Essay Example on Amazon Focuses on Data Analytics to Improve Customer Relationships

Introduction

Amazon's mission concentrates on four areas: customer-centric, invention and innovations, sustainable thinking, and operational superiority (Amazon.com, 2020). The proposed data analytics initiative is customer-centric, and it aims at gathering and analyzing customers' information to develop a more in-depth insight into their needs (EMC Education Services, 2015). As a result, the company can build a robust relationship with clients by making decisions that match their requirements. The company must be innovative to offer a unique experience to customers. The initiative will use customers' feedback, referrals, and rating to gauge the level of satisfaction.

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Data Analytics Lifestyle

Business insight - it involves understanding the problem to solve and formulation of hypothesis (Hodeghatta & Nayak, 2016). The primary objective of the initiative is to improve customers relationship by assessing their feedback.

Data understanding - studying dataset with the organization to understanding their patterns before analyzing them.

Data preparation - the process of cleaning the raw data about the client's feedback and rating to clean the dataset.

Data modeling - use of analytical techniques, such as SQL, to understand different variables and their relationship with the problem studied (Otneim, 2017).

Evaluation - testing the accuracy and reliability of the analytical model using customer feedback.

Communicating outcomes - assessing results from the analysis using the developed model.

Operationalization - putting the initiative in practice by the use of a pilot project to test the performance of the initiative (Zikopoulos & Eaton, 2011).

Value of Life cycle

The life cycle is fundamental for testing the essence of a data analytic initiative (Prajapati, 2013). The proposed data analytics project should assess the patterns in the analyzed data to project potential opportunities and risks (Vashisht & Gupta, 2015). The data analytic life cycle should find the relationship between the market share and consumers' feedback and rating. Also, the use of different datasets should lead to similar results from the prior datasets.

Data

The current data to analyze is critical for developing insight into the analytical problem. The use of the existing makes it possible to evaluate the pattern of variables (Demchenko, De Laat, & Membrey, 2014). However, it may be subjective. As such, there is a possibility of biased or inaccurate final results.

References

Amazon.jobs. (2020). Amazon's Global Career Site. Available at: <https://www.amazon.jobs/en>

Demchenko, Y., De Laat, C., & Membrey, P. (2014, May). Defining architecture components of the Big Data Ecosystem. In 2014 International Conference on Collaboration Technologies and Systems (CTS) (pp. 104-112). IEEE

EMC Education Services. (2015). Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data. Wile

Hodeghatta, U. R., & Nayak, U. (2016). Business Analytics Using R-A Practical Approach. Apress.

Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1), 1.

Otneim, H. (2017). Business Analytics Using R-A Practical Approach. Journal of Statistical Software, 81(1), 1-6.

Prajapati, V. (2013). Big data analytics with R and Hadoop. Packt Publishing Ltd.

Russom, P. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19(4), 1-34

Vashisht, P., & Gupta, V. (2015, October). Big data analytics techniques: A survey. In 2015 International Conference on Green Computing and Internet of Things (ICGCIoT) (pp. 264-269). IEEE

Zikopoulos, P., & Eaton, C. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media.