ABSTRACT
As a common choice strategy for consumers, variety-seeking has a direct impact on the business performance of enterprises, and has been studied for decades in management science and marketing research. Existing research tends to find factors which influence variety-seeking by means of questionnaires and laboratory experiments. Based on the important factors, marketers develop marketing plans which are tailored to consumers. However, due to personal motivation, the results of questionnaires and laboratory experiments may not fully reflect internal states of participants. Thus, we propose a data-driven framework, using ensemble learning for predicting variety-seeking behavior based on real student consumption data. Experiments demonstrate that our model outperforms separate machine learning algorithms and can effectively predict variety-seeking behavior. We further analyze the contribution of each feature towards the prediction, achieving some useful conclusions for the study of variety-seeking intervention mechanism.
- Wilson T D, Dunn E W. Self-knowledge: Its limits, value, and potential for improvement{J}. Annual review of psychology, 2004, 55.Google Scholar
- Hoyer w d, ridgway n m. Variety seeking as an explanation for exploratory purchase behavior: a theoretical model{j}. Acr north american advances, 1984.Google Scholar
- Jian j, zhang y, zhang c. Predicting consumer variety-seeking through weather data analytics{j}. Electronic commerce research and applications, 2018, 28: 194--207.Google Scholar
- Chuang s c, cheng y h, wang s m, et al. The impact of the opinions of others on variety-seeking behavior{j}. Journal of applied social psychology, 2013, 43(5): 917--927.Google Scholar
- Ariely D, Levav J. Sequential choice in group settings: Taking the road less traveled and less enjoyed{J}. Journal of consumer Research, 2000, 27(3): 279--290.Google Scholar
- Ratner R K, Kahn B E. The impact of private versus public consumption on variety-seeking behavior{J}. Journal of Consumer Research, 2002, 29(2): 246--257.Google ScholarCross Ref
- Morales A, Kahn B E, McAlister L, et al. Perceptions of assortment variety: The effects of congruency between consumers' internal and retailers' external organization{J}. Journal of Retailing, 2005, 81(2): 159--169.Google ScholarCross Ref
- Ha J, Jang S C S. Variety seeking in restaurant choice and its drivers{J}. International Journal of Hospitality Management, 2013, 32: 155--168.Google ScholarCross Ref
- Ayhan s, costas p, samet h. Predicting estimated time of arrival for commercial flights{c}//proceedings of the 24th acm sigkdd international conference on knowledge discovery & data mining. Acm, 2018: 33--42. Google ScholarDigital Library
- Li j, rong y, meng h, et al. Tatc: predicting alzheimer's disease with actigraphy data{c}//proceedings of the 24th acm sigkdd international conference on knowledge discovery & data mining. Acm, 2018: 509--518. Google ScholarDigital Library
- Su y, liu q, liu q, et al. Exercise-enhanced sequential modeling for student performance prediction{c}//thirty-second aaai conference on artificial intelligence. 2018.Google Scholar
- Yao h, wu f, ke j, et al. Deep multi-view spatial-temporal network for taxi demand prediction{c}//thirty-second aaai conference on artificial intelligence. 2018.Google Scholar
- Ballinger b, hsieh j, singh a, et al. Deepheart: semi-supervised sequence learning for cardiovascular risk prediction{c}//thirty-second aaai conference on artificial intelligence. 2018.Google Scholar
- Jiao b, zhang j, wu c, et al. Deep sequence learning with auxiliary information for traffic prediction{c}//proceedings of the 24th acm sigkdd international conference on knowledge discovery & data mining. Acm, 2018: 537--546. Google ScholarDigital Library
- Legohérel P, Hsu C H C, Daucé B. Variety-seeking: Using the CHAID segmentation approach in analyzing the international traveler market{J}. Tourism Management, 2015, 46: 359--366.Google ScholarCross Ref
- Kim H S, Drolet A. Choice and self-expression: A cultural analysis of variety-seeking{J}. Journal of personality and social psychology, 2003, 85(2): 373.Google Scholar
- Chawla n v, bowyer k w, hall l o, et al. Smote: synthetic minority over-sampling technique{j}. Journal of artificial intelligence research, 2002, 16: 321--357. Google ScholarDigital Library
- Dietterich T G. Ensemble methods in machine learning{C}//International workshop on multiple classifier systems. Springer, Berlin, Heidelberg, 2000: 1--15. Google ScholarDigital Library
Index Terms
- Exploring the Prediction of Variety-seeking Behavior
Recommendations
Modeling Inertia and Variety Seeking Tendencies in Brand Choice Behavior
Theories of exploratory behavior suggest that inertia and variety-seeking tendencies may coexist within the individual, implying that the same individual may exhibit inertia and variety-seeking at different times depending on his/her choice history. ...
Predicting consumer variety-seeking through weather data analytics
Highlights- Assess why and how weather conditions may influence consumers’ variety-seeking.
AbstractMarketing decision support systems (MDSS) incorporate both internal and external data in performing analytics to improve business effectiveness. Weather data have long been considered a crucial external data input in practitioners’ ...
Vertical Differentiation with Variety-Seeking Consumers
We analyze price and quality competition in a vertically differentiated duopoly in which consumers have a preference for variety. The preference for variety is a consequence of diminishing marginal utility for repeated experiences with the same product. ...
Comments