Relationship of AIDA Model towards Data Analytics Capabilities, Marketing Strategies and Digital Marketing Performance on Small and Medium Enterprises (SMEs)

Rahmat Aidil Djubair¹, Winnie Wong Poh Ming²

^1,2 School of Business and Management, University of Technology Sarawak, Malaysia

ABSTRACT: AIDA Model (Action, Interest, Desire and Action) is one of the most used and popular steps being used and examine when focusing on consumer purchase decision-making processes. By looking on the relationship between AIDA Model’s steps and how much they influence marketing strategies, the research would like to finally examine if the relationship is mediated by the abilities of the SMEs digital marketing players and in the end to the digital marketing performance consist of financial and non-financial performance. The research concluded that SMEs in Sarawak still yet able to fully utilized most of the steps in AIDA Model for the benefits of their digital marketing strategies. However, their capabilities and realization towards the important of data analytics especially those that provided by digital marketing platforms and tools seems to have positively impacting their marketing strategies and therefore digital marketing performance.

KEYWORDS: Digital Marketing Performance, Data Analytics Capabilities, Sarawak SME, AIDA Model

1. BACKGROUND OF THE STUDY

This research aims to measure the current digital and e-commerce capabilities of small and medium-sized enterprises (SMEs) in Sarawak. This will be accomplished by gauging the respondents’ levels of knowledge, experience, and performance with the help of the Awareness, Interest, Desire, and Action (AIDA) Model. Other studies have also focused on the performance of Digital Marketing (DM). However, the measurement in this study is improved further by the addition of a further essential component in assessing the effectiveness of DM activities called data analysis capabilities (Gever, 2017). These capabilities can alter a company’s marketing strategy, which in turn can affect the performance of digital marketing. The AIDA Model will be incorporated into the study questionnaire in order to assess the respondents’ use of the data gained from DM operations in all scenarios relevant to the model.

At this time, all of the people and places on earth are linked together through a single network known as the Internet. Over half of the world’s population, or around 4.5 billion people, are connected to the internet; of this number, 3.8 billion are considered to be active social media users. There are currently 7 billion people in the globe (Hootsuite, 2020). The Internet is the only single system that is used consistently by a significant majority of people all over the world. It is also possible to draw a conclusion from the same data, which is that out of the total population of 7.75 billion people in the globe, 5.19 billion people use mobile phones, and 4.64 billion people, or 59%, utilize the internet. Active social media users make up around 49 percent of the world’s population, which is equal to 3.80 billion people (Hootsuite, 2020). Because of the lightning-fast pace at which the Internet was developed and put into use, human existence has been thrust into the digital age, which has prompted literary works to investigate the relationship between the digital and the real worlds. (Wang & Loo, 2019). Smartphone applications, social media platforms, the Internet, and a wide variety of other forms of digital networking tools are now ingrained in the daily routines of billions of people all over the world. At this time, there are 4.54 billion individuals who have access to the Internet, which is equivalent to 59 percent of the entire population of the globe (Saura, 2021). The consumption of digital media is a significant component for a great number of people. In 2019, 2.95 billion individuals throughout the world were active in digital activities; this number is projected to climb to roughly 3.43 billion by 2023 (• Digital Users Worldwide 2020 | Statista, n.d. 2020).

Data is the most important factor in assessing the success of any online marketing endeavor in the present climate of digital marketing (Singaraju & Niininen, 2021). Both small and medium-sized enterprises (SMEs) and large organizations (corporations) are finding success with various digital marketing strategies. SMEs stand for small and medium-sized businesses. These companies are employing data science and digital marketing in an effort to expand their customer base, boost brand awareness, and break into new market niches (Saura, 2021). This research study aims to ascertain the valid and reliable determinants of digital marketing performance on the Sarawak’s Small and Medium Enterprises (SME) digital marketing players through the effectiveness of their marketing strategies derived from data analytics capability. Since data and the decision-making process play an important role in evaluating the performance of digital marketing (Khade, 2016), the purpose of this research study is to determine the valid and reliable determinants of digital marketing performance on the Sarawak’s Small and Medium Enterprises (SMEs)

