How Big Data Analytics Enables Service Innovation in

Amazon

Writer, you write about Specific features of the DBA. To ensure student ability to analyze and evaluate effectiveness of strategic use of information systems in a business firm, you are assigned to work to conduct a case study and write a case analysis paper based on this case study. This is my teammates overall work. You must cite your reference sources through the text and include a reference list using APA style at the end. The paper is to be typed, 1.5-spaced, and in a readable 12-point Arial type font with one- inch margins on four sides. To help you understand how to write this case analysis paper and to complete this project, a case study paper by Lehrer, Wieneke, Brocke, Jung, and Seidel (2018) is attached in PDF, and read my classmates readings to go with their flow and good luck. Do not plagiarize nor cheat, I will know. Again, good luck!

Structure

1. Introduction of the case company (must include the key business strategy for service improvement);

2. The purpose of DBA or DA in terms of supporting the identified business strategy;

3. The DBA or DA technologies and other supporting ISs in the company;

4. Specific features of the DBA You need to research the different ways they collect data, For example they collect date from customers from product reviews, my teammates wrote a couple of paragraphs for suggestions for data collection its right above the references. Discuss the features of the BDA amazon uses.

5. How did the company use DBA/DA and the effectiveness of the DBA/DA features in serving the purpose;

6. Your conclusion;

7. Tables and figures are helpful but optional;

8. Length: roughly 1,000 words;

9. Search for relevant literature in roughly the most recent five years (i.e., 2014 or 2015-2020).

Introduction

Service innovation is a new service product or service process offered by a company that substantially improves or provides a new benefit to a customer. Because the innovative service has never existed before, it allows a firm to differentiate themselves from their competition and take advantage of earning more revenue. The development of service innovation is achieved with the use of “Big Data (BD) and Big Data Analytics” (BDA). Big Data is produced daily from a plethora of sources and defined in terms of volume, velocity, variety, variability, and veracity. The data is generated at high volume and velocity and relies upon even faster processing to collect, store, and analyze. “Big Data Analytics” is an analytical tool used to gather, process, and analyze sets of structured or unstructured big data. The information derived from the analysis provides insight to firms, so it can identify problems and needs. The analysis not only aids in development of customer centric innovative services, but can also be used to redesign and develop competitive advantages.

The research paper written by Lehrer, C. et al "How Big Data Analytics Enables Service Innovation: Materiality, Affordance, and the Individualization of Service", investigated how several companies utilized material features of big data analytics technologies to produce service innovation. The investigation found BDA technologies enable service innovation two ways: automation of customer-sensitive service provision (Lehrer, C. et al, 2018, vol. 35 pg. 446)  and IT-supported service delivery by human service actors (i.e., human-material service practices); that is, the technology afforded human service actors new actions that led to fundamentally revamped practices (Lehrer, C. et al, 2018, vol. 35 pg. 431). BDA generativity and reprogrammability as digital technology, is essential for trigger based service and preference based service type of automation processes. BDA facilitates an organization’s ability to act on the customer’s behalf in real time, deliver proactive service provisions based on customer context, and provide service individualization. Methodologies developed to capture empirical data to provide quantitative approach to determine relationship of BDA materiality in service innovation can be useful to practitioners.Currently a gap exists in availability of empirical evidence in relation to impact of BDA on service innovation. A gap also exists in standardizing a way to interpret coding and analyze data in qualitative research that can be used to determine a firm's need for automation.

Amazon continues to use BDA to provide customers with high value propositions. The goal of this documentary study is to provide an insight into the role big data analytics has been used by Amazon to create service innovation.

