How Amazon is using Big Data to Enhance Customer Engagement and Increase Purchases
Task: Write a dissertation on a selected topic. Make sure that you address each element of the dissertation which includes introduction, literature review, findings and discussion, conclusion and recommendations.
Chapter 1: Introduction
The industry where goods and services are sold directly to the consumers is known as retail industry. Grocery stores, stores that sell electronic gadgets are all part of retail industry. Amazon is one of the biggest retailer that has witnessed growth in the past decade because advanced technology has impacted the industry in various positive ways. Big data is a technical aspect that have impacted industries worldwide. Data that are large, complex and have varieties are known as big data. Big data is generally used by leaders across the world to understand the patterns and chaos of wide range of information. As mentioned earlier, big data has explored in several industries and it is expected to foster future innovation in various sectors across the world (Hasan, Popp and Oláh 2020). The primary objective of big data analytics is to analyse the large volume of data related to the Amazon retailer. Big data analytics help in involvement of Artificial Intelligence in big datasets (Hariri, Fredericks, and Bowers, 2019). This is how big data analytics hold data from different sectors.
Retail industry has witnessed the importance of big data analytics in the current business environment. It is said that big data analytics is one such technological resource that has given rise to a new concept named Retail analytics. The process where big data is used for analyzing the change in needs and demands of customers and for optimizing pricing strategies is known as retail analytics. Stores that are using this analytical tool are known as smart stores and their dependence on retail analytics have helped them to optimize profit in various ways (Gregorczuk, 2022). Moreover, big data analytics is generally used in retail industries to help customers with product information. Detailed information about products is helping consumers to make informed decisions. This is further having a positive impact on the profitability of the firm. Big data analytics have also helped retail industry to overcome one of the recent crises i.e., spread of Corona virus. The spread of this virus has given rise to the data volume. These data are further used for making decisions (Sheng, et.al., 2021). The change in customer needs is also analysed with the help of this data volume. This is how retail industry and data analytics have become interconnected with each other.
1.2 Research rationale
The objective of this part of the research study is to provide justification for choosing this research topic. Customers are the most important stakeholder of any company and there is no exception in case of Amazon. In this volatile business environment, acquiring customers is one of the most difficult tasks and responsibilities for the company. Industry leaders across nations have adopted various strategies to acquire customers because customers primarily control the financial performance of an organization. Engagement and interest of customers in the core activities and products/services of an organization is determined by the industry leaders before acquiring them (Zheng, Li and Na, 2022). Apart from volatility in the international market, intense competition is another important factor that is faced by retail industry leaders across the world. Amazon companyis adopting unique strategies to drive the interest of the customers from other competitors. Retail market is huge and heterogeneous. Using data analytics can help retail leaders segment customers in smaller groups that are homogeneous in nature (Yoseph, et.al., 2020). Thus, it can be said that this research study is important because it help customers understand the process in which customers can be segmented conveniently and quickly in Amazon company.
1.3 Problem statement
Providing high-quality services to the customers of the retail industry is one of the major challenges faced by organizational leaders of Amazon. In the 21st century the needs and demands of customers are changing in every retailer company. It is the core responsibility of a retail leader to serve customers in both online and offline stores based on the purchasing pattern of consumers. While serving customers, inventory managers of Amazon often find it difficult to understand whether they should keep the online store open even if products are running out of stock (Hole, Pawar and Khedkar 2019).This problem has been identified as major problem. Furthermore, with the increase in adoption of omni-channel strategies within the company, the concept of omni-channel marketing has gained popularity. Marketing managers of Amazon often find it difficult to practice omni-channel marketing because they have limited information about this marketing strategy. Ineffective marketing strategies might prevent managers of the retail industry to retain customers. This is how Amazonlose competitive advantage and their market share can also decrease.The identified problem has been considered as the key problem statement for this research work.
1.4 Research scope
This research has a large scope of doing comprehensive research on the identified problem context. Both the qualitative and quantitative data analysis could be done during this research work. The objective of this part of the research study is to focus on parameters of the research study and the extent to which this research topic is explored by the researcher. In this research study, the researcher primarily focuses on utilization of big data in acquisition of customers of the retail industry. In this research study, the primary focus has been given on the Amazon company and its business operations. This research study gives basic and in-depth information about change in the needs and demands of customers in Amazon retailer.
