Business Analytics Commons^™

Escaping The Promotion Trap: A Machine Learning Framework For Brand Equity Preservation In Beverage Cpg, Lucas P. Jones

Data Science Undergraduate Honors Theses

When companies acquire beverage brands, they typically value them based on total sales revenue. This traditional approach treats all sales equally over time, whether they are driven by genuine consumer demand or temporary discounts. This is important because while promotions can boost short-term sales, they tend to erode brand value over long periods of time. The measurement problem extends to acquisitions, where buyers lack the tools to distinguish real consumer demand from artificial promotional inflation.

This thesis develops a framework to separate genuine baseline demand from promotional dependence using Nielsen scanner data covering 189 beverage brands across 188,304 weekly observations …

Face Presence In User-Generated Photos And Its Effect On Review Helpfulness, Anh Dang, Bridget Nichols, Mark Nichols

Atlantic Marketing Association Proceedings

No abstract provided.

Leveraging Accounting Analytics To Enhance Payroll Accuracy And Fraud Detection In U.S. Public Sector Institutions: A Case Study Approach, Regina Debrah

Beacom School of Business Student Publications

This study explores how accounting analytics can be leveraged to enhance payroll accuracy and improve fraud detection in U.S. public-sector institutions, addressing persistent irregularities amid rising demands for fiscal transparency. The research employs a qualitative design with secondary sources including academic literature, reports, and case studies. The literature identifies successful analytics implementation, such as Treasury OPI’s machine learning for data integration for unemployment claims. These precedents demonstrate direct transferability to payroll’s high volume and rules-based structure. Findings show that analytics significantly reduce improper payments through real-time screening, data integration, and risk prioritization when embedded in workflows. The findings also show …

Predicting House Prices Using Machine Learning, Pejal Rath, Robert Slater

SMU Data Science Review

This study explores the use of Machine Learning methods to predict housing prices, a problem of significant interest in real estate economics, and data science. Traditional hedonic pricing models have long been used to evaluate the impact of housing attributes on property values, but they often lack the flexibility to capture nonlinear relationships and complex feature interactions. Recent advancements in regression based and Machine Learning approaches provide promising alternatives that may improve prediction of accuracy and market insights. This research will investigate how models such as linear regression, random forest, and gradient boosting can be applied to publicly available housing …

Ai-Driven Risk Forecasting For Strengthening The United States Food Supply Chain Resilience: Case Study: A National Ai-Enabled Food Supply Chain Risk Forecasting Framework And A Data Analytics Approach To Predicting Disruptions, Sarah Onyeche Usoro, Itua Austin Omogiate, Rahab Zhewe Felix, David Azikutenyi Galadima

Student Publications

The United States food supply chain is one of the most complex and interconnected systems in the world, spanning agricultural production, processing, transportation, storage, and retail distribution. While this complexity enables efficiency and scale, it also increases vulnerability to disruptions caused by climate change, labor shortages, geopolitical shocks, transportation failures, cyber threats, and public health crises. Conventional risk management methods, often reactive and siloed, have proven inadequate in predicting and mitigating systemic shocks that have become frequent occurrences in today’s world. This article examines how artificial intelligence (AI) and data analytics can transform risk forecasting in U.S. food supply chains …

Forecasting Aviation Carbon Emissions With Tree-Based Machine Learning: A Case Study Of Turkish Airlines Operational Data, Demet Dağlı Phd, Ednan Ayvaz Phd

International Journal of Aviation, Aeronautics, and Aerospace

The global aviation industry plays a critical role in economic development and international connectivity. However, it also contributes significantly to environmental challenges, particularly through fuel consumption and carbon emissions. The aviation sector is responsible for approximately 1–2% of global CO₂ emissions, and its environmental impact is expected to increase substantially by 2050. This study proposes a machine learning (ML)-based decision support framework to forecast carbon emissions in the airline sector. Using Turkish Airlines’ quarterly operational data from 2008 to 2023, Decision Trees, Random Forest, XGBoost, and Extra Trees models were applied to predict carbon emissions. Key variables include revenue passenger …

Exploring The Interplay Between Economic Growth And Sustainable Development: A Complex Systems Approach To Gsdp And Sdgs In Indian States, Rosewine Joy, Helen Josephine, Divya D, Midhun Raj

Northeast Journal of Complex Systems (NEJCS)

Pursuing Sustainable Development Goals (SDGs) necessitates aligning business and management practices on a global scale. This paper delves into the intricate dynamics between Gross State Domestic Product (GSDP) and SDGs across diverse states in India, offering nuanced insights to policymakers, businesses, and stakeholders. This paper explores the dynamic relationship between Gross State Domestic Product (GSDP) and the Sustainable Development Goals (SDGs) in the context of India's diverse states by applying modern machine learning techniques such as XG boost, Decision trees, and K mean clustering. The study delves into how economic growth influences the progress towards SDGs. The research integrates complex …

