By Mia Harder ’22
LMU Master of Science in Business Analytics (MSBA) students applied their newfound knowledge and skills to real-world business challenges as part of their capstone projects. Six student teams presented their research and data analysis in a virtual forum to peers, faculty and representatives from four high-profile corporate partners: computer software giant Adobe; Mexican restaurant chain Qdoba; full-service marketing agency RPA; and Children’s Hospital Los Angeles (CHLA).
The MSBA capstone projects give students an opportunity to provide business analytics solutions to real organizations under the guidance of on-site corporate managers and faculty advisors. The student teams collect real data from the corporate partners and make recommendations based on their analytics findings. Here is a summary of each project:
Adobe – “Through the Social Lens: Improving Adobe’s Marketing Effort Accuracy”
The team focused on improving Adobe’s marketing strategy by conducting in-depth analyses of social media mentions, sales variance and industry patterns. The students used the correlation between Sprinklr data and product sales to reach their recommendations. Sprinklr provides a unified SaaS-platform of products designed to help companies monitor and interact with customers and prospects over social media channels. In this instance, Sprinklr data was used to track the positive and negative mentions of Adobe’s products on social media.
“The student presentation models gave helpful guidance on how Adobe can hit its financial targets and how much the company should invest in marketing,” said Ryan Komagome, director of strategic planning, analytics and market expansion at Adobe. “Their presentation also provided great insights into how we are trending within the industry, especially with the ongoing pandemic.”
Qdoba Team 1 – “Assessing the Performance of Stores in Multi-unit Retailing Systems: An Application of the Synthetic Control Method”
Qdoba Team 2 – “A Comprehensive Model for Retailing Store Survival Analysis: Using Restaurants in L.A. as Example”
Two teams partnered with Qdoba. One team focused on assessing the performance of stores by using the Synthetic Control Method, a statistical method used to account for the variability in a comparative case study. Students used informational inputs, sales and consumer demographics to get a benchmark performance for the store in question. The second team created a survival model that evaluated specific features that can affect a restaurant’s success. Both teams provided impactful insights and solutions to enhance Qdoba’s business ventures.
RPA – “On the Right Path: From Retail Foot Traffic Data to Business Findings”
The team analyzed a large dynamic geolocation dataset based upon foot tracking technology consisting of billions of observations. Students analyzed these Big Data utilizing Google BigQuery, SQL and Tableau to better market CKE Restaurants. Controlling for competition in the market, they explored geographic trends for various designated market areas (DMA), to identify locations where advertising was likely to have the greatest return. Seasonal trends were also analyzed and the team made recommendations for best days of the week to optimize marketing efforts.
CHLA Team 1 – “Building a predictive model for patient appointment cancelations and no-shows for the ambulatory clinics of CHLA”
CHLA Team 2 – “A predictive scheduling and resource management solution using simulation for one of the ambulatory clinics of CHLA”
Two teams partnered with Children’s Hospital Los Angeles. One team focused on building a predictive model for patient appointments and cancellations. Their algorithmic model was sufficient in identifying whether the patient would attend an appointment, benefiting the hospital to have more available time slots for patients and optimizing work schedules for staff. The second team concentrated on predictive scheduling and resource management using a simulation for one of the ambulatory clinics. Their model was useful as it optimized staffing levels, lowering wait times and reducing labor costs. The team was able to successfully lower labor costs without sacrificing patient services.
“The six teams did an excellent job of demonstrating their passion and talents to generate innovative business solutions through advanced analytics,” said Sijun Wang, marketing professor and co-program director of the MSBA program. “On behalf of the MSBA faculty and advisory board, we could not be more proud of them!”
