This respiratory dedicates in the marketing analytics projects Jasper and his teammates accompolished back in Fall 2020 at Robert H. Smith Business School, University of Maryland, College Park.
From Project 1 to 5, we extensively used SAS to clean up, sort out, and model with various regressions(Linear Regression Models, Generalized Linear Models, Mixture Regression Models etc.); we used Advanced Excel (Pivot Table, macros, VLOOKUP, etc) and Tableau to showcase our findings to various stakeholders.
Our projects are well-formated that include sections of executive summary, Intro, methodology, key findings, recommendations and it was intented preparing in a level of standard for the company stakeholders.
Topic: Retail pricing decisions based on point-of-sales scanner data
Models Involved: Linear, semi-log, log-log SAS regression models
Software: SAS
Topic: Analyzing print ad designs using eye-movement data
Models Involved: Generalized linear models: SAS Poisson regression, logit model
Software: SAS
Topic: Evaluating the effectiveness of sales promotions based on scanner panel data
Models Involved: Models of incidence, choice and quantity: semi-log regression, logit model, multinomial logit model
Software: SAS
Topic: International market segmentation for global retailers
Models Involved: Mixture Regression Models
Software: GLIMMIX, Tableau
Topic: New product development using choice-based conjoint analysis for coffee makers
Models Involved: Multinomial logit (MNL) models and mixture MNL models
Software: GLIMMIX