Top

Information Systems Frontiers

Published in:

12-11-2016

Keep the conversations going: engagement-based customer segmentation on online social service platforms

Authors: Nripesh Trivedi, Daniel Adomako Asamoah, Derek Doran

Published in: Information Systems Frontiers | Issue 2/2018

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Most businesses and organizations develop online services as a value-added offering, which is a significant revenue stream from their existing user base. Such services may be enhanced with social elements to serve as value-added tools for user attraction and retention. Social elements may allow users to post content, share information and directly interact with each other. Investments in these social features are for naught if they do not encourage users to engage on the platform effectively. However, common ways to segment customers by their engagement is hindered by the statistical nature of behavioral data based on social elements. To address this important concern, this paper presents a methodological framework for engagement-based customer segmentation able to appropriately consider signals from social elements. It argues why the traditional approaches for user segmentation is ill-suited and advocates for the integration of kernel functions with clustering to segment, identify and understand user engagement profiles. The framework is demonstrated with real data from a large, very active OSS.

previous article Information fusion-based meta-classification predictive modeling for ETF performance

next article Project description and crowdfunding success: an exploratory study

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Adamic, L.A., & Glance, N. (2005). The Political Blogosphere and the 2004 U.S. Election. In Proceedings of the 3rd international workshop on Link discovery - LinkKDD ’05 (pp. 36–43). http://dl.acm.org/citation.cfm?id=1134271.1134277\nhttp://portal.acm.org/citation.cfm?doid=1134271.1134277.

Agrawal, R., Gehrke, J., Gunopulos, D., & Raghavan, P. (1998). Automatic subspace clustering of high dimensional data for data mining applications (vol. 27). ACM.

Anderson, A., Huttenlocher, D., & Kleinberg, J. (2012). Discovering Value from community activity on focused question answering sites : a case study of stack overflow. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 850–858).

Arapakis, I., Lalmas, M., Cambazoglu, B. B., Marcos, M., & Jose, J. M. (2014). User engagement in online news: under the scope of sentiment, interest, affect, and gaze. Journal of the Association for Information Science and Technology, 65(10), 1988–2005.CrossRef

Attfield, S., Kazai, G., Lalmas, M., & Piwowarski, B. (2011). Towards a science of user engagement (position paper). WSDM Workshop on User Modelling for Web Applications. http://www.dcs.gla.ac.uk/~mounia/Papers/engagement.pdf.

Benevenuto, F., Rodrigues, T., Cha, M., & Almeida, V. (2009). Characterizing user behavior in online social networks. Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference, (17313374869111495992related:ON2QmLZ2RfAJ) (pp. 49–62).

Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13, 281–305.

Booth, D., & Jansen, B. (2008). A review of methodologies for analyzing websites. Handbook of Research on Web Log Analysis, 141–162. http://faculty.ist.psu.edu/jjansen/academic/jansen_website_analysis.pdf.

Brooke, J. (1996). SUS-A quick and dirty usability scale. Usability Evaluation in Industry (pp. 4–7).

Calzarossa, M. C., Massari, L., Doran, D., Yelne, S., Trivedi, N., & Moriarty, G. (2016). Measuring the users and conversations of a vibrant online emotional support system. In IEEE Symposium on Computers and Communications, Messina, Italy.

Camps-Valls, G., Rojo-Alvarez, J. L., & Martinez-Ramon, M. (2007). Kernel methods in bioengineering, signal and image processing.

Chang, H.-J., Hung, L.-P., & Ho, C.-L. (2007). An anticipation model of potential customers’ purchasing behavior based on clustering analysis and association rules analysis. Expert Systems with Applications, 32(3), 753–764. http://linkinghub.elsevier.com/retrieve/pii/S0957417406000327.

Chen, Y.Y., Lai, F.W., Goh, K. N., & Daud, S.C. (2013). The effect of integrating social plugins into e-commerce website : a study on online consumer behaviour. In International Conference on Ubiquitous Information Management and Communication. ACM (pp. 0–5). doi:10.1145/2448556.2448612.

