Rafael Becerril Arreola


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Quantitative Marketer and Operations Researcher
Computer and Automation Engineer
Analytics Professional and Data Scientist

Associate Professor at the University of South Carolina.




I leverage my rigorous training in data analysis, computer engineering, automation, business, and economics to study how social factors (e.g., income, social status) and digital technologies (e.g., marketing mix automation, pricing algorithms) interact in consumer markets. I use this new knowledge to develop theory-informed methods and technologies that balance business peformance and consumer well-being.










Projects

Projects

I study the overall and disparate effects of socioeconomic factors (e.g., income and socioeconomic status) and digital technologies (e.g., algorithms and automation) on consumer behavior (e.g., spending and brand choice), business decisions (e.g., assortment and pricing policies), and market outcomes (e.g., sales and product availability).

My current and recent projects belong to one of the following groups:

Lights

Computer algorithms provide great value to businesses and consumers but can also heavily harm their welfare. Even when programmed to help, conceptual or technical deficiencies can cause algorithms to fail or discretely create disparities among consumer groups.

In a first project, I propose an auditing platform to collect data from online retailers and platforms in a way such that the data can be used to assess both whether the algorithms create disparate impact and also whether any disparate impact is associated with business necessities. The application of the platform is illustrated with the case of a leading online retailer, whose algorithms do appear to create disparities in prices although much of those disparities appear to be consistent with business necessities.

The data for this project are available here together with their data dictionary.

In a second project (work in progress), I separate the decisions made by personalization algorithms from the localization decisions of retailers and explore whether the personalization algorithms induce disparities by themselves. Preliminary results from the analysis of a pricing algorithm suggest that, after accounting for costs, the algorithm maximizes profits for the larger groups of minority consumers but fails to accurately infer the price sensitivities of the smaller minority groups.

Lights

Changes in the distribution of income are reshaping markets, leading consumers to adjust their consumption according to their new levels of purchasing power. This in turns leads manufacturers and retailers of consumer products to change their products and their assortments.

In a first project, my coauthors and I analyzed seven years of sales of consumer packaged goods across 944 product categories and across the U.S. to find that increasing income inequality leads to smaller assortments in the mainstream grocery channel. We investigate three potential demand explanations and find that the effect is mostly the result of the income losses of the middle-income households (consistent with Engel's Law of quantity).

In a second project (work in progress), my coauthor and I investigate the role of Permanent Income in outcomes of relevance to marketers (e.g., market basket size and composition). We find that using permanent income, as opposed to reported income, allows us to uncover income effects that are significantly larger than those previously documented in the literature.

Collaborators:

Lights

An important effect of socioeconomic and technological change on consumer markets happens through social processes. Socioeconomic and technological changes transform the way people communicate and how they interact and this impacts their consumption.

In a first project, derived from my doctoral dissertation, I explore how the prices the different products of a brand (i.e., automobile make) affect the sales of all products of the brand. Among other findings, I show that the price of the entry-level product, if set low, helps the sales of the luxury brands but hurts the sales of non-luxury This is likely because low prices harm the prestige of non-luxury brands but make luxury-brands more affordable, and justifies common concerns in the automobile industry.

In a second study, my coauthor and I quantify the wealth-signaling effects of prices, finding that consumers often counter-signal wealth. That is, it is not uncommon for consumers to prefer inexpensive products to avoid claiming high-social status.

In a third project (work in progress), my coauthor and I propose a method to quantify how much households use different product categories to signal wealth using expenditure data.

Lights

In online markets as well, socioeconomic and technological changes transform what consumers can afford as well as the way people inform their choices.

Service quality Competitive reactions Blockchain

Working papers and preprints available from:

DMSB profile page google scholar profile
ResearchGate page SSRN profile
CV OCRID profile









Contribution

Research

Research

I work on projects that are relevant to businesses, consumer welfare, and policy makers. In particular, my projects lay foundations for the evaluation of managerial decisions, consumer decisions, and public policies.

My goal is to discover paths towards economically-sustainable, business practices that promote and balance business profitability and consumer welfare.

I also work with companies to devise and implement technologies that help them make decisions that are both competitive and socially responsible.









Teaching

Lights

I teach courses on data analytics and data science, emphasizing industry's best practices and a think-first-then-do approach.

"Every battle is won before it is ever fought (Sun Tzu)."

With a solid understanding of data and methods, my students learn to pursue long-term competitive advantage while identifying potential biases and detrimental byproducts of data-based solutions.










Service

Service

Involvement in multiple committees have allowed me to contribute to my workplace and community at multiple levels. Mostly, I serve in initiatives related to data analytics, computing, and governance. I am also keen to support the growth of students at levels ranging high-school to doctoral programs.

More broadly, I serve for multiple journals in Marketing, Operations, Information Systems, and Computer Science.











About

Background



I cultivate the three pillars of data science:

The three pilars of data science

My keen interest in mathematics and computers led me through years of solid training in electrical and computer engineering and automatic control (ITESM, U. of Toronto). At the same time, I discovered the beauty of the social sciences and became an avid learner of economics, psychology, and sociology. The formal training process culminated with a Ph.D. in quantitative marketing (UCLA), with an emphasis in economics and statistics. Yet the learning process never ends.

I combine these diverse skillsets in my research but also in my teaching, to help students become well-rounded analytics professionals / data scientists.

My expertise is in the following areas:

Industries: CPG, retailing, online retailing, online marketplaces, automotive
Functional areas: Pricing, assortments, product lines, last-mile delivery
Technologies: Data analytics, machine learning, optimization, digital automation

My CV is available here. My private LinkedIn profile is available here.











Interests

Books

I grew up building my own toys and programming computers, so I love technology. Yet Understanding human behavior fascinates me too and I enjoy reading about history, psychology, sociology, business, technology, and specially anything that puts them together.

Some books I appreciate:











Contact

Darla Moore School of Business, Room 472

1014 Greene Street, Columbia SC

rafael[dot]becerril[at]moore[dot]sc[dot]edu