Research Method

A deductive research approach was adopted for this case study because it allows for the operationalization of pre-existing parameters for big data analytics at Amazon. Qualitative metrics alongside theoretical models were used to consolidate the variables of the study (Chenail, 1715). Special linkages were established between the concepts to foster standardization of approaches. Since it was a qualitative study, the researchers relied on the views of the participants drawn from service innovation domains at the company. The data collection and analysis were done subjectively as the researchers sought to describe the themes in a manner that is consistent with the existing knowledge paradigm on the subject. It was crucial that the study measured the validity of the hypothesis. Interviews were conducted to examine the innovation in services through digital technological advancement. Data validity and reliability are critical aspects of the study because they define the efficacy of the outcomes and provide for the acceptability of the results in the survey (Mohajan, 58). Data validity was guaranteed by double-checking the consistency of questions and answers against the established theoretical ideals of the study. The data editing process involved the removal of responses that were widely inconsistent, and which failed to meet the threshold of conformability.

Semi-structured interviews put the study at the risk of being affected by the bias of the researchers. However, this threat was countered by ensuring that the respondents are limited to experts drawn from selected fields. Convenience sampling technique was adopted in the determination of participants for the interview sessions because BDA is a sophisticated theme that requires expert analysis. Convenience sampling is the best method of focusing on the resources of research for obtaining the most reliable information, and its adoption allowed the researchers to focus on operationalizing the variables and measures instead of covering broad areas (Farrokhi and Mahmoudi-Hamidabad, 786). Before the researchers took the participants' views on BDA, they were given a short theme description test to help qualify the reliability of the information they provided for the study. The methodology focused on establishing the purpose of BDA in the business strategy and its description as a critical analytical tool in business.

Case Organization Selection

Big data analytics remains a crucial component of the systems architecture at Amazon. The company is one of the leaders in technology integration in its value chain, and it leverages its performance on online marketing and e-commerce structures. Generally, the function domain at Amazon is anchored on the operations under the chief technology officer (CTO) who is responsible for maintaining the function for the company (Marr). Amazon further relies on online systems to provide differentiated services to its customers and getting essential insights into BDA integration will foster service innovation in the company.

Moreover, Amazon uses BDA to understand its customers and foster close linkages to the market. The company maintains that it provides the broadest and deepest portfolio of purpose-built analytics tools so that it can quickly get insights from consumer data (Rijmenam). The main facets of BDA services include interactive analytics, big data processing, data warehousing, real-time analytics, dashboards and visualizations, and operational analytics. Some of the most common AWS services that the study considered as variables include Amazon Redshift, Amazon Kinesis, Amazon Quick Sight, AWS S3 Glacier, and Amazon Deep Learning AMIs (Amazon Website). The general operational philosophy in BDA integration that was also studied is collaborative filtering and the development of a 360-degree view of consumer demands in big-data-driven customer service.

Discussion

In conclusion, the main aim of the study was to investigate how BDA is used in Amazon Company to enable service innovation. Amazon largely relies on offering services to its customers. Therefore, it has to ensure that it keeps improving its services to maintain its market share and market leadership in e-commerce and technology sectors. The company works with Big Data to generate essential insights which it uses to improve its value to stakeholders. It, therefore, uses BDA to create valuable insights to support decision making regarding service innovations. Some of the big data applications used in the company include buying behavior predictions and data visualization.  The study focused on determining how BDA is used in the company to contribute to service innovations.

Contribution to Digital Innovation Literature

The study is significant to digital innovation literature in different ways. First off, the study focused on determining the contribution of BDA to service innovations in one of the biggest companies in the world. The findings show the importance of Big Data and Big Data Analytics to modern companies.  Secondly, the study shows the importance of digital innovations to the competitiveness of companies.

Implications of BDA Practice

The study findings show that BDA is important to modern companies, especially when companies want to base their service innovations on data. The use of BDA benefits companies by giving them a competitive edge in their respective industries. The study focuses on investigating the contribution of BDA to service innovations, and therefore it promotes BDA practice.

Data Analysis

How did the company use DBA/DA and the effectiveness of the DBA/DA features in serving the purpose;

A company like Amazon is no stranger to big data analytics. Amazon has been a leader of utilizing big data in a meaningful way for many years. Amazon has multiple big data analytics and continues to improve; this literature review cannot discuss them all. However, there is some stand out technology and big data leading the future in Amazon. Amazon has combined big data and cloud computing to create a great marriage of analytics. Being able to collect the data is not enough, developing the technology is the start. Amazon has used their technology to change the world of how e-commerce operates.

