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I am Alex Chaudhry. I am working as an Assistant 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

MKTG 6300 Marketing Analytics

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.

Syllabus

Research

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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