2. LITERATURE REVIEW

2.1          AIDA Model

Business, as defined by Mackey et al., (2013), “is the voluntary exchange of value generated by two parties.” As such, it is crucial for business executives to understand the decision-making process clients go through when making a purchase (Kusumawati, 2019). Marketing campaigns and advertisements may sway consumers at this phase of the buying process (Dahiya & Gayatri, 2018), and pricing and other visible details may influence their decisions. Awareness, Interest, Desire, and Action are the meanings behind the letters AIDA. The advertising industry makes use of this method for classifying the various responses a customer may have to an advertisement. When it comes to managing marketing and advertising campaigns, AIDA is generally seen as the gold standard measuring the steps of purchase decision (Purbaningsih et al., 2022). Elmo Lewis introduced the AIDA model in 1898 (Ganesh*, 2020). Since then, the AIDA framework has been shown to be useful in a wide range of social settings. The AIDA idea is very useful in the advertising industry since it is used to evaluate the success of marketing initiatives.

2.1          Digital Marketing

The term “digital marketing” refers to the practice of using the Internet or other forms of online advertising to attract new clients (Sridevi et al., 2021). Literature defines digital marketing as “the process of promoting a business’s goods and services utilizing any and all forms of electronic media and communication” (Omar et al., 2020). To a large extent, this definition accords with what (Chaffey, 2010) describes as the online definition of marketing: the effort to employ digital technology to achieve the marketing objective. According to Chaffey’s findings, digital marketing can effectively identify, forecast, and respond to customers’ needs in real time. Relatedly, web marketing helps keep customers happy and satisfied. In digital marketing, the connection with the customer is managed in a way that yields financial gain for the business and additional equity for the customer (Ghorbani et al., 2021). Internet marketing, as defined by (Tiago & Veríssimo, 2016), encompasses any and all efforts made, both online and offline, to encourage others to make a purchase from a website or other media on the internet; accordingly, internet marketing is carried out with the use of technological tools.

2.2          Digital Marketing Performance

The success of a company’s marketing efforts conducted via the use of any and all digital marketing channels is measured by the latter’s “digital marketing performance” (Facebook, Instagram, TikTok, Google Ads, other message applications, etc). (Brar, 2021) research utilized analytics data from digital marketing to evaluate the efficacy of these strategies. The success of a company’s digital marketing efforts can be measured using the scorecard approach developed by (Fatin & Rahman, 2020). This approach takes into account the following factors: the company’s capacity for innovation and learning, the effectiveness of its internal business processes, and the level of its customers’ satisfaction. Similar to the emphasis placed on digital analytics by (Mero, 2016) recommend using web analytics like Google Analytics, Facebook Insights, SEMrush, and many other online tools to evaluate the success of your digital marketing campaigns.

2.3          Marketing Strategies

For businesses to success in any business environment especially related to digitalization, marketing strategy is a must and crucial. According to González Moreno et al., (2022), a business whose marketing campaigns had a significant impact on the local community who would include the company’s heavy use of social media and merchandise would stand out in the market. Marketing strategy is being used in order to increased competitiveness and therefore the most effective in product promotion and for quality development (Ariadini, 2022). In research by Brar et al., (2021), the research found that marketing strategy can also be designed in order to assist a discovering process of market strategy, to have better perspectives of competitive marketing environment including on how to effectively handling situation related to business decision making process. Pricing, product, promotion, distribution, marketing research, sales, advertising, merchandising, etc., may all play a role in the overall framework of a marketing strategy (Proctor, 2020).

2.4          Data Analytics Capabilities

Data management expertise may provide a corporation a competitive edge that is difficult to duplicate. The extensive use of digital platforms and the general availability of the internet have also led to a radical shift in the last decade in how firms’ skills for value creation and innovation are developed (Ducange et al., 2018). New and challenging opportunities to meet the needs of customers all over the world have arisen as a result of the widespread adoption of digital platforms. With so much information accessible on the wants and preferences of people throughout the world, businesses face a variety of difficulties (Coviello et al 2017). We call these massive, never-before-seen troves of information “big data,” because they are truly revolutionary (Gupta & George, 2016). Big data text analytics on customer reviews, for instance, might help companies learn more about their clients’ propensities to make purchases after reading about their products and services (Apampa, 2020).