Understanding The Incidents On Legacy Airlines With Explainable Artificial Intelligence And Text Mining: Case Study Top 5 Us Airlines, Dean Sutherland, Mehmet Burak Cankaya

Beyond: Undergraduate Research Journal

Aircraft incident data from the Federal Aviation Administration between 1978-2023 was analyzed for the top five airlines in the United States: Delta Air Lines Inc., American Airlines Inc., Alaska Airlines Inc., Southwest Airlines Co., and United Airlines Inc. This analysis aimed to predict conditions with a higher chance of causing injury and determine the best machine-learning model for this prediction. The target variable of "injuries occurred" is processed through machine learning methods such as Naïve Bayes, logistic regression, and XGBoost. This was to predict the injuries and understand and explain the complex fault mechanism with explainable artificial intelligence. The XGBoost …

Enhancing Online Education Through Sentiment Analysis And Complex Systems Modelling, Helen Josephine V L, Sharad Gupta, Rosewine Joy, Manjari Sharma

Northeast Journal of Complex Systems (NEJCS)

This research explores the application of sentiment analysis through the lens of complex systems modelling to enhance the quality of online certification courses, with a particular focus on global platforms such as Coursera. The COVID-19 pandemic catalyzed significant growth in online learning, creating an urgent need for adaptive and student-centric approaches to ensure relevance and effectiveness. Leveraging unstructured textual data from student reviews of courses, this study integrates methodologies from systems science, computer science, and education to address real-world challenges in online education. By employing both lexicon-based (SentiWordNet and VADER) and supervised machine learning techniques (Multinomial Naive Bayes, Support Vector …

Mortgage Default Classification Modeling For Variable Analysis, Brendan R. Goggins

Honors College Theses

The financial crisis of the early 2000’s is a prime example of the severe consequences that mortgage default and borrower insolvency can have on economies at large. Mortgage default specifically is a prime case with the popularization of mortgage backed securities and the commonality of this loan structure. Multiple hypotheses and models have been formed to understand the reasons, causes, and consequences of mortgage default. This paper uses both machine learning and statistical classification models to inform an understanding of the variables most significant and impactful to the default outcome of mortgages. Consideration is given to both loan-level microeconomic variables …

Spatiotemporal Negative Inventory Outlier Decomposition For Supply Chain Applications In Consumer-Packaged Goods (Cpg), Hayden Mcdonald

Data Science Undergraduate Honors Theses

Coca-Cola is a popular soft drink brand with sales occurring in every Walmart store across the world, which generates large quantities of data and requires a robust supply chain system. However, the company does not currently have a sophisticated, automated, and/or prescriptive system for detecting where, when, and why inventory outages occur and applying preventative measures to avoid loss of revenue from the absence of inventory on store shelves. This thesis proposes and applies a novel, prescriptive system for this purpose. An inventory outage can be seen as a ‘negative’ statistical outlier in a time series of inventory for an …

A Review On Machine Learning Methods For Customer Churn Prediction And Recommendations For Business Practitioners, Awais Manzoor, M. Atif Qureshi, Etain Kidney, Luca Longo

Articles

Due to market deregulation and globalisation, competitive environments in various sectors continuously evolve, leading to increased customer churn. Effectively anticipating and mitigating customer churn is vital for businesses to retain their customer base and sustain business growth. This research scrutinizes 212 published articles from 2015 to 2023, delving into customer churn prediction using machine learning methods. Distinctive in its scope, this work covers key stages of churn prediction models comprehensively, contrary to published reviews, which focus on some aspects of churn prediction, such as model development, feature engineering and model evaluation using traditional machine learning-based evaluation metrics. The review emphasises …

Improving Credit Card Fraud Detection Using Transfer Learning And Data Resampling Techniques, Charmaine Eunice Mena Vinarta

Electronic Theses, Projects, and Dissertations

This Culminating Experience Project explores the use of machine learning algorithms to detect credit card fraud. The research questions are: Q1. What cross-domain techniques developed in other domains can be effectively adapted and applied to mitigate or eliminate credit card fraud, and how do these techniques compare in terms of fraud detection accuracy and efficiency? Q2. To what extent do synthetic data generation methods effectively mitigate the challenges posed by imbalanced datasets in credit card fraud detection, and how do these methods impact classification performance? Q3. To what extent can the combination of transfer learning and innovative data resampling techniques …

Accounting And Financial Statements Auto Analysis System, Zhen Jia

Electronic Theses, Projects, and Dissertations

This project was motivated by the need to revolutionize the generation of financial statements and financial analysis process thus speeding up business decision making. The research questions were: 1) How can machine learning increase the speed of financial statement preparation and automate financial statements analysis? 2) How can businesses balance the benefits of automating financial analysis with potential concerns around privacy, data security, and bias? 3) Can the Java J2EE framework provide a reliable running environment for machine learning?

