CABA and UConn School of Business faculty have been great partners of Cigna over Fall 202. As we embarked on our Technology Operations AIOps journey, UConn students were provided with two problem statements focused around validating if our data had enough predictive power to help improve operational efficiency at Cigna.
Students were provided with de-identified dataset with multiple parameters. It was interesting to observe how each of the 8 teams approached the problem statements in their own unique way and their collective strength achieved the scale of experimentation that would not have been otherwise possible. To be a UConn BAPM Alum and work with the current cohort was a unique experience and is great to see the advances the program has made in the past 5 years.
Lastly, I want to sincerely thank John Wilson and Jennifer Eigo for helping us at each step of this partnership. Will definitely look forward to additional opportunities to partner with the BAPM program.
Shashank Navuduri, Senior Manager
Travelers is a top 10 personal insurance carrier with millions of customers and product offerings spanning auto, property, and specialty insurance. Today, Travelers customers receive several forms of communication, including messages regarding their billing and policy information, as well as product and service offerings. Historically, these forms of communication have been executed and measured in siloed business units.
Students were provided a sample dataset containing customer attributes and outbound communications and were tasked with examining the relationship between communications and customer behavior. They were encouraged to build visualizations and statistical models to find insights, quantify the effect of communication on customer behavior, and create a story based on their recommendations.
The students did a great job of using the sponsor dataset to visualize customer journeys and profile customer attributes. They provided insights around customers’ paperless preferences and electronic communications, characteristics that correlated to customer churn, and communication types with positive engagement. Several teams supplemented their findings and recommendations with external research. The students also did a phenomenal job of balancing detailed analytics and recommendations when presenting their work to both technical and business audiences.
“The UConn capstone gave us an opportunity to work closely with 8 talented teams, each with a different perspective and story to tell. Their recommendations had real world applicability and, in some cases, re-affirmed our internal findings. To be a BAPM alum with a chance to sponsor a capstone was a truly unique and exciting opportunity.”
– Prasanthi Lingamallu, Director, Personal Insurance Marketing Analytics
Pentation Analytics is a Big Data Analytics company that enables insurers and intermediaries to better engage with policy holders. We provide predictive intelligence and process automation tools that address the core insurance use-cases of increasing retention, cross-sales and optimizing claims. We are based out of Hartford, Connecticut and we are highly engaged with UConn’s Center for the Advancement of Business Analytics (CABA).
Pentation Analytics and CABA organized a hackathon focused on customer retention. Customer retention is a global business problem area in which much work is being done, but it is still not enough. The problem statement read “Insurance customer retention insights through dashboards”. Students were given P&C Insurance carrier data, both structured and unstructured. Student turnout was overwhelming and it was very interesting how the teams approached the problem. In addition, seeing data from their eyes was an amazing and enriching experience. In terms of the student experience, we hope inputs provided by our team during presentations will help these talented students to bridge the academic-Industry gap.
The hackathon was very successful and we look forward to many more fruitful endeavors between Pentation and CABA.
-Prahant Roy, Pentation Analytics
Aureus Analytics was born to fulfill the need to do more with your data but with lesser tools. What started as a need to have access to real time insights at the point of decision, has now blossomed into a smorgasbord of highly energetic and restless individuals creating a great product to revolutionize customer experience in Insurance Industry.
After meeting with CABA and learning about the program structure and how CABA engages with industry for the benefit of the students as well as the industry, we were impressed. The endeavor to keep the engagements symbiotic was very visible and absolutely novel. In our CABA Data Challenge, our approach was to engage the students by introducing them to something new and exciting. We built the challenge around a customer experience paradigm and demonstrated our concepts and technology to them in an extensive two phase process. In phase I, the students were exposed to our application and its use via a simulated demo, and a simple quiz was then used to assess and select the suitable candidates for phase II. Phase II was more extensive. Direct access to our technologies and sample data was provided to them to evaluate the customer events and experience paradigm.
The commitment and feedback from the students was phenomenal and encouraging. They understood and critiqued the approach in a very constructive way. They also took the initiative and analyzed the data from a customer perspective. We are looking forward to selecting a few students for an internship to carry forward the engagement that was established with this Data Challenge. We sincerely thank the CABA team of Anna Radziwillowicz, Jennifer Eigo & John Wilson for all their help and support. We look forward to more similar engagements, projects and Data Challenges.
-Nitin Purohit, Co-founder & CTO
LIMRA asked the students to build a model to predict variable annuity cancellations, based on a large data set with dozens of variables. In parallel with our internal efforts, the students adeptly explored the data, built models, developed theories, and presented their recommendations. We used their results to confirm our findings, and their fresh perspective even gave us some new ideas. We are planning to publish our findings this year, in no small part due to the students’ contributions.
Buyer / Non-Buyer Project
LIMRA asked the students to build a model to predict who was likely to buy individual life insurance, based on a consumer survey. Despite having a limited amount of data, the students were able to identify several personal characteristics and life events that make someone more likely to buy. Their work has helped us develop profiles of types of customers who are most likely to buy, which we are planning to publish this year.