3. RESEARCH FRAMEWORK

This research aims to investigate the moderating effect of data management capabilities on the direct and indirect impact of interest as one of the customers’ decision-making processes (AIDA) adopted by digital marketing players as guidance in performing digital marketing activities on digital marketing performance. Figure 1 shows a potential pathway between AIDA and successful digital marketing; this study aims to investigate if data analytics capabilities mitigate this connection. An extensive literature search was conducted, and a theoretical framework was created to help reach the study’s goals. An intriguing aspect of this research is its focus on the factors that influence customers’ final purchasing decisions. One major goal of this study was to develop a research framework that would encompass all of the critical factors that shape and affect the digital marketing players of Sarawak’s SMEs toward digital marketing performance. The research also confirms previous findings that show how data management skills might regulate the relationship between interest and digital marketing success.

(See in PDF File)

Fig. 1. Proposed research framework

4. METHOD

A self-administered questionnaire written in English was used to collect information from residents of all of Sarawak’s major cities. Data collection was done over the course of four months in an effort to reduce bias. The survey was completed independently and sent electronically (through Google Form), and many follow-up activities were taken to persuade the intended audience to complete it. Participants came from the following locations: Betong; Bintulu; Kapit; Kuching; Limbang; Miri; Mukah; Samarahan; Sarikei; Serian; Sibu; and Sri Aman. There was a total of 10 questions used for the assessment, and each item was assessed on a seven-point Likert scale in order to determine how well each predictor performed. Meanwhile, the capabilities of data management included twenty indicators in four domains (data collection, data processing, data interpretation, and data implementation). In the end, we selected five metrics to evaluate the efficacy of digital marketing in four distinct areas. The questionnaire was pilot tested with the SPSS software once it was drafted for demographic frequencies and the SmartPLS 4.0 we used in order to analyze the Confirmatory Factor Analysis (CFA) and to examine the direct and indirect relationship of the study proposed research framework.

5. CONFIRMATORY FACTOR ANALYSIS (CFA)

With the total numbers of 179 respondents, the replies came from every single person who were targeted for the survey consist of SMEs actively participating in digital marketing activities. A CFA (confirmatory factor analysis) was performed on the retrieved data to verify the reliability of the measuring scale before continuing with the structural modeling. Tabulated in Table 1 below are the findings of the factor analysis with the value of Cronbach alpha (CA) Composite Reliability (CR) and Average Variant Extracted (AVE). the CFA result shows that the research obtained good model fit and having adequate and if not high value of reliability. Based on the study’s applicability in light of its relevance and the issue at hand, the quantitative data strategy is the approach that should be implemented. Consistent with the approach provided above, a questionnaire handled by the digital marketing actors was used to collect the required data. Also, this study used a cross-sectional design, and measurements of several variables were taken at the same time.

Table 1. The confirmatory factor analysis result

6. DIRECT EFFECTS

Table 2 shows that there is a correlation between the customers’ decision-making steps (AIDA) and the success of digital marketing. PLS analysis showed a positive correlation between all of the relevant factors and the success of digital marketing. Both the T statistics and P values of the result shows values of significant relationship between all the suggested variables

Table 2. Path Coefficients between Independent Variables and Digital Marketing Performance (Direct Impact)

7. INDIRECT IMPACTS

The results of the mediation analysis are shown in table 3, where there is a significant positive correlation between AIDA and digital marketing strategies and a mediating impact of data analytics capabilities (t=8.26, p = 0.00). These findings suggested that data analytics capabilities mediated the connection between AIDA and the planning of digital marketing strategies.

Table 3. The Mediating Impact of Data Management Capability on Interest with Digital Marketing Performance