1.5 Research aim
Primary aim of the research to do comprehensive analysis on the selected research topic. However, a complete information analysis on the implementation of big data analytics in Amazon will be conducted in this paper. On the other hand, both the qualitative and quantitative research will be performed to meet the research aim. Depending on the primary aim of the research, entire project will be conducted.
1.6 Research objective
In order to achieve the project aim, it is important to set few project objectives. In the below points, major objectives of the project have been illustrated:
• To explore importance of customer acquisition in the retail industry.
• To explore all the major concepts of big data associated with the business operation in retail industry.
• To critically analyze the impact of big data on Amazon retailer.
• To explore various ways in which big data is being leveraged by Amazon to acquire customers.
• To recommend actions that can further help Amazon marketing managers in effective utilization of big data.
1.7 Research questions
In order to conduct a brief research on the identified problem context, it is important to identify few research questions which will be addressed during the research project.
• What are major aspects of customer acquisition that could be addressed by the big data technologies?
• In which aspects big data can enhance retail sales or business performance of Amazon?
• What are major challenges that could be faced by the business developer during the implementation of big data technologies in Amazon?
• How big data is being utilized by the marketing managers of Amazon to enhance customer engagement?
The above research questions will be addressed during the entire project work.
1.8 Structure of the research study
The research study starts with an introduction. In this introduction chapter, the background of the research topic and problems that are associated with the topic have been discussed in details. Apart from that the research scope has also been explained in this chapter. The objective behind mentioning the research scope is to analyze the parameters that are associated with utilization of big data analytics in the retail industry. At the end of the of this chapter, the aim and objectives of the research have also been mentioned. The researcher focuses on accomplishing these research objectives in the following part of the study. The second chapter comprises of the Literature review. The objective of this chapter is to present work of scholars across the world. Finally, the research ends with conclusion and recommendation. Each research objective is linked with conclusion and at the end of the report few recommendations are provided to the industry leaders that can further help them in effective utilization of big data.
Chapter 2: Literature review
The objective of this chapter of the research study is to present work of several scholars across the nations. Works that are associated with big data analytics, its impact on retail industry and how it is used to acquire customers of the retail industry are presented. Moreover, the purpose behind presenting the Literature Review section is to provide in-depth information of the selected topic. Readers can also understand the contradicting viewpoint that are associated with big data analytics with the help of the Literature review. In order to acquire related information on the chosen topic, a brief literature review has been performed in this section.
2.2 Overview of the industry
UK is one of the leading nations in the world. The country is categorized under developed nations, and have experienced growth in various over the last decade. There is no exception in the retail industry. According to the study conducted by Hasan, (2021), Amazon is one of the major retail company that plays an important and critical role in the service industry of UK. This company is known for offering products or services directly to its customers. The author states that performance of the retail industry also have an impact on the growth of whole UK economy. Retail leaders like Amazon in UK are therefore adopting various strategies to optimize profit and reduce costs in the industry. The company is also focused on international expansion. Thus, it can be said that activities and performance of Amazon retail companyholds structured significance. However, as opined by Seidu, et.al., (2021), the retail industry in UK currently at its lowest point because of several uncertainties in the international market. The intention of online shopping has increased and the shift in this purchasing behaviour has increased the demand of warehouse space. Moreover, Brexit has become one of the biggest contributors behind the change in the UK retail industry. Such changes are expected to have an impact on the process of procurement. Thus, it is the responsibility of the industry leaders to forecast problems, analyse data and take relevant actions to avoid any kind of discrepancies within the sector.
2.3 Customer acquisition
The process of getting customers who are potential and can contribute to the sales of the firm is known as customer acquisition. According to the study conducted by CORDOVA-BUIZA, et.al., (2022), unlike organizational leaders of traditional firms, current leaders are emphasizing on digital marketing. The objective behind adopting this strategy is to get attention of customers who are suitable for the products/services of the customer. Thus, it can be said that digital marketing strategies are used by retail leaders to acquire customers. Big data analysis is one of the major digital marketing strategy that has been adopted by Amazon to get proper insights on their current business process and operations. With the help of new technologies and analytical platform, historical data of the company is analysed and further key decision are taken by the company. Key decision on the customer engagement and policy making are developed by considering big data result analysis.
