Analysis of Web Server Logs to Understand Internet User Behavior and Develop Digital Marketing Strategies

  • Authors

    • Saleh Mowla
    • Nisha P. Shetty
    2018-12-19
    https://doi.org/10.14419/ijet.v7i4.41.24290
  • digital marketing, pattern discovery, server logs, web analytics, web usage mining
  • With the advancement of technology and widespread availability of information through the internet and the World Wide Web, it has become possible for small-scale industries to convert and expand themselves by increasing awareness and exposure of their businesses. One of the most popular and easiest way to increase a customer base is by creating websites and thus engaging with customers online. However, with the increase in the number of websites and other available source of advertisements, it has become a need to design websites in a way that keeps the customers and viewers engaged and interested so that they return with positive expectations. One way of analyzing website popularity and online customer behavior is by analyzing web server logs with the help of web usage mining techniques to find unknown patterns and generate insights about different aspects of the website. The paper discusses a live scenario where logs of a blogging website have been mined and analyzed to better improve the structure of the site as well as understand the behavior of the viewers who have visited the site. The paper also provides recommendations on improving the structure of the website to adopt effective digital marketing strategies.

     

     

     
  • References

    1. [1] A. Kakkar, R. Majumdar and A. Kumar. Search engine optimization: A game of page ranking. In 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, 2015 (pp. 206-210).

      [2] D. Dixit and J. Gadge. Automatic Recommendation for Online Users Using Web Usage Mining. In International Journal of Managing Information Technology (IJMIT), Vol. 2, No. 3, 2010.

      [3] D. Tanasa and B. Trousse. Advanced data preprocessing for intersites Web usage mining. In IEEE Intelligent Systems (vol. 19, no. 2, pp. 59-65), Mar-Apr 2004.

      [4] M. Aldekhail. Application and Significance of Web Usage Mining in the 21st Century: A Literature Review. In International Journal of Computer Theory and Engineering, Vol. 8, No. 1, 2016

      [5] P. Verma and N. Kesswani. Comparative analysis of algorithms for identification of session on the basis of threshold value. 2016 3rd International Conference on Computing for Sustainable Global Development (INDIA Com), New Delhi, 2016 (pp. 3724-3730).

      [6] S. K. Dwivedi and B. Rawat. A review paper on data preprocessing: A critical phase in web usage mining process. 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), Noida, 2015 (pp. 506-510).

      [7] P. Sharma, S. Yadav and B. Bohra. A review study of server log formats for efficient web mining. 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), Noida, 2015 (pp. 1373-1377).

      [8] D. S. Sisodia and S. Verma. Web usage pattern analysis through web logs: A review. 2012 Ninth International Conference on Computer Science and Software Engineering (JCSSE), Bangkok, 2012 (pp. 49-53).

      [9] Chaffey, D. & Patron, M. “From web analytics to digital marketing optimization: Increasing the commercial value of digital analyticsâ€. Journal of Direct, Data Digital Marketing Practice, (2012) 14: 30. https://doi.org/10.1057/dddmp.2012.20

      [10] Xun, J. Return on web site visit duration: Applying web analytics data. Journal of Direct, Data Digital Marketing Practice, (2015) 17: 54. https://doi.org/10.1057/dddmp.2015.33

      [11] Vorvoreanu, M., Boisvenue, G., Wojtalewicz, C. et al. “Social media marketing analytics: A case study of the public's perception of Indianapolis as Super Bowl XLVI host city†Journal of Direct, Data Digital Marketing Practice (2013) 14: 321. https://doi.org/10.1057/dddmp.2013.18

      [12] Moissa B., de Carvalho L.S., Gasparini I. (2014) A Web Analytics and Visualization Tool to Understand Students’ Behavior in an Adaptive E-Learning System. In: Zaphiris P., Ioannou A. (eds) Learning and Collaboration Technologies. Designing and Developing Novel Learning Experiences. LCT 2014. Lecture Notes in Computer Science, vol 8523. Springer, Cham. https://doi.org/10.1007/978-3-319-07482-5_30

      [13] HASAN, L., MORRIS, A. and PROBETS, S., 2009. Using Google Analytics to evaluate the usability of e-commerce sites. IN: Kurosu, M. (ed.). Human Centered Design, HCII 2009, Lecture Notes in Computer Science 5619, pp. 697-706.

      [14] Mo Wang and Juanle Wang. A data preprocessing framework of geoscience data sharing portal for user behavior mining. 2015 23rd International Conference on Geoinformatics, Wuhan, 2015 (pp. 1-5).

      [15] G. Neelima and S. Rodda. Predicting user behavior through sessions using the web log mining. 2016 International Conference on Advances in Human Machine Interaction (HMI), Doddaballapur, 2016 (pp. 1-5).

      [16] N. Neelima and Syeda Farha Shazmeen. Visual data mining to discover knowledge patterns from Web navigational trends. 2011 International Conference on Recent Trends in Information Systems, Kolkata, 2011 (pp. 117-120).

      [17] T. Badriyah, E. T. Wijayanto, I. Syarif and P. Kristalina. A hybrid recommendation system for E-commerce based on product description and user profile. 2017 Seventh International Conference on Innovative Computing Technology (INTECH), Luton, 2017 (pp. 95-100).

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  • How to Cite

    Mowla, S., & P. Shetty, N. (2018). Analysis of Web Server Logs to Understand Internet User Behavior and Develop Digital Marketing Strategies. International Journal of Engineering & Technology, 7(4.41), 15-21. https://doi.org/10.14419/ijet.v7i4.41.24290