8. CONCLUSION

The result from this study proved that SMEs digital marketing players in Sarawak show an amazing realization an interest when towards the steps in consumer decision-making process when implementing planning and implementing their digital marketing strategies. This research has shown that the Awareness, Interest, Desire, and Action (AIDA) model of the customer’s decision-making process may be used to explain the causal relationship between the proposed predictors and digital marketing performance. The AIDA model can help those involved in digital marketing gain a deeper comprehension of the consumer decision-making process. Based on the findings, it’s clear that the AIDA framework must be strictly adhered to while carrying out digital marketing campaigns. This study also demonstrated the extent to which digital marketing players among Sarawak’s SMEs understand the value of data and how it can help them take their digital marketing campaigns to the next level of success. The findings of this study show that the effectiveness of digital marketing campaigns may be influenced by a company’s data management capabilities. Consequently, it provides theoretical support for the findings of (Saidali et al., 2019), who demonstrated that data management skill was a key driver of success across all marketing efforts, particularly those involving digitalization. Because data management skills are crucial to the success of the process of segmenting online consumers, companies engaged in digital marketing need to ensure they have access to this potent information. This is because only those with skill in data management will be able to come up with effective advertising plans. Any issue might be exposed along with its remedy if only we had access to enough data. Using data, they were able to develop an original strategy, setting them apart from the competition. Those suggestions would be truly original, completely out of the box, and in step with the latest market tendencies.

Moreover, to the best of our knowledge, no previous studies have investigated the proposed dimensions employed in this study and their direct association with the success of digital marketing in Sarawak or anywhere else in the world. The findings of this research add to the existing body of knowledge about the connection between digital marketing performance in Sarawak and factors such as digital marketing professionals’ level of awareness, interest, desire, action, data management competence, digital marketing strategies and digital marketing performance performance. In fact, these findings shed new light on and expand our understanding of the literature on both the performance of digital marketing and the behavior of digital marketing actors. From a scholarly point of view, this study provides new insight into the mindsets and opportunities for growth among Sarawak’s digital marketing professionals. Those that rely on digital marketing performance as a proxy for their company’s success should utilize the findings of this study to their advantage by concentrating on the activities most likely to enhance online consumers’ decision-making stages for the benefit of digital marketing players. This study’s findings might inform industry participants on the kinds of digital marketing knowledge and skills they should acquire.

REFERENCES

1. Digital users worldwide 2020 | Statista. (n.d.). Retrieved August 25, 2020, from https://www.statista.com/statistics/617136/digital-population-worldwide/
2. Apampa, O. (2020). USING BIG DATA ANALYTICS TO ENHANCE CONSUMERS’ SUSTAINABLE PURCHASE DECISION-MAKING OF GROCERIES ONLINE.
3. Ariadini, M. (2022). A Marketing Strategy Analysis of Marketing Strategies to Increase Competitiveness at Kopili Coffee. INCOME: Innovation of Economics and Management, 2, 22–25. https://doi.org/10.32764/income.v2i1.2653
4. Brar, T. P. (2021). Digital Marketing Performance: Understanding the Challenges and Measuring the Outcomes (pp. 51–63). https://doi.org/10.4018/978-1-7998-7231-3.ch004
5. Chaffey, D. (2010). Chaffey, D. (2007b). Top 10 E-marketing strategies of today and tomorrow. Site implemented by Dave Chaffey. Retrieved November 29, 2010, from http://www.davechaffey.com/E-marketing- Insights/Internet-marketing-articles/Top-10-E-marketing-strategies.
6. Dahiya, R., & Gayatri, G. (2018). A Research Paper on Digital Marketing Communication and Consumer Buying Decision Process: An Empirical Study in the Indian Passenger Car Market. Journal of Global Marketing, 31(2), 73–95. https://doi.org/10.1080/08911762.2017.1365991
7. Ducange, P., Pecori, R., & Mezzina, P. (2018). A glimpse on big data analytics in the framework of marketing strategies. Soft Computing, 22. https://doi.org/10.1007/s00500-017-2536-4
8. Fatin, T., & Rahman, N. (2020). Measuring Digital Marketing Performance: A Balanced Scorecard Approach. International Journal of Applied Management Theory and Research (IJAMTR), 2(1), 1–15.
9. Ganesh*, C. N. (2020). Aida Model – A Panacea for Promoting Products. International Journal of Recent Technology and Engineering (IJRTE), 8(5), 1572–1576. https://doi.org/10.35940/ijrte.d7346.018520
10. Gever, C. V. (2017). Testing the AIDA Model Hypothesis Vis-À-Vis Subscribers’ Response to Unsolicited SMS Adverts. International Journal of Communication, 20(1).
11. Ghorbani, Z., Kargaran, S., Saberi, A., Haghighinasab, M., Jamali, S. M., & Ale Ebrahim, N. (2021). Trends and patterns in digital marketing research: bibliometric analysis. Mark. Anal. https://doi.org/10.1057/s41270-021-00116-9
12. González Moreno, S., Palma-Ruiz, J. M., & Caro Lazos, L. (2022). Marketing Strategies for Esports (pp. 52–68).