Chitta, R., Jin, R., Havens, T. C., & Jain, A. K. (2014). Scalable Kernel clustering : approximate Kernel k-means. arXiv.

Chu, C.-W., Holliday, J.D., & Willett, P. (2009). Effect of data standardization on chemical clustering and similarity searching. Journal of Chemical Information and Modeling, 49(2), 155–61. http://www.ncbi.nlm.nih.gov/pubmed/19434820.

Chuang, H. H. C., Lu, G., Peng, D. X., & Heim, G. R. (2014). Impact of value-added service features in e-retailing processes: an econometric analysis of web site functions. Decision Sciences Journal of Innovative Education, 45(6), 1159–1186.CrossRef

Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2009). Power-law distributions in empirical data. SIAM Review, 51(4), 661. doi:10.1137/070710111.CrossRef

Constant, D., Sproull, L., & Kiesler, S. (1996). The kindness of strangers: the usefulness of electronic weak ties for technical advice. Organization Science, 7(2), 119–135.CrossRef

Cyr, D., Hassanein, K., Head, M., & Ivanov, A. (2007). The role of social presence in establishing loyalty in e-service environments. Interacting wih Computers, 19(1), 43–56. doi:10.1016/j.intcom.2006.07.010.CrossRef

Davis, D., Allen, J., & Cosenza, R. M. (1988). Segmenting local residents by their attitudes, interests, and opinions toward tourism. Journal of Travel Research, 27(2), 2–8.CrossRef

De la Torre, F., & Vinyals, O. (2007). Learning Kernel expansions for image classification. In Computer Vision and Pattern Recognition, 2007. CVPR ’07. IEEE Conference on (pp. 1–7).

Doran, D., Yelne, S., Massari, L., Calzarossa, M.-C., Jackson, L., & Moriarty, G. (2015). Stay awhile and listen: user interactions in a crowdsourced platform offering emotional support. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 667–674). Paris, France: ACM.

Farajian, M., & Mohammadi, S. (2010). Mining the banking customer behavior using clustering and association rules methods. International Journal of Industrial Engineering.

Fredline, E., & Faulkner, B. (2000). Host community reactions: a cluster analysis. Annals of Tourism Research, 27(3), 763–784.CrossRef

Gagné, C., & Godin, G. (2005). Improving self-report measures of non-adherence to HIV medications. Psychology & Health, 20(6), 803–816. doi:10.1080/14768320500386441.CrossRef

Gong, N. Z., Xu, W., Huang, L., Mittal, P., Stefanov, E., Sekar, V., & Song, D. (2012). Evolution of social-attribute networks: measurements, modeling, and implications using Google+. Internet Measurement Conference, 131–144. doi:10.1145/2398776.2398792.

Gummerus, J., Liljander, V., Pura, M., & Riel, A. V. (2004). Customer loyalty to content-based web sites: the case of an online health-care service. Journal of Services Marketing, 18(3), 175–186.CrossRef

Hawkshead, J., & Krousel-Wood, M. A. (2007). Techniques for measuring medication adherence in hypertensive patients in outpatient settings: advantages and limitations. Disease Management and Health Outcomes, 15(2), 109–118. http://ideas.repec.org/a/wkh/dmhout/v15y2007i2p109-118.html.

Huang, Z., & Benyoucef, M. (2013). From e-commerce to social commerce: a close look at design features. Electronic Commerce Research and Applications, 12(4), 246–259. doi:10.1016/j.elerap.2012.12.003.CrossRef

Iba, T., Nemoto, K., Peters, B., & Gloor, P. A. (2010). Analyzing the creative editing behavior of wikipedia editors: through dynamic social network analysis. Procedia - Social and Behavioral Sciences, 2(4), 6441–6456.CrossRef

Ikehara, C., & Crosby, M. (2005). Assessing cognitive load with physiological sensors. Proceedings of the 38th Annual Hawaii International Conference on System Sciences, 00(C), (pp. 1–9).

