Students participating in this capstone project will partner with ArosaPay, a payment technology company that allows customers to pay only when they’re satisfied with their purchases. The project challenges students to explore how predictive analytics can support this revolutionary model by identifying satisfied vs. unsatisfied customers before payment decisions are triggered.

The primary goal is to deliver a research-backed, innovative, and feasible business solution that outlines how machine learning and data-driven insights can be used to score customer satisfaction in real time, creating value through more confident, automated payment experiences. Students will not build or implement a technical product but will act as strategic advisors to define, validate, and recommend a scalable solution that aligns with ArosaPay’s disruptive business model.

Capstone students will serve as strategic innovation consultants. Their job is to investigate the potential for AI-based customer satisfaction prediction as a tool for payment automation, customer experience optimization, and fraud reduction in e-commerce environments.

They will:

• Identify market opportunities and pain points around payment risk, satisfaction ambiguity, and returns
• Conduct primary and secondary research to understand how predictive models can improve trust in post-purchase payment systems
• Propose a non-technical solution architecture for a predictive scoring system
• Build a business case for the solution’s implementation, scalability, and financial sustainability
• Recommend digital tools and metrics for assessing satisfaction accurately and ethically

Validated Business Problem Statement

A clearly articulated, research-backed description of the key challenge ArosaPay faces in enabling post-purchase, satisfaction-based payments, focused on the difficulty of reliably predicting customer satisfaction.

Innovation Concept Proposal

A strategic, non-technical concept for a machine learning–supported solution that predicts customer satisfaction to automate payment decisions. Includes potential use cases, ethical considerations, and customer impact.

Market Analysis & Opportunity Assessment

A review of the competitive landscape, market trends, and potential adoption barriers or enablers. May include benchmarking of existing satisfaction or predictive scoring tools in e-commerce and fintech.

Business Model & Value Proposition

A structured business model (e.g., Lean Canvas or Business Model Canvas) outlining how the satisfaction prediction concept fits into ArosaPay’s platform and value chain. Includes a clear customer value proposition and monetization options.

Financial Forecast & ROI Estimate

A simplified financial projection including potential implementation costs, revenue models (e.g., SaaS, transaction-based), ROI timeline, and assumptions for market uptake. Includes risk and sensitivity analysis.

Implementation Roadmap

A proposed go-to-market or pilot strategy including timelines, resources needed, risk mitigation steps, and success milestones. May also recommend potential technology or research partners.

Digital Tools & Technology Strategy

A high-level plan for leveraging digital tools (e.g., low-code AI platforms, analytics tools, cloud infrastructure) that could power satisfaction prediction. Also includes ethical and compliance considerations.

Analytics & Metrics Framework

A proposed set of KPIs, validation techniques, and data sources to support ongoing evaluation of satisfaction prediction accuracy and impact on payment outcomes.

Strategic Report (20–25 pages)

A comprehensive, professional-grade final report that compiles all research, analyses, solution designs, and recommendations into a cohesive business case for ArosaPay.

Final Presentation & Q&A Session

A 15-minute visual presentation (e.g., pitch deck) summarizing the opportunity, strategy, and solution, followed by a Q&A with ArosaPay stakeholders. Includes insights into feasibility, potential next steps, and student reflections.