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Marketing Analytics And Big Data

Executive Summary of ASICS Marketing Case Study

The current study deals with the case study of ASICS in terms of chasing its vision in 2020. The company aims at the development of the operational instances alongside development of fitness application that helps the customers in achieving the needs of fitness. Based on the aims of the company, big data can be effective in the marketing analytics. Proper implementation of big data will be providing the company with the benefit of deriving 360 degree of knowledge regarding the customers alongside promoting brand advertisement through native advertising and many others. It helps in satisfying the customers through effective production adhering to the needs of the customers. However, challenges of improper real time data, alongside poor quality of data and others are liked with the usage of big data that can be effectively mitigated through proper training, using effective database and others recommended in the study below.

Table of Contents



Identification and explanation of the importance of marketing analytics using big data in customer retention

Analysing the use of marketing analytics in gaining an insight into the targeted audience.

Description of using big data and analytics in increasing brand awareness.

Explanation of four challenges of using appropriate techniques for strategic decision making of marketing



Reference list

Introduction to ASICS Marketing Case Study

Market analytics helps in analysing the marketing strategies that tend to be adopted by the companies to reckon their position within the market. Big data, on the other hand, helps in analysing the marketing information alongside the behaviour of the consumers within the market. Big data in this respect is useful in assisting marketing analytics to derive its actual goals. In this study, ASICS a company based on the designing of sports equipment will be highly focused. The case study of ASICS in terms of chasing vision in 2020 will be highly concentrated for the purpose of analysing the marketing analytics and use of big data within its achieve its goal.

Findings of ASICS Marketing Case Study

Identification and Explanation of The Importance of Marketing Analytics Using Big Data in Customer Retention

Marketing analytics with the help of big data is useful in analysing the different situations within a market. In the opinion of Evans et al. (2017), great info helps to explain the market circumstances alongside the insight of consumer information. It helps the company in analysing the preferences of the customers present within the market alongside the recent trend within it. Supported by Bouzenita & Boulanouar (2016), big data also reveal the needs of the business based on the targeted audience in the market segment. It also assists in analysing the behaviour of the consumer so that the companies within the market are useful in figuring the actual needs of the market that can boost organisational growth. Hence, big data is helpful in marketing analytics as it provides insight into the customer's preferences. In addition to this, big data also helps marketing analytics in identifying the opportunities within the market, because of this; the companies can deliver unique value that is expected by the customers further leading to customer retention (Ryan et al. 2020).

It is because unique and compelling opportunities provided to the customers increases the satisfaction level. As denoted in the theory of Maslow Hierarchy of Needs by Schulte (2018), increased satisfaction level tends to meet the needs of the customers further increasing the motivational level of the customers. This helps in increasing the loyalty of the customers, and because of this, the retention of the customers is increased. This depicts the fact that big data within the marketing analytics helps the company to satisfy the needs of the customers through useful knowledge about their demand. In addition to this, as per Barba-Sánchez & Atienza-Sahuquillo (2017), the mentioned aspects also helps in analysing the buying behaviour of the customers further providing the scope of designing the selling pattern of the company. 

Besides, Vroom’s Expectancy Theory also denotes the fact that meeting the expectation of the customers can highly satisfy those (Lloyd & Mertens, 2018) (Refer to Appendix 1). This helps in increasing the retention of the customers, as they are highly satisfied with the services provided by the company. Based on the current case study, it can be revealed that using big data within the marketing analytics, ASICS will be useful in analysing the customers buying pattern alongside an insight into the customer's demand. It will further provide the company with the opportunity of changing the vision targeted in 2020 (Ofek, Sato & Kanno, 2020). Hence, using the big data in the marketing analytics, ASICS will be useful in increasing the retention of the customers through the increase of customer loyalty and motivational level.

Analysing the Use of Marketing Analytics in Gaining an Insight Into the Targeted Audience

Marketing analytics is useful in gaining a 360-degree view of the targeted audience of the company. In the opinion of Soyoung & Sungchan (2017), "know your customer" (KYC) was introduced initially by the banks several years ago for preventing any types of fraud within the bank. KYC tends to provide insight into the behaviour of the customers. However, it was limited to the financial institution several years ago. In the current scenario, it can be observed that due to the availability of the big data system, KYC is available for all types of business starting from small to large within the business domain. As per Tallapragada, Rao & Kanapala (2017), big data in terms of marketing analytics tends to provide the companies with a detailed overview of the audience with the help of KYC.

