Identifying Decision Makers from Professional Social Networks

Sales professionals help organizations win clients for products and services. Generating new clients starts with identifying the right decision makers at the target organization. For the past decade, online professional networks have collected tremendous amount of data on people's identity, their network and behavior data of buyers and sellers building relationships with each other for a variety of use-cases. Sales professionals are increasingly relying on these networks to research, identify and reach out to potential prospects, but it is often hard to find the right people effectively and efficiently. In this paper we present LDMS, the LinkedIn Decision Maker Score, to quantify the ability of making a sales decision for each of the 400M+ LinkedIn members. It is the key data-driven technology underlying Sales Navigator, a proprietary LinkedIn product that is designed for sales professionals. We will specifically discuss the modeling challenges of LDMS, and present two graph-based approaches to tackle this problem by leveraging the professional network data at LinkedIn. Both approaches are able to leverage both the graph information and the contextual information on the vertices, deal with small amount of labels on the graph, and handle heterogeneous graphs among different types of vertices. We will show some offline evaluations of LDMS on historical data, and also discuss its online usage in multiple applications in live production systems as well as future use cases within the LinkedIn ecosystem.

[1]  Matthew Richardson,et al.  The Intelligent surfer: Probabilistic Combination of Link and Content Information in PageRank , 2001, NIPS.

[2]  Hongyuan Zha,et al.  Co-ranking Authors and Documents in a Heterogeneous Network , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[3]  Stephen P. Boyd,et al.  Accuracy at the Top , 2012, NIPS.

[4]  Zoubin Ghahramani,et al.  Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.

[5]  Virgílio A. F. Almeida,et al.  Finding trendsetters in information networks , 2012, KDD.

[6]  Bernhard Schölkopf,et al.  Learning with Local and Global Consistency , 2003, NIPS.

[7]  Bernhard Schölkopf,et al.  Learning from labeled and unlabeled data on a directed graph , 2005, ICML.

[8]  Brendan Andrew Duncan,et al.  Probabilistic Modeling of a Sales Funnel to Prioritize Leads , 2015, KDD.

[9]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[10]  Jiawei Han,et al.  Mining topic-level influence in heterogeneous networks , 2010, CIKM.

[11]  Sepandar D. Kamvar,et al.  An Analytical Comparison of Approaches to Personalizing PageRank , 2003 .

[12]  Scott Counts,et al.  Identifying topical authorities in microblogs , 2011, WSDM '11.

[13]  Michael I. Jordan,et al.  Advances in Neural Information Processing Systems 30 , 1995 .

[14]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[15]  Tie-Yan Liu,et al.  BrowseRank: letting web users vote for page importance , 2008, SIGIR '08.

[16]  Qi He,et al.  TwitterRank: finding topic-sensitive influential twitterers , 2010, WSDM '10.

[17]  K. Schmidt,et al.  Making the consensus sale , 2015 .

[18]  Hector Garcia-Molina,et al.  Combating Web Spam with TrustRank , 2004, VLDB.

[19]  Yi Chang,et al.  Yahoo! Learning to Rank Challenge Overview , 2010, Yahoo! Learning to Rank Challenge.

[20]  Thomas Hofmann,et al.  Semi-supervised Learning on Directed Graphs , 2004, NIPS.

[21]  Taher H. Haveliwala.  Topic-sensitive PageRank , 2002, IEEE Trans. Knowl. Data Eng..

[22]  Paul J. Kennedy,et al.  Discovering influential authors in heterogeneous academic networks by a co-ranking method , 2013, CIKM.