2.4 Importance of Customer Acquisition in Retail industry
According to the study conducted by Vashishtha and Sharma, (2016), retailers in the current scenario are making tireless efforts to experience growth within the industry. Retail leaders know acquisition of potential customers can only bring significant growth to the customers. The sales strategies that are adopted by Amazon is important and it is meant to attract customers that can further contribute to the growth of the company. The author also states that customer acquisition are assets of the company and they should be managed well for the profit optimization of the company. In the current business environment, organizational leaders are hiring customer relationship managers. These managers and their team analyse the needs and preferences of customers. After the analysis customers are segmented and targeted. This is how customer acquisition is done. In addition to this, according to the study conducted by Lehrke, et.al., (2018), customers are acquired with the help of digital marketing strategies. Marketing managers of Amazon thinks that if customers can be acquired rightfully by retail leaders, then it can enhance the presence of brand within the industry. The author also states that retail leaders who are focusing on acquiring customers are adopting various strategies by using advanced technologies. These technologies are not only helping them to attract or retain customers but they are also responsible for creating seamless customer experiences. Thus, from the above discussion, acquisition of customers in the retail industry increases the value of the brand, optimize profit, and help companies to gain competitive advantage.
2.5 Big Data and Customer acquisition
In the above part of the Literature review section, it has been mentioned that various unique strategies and technologies are taken into consideration while acquiring customers in the retail industry. Big data analytics is one such technology. According to the study conducted by Liu, et.al., (2020), big data analytics generally consist of two different concept. One is data that are hold by a company and the second aspect tools and techniques that are used to analyse the data. The author states that big data analytics is helping Amazon to acquire customers by analysing their purchasing behaviour. The purchasing behaviour analysis is further helping Amazon to set the price of the product, ensure profit optimization and improve the performance of the firm. The author states that there are steps that are leveraged by Amazon while acquiring customers using big data analytics. Big data analysis focuses on data mining which is a major technique in big data analytics. It also comprises of neural networks that further helps in social media analysis. However, as opined by Kitchens, et.al., (2018), customer analytics is an integral part of big data analytics in Amazon. Customer analytics is generally used by marketing managers to understand the behaviour of customers at a time. These relationship-oriented data further help in deeper analysis of customers. Finally, these analysis help retail leaders to acquire or retain customers in various ways.
Furthermore, according to the study conducted by Shabbir and Gardezi, (2020), big data analytics reduces the cost of customer acquisition by a certain percentage. The author states that with the decrease in the rate of customer acquisition, the profitability of the firm along with organization’s efficiency increases. The author states that big data analytics not only help in effective acquisition of customers but it is also responsible for enhancing organization’s performance. Big data analytics help retail leaders to access large volume of data from sources that are diverse. Analytical techniques are also used to analyse semi-structured data. These data are further used efficiently by these leaders. Finally, it can be said that big data analytics helps managers to get perceptions about businesses. These perceptions are further used for acquiring customers and retaining them.
2.6 Critical analysis of big data on retail industry
Retail executives have started recognizing opportunities related to usage of big data in Amazon. As pointed out by Mercier, Richards& Shockley (2013), consumers are now increasingly using data and technology for having control over their shopping experience. Data is now crucial for the businesses as a result of the multichannel shopping experiences and technology adoption that have become standards on Amazon. The synchronisation of multi-channel purchasing interactions and new data capabilities allows Amazon to manage, integrate, and comprehend a wide array of data.
According to Timofeeva (2019), big data helps in the storage, processing and analysis of vast information that further contributes towards sustainable development of Amazon. While the adoption of this technology is still midlevel in the rest of retail industry. It is expected that the retail industry will experience rapid growth in its implementation in the coming years. Huge amount of data circulates in the retail industry involving retailers, shoppers, suppliers, marketers and other actors.
Furthermore, Cheahand Wang (2017) have opined that big data along with internet and IoT devices can help in offering business opportunities to traditional retail companies by enabling them to connect with their users and engage them in unique ways. In this regard, companies are required to follow three key principles. According to Sun, et al. (2016), Amazon is increasingly considering big data for gaining competitive advantage in the market. However, there are various factors that influence the organizational adoption of big data in the identified organization. These factors can either act as motivators or obstacles in adopting and implementing the big data technology in the operations of retail firms.
