Implications of Data Analytics in Marketing and Best Practices for Responsible Use: A Survey Analysis

DOI : 10.17577/IJERTCONV12IS03022

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Implications of Data Analytics in Marketing and Best Practices for Responsible Use: A Survey Analysis

Implications of Data Analytics in Marketing and Best Practices for Responsible Use: A Survey Analysis

Dr. Jaffar Ali Akbar Ali

College of Computing and Information Science University of Technology and Applied Sciences, Sohar Email: akbarose@yahoo.com

Mobile: 9597482597 (WhatsApp)

I, INTRODUCTION

The emergence of Big Data analytics has changed the marketing landscape, allowing companies to dive deep into vast repositories of data to extract valuable insights. By exploiting the power of advanced algorithms and machine learning techniques, marketers can now personalize offers, optimize campaigns and improve customer experiences like never before. However, the increase in data also raises concerns about privacy, consent and ethical aspects. The purpose of this article is to explore both the opportunities and challenges of Big Data analytics in marketing and provide recommendations for responsible use.

  1. Big Data:

    Big data refers to large volumes of data, both structured and unstructured, that inundates a business on a day-to-day basis. But its not the amount of data that's important; it's what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.

  2. Characteristics of Big Data: Big Data has the following characteristics

    Volume: It refers to the vast amounts of data generated every second by various sources such as social media, sensors, etc.

    Velocity: It is the speed at which new data is generated and the pace at which data must be processed.

    Variety: It refers to the different types of data, including structured data (like numbers in a database), semi-structured data (like XML files), and unstructured data (like social media posts or images).

    Value: The ultimate goal of big data is to extract value from data by turning it into insights that lead to better decisions and strategic business moves.

    Veracity: Refers to the trustworthiness of the data being captured. With the rise of big data, ensuring data quality becomes a significant concern.

  3. Big Data Analytics:

Big data analytics helps businesses, used to improve healthcare sectors, used in manufacturing, useful for government operation, and educational institutions, etc.

Data analytics can show the entire customer lifecycle, from unmet needs and awareness of your products or services, to interactions with your company, to purchase and engagement. These same customers may even become product/company representatives who share their experiences with potential new customers.

One of the most important ways big data affects marketing strategies is by providing insights into consumer behavior. By analyzing customer data, companies can understand their customers' needs, preferences and buying habits. In this article we will discuss the implications of big data analytics in marketing.

II. RESEARCH

  1. Research Questions:

    1. What are the primary advantages of employing Big Data analytics in marketing?

    2. What are the key challenges and risks associated with Big Data analytics in marketing?

    3. What are the best practices for ensuring responsible and ethical use of Big Data in marketing strategies?

  2. Research Methodologies:

    This study employs a mixed-method approach, combining a comprehensive literature review with survey data analysis. The literature review provides a theoretical framework for understanding the implications of Big Data analytics in marketing, while survey data offers empirical insights into industry practices and perceptions.

  3. Survey Questions:

    1. What is your role within the marketing industry?

    2. To what extent does your organization utilize Big Data analytics for marketing purposes?

    3. What do you perceive as the primary advantages of using Big Data analytics in marketing?

    4. What are the main challenges or concerns you encounter when employing Big Data analytics in marketing strategies?

    5. What measures does your organization implement to ensure the responsible and ethical use of Big Data in marketing practices?

III. SURVEY DATA & RESULTS

  • Role within the marketing industry:

    • Marketer: 60%

    • Data Analyst: 20%

    • Executive/Manager: 15%

    • Other: 5%

  • Extent of Big Data analytics utilization:

    • Extensively: 40%

    • Moderately: 35%

    • Minimally: 20%

    • Not at all: 5%

  • Primary advantages of using Big Data analytics in marketing:

    • Enhanced targeting and personalization: 65%

    • Improved decision-making: 20%

    • Better understanding of customer behavior: 10%

    • Increased ROI: 5%

  • Main challenges/concerns:

    • Data privacy and security: 45%

    • Lack of skilled personnel: 25%

    • Interpretation of results: 15%

    • Ethical considerations: 15%

Advantages:

  • Enhanced targeting and personalization

  • Improved decision-making

  • Better understanding of customer behavior

  • Increased ROI

    Disadvantages:

  • Data privacy and security concerns

  • Lack of skilled personnel

  • Interpretation challenges

  • Ethical considerations

V. CONCLUSION

In conclusion, big data analytics has enormous potential to transform marketing practices and drive business growth. By

harnessing the power of data, marketers can gain deeper insights into consumer behavior, personalize experiences and optimize campaign performance. However, the responsible use of big data analytics is paramount to reducing the risks associated with privacy breaches, algorithms and ethical lapses.

REFERENCES

[1]. Chen, M., Mao, S., & Liu, Y. (2014).

Big data: A survey. Mobile Networks and Applications, 19(2), 171-209.

[2]. Davenport, T. H., & Harris, J. (2007). Competing on analytics: The new science of winning. Harvard Business Press.

[3]. Kim, J., & Han, S. (2014). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.

[4]. Mayer-Schönberger, V., &Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt.

[5]. Mittal, S., & Khan, M. L. (2017). Big data in marketing: A review. Journal of Marketing Anlytics, 5(4), 138-

146.

[6]. Schermann, M., Kolb, D., &Flath, C.

M. (2017). Big data analytics in marketing research. Business & Information Systems Engineering, 59(6), 437-448.

[7]. Smith, A. N., Fischer, E., &Yongjian,

C. (2012). How does brand-related user-generated content differ across YouTube, Facebook, and Twitter? Journal of Interactive Marketing, 26(2), 102-113.

[8]. Wang, R., & Strong, D. M. (1996).

Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5-33.

[9]. Wamba, S. F., Akter, S., Trinchera, L., de Bourmont, M., &Boada-Grau, J. (2017). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 70, 356-369.

[10]. Zikopoulos, P., Eaton, C., Deroos, D., Deutsch, T., & Lapis, G. (2012). Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media.