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I am Alex Chaudhry and work as an Associate Professor of Business Analytics at Southern Utah University. My PhD in Marketing is from Rice University. Some of my research interests are:

  • consumer choice dynamics
  • causal inference with big data
  • social science applications of machine learning

Are managers selecting optimal one-to-one customized promotional campaigns? Should managers respond to online reviews? How do social influences alter consumer opinions? How do video advertising dynamics affect consumer product expectations and firm profits? These are some of the questions I address in my research.

I have found that my personality and temperament forces me to learn constantly by studying, researching and collaborating with my peers. I take my love for learning to another level when teaching by engaging the curious minds of my students to find their own passion in the world of analytics.

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Teaching

ANLY 6510 Generative AI System

Syllabus

Course Description & Objectives

This is a seven-week graduate course in the Master of Science in Business Analytics (MSBA) program. It builds practical, job-ready generative-AI skills that students can apply immediately in any analytics or business role. The course follows the seven topics of the Google AI Professional Certificate: AI Fundamentals, Brainstorming and Planning, Research and Insights, Writing and Communicating, Content Creation, Data Analysis, and App Building.

Learning Outcomes

• Explain how generative AI and large language models work, construct effective structured prompts, and judge where AI collaboration adds value versus where human judgement is essential.

• Apply AI across core business workflows – research and document intelligence, data analysis, and written and visual communication – to produce professional, decision-ready deliverables.

• Design, build, and deploy an AI-powered tool or workflow that solves a real business problem.

ANLY 6100 Data Analytics I

Syllabus

Course Description & Objectives

This course is an introduction to the use of secondary data in business settings. This course will provide you a general introduction to common statistical and data analysis skills. The skills and knowledge you will pick up along the way will be valuable in our increasingly data-driven economy. The main topics of the course can be organized as follows:

Analytical topics

• Summarizing data and descriptive statistics

• Regression analysis for continuous dependent variables

• Clustering Methods

Technical topics

• Fundamentals of programming in Python

• Use of open source data analysis libraries including Pandas, Numpy, Statsmodels, Sklearn, etc.

• Use of the notebook environment, the industry standard mode of producing replicable data analysis.

• GitHub – for building a public project portfolio at the end of the semester.

Learning Objectives

• Gain an appreciation of how data can inform business decisions.

• Learn how to communicate stories with data effectively using the industry standard medium for data analytics: Jupyter Notebooks

• Build a foundation for programming for data analytics using Python.

• Understand fundamentals of how to work with real world data.

• Be able to conceptualize business questions in terms of relationships in data.

• Be able to apply regression analysis to demonstrate relationships between continuous outcomes and their determinants.

• Be able to apply classification analysis to demonstrate relationships between discrete outcomes and their determinants.

• Be able to apply clustering algorithms for segmentation.

ANLY 6110 Data Analytics II

Syllabus

Course Description & Objectives

This course is an introduction to the use of secondary data in business settings. This course will provide you a general introduction to common statistical and data analysis skills. The skills and knowledge you will pick up along the way will be valuable in our increasingly data-driven economy. The main topics of the course can be organized as follows:

Analytical topics

• Logit analysis for discrete dependent variables

• Regression analysis for nonlinear and heterogeneous relationships

• Natural language processing

• Predictive Analytics

• Data Acquisition (Web scraping & API access)

Technical topics

• Fundamentals of programming in Python

• Use of open source data analysis libraries including Spacy, Gensim, pyLDAvis, t-SNE, Scipy, etc.

• Use of the notebook environment, the industry standard mode of producing replicable data analysis.

Learning Objectives

• Gain an appreciation of how data can inform business decisions.

• Learn how to communicate stories with data effectively using the industry standard medium for data analytics: Jupyter Notebooks

• Build a foundation for programming for data analytics using Python.

• Understand fundamentals of how to work with real world data.

• Be able to conceptualize business questions in terms of relationships in data.

