Technology & Innovation

Customer acquisition and user experience are key metrics for startups, companies and financial institutions. Data science allows businesses to gain insights from user interactive data, such as unstructured texts from customer service chats and surveys. Two of the Data to Knowledge Lab’s Spring 2021 Capstone teams aim to help businesses gain these insights and ultimately improve customer experience.

D2K Capstone Project - Data Science Meet Emotion

Project #1 - Data Science Meet Emotion: Increasing Empathy in Customer Service Interactions Using Natural Language Processing

This project aims to build a model which predicts a real-time probability that a customer will have a positive experience during live customer support chats. To achieve that, five Rice students named the CS Chats team performed sentiment analysis and topic modeling with the data sets provided by D2K Affiliate member

Project #2 - The Positive Feedback Cycle: Using Customer Feedback to Understand User Pain Points.

The goal of the customer feedback project is to build data science tools to help the product management team at understand what types of users like and dislike the product.

Watch the video highlight of these two capstone projects: CS Chats Team Customer Feedback Team


Read the full story of these two projects >

Payment Acceleration Risk Model for Small Businesses

Team Members: Graham Curtis, Namanh Kapur, Grace Morgan, Daniel Tang, James Warner, Alex Yang

D2K Team is working with to develop a model that determines the risk that transactions sent from client organizations will default. Knowing an organization's risk of default, our model will be able to selectively accelerate the payments of stable organizations, bringing value to client organizations in the form of greatly reduced payment lead times while extending minimal financial risk for


Sponsor Mentor: Han Shi, Eitan Anzenberg, Sangam Singh

D2K Fellow: Jack Wang

Connecting the dots: How the entire financial world is connected

Team Members: Ye Chen, Seth Kimmel, Ankit Narasimhan, Jordan Pflum, Yifan Zhang

Building upon previous work done to model inter-relatedness of future contracts, we seek to understand market conditions under which such inter-relatedness occurs. This gives us insight into how the futures market operates and allows us to build powerful price prediction models using this knowledge.

Sponsor: Belvedere Trading LLC

Sponsor Mentor: Dr. Andrew Wendorff

D2K Fellow: Weilie Nie

Faculty Mentor: Dr. Dan Kowal

Cross-Community Comparison of Fire and Emergency Medical Services

Team Members: Toby Han, Sue Kim, Augi Liebster, Matthew Mutammara, Chris Yum

Our work aims to organize fire departments into cohorts based on demographic/incident factors via statistical clustering methods. This context will allow departments to compare how well they provide services to their communities and explore ways that similar departments have improved their operations and outcomes.

Sponsor: Intterra

Sponsor Mentor: Brian Collins, Molly Hausmann, Jason Posthuma, Amy Ehm

D2K Fellow: Daniel Bourgeois