Competitive Marketing Analysis

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This course applies data analytics to problems in marketing to better understand customer needs and preferences and to help organizations accomplish their strategic goals and objectives.  Special emphasis will be placed on summarizing marketing data, forecasting new products, pricing strategies, estimating demand, segmenting markets, calculating customer lifetime value, retailing, advertising, and internet and social media marketing.  Students will also learn how to construct models to support decisions in the areas of customer acquisition, engagement, satisfaction, and retention.  Finally, Competitive Marketing Analytics will integrate the fundamentals of business analytics in a marketing context with research design best practices; survey design, execution, and analysis; and qualitative methods including focus groups, case studies, and interviews.  

Topics covered in Competitive Marketing Analytics will include data management, applied descriptive statistics, graphical analyses, dealing with missing data and outliers, dealing with violations of model assumptions, exploratory factor analysis, cluster analysis, discriminant analysis, multiple regression analysis, analysis of variance (ANOVA), multiple analysis of variance (MANOVA), regression with binary dependent variables, and structural equation modeling.

PREREQUISITE:  BUS3310 - Competitive Analysis (or its equivalent).


  • Collecting, storing, cleaning and managing marketing analytics data
  • Synthesizing and applying universal ethical principles to competitive marketing analytics in modern organizations
  • Summarizing marketing data using descriptive statistics and graphical techniques
  • Examining marketing data for missing information, outliers, normality, homoskedasticity, and linearity
  • Specifying marketing models in consideration of data availability and limitations 
  • Exploring the structure of data using principal component, confirmatory, and exploratory factor analysis
  • Constructing marketing models using binary and multiple regression analysis
  • Segmenting markets using hierarchical and nonhierarchical cluster analysis
  • Implementing the marketing research process including constructing a problem statement, performing data analysis, and interpreting results
  • Utilizing a statistical processing software package to collect, organize, and analyze marketing data 
  • Utilizing data obtained from multiple sources including customer surveys, focus groups, case studies, and questionnaires 
  • Applying regression analysis to problems in marketing analytics
  • Applying analysis of variance, multiple analysis of variance, and discriminant analysis to problems in marketing analytics
  • Applying structural equation modeling to problems in marketing analytics
  • Identifying the most appropriate marketing model in consideration of research design, sampling, and measurement issues
  • Integrating quantitative marketing analysis with qualitative techniques including focus group, case studies, and survey analysis best practices 

Course Syllabi

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