• Be able to apply regression analysis to demonstrate relationships between continuous outcomes and their determinants.

• Understand what heterogeneous effects are and why they might be important.

• Be able to run and interpret a regression with interactions of discrete and continuous variables

• Be able to apply Natural Language Processing techniques.

• Be able to apply collaborative filtering techniques.

• Be able to extract data from websites.

• Be able to acquire data via website APIs.

ANLY 6400 Big Data for Business

Syllabus

Course Description & Objectives

This course is an introduction to the use of cloud computing services in business settings. This course will provide you a general introduction to common big data frameworks for both storage and analysis. The skills and knowledge you will pick up along the way will be valuable in our increasingly data-driven economy. The main topics of the course can be organized as follows:

Learning Objectives

• Gain an appreciation of how data can inform business decisions.

• Understand fundamentals of how to work with real world data.

• Become familiar with cloud computing services.

• Familiarity with data mining and machine learning techniques.

• Perform large-scale model training, analysis and reporting.

ANLY 6900 MSBA Capstone

Syllabus

Course Description:

With faculty input and supervision, students carry out a complex analytics project that applies methodologies from their coursework to enhance the performance of a business.

Course Objectives:

Upon completion of this course, students will have a complete end-to-end business analytics project and report. In addition, students will complete an assurance of learning measurement for the MSBA program.

MKTG 6300 Marketing Analytics

Syllabus

In this course, implement state-of-the-art data manipulation techniques leading to text classification, and apply analytics to develop business strategies. We will cover the following quantitative methods: regression analysis, demand modeling, choice modeling, conjoint analysis and machine learning.

Research

Community Gifting and Recipients’ Engagement with Peers and Influencers: Evidence from Natural Experiments on Twitch

with Yang Wang and Erya Ouyang


Community gifting, the phenomenon of donating digital goods to peers without selecting specific recipients, is one of live streaming’s key social technologies for engaging online communities. In this study, we investigate the causal relationship between receiving a community gift and the recipient’s subsequent social and monetary engagement behaviors by exploiting the randomization of recipient assignment on the popular live streaming platform, Twitch. We find that, relative to nonrecipients within a five-minute window of a community gifting event, community gift recipients exhibit a 69% chance of directing an additional message toward their peers, a 35% chance of directing an additional message toward the streamer and a 5% chance of gifting an incremental subscription to the community. However, recipients are no more likely to tip the streamer. We apply computational linguistics methods to illustrate that recipients’ increased social engagement is accompanied by elevated sentiment and an increased likelihood of joining existing conversations. Finally, we conduct a series of moderator analyses, and find that recipients’ gifting behavior is less frequent when there are more gifting events prior to the focal community gift, i.e., allowing the recipient to hide. Moreover, the social engagement effect of receiving a community gift is greater when prior chatter is more voluminous and discontinuous, i.e., when it is easier for the recipient to jump into the chat. We conclude that the spillovers to social engagement are more important than those to financial reciprocity given the positive feedback loop implied by our moderator analysis. Our results reveal how and when community gifting impacts audience engagement.

Chaudhry, A., Wang. Y., Ouyang, E. (2024), “Community Gifting and Recipients’ Engagement with Peers and Influencers: Evidence from Natural Experiments on Twitch,” Journal of Interactive Marketing. (Article)

When and how Managers’ Responses to Online Reviews Affect Subsequent Reviews

with Yang Wang

In this study, the authors investigate the externalities of managers’ responses (MRs) to online reviews on popular travel websites. Specifically, the authors examine the effect of publicly responding to hotel guests’ reviews on subsequent reviewer ratings. The authors find that manager responses to negative reviews (MR-N) can significantly influence subsequent opinion in a positive way if those responses are observable at the time of reviewing. Notably, the findings show this externality to be negative for manager responses to positive reviews (MR-P). The authors conduct a topic analysis on review texts and corresponding MRs to study the moderating role of response tailoring on the opinion externalities of MR. The authors show that tailored MR amplifies the positive (negative) impact of MR-N (MR-P) on subsequent opinion. Intuitively, tailoring an MR-N adds specificity to the hotel’s complaint management strategy, bolstering the positive effects of MR-N on subsequent opinion. However, by highlighting specific positive elements of a review, managers’ intent for responding is brought into question as they take advantage of reviewers’ positive feedback to promote their hotel.

