Open Access   Article Go Back

Processes and Techniques in Digital Marketing Analytics

Sanjana Karanam1 , Rajashree Shettar2

  1. Department of Computer Science and Engineering, R.V. College of Engineering, Bangalore, India.
  2. Department of Computer Science and Engineering, R.V. College of Engineering, Bangalore, India.

Section:Research Paper, Product Type: Journal Paper
Volume-8 , Issue-4 , Page no. 28-33, Apr-2020

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v8i4.2833

Online published on Apr 30, 2020

Copyright © Sanjana Karanam, Rajashree Shettar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Sanjana Karanam, Rajashree Shettar, “Processes and Techniques in Digital Marketing Analytics,” International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.28-33, 2020.

MLA Style Citation: Sanjana Karanam, Rajashree Shettar "Processes and Techniques in Digital Marketing Analytics." International Journal of Computer Sciences and Engineering 8.4 (2020): 28-33.

APA Style Citation: Sanjana Karanam, Rajashree Shettar, (2020). Processes and Techniques in Digital Marketing Analytics. International Journal of Computer Sciences and Engineering, 8(4), 28-33.

BibTex Style Citation:
@article{Karanam_2020,
author = {Sanjana Karanam, Rajashree Shettar},
title = {Processes and Techniques in Digital Marketing Analytics},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2020},
volume = {8},
Issue = {4},
month = {4},
year = {2020},
issn = {2347-2693},
pages = {28-33},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5070},
doi = {https://doi.org/10.26438/ijcse/v8i4.2833}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v8i4.2833}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5070
TI - Processes and Techniques in Digital Marketing Analytics
T2 - International Journal of Computer Sciences and Engineering
AU - Sanjana Karanam, Rajashree Shettar
PY - 2020
DA - 2020/04/30
PB - IJCSE, Indore, INDIA
SP - 28-33
IS - 4
VL - 8
SN - 2347-2693
ER -

VIEWS PDF XML
278 375 downloads 163 downloads
  
  
           

Abstract

With the growing technologies and the dominance of digital media, the way companies market has changed and businesses are doing all they can to surpass their competitors. With the advancements in technology, as of 2019, 82% of businesses engage in digital marketing. With ever increasing data analysis tools and growth of statistical machine learning as a field, digital marketing channels have seen tremendous growth, and are now considered as an essential part of every company. Though each “company” carries out its analysis in their own way, there are five basic steps involved in digital marketing analysis. This paper presents the steps involved in the process of digital marketing analytics, along with their importance and the most prominent method in each step by exploring popular machine learning tools and proposes a general framework for digital marketing analytics.

