Enhancing Consumer Behavior Using Visual Cues and Sonic Branding Theodore Hayduk III, Doctoral Student, Texas A&M University Matthew Walker, PhD, Texas A&M University Assessing Consumer Behavior in Real Time Path Diagram Abstract Sonic Branding is rapidly becoming an area of interest for researchers and practitioners alike. Historically, the sport branding process has focused on well-established visual techniques designed to convey the personality and ethos of an organization (Bartholme, 1996). More recent research builds on that idea by highlighting the importance of sonic branding via the use of organization-specific music in a retail environment (Ballouli & Bennett, 2014). Specifically, this research hinges on the notion that consumer purchase behaviors can be amplified when consumers hear branded, organization-specific music as opposed to generic popular music. However, for consumers who did not notice the music, purchase behaviors were not nearly as affected. Therefore, a sport marketer’s goal should be to get retail consumers to notice the use of branded music in the first place. The study proposed herein will attempt to assess the effect of subtle, synchronized visual stimuli when used in conjunction with branded music. Examples of subtle visual cues include pulsating track lighting synchronized to the beat of branded music, or a video screen playing team highlights wherein the on-screen action is synchronized with team-specific background music. Three environments will be created in the bookstore of a large, Division I university located in the southwestern United States. The three environments will include (1) a sport retail environment playing generic popular music (the control group) (2) a sport retail environment playing branded music, and (3) a sport retail environment playing branded music synchronized to subtle visual cues. Data gathering will be completed using two methods. The primary method of data collection will be via the use of advanced internet protocol cameras and intelligent software analytics. These cameras and analytics are capable of tracking purchasing behaviors and impression generations of customers. They track information such as the number of customers, demographics, duration of each impression, mood, and eventual purchase decision. This data will be augmented via a survey of departing customers consisting of three dichotomously coded outcome variables. As we hypothesize significant differences between all three environments, the null hypothesis and alternative hypothesis can be expressed as follows, where μ shall represent customers’ purchase behaviors: Ho: μ1=μ2=μ3 H1: μ1<μ2<μ3 Confirmatory Factor Analysis and Structural Equation Modeling will be used to assess the relationships between variables. A model of the proposed framework is presented below. Fixed dome network cameras for overall coverage Pan, Tilt, and Zoom (PTZ) network cameras for object and person tracking Fixed discrete network cameras for highly focused coverage Network Video Recorder: desktop terminal Network monitor Infrastructure Logistics and Operations Crowd movement Duration of impression Type of Impression (positive, negative, neutral) Purchase outcome People tracking POS statistics Target marketing with demographic capture Event, object, and patron heat mapping Trend forecasting Real Time Analytics Dependent Variables (Consumer behavior) captured via camera analytics Moderating variable (Recognition of branded music) captured via survey Allows for the interpretation of actual consumer behavior, as opposed to consumer intentions Interpretation Demographics and Impression Capture