One product Amazon uses which has an effect on e-commerce is Amazon Kinesis. Amazon Kinesis is a clickstream data analytic technology using cloud computing. This technology takes heavy loads of clickstream data records and can produce real time analytics of the data (Gulabani, 2017). With the clickstream data Amazon can create statistical analysis on products and target the marketing for the product. The statistical data can be from the duration of computer time a customer is on a specific product. Amazon can use the data to predict profitability of a product and optimize prices. Amazon Kinesis is one of many Amazon data analytic products.

Amazon Web Services provides multiple big data analytic features for the company to provide cloud computing platforms. Amazon Web Services (AWS) offer products to other companies to utilize affordable data analytic technology. However, Amazon also uses products under the AWS umbrella like Amazon Elastic Computing Cloud (EC2) and deep learning Amazon Machine Images (AMI). EC2 is a secure cost effective and efficient way to be able to store data on the cloud. This tool Amazon EC2 service allows easy adaptation by the platform being affordable but also being able to adapt to different sized of data for big data usage (Zhang et al, 2018). Combining EC2 and Amazon’s deep learning the company offers data analysis at its finest. The learning technologies and space for large data it is a framework for predictive analytics and machine learning. The technology takes the customer transactions, search results, and products to optimize prices, personalize marketing, and research.

Salesforce is not an Amazon created product, but Amazon partners AWS and Salesforce to create a powerful Customer Relationship Management (CRM). Amazon uses Salesforce by collecting and interpreting customers information through their sales transactions to create relationships with the company. Amazon can collect data from the sales transactions and provide personalized services to customers. The predictive technology collects the data from customer sales and can provide customers with new products and services which fits their future needs (Neal, 2020). Since Amazon offers various products, the technology filters through the variety of products purchased from a customer and the company is able to send customer products suggestions.

Amazon uses voice-initiated products like Amazon Echo and Alexa. These two products are not only in a large amount of homes around the world, but there are multiple products in one home. Clark discusses CRM software using Amazon’s Choice algorithm through Amazon’s Echo and Alexa data to provide products which are balanced between their customer reviews and the price of the product (2018). Not only does Amazon take this data to suggest the newest best product, the company also can gather important data to invest in future products.

When discussing big data analytics and Amazon, there is no one developmental tool which supports the company. Amazon uses a combination of these technologies to create individual service to customers through data analytics. Amazon takes the data from customer reviews, retailer customer transactions, and clickstream to be able to set prices and produce new products through this type of data. When comparing the way Amazon uses big data analytics to the company in the Lehier et al. case study, How Big Data Analytics Enables Service Innovation: Materiality, Affordance, and the Individualization of Service, the companies are very similar (2018).

In the case study the company uses big data analytics to support the ecommerce travel company. The case study company utilizes the analytics of clickstream data through the customers usage on the websites and mobile applications. The company would provide personalized customer service for customers who utilized the business. The company would track the customers location and be able to provide services during their travel by suggesting products like entertainment which the customer fit the customers interest through their digital footprint. The company provides unique customer service to their customers.

Although Amazon is a major corporation which has large amounts of data, the way the case study company analyzes their data is similar to the way Amazon provides customer service to their customer. Both goals of the company are achieved by providing personalized customer innovation to the customers. However, the company in the case study would be able to call their customers while traveling with a more hands on approach. As both companies track customer locations, the case study company detects the customers location while the customer travels and the company will send customer personalized messages to the customer and suggest tourist attractions or other entertainment straight to the customer. Even though the companies have different amounts of data, both have large amounts of data and are able to analyze and utilize the analysis effectively.

