Overview of Today’s Discussion Topics Case Discussion: Walmart Labs Acquisition of Inkiru

Overview of Today’s Discussion Topics • Case Discussion: Walmart Labs Acquisition of Inkiru • Market Segmentation (Additional Insights) • Market Segmentation Case Discussion: Back to School Lunch boxes and backpacks • Market Segmentation Case Discussion: Honda • Cluster Analysis vs. Factor Analysis • FA example vs. CA example on motivation data • Product/Brand Positioning and Firm Strategy • Case Discussions • Simplified Hierarchical Values Map Modeling of Data through Partial Correlation Analysis

Segmentation vs. Data Reduction Dr. Jared Hansen UNC Charlotte

Case Discussion: Walmart Labs Acquisition of Inkiru • Market Segmentation (Additional Insights) • Market Segmentation Case Discussion: Back to School Lunch boxes and backpacks • Market Segmentation Case Discussion: Honda • Cluster Analysis vs. Factor Analysis • FA example vs. CA example on motivation data • Product/Brand Positioning and Firm Strategy • Case Discussions • Simplified Hierarchical Values Map Modeling of Data through Partial Correlation Analysis

Discussion of Walmart Labs Acquisition of Inkiru • http://techcrunch.com/2013/06/10/walmart-labs-buys-data-analytics-and-predictive-intelligence-startup-inkiru/

Market Segment Targeting Criteria Targeted Segments Must Be: • Identifiable • Reachable • Sizeable And Should Be: • Profitable

Case Discussion How Should They Segment the Market? • Using What Type of Data? • Collected from Whom (and how)? • And what tools should they use to mathematically create the segments?

Case Discussion

Case Discussion: Honda Segmentation • http://www.youtube.com/watch?feature=player_detailpage&v=w4BcgS85U14 • What market segment are they targeting? • Is this a good idea? (What are the Pros and Cons) • What type of data should they use to identify this segment?

Factor Analysis • Goal: more accurate measurement of unobserved (latent) concepts like loyalty, satisfaction, motivation, etc., for which we like to use multiple scale items to improve accuracy of measurement. • Statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. • Factor analysis searches for such joint variations in response to unobserved latent variables. • The observed variables are modelled as linear combinations of the potential factors, plus "error" terms. • The information gained about the interdependencies between observed variables can be used later to reduce the set of variables in a dataset. • Factor analysis originated in psychometrics, and is used in behavioral sciences, social sciences, marketing, product management, operations research, and other applied sciences that deal with large quantities of data.

Reflective vs. Formative Structural Equation Modeling of Latent Factors

Overview of Today’s Discussion Topics Case Discussion: Walmart Labs Acquisition of Inkiru