Jacques, R. D. (1996). The nature of engagement and its role in hypermedia evaluation and design. Retrieved from http://ethos.bl.uk/OrderDetails.do?uin = uk.bl.ethos.336369.

Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666. doi:10.1016/j.patrec.2009.09.011.CrossRef

Kailing, K., Kriegel, H.-P., & Kröger, P. (2004). Density-connected subspace clustering for high-dimensional data. In Proc. SDM (vol. 4). SIAM.

Kim, E. (2013). Everything you wanted to know about the Kernel trick (but were too afraid to ask). http://www.eric-kim.net/eric-kim-net/posts/1/kernel_trick.html. Accessed 1 Aug 2014.

Kodinariya, T. M., & Makwana, P. R. (2013). Review on determining number of cluster in K-means clustering. International Journal of Advance Research in Computer Science and Management Studies, 1(6), 90–95.

Konradt, U., & Sulz, K. (2001). The experience of flow in interacting with a hypermedia learning environment. Journal of Educational Multimedia and Hypermedia, 10(1), 69–84. http://www.editlib.org/index.cfm?fuseaction = Reader.ViewAbstract&paper_id = 7992&from = NEWDL.

Kriegel, H.-P., Kroger, P., Renz, M., & Wurst, S. (2005). A generic framework for efficient subspace clustering of high-dimensional data. In Fifth IEEE International Conference on Data Mining (ICDM’05). IEEE.

Kunegis, J., Lommatzsch, A., & Bauckhage, C. (2009). The Slashdot zoo: mining a social network with negative edges. Proceedings of the 18th international conference on World wide web (pp. 741–750). doi:10.1145/1526709.1526809.

Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, s social network or a news media? The International World Wide Web Conference Committee (IW3C2) (pp. 1–10).

Lagun, D., Hsieh, C.-H., Webster, D., & Navalpakkam, V. (2014). Towards better measurement of attention and satisfaction in mobile search. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval (pp. 113–122). ACM.

Lalmas, M., O’Brien, H. L., & Yom-Tov, E. (2013). Measuring user engagement. In Tutorial s of the 22nd International World Wide Web Conference.

Lehmann, J., Lalmas, M., Yom-Tov, E., & Dupret, G. (2012). Models of user engagement. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7379 LNCS (pp. 164–175). doi:10.1007/978-3-642-31454-4_14

Lima, A., Rossi, L., & Musolesi, M. (2014). Coding together at scale: GitHub as a collaborative social network. In Proceedings of the 8th International Conference on Weblogs and Social Media, ICWSM 2014 (pp. 295–304). http://www.scopus.com/inward/record.url?eid=2-s2.0-84909956490&partnerID=tZOtx3y1.

Lipsky, L. (2009). Queueing theory : a linear algebraic approach. doi:10.1007/ 978-0-387-49706-8.

Madrigal, R. (1995). Residents’ perceptions and the role of government. Annals of Tourism Research, 22(1), 86–102.CrossRef

Mislove, A., Koppula, H.S., Gummadi, K.P., Druschel, P., & Bhattacharjee, B. (2008). Growth of the flickr social network. In Proceedings of the first workshop on Online social networks - WOSP ’08 (pp. 25–30). doi:10.1145/1397735.1397742.

Nov, O. (2007). What motivates Wikipedians? Communications of the ACM, 50(11), 60–64.CrossRef

O’Brien, H. L., & Toms, E. G. (2008). What is user engagement? A conceptual framework for defining user engagement with technology. Journal of the American Society for Information Science and Technology, 59(6), 938–955.CrossRef

O’Brien, H. L., & Toms, E. G. (2010). The development and evaluation of a survey to measure user engagement. Journal of the American Society for Information Science and Technology, 61(1), 50–69.CrossRef

Ogonowski, A., Montandon, A., Botha, E., & Reyneke, M. (2014). Should new online stores invest in social presence elements? The effect of social presence on initial trust formation. Journal of Retailing and Consumer Services, 21(4), 482–491. doi:10.1016/j.jretconser.2014.03.004.CrossRef

Pang-Ning, T., Steinbach, M., & Kumar, V. (2006). Introduction to data mining. Library of Congress. doi:10.1016/0022-4405(81)90007-8.

