Martin Spann

77 posts

Martin Spann

Martin Spann

@MartinSpann

Prof. @LMU_Muenchen

Katılım Şubat 2015
174 Takip Edilen195 Takipçiler
Martin Spann retweetledi
NBER
NBER@nberpubs·
Implications and challenges of algorithmic pricing for consumers, managers, and regulators, from Spann, Bertini, Koenigsberg, Zeithammer, Aparicio, Chen, Fantini, Zhe Jin, Morwitz, Popkowski Leszczyc, Ana Vitorino, Yalcin Williams, and Yoo nber.org/papers/w32540
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Marketing Science
Marketing Science@Mrktng_Science·
Newly Accepted Articles 6/23 (3 of 5) "Frontiers: The Interplay of User-Generated Content, Content Industry Revenues, and Safe Harbor Provisions: Quasi-Experimental Evidence from YouTube" by Wlömert, Nils; Papies, Dominik; Clement, Michel; Spann, Martin @DPapies @MartinSpann
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Dominik Molitor
Dominik Molitor@dommolitor·
Our study shows that understanding location-specific demands is key. Retailers can leverage this knowledge, alongside mobile tech, for optimized in-app targeting and better assortment decisions, leading to an enhanced customer experience and increased sales. 5/5
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Dominik Molitor
Dominik Molitor@dommolitor·
Also, we find that the number of products within the same category that are searched together (search breadth) increases as store distance and density increase. Durable products influence these patterns. 4/5
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Dominik Molitor
Dominik Molitor@dommolitor·
Key findings? Store distance and density significantly influence search behavior. Detailed searches for product-specific attributes (search depth) increase as store distance decreases and store density increases. 3/5
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Dominik Molitor
Dominik Molitor@dommolitor·
We introduce two new metrics: 'Present Store Distance' and 'Present Store Density.' These measures help us understand how a shopper's location relative to stores affects their search behavior. 2/5
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Werner Reinartz
Werner Reinartz@ReinartzWerner·
Kakatkar and @MartinSpann propose a new approach that lets #retailers triangulate the challenge of (a) deriving insights from highly unstructured #shopper data in the #store, (b) guarding #consumer #privacy by using non-personalized data, and (c) achieving commercial objectives.
International Journal of Research in Marketing@IJRMktg

Kakatkar and Spann propose a method to use anonymized and fragmented data to provide marketing analytics that are useful and still anonymized, and they validate this approach with a field experiment. #marketing #analytics #privacy sciencedirect.com/science/articl…

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