Yang Wang and Alexander Chaudhry (2018) When and How Managers’ Responses to Online Reviews Affect Subsequent Reviews. Journal of Marketing Research: April 2018, Vol. 55, No. 2, pp. 163-177.

Do online reviews improve product quality? Evidence from hotel reviews on travel sites.

with Yang Wang and Amit Pazgal

In this study, we use a game theoretic model to argue that the presence of online reviews can lead to product quality improvements for independent firms selling experience goods. Exploiting heterogeneous review plat- form penetration across markets, we test the predictions of our model using a dataset covering 40 thousand U.S. hotels and show that markets with greater platform penetration exhibit greater gains in independent hotel quality. Independent hotels located in median peak penetration platform-defined markets improved their quality by an average of .129 stars as measured using composite online travel agent (OTA) star ratings, eroding 41% of the advantage held by chains in the absence of online reviews. We address measurement noise challenges for quality and platform penetration using state space models to reveal persistent quality and platform penetration trends. Additionally, we resolve endogeneity due to potential unobserved confounds correlated with penetration and quality across markets and time. We do so by exploiting review platforms’ imperfect market definitions that divide areas of hotel agglomeration into separate review platform markets, thus quasi-exogenously assigning hotels in the same area to varying levels of online review exposure. Our research suggests that online reviews play an important role in facilitating competition on quality.

Paper    Appendix

Measuring the Effects of Customized Targeted Promotions on Retailer Profits: Prescriptive Analytics Using Basket-Level Econometric Analysis

with Carrie Heilman and Seethu Seetharaman

This study empirically estimates the expected basket-level demand effects, as well as the expected store profit effects, of three different types of retailer targeted promotions varying in their customization level (high, medium, low customization). Using data from a national grocery store retailer that targets households with different types of promotions, we build and estimate an econometric model of a household’s contemporaneous purchase incidence outcomes in 28 frequently shopped product categories. Estimating the cross-category dependencies in purchase incidence as a function of exposure to different levels of customized promotions, allows us to measure the effect of each campaign type on expected retailer profit and implement prescriptive analytics to identify the appropriate multi-level coupon mix for maximizing store profits. The findings reveal that all three levels of coupon customization result in per-customer returns, but that medium customization leads to the highest incremental expected profit, while high customization generates the highest expected profit. The results provide insights to retailers about the values of investing in more customized promotional efforts, with a detailed cross- category perspective into where such value is gained.

Paper    Appendix

Geodemographic Drivers of Store-Level Demand and Marketing Mix Sensitivities

with Seethu Seetharaman

To provide visual perspective on the extent of geographic dispersion in the estimated price sensitivities across stores, we use different shades of red (darker shade denoting greater price sensitivity) for different stores in the Houston metropolitan area. We also show the degree of dispersion across all stores on the national map of the United States.

Using different symbols for different store formats (hexagons for food stores, squares for mass merchandisers, triangles for drug stores), we also report the geographic dispersion separately for the three store formats. Using data visualizations such as these, retail chain managers can better understand and rationalize the incentives for store-level pricing over zonal pricing in the three categories under study. City planners, on the basis of the revealed differences in estimated marketing mix sensitivities across different city neighborhoods, can better understand the real-world implications of approving zoning permits for retail stores of different store formats at different locations in the city. For example, there could be different tax revenue implications, on account of differential price sensitivities of consumers for different store formats, for approving a zonal permit for a food store versus a mass merchandiser in a given zip code.

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