As pointed out by Chauhan, Mahajanand Lohare (2017), large amounts of data are generated in Amazon because of various customer interactions. The organization is using big data analytics here for obtaining a unified picture of customers’ habits, tastes and preferences across various stores or online channels. These data are further used for making strategic decisions by utilizing the valuable insights gained about consumer behaviour. It helps in contributing positively towards the growth and development of the company. Here, the authors have further opined that big data has the capability of emphasizing on unstructured data gathered from social media, sensors and other devices, customer transactions, e-commerce transactions, medical records and others (Chauhan, Mahajan and Lohare 2017). Customer profiling has also been used that enables Amazon in enhancing customer experience and obtain their loyalty towards brands. Thus, business analysts of Amazoncollects massive amounts of data with the help of this technology and ultimately help in ensuring customer-centricity in their operations and services.
2.7 Literature Gap
The objective of this part of the literature review section is to discuss about some gaps into the literature review. Due to time and budget constraints, the scholarly works of few authors have been considered for presenting the literature review section. There are many other authors who have worked on this topic that could be considered for better information acquisition. Since works of all authors have not been explored in this chapter therefore, it can be marked as one of the primary literature gaps. Moreover, the researcher has considered research work which is not less than 10 years old. Therefore, the researcher has not presented the factors associated with big data analytics before 10 years. This can be marked as another example of insufficient information. Thus, it can be said that more insights and in-depth analysis of the research could have made the research more authentic and reliable.
The objective of the literature review was to find out ways in which business analysts use big data analytics for the acquiring customers of Amazon. The research study comes to the conclusion that the UK retail business is very significant since it has a direct impact on the expansion of the national economy. In addition, this sector of the economy has recently seen fierce competition. As a result, the company's main goals have been to establish a competitive edge and boost its market share on a global scale. Therefore, corporate organizations are concentrated on obtaining consumers in order to achieve competitive advantage. Big data analytics is an analytical technique that is assisting customers in gaining insights about customers, according to the literature review section. With the use of a web traffic analytics tool, clients' browsing and purchasing behavior is examined.
Chapter 4: Findings and discussion
After doing a brief analysis on the selected research topic, all the major aspects of big data analysis and technology adoption by Amazon has been addressed. However, all the identified research questions and project aim has been addressed in this report. In this section, a result analysis or discussion on findings of this research project will be illustrated. All the necessary aspects of result analysis will be considered in this section. Here, the result of data analysis both the qualitative and quantitative analysis will be illustrated. Based on the analysis performed during this project, major findings will be identified that could give some effective business ideas for organizations. On the other hand, a number of challenges and issues will also be identified that have been faced by the business analysts of the company during the implementation of big data analysis approach. At the same time, a result discussion on the selected research topic will also be illustrated. Most of acquired data and information will be considered during the result discussion. This will give a comprehensive information on the identified problem context. Amazon company has been considered in this research project which has been successfully adopting smart technologies to enhance their business operations.However, some of the aspects in big data analysis in retail business has been introduced.
4.2 Data analysis
For this descriptive analysis, the grounded theory approach which is particularly effective for examining emerging trends that extend beyond existing ideas has been applied. Information material from a variety of sources has been collected , such as publicly available business information, financial analyst reports, publication and newspaper publications, casual conversations with coworkers, formal questionnaires, research projects, papers supplied by the case firms, and involved in joint practice research projects with two outsourcers (Saltz 2015).The Innovator's moral conundrum and risk prevention, failure to find creative use cases, dissatisfaction as a result of over implementation and inflated anticipations brought on by excitement, and incapacity to deal with the difficulty that comes with system advancement transformation are just a few of the reasons why many manufacturers in the not adopting classes are unable to start moving to deployment.
In the above figure, a bar chart has been shown which gives an overview on the use of big data analytics in retail industry. Due to the extended benefits of the big data technologies, the application of this technologies is increasing year by year. In near future, there will be lots of automated big data applications which will be deployed by the organizations to get proper insights into their business operations. A number of programming approach has also been utilized to get proper insights on the business operations. Statistical reasoning informs us that just about everything may be regarded as a procedure (Hasan, Popp and Oláh, 2020). If that is the situation, data analysis may then be seen of as a mechanism that can be examined and enhanced using the methodological approach.