Romero, R., Iglesias, E., & Borrajo, L. (2014). A linear-RBF multikernel SVM to classify big text corpora. BioMed Research International. http://www.hindawi.com/journals/bmri/aa/878291/.

Seah, M., & Cairns, P. (2008). From immersion to addiction in videogames. In Proceedings of BCS HCI (pp. 55–63). http://eprints.whiterose.ac.uk/68181/.

Shah, S. K. (2006). Motivation, governance, and the viability of hybrid forms in open source software development. Management Science, 52(7), 1000–1014.CrossRef

Shukla, S., & Naganna, S. (2014). A review on K-means data clustering approach. International Journal of Information and Computation Technology, 4(17), 1847–1860.

Szell, M., & Thurner, S. (2010). Measuring social dynamics in a massive multiplayer online game. Social Networks, 32(4), 313–329. doi:10.1016/j.socnet.2010.06.001.CrossRef

Taniar, D. (2008). Data mining and knowledge discovery technologies. IGI Global.

Torkjazi, M., Rejaie, R., & Willinger, W. (2009). Hot today, gone tomorrow : on the migration of myspace users. … of the 2nd ACM workshop on Online … (pp. 43–48). doi:10.1145/1592665.1592676.

Ugander, J., Karrer, B., Backstrom, L., Marlow, C., & Alto, P. (2011). The anatomy of the facebook social graph. Arxiv preprint arXiv, abs/1111.4(July) (pp. 1–17). doi:10.1.1.31.1768.

van Dam, J.-W., & van de Velden, M. (2014). Online profiling and clustering of Facebook users. Decision Support Systems, 70, 60–72. http://www.sciencedirect.com/science/article/pii/S0167923614002796.

Viswanath, B., Mislove, A., Cha, M., & Gummadi, K. P. (2009). On the evolution of user interaction in Facebook. Proceedings of the 2nd ACM workshop on Online social networks - WOSN ’09, 37. doi:.10.1145/1592665.1592675.

Wasko, M. M., & Faraj, S. (2005). Why should i share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Quarterly, 29(1), 35–57.CrossRef

Webster, J., & Ahuja, J.S. (2006). Enhancing the Design of web navigation systems: the influence of user disorientation on engagement and performance. Mis Quarterly, 661–678.

Webster, J., & Ho, H. (1997). Audience engagement in multimedia presentations. ACM SIGMIS Database, 28(2), 63–77.CrossRef

Xia, M., Huang, Y., Duan, W., & Whinston, A. B. (2012). Research note-to continue sharing or not to continue sharing? An empirical analysis of user decision in peer-to-peer sharing networks. Information Systems Research, 23(1), 247–259.CrossRef

Zhong, C., Salehi, M., Shah, S., Cobzarenco, M., Sastry, N., & Cha, M. (2014). Social bootstrapping : how pinterest and last. fm Social Communities Benefit by Borrowing Links from Facebook. Www2014 (pp. 305–314). doi:10.1145/2566486.2568031.

Title: Keep the conversations going: engagement-based customer segmentation on online social service platforms
Authors: Nripesh Trivedi
Daniel Adomako Asamoah
Derek Doran
Publication date: 12-11-2016
Publisher: Springer US
Published in: Information Systems Frontiers / Issue 2/2018
Print ISSN: 1387-3326
Electronic ISSN: 1572-9419
DOI: https://doi.org/10.1007/s10796-016-9719-x

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 2/2018

Erratum to: Weighted subspace modeling for semantic concept retrieval using gaussian mixture models

Business process flexibility - a systematic literature review with a software systems perspective

The impact of big data analytics on firms’ high value business performance

Big Data & Analytics for Societal Impact: Recent Research and Trends

Enabling self-service BI: A methodology and a case study for a model management warehouse

A conceptual framework for quality healthcare accessibility: a scalable approach for big data technologies

Premium Partner