Impact of Predictive Business Analytics on Digital Marketing Performance

Authors

  • Ritika Juneja

DOI:

https://doi.org/10.64882/ijrt.v5.i1.759

Keywords:

Predictive Business Analytics, Digital Marketing, Marketing Performance, Customer Behavior Prediction

Abstract

Predictive Business Analytics has become an effective instrument of improving digital marketing efficiencies in a more competitive and data-rich business climate. This paper discusses the relevance of predictive analytics methods to make use of past and current data to predict consumer behavior, optimize marketing campaigns and enhance decision-making processes across the digital platform. Through customer interaction trends, customer online browsing behavior, customer purchase patterns and engagement indicators, predictive analytics helps marketers to understand or predict customer needs, tailoring marketing messages and resource allocation to be more effective. The paper notes that predictive analytics helps to increase campaign efficiency because it improves targeting accuracy, lowers customer acquisition costs and improves conversion rates. Moreover, the assimilation of predictive analytics into the digital marketing approach helps in the proactive planning, measurement of performance and ongoing optimization. The paper recommends that predictive business analytics is important in making digital marketing a strategic performance-oriented rather than a reactive operation that creates quantifiable business value and sustainable competitive advantage.

References

Kotler, P., & Keller, K. L. (2016). Marketing management (15th ed.). Pearson Education.

Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121.

Germann, F., Lilien, G. L., & Rangaswamy, A. (2015). Marketing analytics and firm performance. Journal of Marketing Analytics, 3(1), 4–17.

Leeflang, P. S. H., Verhoef, P. C., Dahlström, P., & Freundt, T. (2015). Challenges and solutions for marketing in a digital era. European Management Journal, 33(1), 1–12. https://doi.org/10.1016/j.emj.2014.12.001

Germann, F., Lilien, G. L., & Rangaswamy, A. (2014). Performance implications of deploying marketing analytics. International Journal of Research in Marketing, 31(2), 114–128. https://doi.org/10.1016/j.ijresmar.2013.09.001

Järvinen, J., & Karjaluoto, H. (2014). The use of web analytics for digital marketing performance measurement. Industrial Marketing Management, 43(8), 1174–1185. https://doi.org/10.1016/j.indmarman.2014.06.009

Davenport, T. H. (2013). Analytics 3.0. Harvard Business Review, 91(12), 64–72.

Germann, F., Lilien, G. L., & Rangaswamy, A. (2013). Performance implications of deploying marketing analytics. International Journal of Research in Marketing, 30(2), 114–128. https://doi.org/10.1016/j.ijresmar.2012.08.001

Davenport, T. H., Barth, P., & Bean, R. (2012). How big data is different. MIT Sloan Management Review, 54(1), 43–46.

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2012). Big data: The next frontier for innovation, competition and productivity. McKinsey Global Institute.

Wedel, M., & Kannan, P. K. (2012). Marketing analytics for data-rich environments. Journal of Marketing, 76(6), 97–121.

Lavalle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21–32.

Lavalle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21–32.

Kumar, V., Venkatesan, R., & Reinartz, W. (2008). Performance implications of adopting a customer-focused sales campaign. Journal of Marketing, 72(5), 50–68. https://doi.org/10.1509/jmkg.72.5.50

Pauwels, K., Ambler, T., Clark, B. H., LaPointe, P., Reibstein, D., Skiera, B., Wierenga, B., & Wiesel, T. (2009). Dashboards as a service: Why, what, how and what research is needed? Journal of Service Research, 12(2), 175–189.

Downloads

How to Cite

Ritika Juneja. (2017). Impact of Predictive Business Analytics on Digital Marketing Performance. International Journal of Research & Technology, 5(1), 78–88. https://doi.org/10.64882/ijrt.v5.i1.759

Similar Articles

<< < 35 36 37 38 39 40 41 > >> 

You may also start an advanced similarity search for this article.