The fact that even these retain or testing dataset collections are frequently a component of the original information, or at least obtained at the very same time under the same surroundings, is a crucial aspect that is sometimes neglected. Returning to our topic of subject matter expertise, matching a set of data even a stand set does not indicate that developers possess a model that can be used in practice and that will be beneficial in the business, when it is likely to encounter new conditions and situations (Vashishtha and Sharma 2016). Investigators need a thorough knowledge of the extent to which the large datasets used mostly for analysis reflect the current and prospective target population, the possible future effects of different factors that were omitted, and the general reliability of the framework to suppositions or other modelling in order to produce implementable models.Big Data initiatives are frequently connected to significant, intricate, and unstructured issues. Data frequently originate from a wide variety of sources. There are frequently several organizational groups engaged, each having its own goals and opinions on the nature of the issue and possible solutions. Because of this, problems are frequently vaguely defined, making it difficult to select which particular issue to focus on and who should comprise the workgroup (Mercier, Richards& Shockley2013).
An overarching, increased solution to solving an issue is referred to as a strategic plan. The technique will frequently entail extra data in along with the initial data acquired, and is generally repetitive in nature, a subject we address further below. Extensive validation process needs continual examination over time, incorporating the modeling process to fresh data sets, obtained under different settings and situations from the initial data, as experienced teachers are typically aware (Hole, Pawar and Khedkar2019). This guarantees that a model may be used more extensively to many circumstances rather than merely fitting the initial training and test set of data, which are normally acquired at the same time. Furthermore, modern technology allows for provenance of statistical studies via dynamic linkages inside of latex publications, such as writer.
4.3 Major findings
Database model has evolved into an art, and those in charge of it in large enterprises ought to be highly skilled professionals (Russom 2016). The domain specialists, on the other side, are able to produce efficient database designs on their own, either to supply them smart tools that will assist in the design process or to completely bypass the design phase and develop a strategy to utilize databases successfully.The methods used by Amazon to query and process big data differ greatly from those employed to analyses typical data collections. Big data is frequently inaccurate, diverse, interactive, and interrelated data that is noisy. Since generalized numbers may be retrieved from recurring patterns and interrelationship analysis often outweighs individual variances and reveals more hidden information, messy big data may actually be more informative than small portions of data. Big Data also creates a vast network of interconnected large datasets, redundant data may be studied to make up for missing data, to validate contradictory situations, to check inaccurate linkages, and to reveal hidden structures.Proper, consistent, trustworthy, and easily accessible data, a standard query interface, a scalability mining method, and a potent cloud computing are just a few of the criteria for data gathering (Tsai et al. 2015). Data analysis itself may help to enhance the accuracy and dependability of the data, clarify its meaning, and provide clever query algorithms. Hospital documents are diverse in nature, spread across different systems, and include inaccuracies, as has already been observed. Here, the value of big data analysis is achieved through applications in retail, such as robustly taking into account all prior challenging circumstances. On the other side, knowledge discovered through analysis and processing can aid in the removal of ambiguity and the correction of mistakes.
If people can't understand the analysis, then it won't be very useful. The company authorities will be given the analysis's findings at the completion of the task for interpretation. It frequently involves revisiting the analysis and testing every stated hypothesis. As has already been demonstrated, mistakes may come from a variety of places, including flaws in software applications, predefined sequence, and inaccurate data that served as the foundation for the conclusions. The results that the database administration produce must be understood and verified by the final consumer, and the computing system must make the customer's job easier. However, this is difficult because of the intricacy of Big Data.For Big Data to be successful, Amazon must adopt a data analytics environment, make sure that all workers are familiar with the key ideas, and encourage data logical approach (Shoro and Soomro 2015). Respondents claim that the application of big data in UK retail companies is significantly influenced by organizational culture. Staff and management members of the organization must comprehend the significance of big data and know how to use it wisely. Additionally, individuals said that organizations must recognize the need to adapt their cultures, incorporate analytics, and stay up with the current developments in the commercial world.