Through the aspects of KYC, marketing analytics is used in gaining 360 degrees insight of the customers. As illustrated by Arridha et al.(2017), it also helps in creating a customer’s engagement that helps in providing a useful solution to the companies in increasing the awareness of the customers present in the market. Analysing the buying pattern of the customer through marketing analytics, the company are useful in identifying the preferences of the customers. It thus helps the company in designing the products that can attract the customers towards the company. In the current case study, ASICS with the help of KYC will get a 360 view of the customers; the company will be useful in analysing the buying pattern of the customers alongside their preferences that further will help achieve the vision in 2020 (Ofek, Sato & Kanno, 2020). 

Besides, it can also be observed from the case study that ASICS is currently equipped with the implementation of technological instances such as app of digital fitness, RunKeeper and many others. As big data is also involved with the implementation of IT instances within the business, therefore, through cloud computing, the company will be able to monitor its program and analyse different information regarding the customers that will help in bringing specific improvement to the launched applications.

Description of Using Big Data and Analytics in Increasing Brand Awareness

From the case study of Aberdeen Group’s Data-Driven Retail, it can be observed that big data is useful for the data-driven procedure adopted by the company. Implementation of big data within the data-driven company is more effective in increasing brand awareness by gaining proper knowledge regarding the targeted customer base. Apart from that, the study also reveals the fact that through the usage of digital media, the company is also useful in creating awareness among the customers within the market. This also helps in increasing the sales of the company and gain targeted goals. However, in the current instance of ASICS, it can be noticed that the company is targeting the implementation of necessary measures for the increase in profit margin and sales within the market (Ofek, Sato & Kanno, 2020). In addition to this, based on the obtainable instances it can also be noticed that the company is targeting to expand its business to the new segments of customers alongside having direct insight of the customers’ mindset, building and communicating a consistent brand in the global scenario.

Other than that, the company has also launched brands of lifestyle that posed the issue of brand architecture. Besides, the 5-year growth plan strategy of the company also consists of the development of the technological application that can assist the customers in its fitness wants. It includes the launch of apps like RunKeeper, implementation of digital competence and many others. However, as argued by Li et al. (2020), big data tends to leverage the use of technological instances for promoting the growth of the company, through the aspects of cloud computing, big data can track the usage of the application launched by the company. It can also be monitoring the growth of the form, further responding to the needs of the customers regarding the launched services. This way, big data in ASICS can be used for promoting the brand value towards its customers. 

Moreover, big data can also be used for promoting several advertisements for the company in the market that will help the customers to gain an insight into the company and its offered value. In the opinion of Cheng et al. (2018), through the facility of native advertisement provided by big data can be useful in winning over new customers. It is because the native ad is the most effective forms of publication that are used for the increase of brand awareness, further attracting more customers to the domain. In addition to this, big data also provides the facility of posting an advertisement on the social media sites that are mostly used by the customers such as Facebook, Twitter and others (Liu, 2016). Hence, through the mentioned aspects, big data can be used by ASICS to create a suitable awareness of the brand among the customers in the marketplace. 

Explanation of Four Challenges of Using Appropriate Techniques for Strategic Decision Making of Marketing

Appropriate techniques are useful in the making of decisions regarding the aspects of marketing within an organisation. In the opinion of Rath (2019), proper techniques used in strategic decision-making helps in taking a suitable decision that can contribute to the future development goals of the company. It also helps in making an informed decision that partially engages all the employees within the domain. Argued by Yassine et al.(2019), techniques in strategic decision making also enables the company to gain different data available in the market. This contributes to the practical decision that can enhance the operational opportunity ion the marketplace. However, as argued by Schulte (2018), there are specific challenges related to the using of appropriate technology in strategic decision-making. One of the significant difficulties influencing the usage of analytical techniques involves the collection of real-time alongside meaningful data. In the opinion of Liu (2016), real-time data influence the decision positively making aspects.