Key-Words / Index Term

digital marketing analytics, digital marketing, machine learning, analytics

References

[1] M. T. P. M. B. Tiago and J. M. C. Veríssimo, “Digital marketing and social media: Why bother?,” Business Horizons, vol. 57, no. 6, pp. 703–708, Nov. 2014.
[2] H. M. Taiminen and H. Karjaluoto, “The usage of digital marketing channels in SMEs,” Journal of Small Business and Enterprise Development, vol. 22, no. 4, pp. 633–651, Nov. 2015.
[3] P. S. H. Leeflang, P. C. Verhoef, P. Dahlström, and T. Freundt, “Challenges and solutions for marketing in a digital era,” European Management Journal, vol. 32, no. 1, pp. 1–12, Feb. 2014.
[4] V. Shankar, J. J. Inman, M. Mantrala, E. Kelley, and R. Rizley, “Innovations in Shopper Marketing: Current Insights and Future Research Issues,” Journal of Retailing, vol. 87, pp. S29–S42, Jul. 2011.
[5] T. Wuest, D. Weimer, C. Irgens, and K.-D. Thoben, “Machine learning in manufacturing: advantages, challenges, and applications,” Production & Manufacturing Research, vol. 4, no. 1, pp. 23–45, Jan. 2016.
[6] H. Li, “Deep learning for natural language processing: advantages and challenges,” National Science Review, vol. 5, no. 1, pp. 24–26, Sep. 2017.
[7] J. Lin and A. Kolcz, “Large-scale machine learning at Twitter,” in Proceedings of the 2012 international conference on Management of Data - SIGMOD ’12, 2012.
[8] M. Badieh Habib Morgan and M. van Keulen, “Information Extraction for Social Media,” in Proceedings of the Third Workshop on Semantic Web and Information Extraction, 2014.
[9] Adarsh Pradhan, Nabanita Paul, Kakali Das, Rupam Jyoti Bordoloi, Dikhya Baruah, “Survey On Speech Recognition Using Hidden MARKOV Model,” International Journal of Computer Sciences and Engineering, Vol.04, Issue.07, pp.158-161, 2016.
[10] L. M. Badea (Stroie), “Predicting Consumer Behavior with Artificial Neural Networks,” Procedia Economics and Finance, vol. 15, pp. 238–246, 2014.
[11] Yunzhong Liu, Yaping Lin and Zhiping Chen, "Using hidden Markov model for information extraction based on multiple templates," International Conference on Natural Language Processing and Knowledge Engineering, 2003. Proceedings. 2003, Beijing, China, 2003, pp. 394-399.
[12] X. Zhao and Y. Ohsawa, “Sentiment Analysis on the Online Reviews Based on Hidden Markov Model,” Journal of Advances in Information Technology, vol. 9, no. 2, pp. 33–38, 2018.
[13] N.-R. Kim, K. Kim, and J.-H. Lee, “Sentiment Analysis in Microblogs Using HMMs with Syntactic and Sentimental Information,” International Journal Of Fuzzy Logic And Intelligent Systems, vol. 17, no. 4, pp. 329–336, Dec. 2017.
[14] D. Voramontri and L. Klieb, “Impact of Social Media on Consumer Behaviour,” International Journal of Information and Decision Sciences, vol. 11, no. 3, p. 1, 2019.
[15] Seyed Mohammad Hossein Hasheminejad and Mojgan Khorrami, “Data mining techniques for analyzing bank customers: A survey,” IDT, vol. 12, no. 3, pp. 303–321, Dec. 2018.
[16] L. Meyer-Waarden and C. Benavent, “The Impact of Loyalty Programmes on Repeat Purchase Behaviour,” Journal of Marketing Management, vol. 22, no. 1–2, pp. 61–88, Feb. 2006.
[17] Rexzy Tarnando, Yuli Karyanti, “Comparison of Text Classification Algorithms of People Sentiments on Twitter (Case: Transjakarta),” International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.8-12, 2019.
[18] A. Zolnierek and B. Rubacha, “The Empirical Study of the Naive Bayes Classifier in the Case of Markov Chain Recognition Task,” in Advances in Soft Computing, Springer Berlin Heidelberg, pp. 329–336.
[19] M. M. A. Grau, M. Tajtakova, and D. A. Aranda, “Machine learning methods for the market segmentation of the performing arts audiences,” International Journal of Business Environment, vol. 2, no. 3, p. 356, 2009.
[20] P. Cal and M. Woźniak, “Parallel Decision Tree for Streaming Data,” in Distributed Computing and Artificial Intelligence, Springer International Publishing, 2013, pp. 27–35.
[21] M. Ramasamy, S. Selvaraj and M. Mayilvaganan, "An empirical analysis of decision tree algorithms," 2015 IEEE International Conference on Engineering and Technology (ICETECH), Coimbatore, 2015, pp. 1-4.
[22] D. L. Olson and B. Chae, “Direct marketing decision support through predictive customer response modeling,” Decision Support Systems, vol. 54, no. 1, pp. 443–451, Dec. 2012.
[23] C. Hartmann, P. Varshney, K. Mehrotra and C. Gerberich, "Application of information theory to the construction of efficient decision trees," in IEEE Transactions on Information Theory, vol. 28, no. 4, pp. 565-577, July 1982.
[24] W. Xiaohu, W. Lele, and L. Nianfeng, “An Application of Decision Tree Based on ID3,” Physics Procedia, vol. 25, pp. 1017–1021, 2012.
[25] F. Sciarrone, "Machine Learning and Learning Analytics: Integrating Data with Learning," 2018 17th International Conference on Information Technology Based Higher Education and Training (ITHET), Olhao, 2018, pp. 1-5.
[26] I. C. Passos, B. Mwangi, and F. Kapczinski, “Big data analytics and machine learning: 2015 and beyond,” The Lancet Psychiatry, vol. 3, no. 1, pp. 13–15, Jan. 2016.
[27] T. Wuest, D. Weimer, C. Irgens, and K.-D. Thoben, “Machine learning in manufacturing: advantages, challenges, and applications,” Production & Manufacturing Research, vol. 4, no. 1, pp. 23–45, Jan. 2016.
[28] Chen, Chiang, and Storey, “Business Intelligence and Analytics: From Big Data to Big Impact,” MIS Quarterly, vol. 36, no. 4, p. 1165, 2012.
[29] Z. Ge, Z. Song, S. X. Ding and B. Huang, "Data Mining and Analytics in the Process Industry: The Role of Machine Learning," in IEEE Access, vol. 5, pp. 20590-20616, 2017.
[30] Z. Sun, L. Sun, and K. Strang, “Big Data Analytics Services for Enhancing Business Intelligence,” Journal of Computer Information Systems, vol. 58, no. 2, pp. 162–169, Oct. 2016.
[31] S. Poornima and M. Pushpalatha, “A survey of predictive analytics using big data with data mining,” International Journal of Bioinformatics Research and Applications, vol. 14, no. 3, p. 269, 2018.
[32] K. Lepenioti, A. Bousdekis, D. Apostolou, and G. Mentzas, “Prescriptive Analytics: A Survey of Approaches and Methods,” in Business Information Systems Workshops, Springer International Publishing, 2019, pp. 449–460.
[33] T. Itoh, A. Kumar, K. Klein, and J. Kim, “High-dimensional data visualization by interactive construction of low-dimensional parallel coordinate plots,” Journal of Visual Languages & Computing, vol. 43, pp. 1–13, Dec. 2017.
[34] G. Chawla, S. Bamal, and R. Khatana, “Big Data Analytics for Data Visualization: Review of Techniques,” International Journal of Computer Applications, vol. 182, no. 21, pp. 37–40, Oct. 2018.
[35] Yunzhong Liu, Yaping Lin and Zhiping Chen, "Using hidden Markov model for information extraction based on multiple templates," International Conference on Natural Language Processing and Knowledge Engineering, 2003. Proceedings. 2003, Beijing, China, 2003, pp. 394-399.
[36] J. Qiu, Z. Lin, and Y. Li, “Predicting customer purchase behavior in the e-commerce context,” Electronic Commerce Research, vol. 15, no. 4, pp. 427–452, Jun. 2015.
[37] Harsh H. Patel, Purvi Prajapati, "Study and Analysis of Decision Tree Based Classification Algorithms", International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.74-78, 2018.