Findings

In Lehier et al. case study, How Big Data Analytics Enables Service Innovation: Materiality, Affordance, and the Individualization of Service, the theoretical model of big data analytics discusses the analysis of automation of customer sensitive service provision and human-material customer-sensitive service practices (2018). With the theoretical model the study of the big data technologies is customer service oriented. While both types of innovations goals are to provide individualized service to customers, they work differently. Automation of customer-sensitive service reacts to an event occurrence and creates rule-based actions. Human-material customer-sensitive service provides services based on trigger interactions with customers digital footprint. The case study shows both models finds innovations for customer service.

As discussed, Amazon has multiple big data analytic technologies, but this literature review only could present some of the technologies. The technologies Amazon uses supports the case studies theoretical model of big data analytics when combining human agency with material agency. Amazon’s goal to provide personalized customer service and uses multiple technologies that interact with each other. Amazon first collects the mass amount of trace data which is the material feature. Amazon then take the data analysis further to proactive approaches to customer interactions. Ultimately provides service individualization for customers.

As with the case study Amazon uses trigger base data along with preference sensitive interactions. With the trigger-based data Amazon can be informative of reactive to customer service. On the other hand, the preference sensitive information Amazon can utilize the customer service in a proactive way. Predictive technology used in both the case study model and in Amazon’s business strategy. These innovative technologies allow the company to evaluate and adjust their service they provide with customer interactions.

Amazon’s service innovations for the ecommerce industry provide different services as some do not require intervention of human actors and some provide better service when human interaction is involved. Amazon differs from the case study theoretical model because of the multilayers of Amazon’s technologies. Amazon uses multitenancy to analyze large amounts of data in the industry which is a complex architecture platform (Khedekar, 2020). Amazon differs from the case study which uses a two-fold strategy. Although the concepts reach the same end goal with having an in-depth understanding of customer needs and taking the information to improve the customer interaction with the company.

Data Collection

The purpose of DBA or DA in terms of supporting the identified business strategy

Specific features of the DBA or DA to serve the identified purpose in Point 2 (The purpose of DBA or DA in terms of supporting the identified business strategy)

Although Amazon creates reliable software services to other companies, the company uses software as a service (SaaS) from other companies. SaaS provides the hardware and application database for collecting and analyzing big data. Salesforce can track the customers and sales data with the already developed program. This product employs multiple data analytic technology like predictive analytics, natural language processing and machine learning to create one cohesive product (Neal, 2020). The software service collects the customer data collected from companies to include using voice data from ordering products.

Amazon provides products which physically go into customers homes like Amazon Echo and Alexa. These products are voice recognition technology tailored to the customer. The Amazon Echo and Alexa are able to provide customers with easily ordering products from Amazon using their infamous Prime. The Echo and Alexa collect data each time a customer speaks to these technologies. These voice platforms are beneficial for retailers and manufacturer companies to collect data (Clark, 2018). 

References

DeJonckheere, M., & Vaughn, L. (2019). Semistructured interviewing in primary care research: a balance of relationship and rigour. Family Medicine And Community Health, 7(2), e000057. doi: 10.1136/fmch-2018-000057

Exploring the reasons behind customer purchase decisions

Crowdsourcing reveals sources of information that could improve product discovery algorithms.

By Jie Yang, Rongting Zhang, Vanessa Murdock March 10, 2020

Jamshed, S. (2014). Qualitative research method-interviewing and observation. Journal Of Basic And Clinical Pharmacy, 5(4), 87–88. doi: 10.4103/0976-0105.141942

Second Spectrum Selects AWS as its Preferred Cloud, Machine Learning, Artificial Intelligence, and Media Services Provider February 14, 2020 

https://www.britannica.com/topic/Amazoncom

Lehrer, C.; Wieneke, A.; Vom Brocke, J.; Jung, R.' & Seidel, S. (2018). "How big data analytics enables service innovation: Materiality, affordance, and the individualization of service." Journal of Management Information Systems. 35(2), pg. 424-460.

Hasnat, B. (2018). "Big data: An institutional perspective on opportunities and challenges." Journal of Economic Issues. 52(2), pg. 580-588.