It allows the company to take a practical decision based on the concerned matter. However, often it can be seen that analytical techniques fail to provide real-time data that affects the decision-making process adversely. In addition to this, other challenges include poor quality of data alongside inaccessible data, and improper visual representation of data are found in the usage of analytical techniques in the strategic decision making (Rath, 2019). Hence, the mentioned challenges are useful in creating confusion within the organisation further leading to ineffective decisions.

Recommendation on ASICS Marketing Case Study

Based on the above instances it can be observed that challenges of gaining real-time data, improper visualisation of data, low accessibility of the data and poor quality of the data is prevailing with the use of analytical techniques. In this respect, it can be recommended that proper training of the staffs regarding the usage of the big data analytical techniques is viable in the current scenario. Other than that, using an active database can be appropriate in mitigating the issue of low accessibility. Besides, building a centralised system within an organisation can be useful in reducing the effect of poor quality of data (Refer to Appendix 2). Hence, the mentioned recommendation will be viable in meeting the issues faced in the implementation of analytic techniques in the decision-making aspects of the company.

Conclusion on ASICS Marketing Case Study

Marketing analytics is recognised as a practice that is being used for the measurement of the performance of the company within the market. It helps in maximising the effectiveness of the company in the market alongside the increase of the Return on Investment (ROI). In the current scenario, ASICS is implementing a new vision in 2020 through the involvement of technological instances. Starting from the development of the business, the company also highly focused on the development of several applications such as RunKeeper and the use of digital fitness. Big data in this respect is highly beneficial for the company, as it will help the company to gain 360-degree knowledge of the customers through KYC aspects. In addition to this, through proper promotion in digital networks, big data will also assist the company in increasing brand awareness and increase the profitability of the company. Based on the challenges of low data quality alongside inaccessible data, gaining real data and others, the company can involve in building a centralised system, using proper database and others. This will help in mitigating the issue of big data that will be adopted by ASICS for chasing its vision in 2020.

Reference List for ASICS Marketing Case Study

Arridha, R., Sukaridhoto, S., Pramadihanto, D., & Funabiki, N. (2017). Classification extension based on IoT-big data analytic for smart environment monitoring and analytic in real-time system. International Journal of Space-Based and Situated Computing, 7(2), 82-93. Retrieved on 27 July 2020, from: https://osf.io/preprints/inarxiv/stmg6/download

Barba-Sánchez, V., & Atienza-Sahuquillo, C. (2017). Entrepreneurial motivation and self-employment: evidence from expectancy theory. International Entrepreneurship and Management Journal, 13(4), 1097-1115. Retrieved on 27 July 2020, from: https://link.springer.com/article/10.1007/s11365-017-0441-z

Bouzenita, A. I., & Boulanouar, A. W. (2016). Maslow’s hierarchy of needs: An Islamic critique. Intellectual Discourse, 24(1). Retrieved on 27 July 2020, from: https://www.researchgate.net/profile/Bronwyn_P_Wood_Aisha-Wood-Boulanouar2/publication/305552656_Maslow%27s_hierarchy_of_needs_An_Islamic_critique/links/5893386745851563f828abcd/Maslows-hierarchy-of-needs-An-Islamic-critique.pdf

Cheng, Y., Hao, Z., Cai, R., & Wen, W. (2018, July). HPC2-ARS: an architecture for real-time analytic of big data streams. In 2018 IEEE International Conference on Web Services (ICWS) (pp. 319-322). IEEE. Retrieved on 27 July 2020, from: https://pdfs.semanticscholar.org/7fe5/92c1ca419670077451f2da00c339f664d251.pdf

Evans, T., Akporuno, O., Owens, K. M., Lickers, B., Marlinga, J., Lin, H. C., & Loh, L. C. (2017). Using Maslow's hierarchy to highlight power imbalances between visiting health professional student volunteers and the host community: an applied qualitative study. Education for Health, 30(2), 133. Retrieved on 27 July 2020, from: http://www.educationforhealth.net/article.asp?issn=1357-6283;year=2017;volume=30;issue=2;spage=133;epage=139;aulast=Evans

