Data Analytics and Related Marketing Risks

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Table of Contents

Introduction
Choice of Type of Organisation
The Importance of Data Analysis in Marketing
Applications of Data Analytics
Potential Risks
Risk Management Techniques
Conclusion
Reference List

Introduction

The motivation for choosing a professional role for this assignment is my personal interest in the field described below, as well as my desire to learn as much as possible about the characteristics and aspects of the role. Specifically, the role chosen is that of a data analyst, which requires the individual to be highly skilled in statistical tools, to have appropriate training, and to have a mathematical and systematic way of thinking that allows them to work with numbers and non-textual information. The data analyst is a highly valuable role that can be useful to almost any organization, regardless of industry. The reason for these multiple manifestations is the profound digitization of today’s world, the rapid transition of businesses to a technology base, and the adoption of deep data practices (Sharma and Biros, 2020; Kossovsky, 2020). Needless to say, the opportunity to work in almost any organization is an additional motivating factor that leads me to choose this particular professional role for the context of the essay.

Choice of Type of Organisation

Given the wide range of practical applications of data analysis, special consideration should be given to the type of organization in which I would prefer to work as a professional. I am most interested in constantly updating large amounts of data, between which it is possible to find correlations and hidden patterns that are not detectable in primary observation. This is why I prefer to work for large marketing organizations or companies like McKinsey & Company. Such companies routinely collect large amounts of diverse data, from the demographics of respondents to their preferences, attitudes toward the object of study, and satisfaction levels (Buhalis and Volchek, 2021). Moreover, the results of their professional performance have a practical reality, as the results of statistical analysis provide insights on how to better build a company, increase sales, and personalize the customer experience. For this reason, this area is the most interesting for me, which is why I dedicate this critical essay to this study.

The Importance of Data Analysis in Marketing

When studying consumer behavior, a great deal of attention should be paid to data because it allows for results that are independent of assumptions and personal beliefs. The advantage of fact-based analysis is that it provides a practically objective and data-only consideration that allows for more reliable and practical decisions (HBS, 2019). Meanwhile, data is becoming an increasingly valuable tool in dealing with consumer behavior, and its role is rapidly expanding. Statistics show that the amount of data used, stored, and consumed globally is growing almost exponentially, with the figure at the end of 2022 estimated to be approximately 150% higher than it was a decade ago, with a steady upward trend expected to continue (Taylor, 2022). This is not surprising, as the number of users accessing the Internet is also on the rise, as is the global population (Vailshery, 2022). Thus, data analytics is highly relevant and on today’s agenda.

Thanks to the vast amount of data with a wide variety of characteristics, companies can process incoming information and modify organizational environments and customer offerings in ways that achieve key objectives, namely increasing profits. Examples of such data that are of increased value to marketing include consumer demographics, information about preferences and attitudes toward specific products or shopping areas, needs, and buying patterns. By collecting, analyzing, and interpreting such data, decision makers can fine-tune sales strategies and offer personalized solutions, including optimized pricing strategies, to improve the customer experience.

Applications of Data Analytics

The essence of applying statistical analysis to collected data is to identify patterns, regularities, and trends. It is helpful to consider some examples that illustrate the benefits of such techniques. For example, information about the timing of visits to a particular shopping mall can be collected and analyzed to more accurately adjust opening hours, offer more discounts during peak hours, and manage staff schedules to avoid store queues (Park, Yuan, and Choe, 2021). In addition, data analysts can use purchase history data to determine which products are most popular among different customer segments; this information can then be used to inform inventory management, product development, and advertising strategies (Zhang et al., 2020). Most commercial organizations also engage in advertising campaigns, so data analytics makes it easier to measure the effectiveness of marketing campaigns. In this case, analysts can track the effectiveness of different communication channels — email, social media, and paid advertising — to determine which ones are generating the most revenue and which ones may be unprofitable (Pierson, 2022). When data analysis is done in conjunction with more serious data processing technologies, such as machine learning and AI, it makes sense to build predictive models (Inanc-Demir and Kozak, 2019). Such models can identify trends that can be used to predict future sales and customer behavior, and thus actually show a company’s management which direction to take. In each of the examples described, data analytics helps to optimize the company’s performance control processes and internal operations, which has significant benefits for the company in both the short and long term.

