Guide to data analytics university
collaborations
Spring Theory pairs companies with universities in semester-long projects that solve big challenges.
Project Stories
Explore our most impactful data science case studies with top universities.
Revolutionizing Lead Conversation Strategy with AI
In a strategic collaboration with Carnegie Mellon University students via Spring Theory, Xometry, an on-demand manufacturing marketplace, successfully transformed a wave of unqualified leads into valuable customer insights. The students, part of a world-leading analytics course, designed an AI-driven lead scoring model to predict potential long-term customers. This short-term project not only led to more efficient sales processes and improved lead conversion rates, but also equipped Xometry with a new, potent tool for its sales pipeline optimization.
"Going into the project we had no idea what we would end up with. We presented students with a problem and supporting data but not the solution. Ten weeks later Xometry had a working model for classifying leads that is now integrated into our system. This outcome was well above expectation." - Michael Dickson, VP, Corporate Development at Xometry,
Predicting e-commerce success with language models and metadata
Pattern collaborated with students of Harvard University’s Institute for Applied Computational Science (IACS) to convert client metadata into priceless business insights. Through rigorous trial and testing, teams developed language models to predict an e-commerce business’s chances of success, equipping Pattern with a new tool to step change their client offerings.
Optimizing Customer Experiences with Data Analytics
Prudential, a financial services company specializing in life insurance and retirement strategies, teamed up with a group of Duke University Masters of Quantitative Management students o develop data-driven solutions for customer experience optimization. The students employed Python, SQL, and Tableau to analyze Prudential's extensive data, including customer survey responses and demographic information, creating a regression model to identify key drivers of customer experience and financial outcomes. This collaboration led to a reusable regression model and a Tableau dashboard that guides Prudential's decision-making and investment strategy, ultimately improving customer experience and driving increased revenue.
Collaboration Goals
Key details on our program, process, and past projects.
Collaborate with a leading university on an customized data project
Graduate students utilize advanced techniques such as gradient boosting, neural networks, and other ensemble methods to develop predictive algorithms. These algorithms are designed to handle a variety of data types, consider potential interactions and non-linear relationships, and manage missing or unstructured data, enabling them to anticipate complex business scenarios effectively.
Students employ natural language processing (NLP) techniques and machine learning to analyze customer sentiments. Advanced techniques like sentiment scoring, topic modeling, and word embeddings are used to dissect large volumes of text data, and interpret the underlying sentiments, thereby facilitating deeper insights into customer attitudes and preferences.
Students develop optimization models using methods such as linear programming, integer programming, and stochastic optimization. These models provide decision-makers with optimal solutions considering resource constraints, aiding in areas like supply chain management, scheduling, and strategic planning.
Employing tools like Tableau, PowerBI, and D3.js, students create dynamic, interactive dashboards and complex visualizations. By effectively communicating multivariate analyses and high-dimensional data, these dashboards enable companies to better understand their data, thus enhancing decision-making.
Leverage student and faculty talent to drive an initiative forward that may be on the backburner
Establish a relationship with top students and university programs through an organized, focused, 3-month project with clear deliverables
Projects are embedded within a graduate-level program in the form of a class project or a final degree project and supervised by a faculty member. Depending on the program, you will have a team of 3-4 students that work on your project for 10-20 hours per week per student for 10-15 weeks.
How This Works
Project Scope and Deliverables
The project is proposed by the sponsoring company, either to Spring Theory to be matched with any matching course, or to fit within a specific opportunity. Typically, companies will have a high level idea of the project goals and will either shape that into a project brief prior to speaking with the professor that is matched, or they may discuss a high-level idea with the professor and based on their feedback develop a brief.
You can find an example brief and a template here
Project Scope and Deliverables
The project is proposed by the sponsoring company, either to Spring Theory to be matched with any matching course, or to fit within a specific opportunity. Typically, companies will have a high level idea of the project goals and will either shape that into a project brief prior to speaking with the professor that is matched, or they may discuss a high-level idea with the professor and based on their feedback develop a brief.
You can find an example brief and a template here
Project Scope and Deliverables
The project is proposed by the sponsoring company, either to Spring Theory to be matched with any matching course, or to fit within a specific opportunity. Typically, companies will have a high level idea of the project goals and will either shape that into a project brief prior to speaking with the professor that is matched, or they may discuss a high-level idea with the professor and based on their feedback develop a brief.
You can find an example brief and a template here
Sumo Logic: Retaining Revenue with Predictive Analytics
Innovative data utilization to predict, prevent customer churn and improve retention
Revolutionizing Revenue Retention
A collaborative endeavor to predict and mitigate customer churn with data analytics.
The ubiquitous cloud-based company, Sumo Logic, collaborated with a team of university data science students to navigate the pressing issue of revenue retention. With their combined skills and resources, they aimed to predict customer churn, enhance customer experience, and ultimately protect millions of dollars in potential revenue loss. Their joint vision: to utilize Sumo Logic's trove of customer usage data to formulate predictive models, which in turn, could provide early warnings of customer attrition and inform strategies to counteract it.
Increasing Revenue, Enhancing Customer Experience
An interdisciplinary exploration into Sumo Logic's data sources to optimize sales and account management.
Sumo Logic's strategy hinged on tapping into their own product data, along with Salesforce's sales tracking data, to offer comprehensive insights into customer behavior. By analyzing and cross-referencing these data sources, the team sought to identify behavioral patterns indicating growth or potential cancellations. A crucial part of this project was the process of transforming large-scale data into actionable strategies, a task both the company and university team approached with the shared goal of enriching Sumo Logic's customer retention capabilities.
Framing the project vision
Utilizing predictive analytics to drive customer retention and ensure business growth.
The outcome of the collaboration surpassed Sumo Logic's expectations. The predictive models developed during the project allowed the company to effectively identify at-risk accounts and enabled timely interventions. This, coupled with the implementation of customer health metrics and dashboards for monitoring, significantly improved the company's ability to retain customers. Sumo Logic's VP of Business Operations, Ben Kwon, praised the effort, stating, "This project was more than an academic endeavor; it was a strategic move that safeguarded substantial revenue, proving the power of predictive analytics in driving customer retention and business growth."
