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Partner-marketing using geo-social media data for smarter commerce

Published: 01 September 2014 Publication History

Abstract

As a result of significantly growing competition from online-only retailers, physical store retailers have resorted to measures such as discount coupons, online price matching, sale events, and a seamless omnichannel customer experience. One important, often overlooked method is partner-marketing, where a retailer promotes products or services offered by other merchants. The main challenge in partner-marketing is finding partners, i.e., determining whom a retailer should choose as partners among many choices. In this paper, we discuss how a retailer can make use of rich information from mobile and geo-social media to find potential partners in a geographic region. The explosion of mobile and geo-social media in recent years has led to increasing insights about consumers' footprintsVwhich shops they visit, where they travel, and where they live and work. In addition, geo-social data also reveals which regional merchants are popular and why. We propose a series of partner-finding techniques that leverage geo-social data at increasing granularity and discuss analytics to rank potential partners. We present compelling business case studies using real data from Foursquare, a social networking website. We also discuss how retailers can combine geo-social data with traditional data such as demographics, weather information, and their own marketing databases to personalize partner-marketing.

References

[1]
P. Jaccard, "Étude comparative de la distribution florale dans une portion des Alpes et des Jura," Bull. Soc. Vaudoise Sci. Nat., vol. 37, pp. 547-579, 1901.
[2]
P. A. Zandbergen, "Accuracy of iPhone locations: A comparison of assisted GPS, WiFi and cellular positioning," Trans. GIS, vol. 13, no. s1, pp. 5-26, Jun. 2009.
[3]
B. Li, Q. Yang, and X. Xue, "Transfer learning for collaborative filtering via a rating-matrix generative model," in Proc. 26th Annu. Int. Conf. Mach. Learn., Montreal, Canada, 2009, pp. 617-624.
[4]
A. Noulas, S. Scellato, C. Mascolo, and M. Pontil, "An empirical study of geographic user activity patterns in foursquare," in Proc. ICWSM, 2011, p. 148.
[5]
S. Robertson, "Understanding inverse document frequency: On theoretical arguments for IDF," J. Documentation, vol. 60, no. 5, pp. 503-520, 2004.
[6]
A. G. Parsons, "The Association between daily weather and daily shopping patterns," Australian Market. J., vol. 9, no. 2, pp. 78-84, 2001.
[7]
Y. Li, M. Steiner, J. Bao, L. Wang, and T. Zhu, "Region sampling and estimation of geosocial data with dynamic range calibration," in Proc. 30th Int. Conf. Data Eng., Chicago, IL, USA, Apr. 2014, pp. 1096-1107.
[8]
Amazon, Amazon.com Associates. [Online]. Available: https://affiliate-program.amazon.com/gp/associates/join/landing/main.html
[9]
Y. Zheng, L. Zhang, X. Xie, and W. Y. Ma, "Mining interesting locations and travel sequences from GPS trajectories," in Proc. 18th Int. Conf. World Wide Web, 2009, pp. 791-800.
[10]
J. Levandoski, M. Sarwat, A. Eldawy, and M. Mokbel, "LARS: A location-aware recommender system," in Proc. IEEE Int. Conf. Data Eng., 2012, pp. 450-461.
[11]
J. Bao, Y. Zheng, and M. F. Mokbel, "Location-based and preference-aware recommendation using sparse geo-social networking data," in Proc. ACM SIGSPATIAL, 2012, pp. 199-208.
[12]
L. Y. Wei, Y. Zheng, and W. C. Peng, "Constructing popular routes from uncertain trajectories," in Proc. 18th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2012, pp. 195-203.
[13]
H. Yoon, Y. Zheng, X. Xie, and W. Woo, "Social itinerary recommendation from user-generated digital trails," Pers. Ubiquitous Comput., vol. 16, no. 5, pp. 469-484, Jun. 2012.
[14]
V. W. Zheng, Y. Zheng, X. Xie, and Q. Yang, "Collaborative location and activity recommendations with GPS history data," in Proc. WWW, 2010, pp. 1029-1038.
[15]
D. Karamshuk, A. Noulas, S. Scellato, V. Nicosia, and C. Mascolo, BGeo-spotting: Mining online location-based services for optimal retail store placement," in Proc. KDD, 2013, pp. 793-801.

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Published In

cover image IBM Journal of Research and Development
IBM Journal of Research and Development  Volume 58, Issue 5-6
September/November 2014
219 pages
ISSN:0018-8646
  • Editors:
  • Jin Dong,
  • Robin Lougee,
  • Chandra Narayanaswami
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IBM Corp.

United States

Publication History

Published: 01 September 2014
Received: 05 February 2014

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