Chen, R.H.L.; Chiang, R. H. L.; & Storey, V. C. (2012). "Business intelligence and analytics: From big data to big impact. MIS Quarterly. 36(4), pg. 1165-1188.

https://digital.hbs.edu/platform-digit/submission/amazon-and-big-data/

https://aws.amazon.com/dynamodb/

Why Do People Buy Seemingly Irrelevant Items in Voice Product Search? On the Relation between Product Relevance and Customer Satisfaction in eCommerce

https://assets.amazon.science/f7/48/0562b2c14338a0b76ccf4f523fa5/why-do-people-buy-irrelevant-items-in-voice-product-search.pdf

Clark, L. (2018). Smart Speakers Open New Channels to Crm Data. Computer Weekly, 22–25.

Gulabani, S. (2017). Practical Amazon EC2, SQS, Kinesis, and S3: A Hands-On Approach to AWS. [electronic book]. Apress.

Khedekar, V., & Tian, Y. (2020). Multi-Tenant Big Data Analytics on AWS Cloud Platform. 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), Computing and Communication Workshop and Conference (CCWC), 2020 10th Annual, 0647–0653. https://doi-org.ruby.uhv.edu/10.1109/CCWC47524.2020.9031133

Lehrer, C.; Wieneke, A.; Vom Brocke, J.; Jung, R.' & Seidel, S. (2018). "How big data analytics enables service innovation: Materiality, affordance, and the individualization of service." Journal of Management Information Systems. 35(2), pg. 424-460.

Neal, R. W. (2020). Where Ai Is at Work. Investment News, 24(2), 10.

Zhang, S., Luo, X., Zhang, Q., Fang, X., & Litvinov, E. (2018). Big Data Analytics Platform and its Application to Frequency Excursion Analysis. 2018 IEEE Power & Energy Society General Meeting (PESGM), Power & Energy Society General Meeting (PESGM), 2018 IEEE, 1–5. https://doi-org.ruby.uhv.edu/10.1109/PESGM.2018.8586512

Amazon Website. Data Lakes and Analytics on AWS. 2020. https://aws.amazon.com/big-data/datalakes-and-analytics/ [Accessed 19 April 2020].

Chenail, Ronald J. "Ten Steps for Conceptualizing and Conducting Qualitative Research Studies in a Pragmatically Curious Manner." The Qualitative Report 16, no.6, 2011, pp.1715-1732.

Farrokhi, Farahman and Asgar Mahmoudi-Hamidabad. "Rethinking convenience sampling: defining quality criteria."Theory and Practice in Language Studies 2, no.4, 2012, pp.785.

Marr, Bernard. Amazon: Using Big Data to understand customers. 2020. www.bernardmarr.com/default.asp?contentID=712 [Accessed 19 April 2020].

Mohajan, Haradhan. "Two criteria for good measurements in research: Validity and reliability." Annals of Spiru Haret University 3, no.1, 2017, pp.58.

Rijmenam, Mark Van. How Amazon is leveraging Big Data. 2020. https://datafloq.com/read/amazon-leveraging-big-data/517 [Accessed 19 April 2020].

 Appendix (Hector)

Open-ended questions

A.       Background

1.         What are your roles and responsibilities in Amazon Inc.?

2.         What is your understanding of “big data”?

3.         Are you involved or aware of any big data initiative in Amazon Company?

4.         How can you describe “big data analytics”?

5.         Is Amazon using big data analytics?

B.       Service innovation at Amazon

1.         Are consumer-oriented services essential in your company? If yes, please explain.

2.         How can you describe “service innovation?

3.         Is service innovation considered essential in your organization? If yes, please elaborate

4.         What do you think has motivated the company to invest in service innovation?

C.       The role of BDA for service innovation

1.                           What role does BDA play for service innovation?

2.                           Why do you think BDA is essential to service innovation and organization?

3.                           Do you think BDA has helped in improving consumer-oriented services?

D.       Conclusion

1.                           Do you have anything that you can add or believe should be discussed regarding the use of BDA in enabling service innovation?

2.                           Could we contact you if we need some clarifications during our data analysis?