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Sina Damangir
San Francisco State University
3Publications
2H-index
55Citations
Publications 3
Newest
Published on May 1, 2018in Journal of Interactive Marketing4.69
Sina Damangir2
Estimated H-index: 2
(SFSU: San Francisco State University),
Rex Yuxing Du9
Estimated H-index: 9
(UH: University of Houston),
Ye Hu10
Estimated H-index: 10
(UH: University of Houston)
Abstract Consumers often consider multiple alternatives from the same product category prior to making a purchase. Uncovering the predominant patterns of such co-considerations can help businesses learn more about the competitive structure of the market in the mind of the consumer. Extant research has shown that various types of online and offline consumer activity data (e.g., shopping baskets, search and browsing histories, social media mentions) can be used to infer product co-considerations. ...
Published on Jan 1, 2015in Journal of Marketing7.82
Rex Yuxing Du9
Estimated H-index: 9
(UH: University of Houston),
Ye Hu10
Estimated H-index: 10
(UH: University of Houston),
Sina Damangir2
Estimated H-index: 2
(SFSU: San Francisco State University)
Evolving tastes can change the relative importance of product features in shaping consumers' purchase decisions, which in turn can shift the relative attractiveness of products with different feature levels. The challenge lies in finding a reliable yet cost-effective way to monitor the weights consumers place on various product features. In the context of the U.S. automotive market, the authors explore the potential of using trends in online searches for feature-related keywords as indicators of...
Published on Jun 1, 2014in Journal of Marketing Research4.20
Ye Hu10
Estimated H-index: 10
(UH: University of Houston),
Rex Yuxing Du9
Estimated H-index: 9
(UH: University of Houston),
Sina Damangir2
Estimated H-index: 2
(UH: University of Houston)
Unlike sales data, data on intermediate stages of the purchase funnel (e.g., how many consumers have searched for information about a product before purchase) are much more difficult to acquire. Consequently, most advertising response models have focused directly on sales and ignored other purchase funnel activities. The authors demonstrate, in the context of the U.S. automotive market, how consumer online search volume data from Google Trends can be combined with sales data to decompose adverti...
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