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Analyzing and Modeling Special Offer Campaigns in Location-based Social Networks

Zhang, Ke and Pelechrinis, Konstantinos and Lappas, Theodoros (2015) Analyzing and Modeling Special Offer Campaigns in Location-based Social Networks. In: AAAI ICWSM. (Unpublished)

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The proliferation of mobile handheld devices in combination with the technological advancements in mobile computing has led to a number of innovative services that make use of the location information available on such devices. Traditional yellow pages websites have now moved to mobile platforms, giving the opportunity to local businesses and potential, near-by, customers to connect. These platforms can offer an affordable advertisement channel to local businesses. One of the mechanisms offered by location-based social networks (LBSNs) allows businesses to provide special offers to their customers that connect through the platform. We collect a large time-series dataset from approximately 14 million venues on Foursquare and analyze the performance of such campaigns using randomization techniques and (non-parametric) hypothesis testing with statistical bootstrapping. Our main finding indicates that this type of promotions are not as effective as anecdote success stories might suggest. Finally, we design classifiers by extracting three different types of features that are able to provide an educated decision on whether a special offer campaign for a local business will succeed or not both in short and long term.


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Item Type: Conference or Workshop Item (Paper)
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Zhang, Kekez11@pitt.eduKEZ11
Pelechrinis, Konstantinoskpele@pitt.eduKPELE0000-0002-6443-3935
Lappas, Theodoros
Date: 2015
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Event Title: AAAI ICWSM
Event Type: Conference
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Telecommunications
Refereed: Yes
Date Deposited: 19 Jun 2015 14:48
Last Modified: 25 Jul 2022 20:25


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