To acquire, maintain, administer, and interpret the gathered data, Big Data needs monetary resources. One regulatory institutions that innovations were fairly expensive, but it was not a factor that would prevent them from adopting them. However, participants said that monetary resources were not one of the grounds why merchants were not using Big Data. A other responder asserted that even though retail companies had a lot of financial resources, only a very tiny portion of those funds were allotted to technologies that would assist initiatives like Big Data.Amazon's capacity to notice external changes and quickly adapt to them in order to preserve a competitive edge is known as business agility in retail. People contend that major retail enterprises' lack of agility is one of the causes they difficulty to integrate big data. Because they have so many processes, regulations, and procedures, large corporations like retail organizations are less nimble. The big retailers are digital immigrants, in contrast to the majority of freshly founded businesses (Kamilaris,Kartakoullis and Prenafeta-Boldú 2017). This study's contextual category sought to identify the environmental elements influencing big data in retail organizations’ acceptance and use of big data.When it comes to the development and execution of big data in enterprises, organizational support is crucial. When asked about performance management for Big Data initiatives in different retailers, the majority of participants claimed that administration in such organizations was in favor of the use of Big Data. However, several traditional retailers reported that management support was insufficient since different departments still appeared to have trouble seeing the value of analytics and applying it to decision-making.
4.4 Issues or challenges faced during analysis
In any technology or new technique, a lots of challenges comes that need to be considered during the implementation phase. In the retail industry, big data implementation is becoming a major challenge for the developers. A lots of challenges and issues faced by the business analysts form the initial stage of data collection to the end of insights building. Big Data is a phrase used to represent information and data that traditional software systems cannot handle or analyze. Big Data refers to massive amounts of structured and unstructured knowledge that must be processed using predictive analysis and visualization techniques to expose hidden trends and find surprising relationships in order to improve decision-making (Hewage et al. 2018). Many companies have a big volume of data that they are unable to use anyway it is still in a difficult raw, fragmented, or various formats. As data grows, businesses are faced with a serious dilemma since a less and smaller portion of the information can be handled. Because of the rapid growth of technology, people today live in the Big Data age. Here, the data handling is the main challenge that has been faced by the business analysts.
In the above figure, all the major challenges and issues have been identified through a bar chart. The task of developing technology that can handle all the technological needs of enormous data streams falls to IT researchers. As data volume increases, IT professionals are receiving more calls asking for more informal assessment and compiled results. If feasible, judgement cannot afford to wait hours or days for answers to their questions. Additionally, potential customers will want tools to access, comprehend, and analyses this data on their own without having to go back to IT for each request.According to the information life cycle, such Big Data difficulties may be divided into three broad categories including information, procedure, and organizational difficulties (Lehrkeet.al. 2018). Data qualities such as quantity, diversity, mobility, authenticity, unpredictability, integrity, exploration, and moralism are what provide the most challenges. The second category of problems relates to the processes that must be followed in order to acquire, collect, convert, and present the findings of data processing using the appropriate model. The organizational problems category, which includes all protection, infrastructure, regulatory, and ethical elements, is the third.
• Although some Big Data practitioners wrongly believe that enormous amounts of data can compensate for poor quality, the truth is that the reverse is true. The importance of quality is increased by the sheer amount of Big Data.
• Furthermore, it is more difficult to judge the reliability of huge amounts since businesses are frequently unable to immediately look through the information set and identify unexpected values (Zheng, Li and Na 2022).
• Additionally, distinct data sets acquired in various locations are frequently used in initiatives of this nature. There is always a chance of combining apples and oranges when data are gathered from several places.
• Inaccurate data, incomplete values, missing variables, and highly varied measurement results are the end consequence. There seems to be an incorrect assumption in the majority of the publications and in data intensity of rivalry sites like kaggle.com that volumes of data internal consistency data able to measure the gives a solid at the suitable frequency and are free of incomplete parameters, exceptions, or other data production problems.
Implemented mathematicians have acquired through experience that information quality is most often crucial to success, and in practice they spend a significant amount of time assessing it. Consequently, it is more common than not that a significant amount of time and effort must be spent on data evaluation, cleaning, and augmentation before a high-quality assessment can be produced.
