Using Social Network Activity Data to Identify and Target Job Seekers

References

• Amemiya T (1984) Tobit models: A survey. J. Econometrics 24(1-2):3–61.Crossref, Google Scholar
• Ansari A, Montoya R, Netzer O (2012) Dynamic learning in behavioral games: A hidden Markov mixture of experts approach. Quant. Marketing Econom. 10(4):475–503.Crossref, Google Scholar
• Ascarza E, Hardie BG (2013) A joint model of usage and churn in contractual settings. Marketing Sci. 32(4):570–590.Link, Google Scholar
• Ascarza E, Netzer O, Hardie BG (2018) Some customers would rather leave without saying goodbye. Marketing Sci. 37(1):54–77.Link, Google Scholar
• Atchadé YF, Rosenthal JS (2005) On adaptive Markov chain Monte Carlo algorithms. Bernoulli 11(5):815–828.Crossref, Google Scholar
• Bradlow ET, Feit EM (2018). Fusion modeling. Homburg C, Klarmann M, Vomberg AE, eds. Handbook of Marketing Research (Springer, Berlin).Google Scholar
• Bronnenberg BJ, Dubé JPH, Gentzkow M (2012) The evolution of brand preferences: Evidence from consumer migration. Amer. Econom. Rev. 102(6):2472–2508.Crossref, Google Scholar
• Bureau of Labor Statistics (2017). Accessed September 2020, https://data.bls.gov/timeseries/LNS14000000.Google Scholar
• Chib S, Greenberg E (1995) Understanding the Metropolis-Hastings algorithm. Amer. Statist. 49:327–335.Google Scholar
• Ebbes P, Grewal R, DeSarbo WS (2010) Modeling strategic group dynamics: A hidden Markov approach. Quant. Marketing Econom. 8(2):241–274.Crossref, Google Scholar
• Eddy SR (1998) Profile hidden Markov models. Bioinformatics 14(9):755–763.Crossref, Google Scholar
• Feit EM, Beltramo MA, Feinberg FM (2010) Reality check: Combining choice experiments with market data to estimate the importance of product attributes. Management Sci. 56(5):785–800.Link, Google Scholar
• Feit EM, Wang P, Bradlow ET, Fader PS (2013) Fusing aggregate and disaggregate data with an application to multi-platform media consumption. J. Marketing Res. 50(3):348–364.Crossref, Google Scholar
• Fong NM, Fang Z, Luo X (2015) Geo-conquesting: Competitive locational targeting of mobile promotions. J. Marketing Res. 52(5):726–735.Crossref, Google Scholar
• Ford BM (1983) An overview of hotdeck procedures. in Madow WG, Olkin I, Rubin DB, eds. Incomplete Data in Sample Surveys, vol 2 (Academic Press, New York), 185–207.Google Scholar
• Garg R, Telang R (2018) To be or not to be linked: Online social networks and job search by unemployed workforce. Management Sci. 64(8):3926–3941.Link, Google Scholar
• Gilula Z, McCulloch RE, Rossi PE (2006) A direct approach to data fusion. J. Marketing Res. 43(1):73–83.Crossref, Google Scholar
• Granovetter M (1973) Weak ties and strong ties. Amer. J. Sociol. 78:1360–1380.Crossref, Google Scholar
• Gupta S (1991) Stochastic models of interpurchase time with time dependent covariates. J. Marketing Res. 28(1):1–15.Crossref, Google Scholar
• Hamilton JD (1989) A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57(2):357–384.Crossref, Google Scholar
• Hand DJ, Till RJ (2001) A Simple generalisation of the area under the ROC curve for multiple mlass classification problems. Machine Learn. 45:171–186.Crossref, Google Scholar
• Hauser JR, Urban GL, Liberali G, Braun M (2009) Website morphing. Marketing Sci. 28(2):202–224.Link, Google Scholar
• Hill K (2012) How Target figured out a teen girl was pregnant before her father did. Forbes (February 16), https://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/?sh=5ae53d946668.Google Scholar
• Hippler HJ, Schwarz N (1987) Response effects in surveys. Social Information Processing and Survey Methodology (Springer, New York), 102–122.Crossref, Google Scholar
• Kamakura WA, Wedel M (1997) Statistical data fusion for cross-tabulation. J. Marketing Res. 34(4):485–498.Crossref, Google Scholar
• Kamakura WA, Wedel M (2000) Factor analysis and missing data. J. Marketing Res. 37(4):490–498.Crossref, Google Scholar