Potential Risks

Despite the clear benefits of data analytics in marketing, there are several risks associated with performing professional tasks. Traditionally, these risks can be divided into two categories: those that are directly related to human activity and those that are not. The first category of risks should include those that arise through the fault of a data analyst and therefore can be eliminated by appropriate methods. These include data misuse, where analysts misuse collected data or use flawed analysis methods. It should also include inappropriate analysis, where the data is used for unauthorized purposes or in any way discriminates against consumers or manipulates their behavior (Griffin House, 2022; Wieringa et al., 2021). In addition, such risks include the quality of the data collected in the first place, as errors in which specific data should be collected lead to erroneous conclusions and impractical results. It is not uncommon for data analysts to discover relationships that were not apparent in their original study — the lack of proper and effective control over decisions based on such discoveries can lead to unintended consequences. Over-reliance on data is also a significant risk, leading to all organizational decisions being made based solely on analytics. Data analytics is a useful and streamlined tool, but it should not rob an organization of creativity and human compassion.

The second category of risk consists of those factors that are rarely man-made and therefore more difficult to correct. Most predictive models use time series, or data that is of historical value to the company (Lim and Zohren, 2021). While forecasts based on past data are helpful, they do not always accurately reflect future uncertainties. Any geopolitical and legal changes, natural disasters, and shifts in public sentiment can affect sales dynamics, which in turn will invalidate projections (Esposito, 2022). Risks of this type should also include comparable data complexity, because the more complex the data, the less analyzable it is, and therefore the results could be erroneous or redundant, which would be detrimental to the company’s operations. A consequence of this risk is also potential problems in integrating data analytics with other systems, such as CRM, ERP, and other internal systems, which can lead to errors or inconsistencies in the company’s operations. Lack of transparency in data analytics can also lead to increased mistrust and skepticism among stakeholders, disrupting previously established communication efficiencies. Limited data access is also a risk: It defines a situation in which the data desired for analysis cannot be collected and therefore cannot be used in analysis. Thus, risks not directly related to human activity pose multiple threats to the organization and require careful management.

A special category of risk, unrelated to the two discussed above, is problems with the confidentiality of the data collected. Because marketing companies are interested in studying consumer behavior patterns, they collect demographic and personal data, the storage of which can be a concern. Leaks of users’ personal data have been widely reported, creating legal and reputational liability risks for organizations (Di Martino et al., 2019). The rapid shift to digital and cloud-based systems creates cybersecurity risks, including hacking through social engineering, which means that the quantity and quality of such risks in organizations are increasing (Aldawood and Skinner, 2019). Therefore, privacy concerns related to the collected data require special attention when planning the implementation of their analytics.

Risk Management Techniques

Once the risks have been identified and it is clear what challenges an organization interested in data analytics may face, the focus must shift to risk management strategies to mitigate their adverse impact on the organization. The main objective of such management is to ensure the confidentiality of stored data, which includes taking measures to protect personal data. The international regulation in force on the territory of the European Union, which regulates the rules of data protection, is the General Data Protection Regulation (GDPR). The GDPR postulates that the data collected must be obtained with the consent of the user (2.7), deleted promptly at the request of the customer (3.17), and the responsibility for its security lies with the firm collecting the data (4.24) (GDPR, 2018a). Among the specific measures that companies can take to ensure privacy, which renders the data invalid even if it is leaked, the GDPR refers to “the pseudonymization and encryption of personal data”, “the ability to ensure the ongoing confidentiality, integrity, availability and resilience of the processing systems and services”, and “a process to regularly test, assess and evaluate the effectiveness of the technical and organizational measures to ensure the security of the processing” (GDPR, 2018b, ch. 4.2.32). Thus, any company working with data analytics must ensure that it is protected, or it will be fully responsible for any breaches.

