Internet Advertising Investment Analysis Based on Beijing and Jinhua Signaling Data

With the popularization of the internet, the media of advertising tends to diversify. Compared to traditional advertising methods, internet advertising is well received for its convenient and fast propagation mode. However due to its mandatory and invasive property, the publicity effect of internet advertising is greatly reduced. To make the delivery of Internet advertising more directional and efficient, we try to analyze the behavioral preferences of netizens from Beijing and Jinhua when using different Apps, and then conclude some rational delivery strategies for internet advertising investors. We use Multi-state Models (MSM) to construct an online Apps transfer model for netizens, which will help to get some intuitive conclusions. After that, basing on online apps transfer model, we adopt entropy method, hierarchical clustering, and Tucker decomposition to mine the potential behavioral preferences of netizens. Finally we provide some suggestions and references with the internet advertisers.

[1]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[2]  Jinhong Xie,et al.  Third-Party Product Review and Firm Marketing Strategy , 2005 .

[3]  Chuck Martin.  The Third Screen: Marketing to Your Customers in a World Gone Mobile , 2011 .

[4]  Eric J. Johnson,et al.  Digitizing Consumer Research , 2001 .

[5]  Paul Levinson,et al.  Cellphone : The Story of the World's Most Mobile Medium and How It Has Transformed Everything! , 2004 .

[6]  Chang-Hoan Cho,et al.  WHY DO PEOPLE AVOID ADVERTISING ON THE INTERNET? , 2004 .

[7]  David M. Blei,et al.  Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations , 2016, ICML.

[8]  S. C. Johnson.  Hierarchical clustering schemes , 1967, Psychometrika.

[9]  Meikang Qiu,et al.  Energy minimization with loop fusion and multi-functional-unit scheduling for multidimensional DSP , 2008, J. Parallel Distributed Comput..

[10]  Meikang Qiu,et al.  Enabling real-time information service on telehealth system over cloud-based big data platform , 2017, J. Syst. Archit..

[11]  Cindy Krum,et al.  Mobile Marketing: Finding Your Customers No Matter Where They Are , 2010 .

[12]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[13]  Min Chen,et al.  SPHA: Smart Personal Health Advisor Based on Deep Analytics , 2018, IEEE Communications Magazine.

[14]  Meikang Qiu,et al.  Voltage Assignment with Guaranteed Probability Satisfying Timing Constraint for Real-time Multiproceesor DSP , 2007, J. VLSI Signal Process..

[15]  Jiao Lu.  Study of Informative Advertising Competition Model in Duopolistic Market with Relative Profit Object , 2017 .

[16]  C. Lynch.  Big data: How do your data grow? , 2008, Nature.

[17]  Salah Bourennane,et al.  Dimensionality Reduction Based on Tensor Modeling for Classification Methods , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Tong Zhang,et al.  Crowd Fraud Detection in Internet Advertising , 2015, WWW.

[19]  Alexandros Labrinidis,et al.  Challenges and Opportunities with Big Data , 2012, Proc. VLDB Endow..