the sponsors business problem. Some examples are:

• Perform data visualization
• Perform various types of analysis: Descriptive, Predictive and Prescriptive, which will be performed based on the provided business problem
• Implement key processes including: data hygiene, ETL, modeling and reporting
• Explain the forecasting modules used in developing the solution(s)

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 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. Customer acquisition and retention
2. Quantifying Customer Lifetime Value
3. Cross-sell and upsell opportunities
4. Develop high propensity target markets
5. Customer segmentation (behavioral or transactional)
6. New Product/Product line development
7. Market Basket Analysis to understand which items are often purchased together
8. Ranking markets by potential revenue
9. Consumer personification

To ensure students’ learning objectives are achieved, we recommend that the datasets are at least 20,000+ rows in size. Data need to be ‘clean’. 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.