• Matz SC, Netzer O (2017) Using Big Data as a window into consumers’ psychology. Current Opin. Behav. Sci. 18:7–12.Crossref, Google Scholar
• Monaco JV, Tappert CC (2018) The partially observable hidden Markov model and its application to keystroke dynamics. Pattern Recognition 76:449–462.Crossref, Google Scholar
• Montgomery A, Li S, Srinivasan K, Liechty J (2004) Modeling online browsing and path analysis using clickstream data. Marketing Sci. 23(4):579–595.Link, Google Scholar
• Morwitz VG, Johnson E, Schmittlein D (1993) Does measuring intent change behavior? J. Consumer Res. 20(1):46–61.Crossref, Google Scholar
• Netzer O, Ebbes P, Bijmolt TH (2017) Hidden Markov models in marketing. Advanced Methods for Modeling Markets (Springer, Cham, Switzerland), 405–449.Crossref, Google Scholar
• Netzer O, Lattin JM, Srinivasan V (2008) A hidden Markov model of customer relationship dynamics. Marketing Sci. 27(2):185–204.Link, Google Scholar
• Qian Y, Xie H (2014) Which brand purchasers are lost to counterfeiters? An application of new data fusion approaches. Marketing Sci. 33(3):437–448.Link, Google Scholar
• Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77:257–286.Crossref, Google Scholar
• Romero J, Van der Lans R, Wierenga B (2013) A partially hidden Markov model of customer dynamics for CLV measurement. J. Interactive Marketing 27(3):185–208.Crossref, Google Scholar
• Rossi PE, McCulloch RE, Allenby GM (1996) The value of purchase history data in target marketing. Marketing Sci. 15(4):321–340.Link, Google Scholar
• Saito T, Rehmsmeier M (2015) The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS One 10(3):e0118432.Crossref, Google Scholar
• Scheffer T, Decomain C, Wrobel S (2001). Active hidden Markov models for information extraction. Hoffmann F, Hand DJ, Adams N, Guimaraes G, eds. Proc. 4th Internat. Conf. Adv. Intelligent Data Anal., (Springer, Berlin, Heidelberg), 309–318.Google Scholar
• Schwartz EM, Bradlow ET, Fader PS (2014) Model selection using database characteristics: Developing a classification tree for longitudinal incidence data. Marketing Sci. 33(2):188–205.Link, Google Scholar
• Schwarz N (1999) Self-reports: how the questions shape the answers. Amer. Psych. 54(2):93.Crossref, Google Scholar
• Schweidel DA, Bradlow ET, Fader PS (2011) Portfolio dynamics for customers of a multiservice provider. Management Sci. 57(3):471–486.Link, Google Scholar
• Stopfer JM, Gosling SD (2013) Online social networks in the work context. Derks D, Bakker A, eds. The Psychology of Digital Media at Work (Psychology Press, London), 39–59.Google Scholar
• Thompson CS, Thomson PJ, Zheng X (2007) Fitting a multisite daily rainfall model to New Zealand data. J. Hydrology (Amsterdam) 340:25–39.Crossref, Google Scholar
• Tourangeau R, Rips LJ, Rasinski K (2000) The Psychology of Survey Response (Cambridge University Press).Crossref, Google Scholar
• Trusov M, Ma L, Jamal Z (2016) Crumbs of the cookie: User profiling in customer-base analysis and behavioral targeting. Marketing Sci. 35(3):405–426.Link, Google Scholar
• Wachtel S, Otter T (2013) Successive sample selection and its relevance for management decisions. Marketing Sci. 32(1):170–185.Link, Google Scholar
• Wedel M, Kannan PK (2016) Marketing analytics for data-rich environments. J. Marketing 80(6):97–121.Crossref, Google Scholar
• Yamato J, Ohya J, Ishii K (1992) Recognizing human action in time-sequential images using hidden markov model. Proc. IEEE Computer Society Conf. Comput. Vision Pattern Recognition (IEEE, New York), 379–385.Google Scholar
• Zarate LE, Nogueira BM, Santos TRA, Song AJM (2006) Techniques for missing value recovering in imbalanced databases: Application in a marketing database with massive missing data. Proc. IEEE Internat. Conf. Systems Man Cybernetics, vol. 3 (IEEE, New York), 2658–2664.Google Scholar
• Zhang JZ, Netzer O, Ansari A (2014) Dynamic targeted pricing in B2B relationships. Marketing Sci. 33(3):317–337.Link, Google Scholar