https://doi.org/10.4324/9781003273691-7

13. Gupta, M., & George, J. (2016). Toward the Development of a Big Data Analytics Capability. Information & Management, 53. https://doi.org/10.1016/j.im.2016.07.004
14. (2020). Digital 2020 Malaysia. Global Digital Insights, 1–92.

https://doi.org/https://datareportal.com/reports/digital-2020-global-digital-overview

15. Khade, A. A. (2016). Performing Customer Behavior Analysis using Big Data Analytics. Procedia Computer Science, 79, 986–992. https://doi.org/10.1016/j.procs.2016.03.125
16. Kusumawati, A. (2019). Impact of digital marketing on student decision-making process of higher education institution: A case of Indonesia. Journal of E-Learning and Higher Education, 1(1), 1–11.
17. Mackey, J., Sisodia, R. S., & George, B. (2013). Conscious Capitalism: Liberating the Heroic Spirit of Business. Cambridge.
18. Mero, J. (2016). The Use of Digital Analytics for Measuring and Optimizing Digital Marketing Performance.
19. Omar, F. I., Zan, U. M. S. M., Hassan, N. A., & Ibrahim, I. (2020). Digital Marketing: An Influence towards Business Performance among Entrepreneurs of Small and Medium Enterprises. International Journal of Academic Research in Business and Social Sciences, 10(9), 126–141. https://doi.org/10.6007/ijarbss/v10-i9/7709
20. Proctor, T. (2020). Marketing strategy (pp. 3–10). https://doi.org/10.4324/9781003005704-2
21. Purbaningsih, Y., Putri, S. E., Bangkara, B. A., Nurofik, A., & Zahari, M. (2022). Understanding the AIDA Model in Marketing Small Business in the Digital Age: Opportunities and Challenges. Budapest International Research and Critics Institute (BIRCI-Journal): Humanities and Social Sciences, 5(3), 19978–19989.
22. Saidali, J., Rahich, H., Tabaa, Y., & Medouri, A. (2019). The combination between Big Data and Marketing Strategies to gain valuable Business Insights for better Production Success. Procedia Manufacturing, 32, 1017–1023.

https://doi.org/10.1016/j.promfg.2019.02.316

23. Saura, J. R. (2021a). Advanced digital marketing strategies in a data-driven era. Advanced Digital Marketing Strategies in a Data-Driven Era, August, 1–342. https://doi.org/10.4018/978-1-7998-8003-5
24. Saura, J. R. (2021b). Using data sciences in digital marketing: Framework, methods, and performance metrics. Journal of Innovation \& Knowledge, 6(2), 92–102.
25. Singaraju, S., & Niininen, O. (2021). Understanding Big Data and its application in the digital marketing landscape. In Contemporary Issues in Digital Marketing (pp. 9–21). Routledge.
26. Sridevi, K. B., S, S. L., Robin, S., A, I. A., P, D. S., Omar, F. I., Zan, U. M. S. M., Hassan, N. A., Ibrahim, I., El-gohary, H., Lane, E. M. M., Yorkshire, W., & Uk, B. D. (2021). Digital Marketing: An Influence towards Business Performance among Entrepreneurs of Small and Medium Enterprises. International Journal of Business Science and Applied Management, 2(11), 126–141.

https://doi.org/10.6007/ijarbss/v10-i9/7709

27. Tiago, M. T. P. M. B., & Veríssimo, J. M. C. (2016). Digital marketing and social media: Why bother? Horiz., 57(6), 703–708. https://doi.org/10.1016/j.bushor.2014.07.002
28. Wang, B., & Loo, B. P. Y. (2019). The hierarchy of cities in Internet news media and Internet search: Some insights from China. Cities, 84, 121–133. https://doi.org/https://doi.org/10.1016/j.cities.2018.07.013