Ensuring the confidentiality and protection of the data used is part of the compliance strategy and policy. The company must comply with existing regulatory requirements, which demonstrate full transparency to the law. This ensures that the company only collects data that it is permitted to collect or store in other countries. One tactic to ensure compliance is to provide ongoing training to employees to educate them on the importance of privacy and how to avoid social engineering. Research shows that such training is an essential step in achieving the desired results, as in its absence, 46% of employees do not change their organizational behavior and thus remain vulnerable to new cyberattacks (Borkovich and Skovira, 2019; Aldawood and Skinner, 2019). Therefore, training should be addressed, and the nature of such training should be based on the principles of continuity and systematicity.

In addition, risk management should include ongoing auditing and testing of internal processes. Such strategies, while costly, allow internal operations to be analyzed to identify and correct errors or inconsistencies. Management should also include regular assessment of data quality and implementation of measures to improve data accuracy and completeness to avoid erroneous or incomplete conclusions. Thus, any risks that arise during the operational management phases should be continually assessed and analyzed so that additional steps can be taken to reduce their adverse impact.

Conclusion

In conclusion, the role of data analytics is rapidly expanding in today’s world, and it would be impractical to ignore its importance to businesses. Data used in marketing allows for fine-tuning of a company’s operational processes, improving and personalizing the customer experience, and optimizing routine processes. There is a particular benefit when data analytics is integrated with AI and machine learning technologies. However, there are risks associated with implementing such solutions in an enterprise, which are discussed in detail in this paper. In addition to a detailed analysis of the potential risks, the paper also discusses how to manage them, which creates opportunities to minimize adverse effects and increase the efficiency of the measures taken.

Reference List

Aldawood, H. and Skinner, G. (2019) ‘Reviewing cyber security social engineering training and awareness programs — pitfalls and ongoing issues’, Future Internet, 11(3), pp. 73-89.

Borkovich, D.J. and Skovira, R.J. (2019) ‘Cybersecurity inertia and social engineering: who’s worse, employees or hackers’, Issues in Information Systems, 20(3), pp. 139-150.

Buhalis, D. and Volchek, K. (2021) ‘Bridging marketing theory and big data analytics: the taxonomy of marketing attribution’, International Journal of Information Management, 56, pp. 1-14.

Di Martino, M., et al. (2019) Personal information leakage by abusing the ‘right of access’. Web.

Esposito, E. (2022) The future of prediction: from statistical uncertainty to algorithmic forecasts. Web.

GDPR (2018a) General Data Protection Regulation. Web.

GDPR (2018b) Security of processing. Web.

Griffin House (2022) Is Google Analytics illegal? Web.

HBS (2019) The advantages of data-driven decision-making. Web.

Inanc–Demir, M. and Kozak, M. (2019) ‘Big data and its supporting elements: implications for tourism and hospitality marketing’, in Sigala, M., Rahimi, R., and Thelwall, M. (eds.) Big data and innovation in tourism, travel, and hospitality. Singapore: Springer, pp. 213-223.

Kossovsky, A. E. (2020) ‘Johannes Kepler — the first data analyst’, in Kossovsky, A. E. (ed.) The birth of science. Cham: Springer, pp. 23-24.

Lim, B., and Zohren, S. (2021) ‘Time-series forecasting with deep learning: a survey’, Philosophical Transactions of the Royal Society A, 379(2194), pp. 1-13.

Park, S., Yuan, Y. and Choe, Y. (2021) ‘Application of graph theory to mining the similarity of travel trajectories’, Tourism Management, 87, pp. 1-16

Pierson, J. (2022) Media and communication studies, privacy and public values: future challenges. Web.

Sharma, M. and Biros, D. (2020) Effects of abilities of data analyst teams and AI development. Web.

Taylor, P. (2022) Amount of data created, consumed, and stored 2010-2020, with forecasts to 2025. Web.

Vailshery, L. S. (2022) Number of IoT connected devices worldwide 2019-2021, with forecasts to 2030. Web.

Wieringa, J., et al. (2021) ‘Data analytics in a privacy-concerned world’, Journal of Business Research, 122, pp. 915-925.

Zhang, Q., et al. (2020) ‘Analysis of purchase history data based on a new latent class model for RFM analysis’, Industrial Engineering & Management Systems, 19(2), pp. 476-483.

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