4.5 Result discussion
There are several aspects of big data implementation into the organization that must be considered by the industry experts. Based on the result acquired during data acquisition and analysis, some of the vital aspects have been addressed by theresearchers.Based on the system architecture of the company, new technologies and innovations could be implemented into the organization. All the possible changes into the organization could be introduced by the company if all the business requirements have been satisfied. Regarding the organizational resources needed for big data, UK based companies have the funding, managerial backing, and governance frameworks in place to enable full implementation (Yin and Kaynak 2015). The survey also revealed that UK based retail organizationsrecognized the problems of human capital, which include the skills needed for Big Data, process improvement, and company behavior, and that various organizations had taken various strategies to overcome the challenges.
Given the abundance of Amazon and the potential for price variations caused by local demands and competition, marketing is one of the most challenging challenges marketing managers face. Charging is more challenging when there are clearance sales and promotions since there is more unpredictability and there isn't any prior data with the same promotional circumstances. It is nearly hard to duplicate a campaign because it depends on what else is readily available on the market, but certain characteristics can be noted for anticipating needs. Big data analysis increases the effectiveness and efficiency of advancement choices by providing more computational and analysis capability (Cheahand Wang2017). With the implementation of big data analytics, all the major business insights could be achieved by the business managers of the company. In each phase of product management to product delivery, business managers could be able to performed their tasks more smoothly. However, a number of challenges have also been mitigated by the users during the implementation of big data technologies.
Big data and the information it yields may be utilized to increase space utilization, increase the range in a constrained space, enhance shop appeal, and increase sales on a constrained range. Big data has lately been included into the procedures that guide selections on where to locate stocks. Retailers would divide their stores into several groups based on demographics and demand trends. This categorization would affect architecture and sufficient space in addition to product placement and assortment. Even though they can still utilize varied sizes, businesses would have a common design according to the various shop categories. The problem when considering usage is to cram more ranges into a small area. Some stores are only able to fit 45% of their inventory into one location.It is not unexpected that this has become a cooperative process with manufacturers as the preparation is based on product or container size (Sunet al. 2016). The container should be large enough to accommodate a whole arriving case onto the shelf, eliminating the need for the store to restock items one at a time. This will improve the efficiency of the advancement procedure. The required minimum exhibition inventory level also would be affected by this. It is okay to have cheap products on the table that can be quickly renewed in order to maintain the appearance of the showcase since the merchant would like to replenish a full case at the same time. Some merchants have indeed started using this strategy, and there are still others who aim to do so shortly.
Chapter 5: Conclusions and recommendations
5.1 Key findings on research
The big data analytics in Amazon retailer has been selected as the research topic which has been considered during the entire work. All the major aspects of data acquisition and analysis for the research paper has been introduced in this paper. Based on the available secondary resources, a number of crucial information and data has been given in this paper. However, it has been seen that a number of advanced technologies and smart devices has been selected by the business analysts of the company to improve the business operations and processes. This research has bene done to do a comprehensive analysis on the selected project topic. By considering business requirements of the company and problem context, all the major information and data has been given in this section.The purpose of this article was to outline big data applications for the retail business. The major retail activities of accessibility, selection, price, and layout design are those that may profit from more sophisticated analysis and processing of information (Santoro et al. 2018). Despite the fact that there are different interpretations of big data, this research provide a complete explanation that encompasses all data collected that can be quickly accessible and analysed to enhance operations and procedures. The current uses of big data in commercial operations centre on complex analysis and quick access to large amounts of data.
There are additional requirements and difficulties that must be met in order to execute big data applications. For instance, businesses must take risks and integrate procedures first. Additionally, as already indicated in the discussion section, the competence needs would change according to the level of intelligence. Generally speaking, procedures must be responsive in order to facilitate the judgement call cycle, and this might be impacted by problems with the proper skill set, provider assistance, and appropriate IT systems. The review of the literature that influenced the discussions served as the foundation for the research's conclusions. Despite the research's modest sample size, the results may still be applied generally and are helpful to merchants and other interested individuals.This study examines some potential future applications for big data in Amazon retailer, as well as practical implications in the categories of accessibility, selection, cost, and layout design (Jain 2010). To quantify the advantages, more study may be done in one of those categories and look at some of the found alternatives. Researchers provide an asset quality with gaps that can be filled and evaluated later.