The findings were: 1) Machine learning can significantly increase the accuracy and speed of financial analysis. Using machine learning algorithms, financial …

A Machine Learning Approach To Revenue Generation Within The Professional Hair Care Industry, Alexander K. Sepenu, Linda Eliasen

SMU Data Science Review

The cosmetic and beauty industry continues to grow and evolve to satisfy its patrons. In the United States, the industry is heavily science-driven, innovative, and fast-paced, suggesting that to remain productive and profitable, companies must seek smart alternatives to their current modus operandi or risk losing out on this multi-billion-dollar industry to fierce competition. In this paper, the authors seek to utilize machine learning models such as clustering and regression to improve the efficiency of current sales and customer segmentation models to help HairCo (pseudonym for confidentiality), a professional hair products manufacturer, strategize their marketing and sales efforts for revenue …

Stock Market Manipulation Detection Using Continuous Wavelet Transform & Machine Learning Classification, Sarah Youssef

Theses and Dissertations

Stock market manipulation detection is important for both investors and regulators. Being able to detect stock manipulation and preventing it gives investors the confidence in the market fairness and integrity. It also helps maintaining liquidity of the stocks and market efficiency. Implementing data mining algorithms in manipulation detection is a relatively recent technique but in the past few years there has been an increasing interest in it's applications in this domain. The benefit of monitoring manipulative trade behavior is that it can be implemented on live feed of stock data, which saves a lot of time in detecting stock price …

Integrating Systems, Processes, And Human Judgment: Three Essays On Value Creation With Supply Chain Analytics, Rebekah Inez Brau

Graduate Theses and Dissertations

Big-data, analytics, automation, and machine learning are changing the role of managers in supply chain and operations functions. Extant research indicates that effective value creation by analytics is achieved through careful attention to three components: technology, people, and processes. As such, the purpose of my dissertation is to improve the integration of people in current supply chain and operations management systems with ever-changing technologies and processes. I focus on one critical function—demand planning—since it is increasingly influenced by predictive analytics yet can still require human judgment. My dissertation is comprised of three essays as follow.

In Essay 1, I implement …

Towards High Performance Stock Market Prediction Methods, Warren M. Landis, Sangwhan Cha

Other Student Works

Stock markets of today, and will continue to in the future, rely on the metrics of timeliness and efficiency to reach optimal profits. A way stock investors have continued to strive for the best of these two factors of the business is through the use of predictive machine learning systems to help aid in their decision making. However, among the many systems currently in use, it could be said that the myriad of data that they are based on may not be sufficient. In an effort to devise an ensemble learning predictive system that will utilize an array of big …

A Predictive Analytics Model For Differentiating Between Transient Ischemic Attacks (Tia) And Its Mimics, Alia C. Stanciu, Mihai Banciu, Alireza Sadighi, Kyle A. Marshall, Neil Holland, Vida Abedi, Ramin Zand

Faculty Journal Articles

Transient ischemic attack (TIA) is a brief episode of neurological dysfunction resulting from cerebral ischemia not associated with permanent cerebral infarction. TIA is associated with high diagnostic errors because of the subjective nature of findings and the lack of clinical and imaging biomarkers. The goal of this study was to design and evaluate a novel multinomial classification model, based on a combination of feature selection mechanisms coupled with logistic regression, to predict the likelihood of TIA, TIA mimics, and minor stroke.

Three Essays On Health Economics And Policy Evaluation, Shishir Shakya

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation consists of three essays on the U.S. Health care policy. Each paragraph below refers to the three abstracts for the three chapters in this dissertation, respectively. I provide quantitative evidence on how much Prescription Drug Monitoring Programs (PDMPs) affects the retail opioid prescribing behaviors. Using the American Community Survey (ACS), I retrieve county-level high dimensional panel data set from 2010 to 2017. I employ three separate identification strategies: difference-in-difference, double selection post-LASSO, and spatial difference-in-difference. I compare how the retail opioid prescribing behaviors of counties, that are mandatory for prescribers to check the PDMP before prescribing controlled substances …

Machine Learning With Multi-Class Regression And Neural Networks: Analysis And Visualization Of Crime Data In Seattle, Erkin David George

Honors Projects

This article examines the implications of machine learning algorithms and models, and the significance of their construction when investigating criminal data. It uses machine learning models and tools to store, clean and analyze data that is fed into a machine learning model. This model is then compared to another model to test for accuracy, biases and patterns that are detected in between the experiments. The data was collected from data.seattle.gov and was published by the City of Seattle Data Portal and was accessed on September 17, 2018. This research will be looking into how machine learning models can be used …

Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater

SMU Data Science Review

The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide best …

Digital Forensic Tools & Cloud-Based Machine Learning For Analyzing Crime Data, Majeed Kayode Raji

College of Graduate Studies: Theses & Dissertations

Digital forensics is a branch of forensic science in which we can recreate past events using forensic tools for legal measure. Also, the increase in the availability of mobile devices has led to their use in criminal activities. Moreover, the rate at which data is being generated has been on the increase which has led to big data problems. With cloud computing, data can now be stored, processed and analyzed as they are generated. This thesis documents consists of three studies related to data analysis. The first study involves analyzing data from an android smartphone while making a comparison between …