Preparing for submission at Journal of Marketing Research
The 41st Annual ISMS Marketing Science Conference, June 20 – 22, 2019, Rome, Italy
The 2019 Haring Symposium, April 19 – 20, 2019, Bloomington, IN.
The 1st Interdisciplinary Research Cluster Day, April 1, 2019, Coral Gables, FL.
The 3rd Annual Three Minute Thesis Competition, Feb 6, 2019, Coral Gables, FL.
No brand is the market leader uniformly across geographic locations. Brands have strong and weak markets. Therefore, analysts need to track brand preferences by location. However, current methods of tracking geographic brand preferences (e.g., surveys, company sales, scanner panel data, Google Analytics, etc.) have limitations: limited sampling of brands, stores, and categories with time and region aggregation. We address these limitations through a spatial, nonparametric approach to track brand preferences at the micro-temporal and micro-geographic level using Twitter. Our approach inherits the intrinsic strengths of social media: micro-temporal, micro-geographic, faster than news media, higher frequency than sales, low cost, competitive monitoring, sentiment inclusion, and social network inclusion. A key feature of our approach is modeling the temporal geographic influence of social connections based on the followers of each post. We estimate our model for carbonated soft drinks brands. Our validation uses Pepsi sales, an apparel fashion brand, and the 2016 U.S. presidential election (a domain where marketing is critical). We find that geographic influence adds predictive power to micro-temporal and micro-geographic brand measures, leads online sales and predicts elections. Our study implies that the micro-temporal and micro-geographic social media measures provide actionable insights for marketing brands and political candidates.
The rapid advances in e-commerce and Web 2.0 technologies have greatly increased the impact of commercial advertisements on the general public. As a key enabling technology, a multitude of recommender systems exists which analyzes user features and browsing patterns to recommend appealing advertisements to users. In this work, we seek to study the characteristics or attributes that characterize an effective advertisement and recommend a useful set of features to aid the designing and production processes of commercial advertisements. We analyze the temporal patterns from multimedia content of advertisement videos including auditory, visual and textual components, and study their individual roles and synergies in the success of an advertisement. The objective of this work is then to measure the effectiveness of an advertisement, and to recommend a useful set of features to advertisement designers to make it more successful and approachable to users. Our proposed framework employs the signal processing technique of cross-modality feature learning where data streams from different components are employed to train separate neural network models and are then fused together to learn a shared representation. Subsequently, a neural network model trained on this joint feature embedding representation is utilized as a classifier to predict advertisement effectiveness. We validate our approach using subjective ratings from a dedicated user study, the sentiment strength of online viewer comments, and a viewer opinion metric of the ratio of the Likes and Views received by each advertisement from an online platform.
Temporal contour shapes are closely linked to the narrative structure of multimedia content and provide important reference points in content-based multimedia timeline analysis. In this paper, multimedia timeline is extracted from content as time varying video and audio signal features. A temporal contour representation is implemented based on sequential pattern discovery algorithm for modeling the variation contours of multimedia features. The proposed contour representation extracts repetitive temporal patterns from a hierarchy of time resolutions or from synchronized video/audio feature dimensions. The statistically significant contour components, depicting the dominant timeline shapes, are utilized as a structural or analytical representation of the timeline. The modeling performance of this proposed temporal modeling framework is demonstrated through empirical validation and subjective evaluations.