Li, Z., Huang, Q., Jiang, Y., & Hu, F. (2020). SOVAS: a scalable online visual analytic system for big climate data analysis. International Journal of Geographical Information Science, 34(6), 1188-1209. Retrieved on 27 July 2020, from: https://www.researchgate.net/profile/Zhenlong_Li3/publication/332738916_SOVAS_A_Scalable_Online_Visual_Analytic_System_for_Big_Climate_Data_Analysis/links/5cc74791a6fdcc1d49b98080/SOVAS-A-Scalable-Online-Visual-Analytic-System-for-Big-Climate-Data-Analysis.pdf

Liu, P. T. S. (2016, November). Medical record system using blockchain, big data and tokenisation. In International conference on information and communications security (pp. 254-261). Springer, Cham. Retrieved on 27 July 2020, from: http://kddlab.zjgsu.edu.cn:7200/research/blockchain/huyiyang-reference/Medical%20Record%20System%20Using%20Blockchain,Big%20Data%20and%20Tokenization.pdf

Lloyd, R., & Mertens, D. (2018). Expecting more out of Expectancy Theory: History urges inclusion of the social context. International Management Review, 14(1), 28-43. Retrieved on 27 July 2020, from: http://americanscholarspress.us/journals/IMR/pdf/IMR-1-2018/IMR-v1-n1-2018-4-19.pdf#page=28

Ofek, E., Sato, N., & Kanno, A., (2020). ASICS: Chasing a 2020 Vision, Harvard Business Review. Retrieved on 27 July 2020, from: https://store.hbr.org/product/asics-chasing-a-2020-vision/517060

Rath, M. (2019). Intelligent Information System for Academic Institutions: Using Big Data Analytic Approach. In Interdisciplinary Approaches to Information Systems and Software Engineering (pp. 207-232). IGI Global. Retrieved on 27 July 2020, from: https://www.igi-global.com/chapter/intelligent-information-system-for-academic-institutions/226401

Ryan, B. J., Coppola, D., Canyon, D. V., Brickhouse, M., & Swienton, R. (2020). COVID-19 Community Stabilization and Sustainability Framework: An Integration of the Maslow Hierarchy of Needs and Social Determinants of Health. Disaster medicine and public health preparedness, 1-7. Retrieved on 27 July 2020, from: https://www.cambridge.org/core/services/aop-cambridge-core/content/view/4A93577CFD109B3939BF9CCAC769A920/S1935789320001093a.pdf/div-class-title-covid-19-community-stabilization-and-sustainability-framework-an-integration-of-the-maslow-hierarchy-of-needs-and-social-determinants-of-health-div.pdf

Schulte, M., (2018). Adult Learning Degree and Career Pathways: Allusions to Maslow's Hierarchy of Needs. The Journal of Continuing Higher Education, 66(1), 62-64. Retrieved on 27 July 2020, from: https://www.tandfonline.com/doi/full/10.1080/07377363.2017.1368767

Soyoung, P. A. R. K., & Sungchan, K. I. M. (2017). The linkage between work unit performance perceptions of US federal employees and their job satisfaction: An expectancy theory. Transylvanian Review of Administrative Sciences, 13(52), 77-93. Retrieved on 27 July 2020, from: https://www.rtsa.ro/tras/index.php/tras/article/download/535/524

Tallapragada, V. S., Rao, N. A., & Kanapala, S. (2017). EMOMETRIC: An IOT integrated big data analytic system for real time retail customer’s emotion tracking and analysis. International Journal of Computational Intelligence Research, 13(5), 673-695. Retrieved on 27 July 2020, from: http://www.ripublication.com/ijcir17/ijcirv13n5_03.pdf

Yassine, A., Singh, S., Hossain, M. S., & Muhammad, G. (2019). IoT big data analytics for smart homes with fog and cloud computing. Future Generation Computer Systems, 91, 563-573. Retrieved on 27 July 2020, from: http://eiu.thaieei.com/box/Technology/73/IoT%20Big%20Data%20Analytics%20for%20Smart%20Homes%20with%20Fog%20and%20Cloud%20Computing.pdf

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