The results of the research could be used to guide business decisions on how business managers of Amazon should utilise big data to improve supply, collection, price, and layout design. This paper evaluate the results from various participants with the existing research and provide them as a development profile that can be used as a standard. Retail logistical challenges, particularly digitally and Information and communication technology real-time logistical support, have innovative and exponentially growing applications like RFID-based product traceability, on-board automotive processes that are incorporated with smart transportation implementations to facilitate with cruising, supervising environmental factors for perishable commodities, and supplemented representation. This field has the same difficulties as big data technologies in the sense of management, a lack of knowledge and ability, and technology compatibility.
This research employed four distinct arrangements of identification and payment situations and evaluated customers' responses to them, depending on studies on developing service procedures in retail establishments. Researchers discovered the gap between consumers' impressions of evolving operational processes and their assessments of telecommunications companies by examining the impact of drivers and inhibitors of emerging services on crucial objectives that offer retailers comparative benefit in the age of big data technologies. Our research is one of the earliest studies to gain a better understanding on the innovative merchants' switch from conventional to exceptional sales channels, which enables them to take use of big data and so significantly benefit both their clients and oneself.
5.2 Area of improvement
The entire research has been done by considering the possible outcome of the big data implementation in retailer industry. On the other hand, all the major changes into the research approach and data analytics could be introduced to improve the research approach. After doing a complete research on the selected research topic, it has been found that there are several aspects that could be improved by the researchers. Based on the available data and information into different online resources, all the major aspects need to be developed. Big data's era has created both previously unheard-of advantages and difficulties for knowledge management. Assessment has received greater responsibilities and field of application as a result of its significance in organizational policymaking. The investigation of appropriate assessment techniques in the context of large data has grown in importance. The introduction and explanation of systematic assessment and enhancement in the big data technologies are the goals of this work.
The assessment strategies based on the primary big data analytical approaches, such as information retrieval, statistical approaches, optimization and modelling, and deep learning, are first reviewed for the target organization. The pertinent assessment techniques are provided with a focus on big data's properties. Researchers also investigate the application domains and research of continuous optimization. It has been found that there are several areas that could be considered for improvement. A number of statistical analysis on the profit and financial statements need to be considered during research. On the other hand, the documentation of the project should be more comprehensive with the graphical representations and inclusion of charts and graphs. On the other hand, a structured report should be developed on all the findings and result outcome of the project. With the help of more primary data collection and analysis, the research should be build more realistic. Based on the available resources, more specific resources need to be introduced in this research.
Evaluation is a process of assessing a thing's qualities and providing accurate findings using particular data and procedures based on specific objectives and criteria which should be introduced by the researchers. It is among the most crucial management roles in research work. The information is enormous, feature space, diverse, disorganized, unfinished, loud, and inaccurate in a big data environment. Traditional assessment techniques are best suited for small constant representative data sets, and it is challenging to apply them to large data sets with frequent changing circumstances. Assessment of the available information, however, has a benefit in research work. Firstly, some types of evaluations, such those that measure business outcomes, employee productivity, capital appreciation, academic success, accreditation, and credit quality, can directly assist management choices.
5.3 Future work
In order to do more advanced research on the selected topic, it is important to do work in near future. There is a huge possibility to do advanced research on the selected project topic. With the help of available resources, a number of changes into the research approach in future could be introduced by the researchers. A number of new technologies and big data approaches are now being developed by the researchers which could be introduced by the retailers companies to enhance their business opportunities.One of the largest sources of big data, IoT is meaningless without the ability to analyse it which could be introduced in future researches. When huge volumes of data are required to be collected, converted, and evaluated frequently, IoT and big data interface. The big data analytics setting is primarily the focus of future work which could be introduced for the advanced research technique. Researchers start by looking into the most recent research on IoT big data handling and monitoring technologies. Second, future researchers should list the several criteria that big data technologies in IoT must meet. Finally, researchers classified the literature. Fourth, they should identify the many possibilities that big data creates. Fifth, they should illustrate how data analytics are used in Internet of Things applications. Sixth, users should outline the unsolved research problems that need to be solved in the future.
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