COIT20249 Professional Skills in Information Communication Technology Assignment Answer

Introduction

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Technological advancements are helping businesses to achieve new heights in their respective sectors. Artificial Intelligence, cloud computing, machine learning, etc., are helping the organisations to compete not only in the local market but also in global ones. In this report, the need for machine learning application in the e-commerce sector, more particularly JD, an online retailing firm was discussed. Furthermore, it talked about how different sectors have successfully incorporated machine learning technology in their operations. The report highlighted the need for the business and areas where it can implement machine learning. Based on the understanding, certain recommendations have been provided for JD regarding the implementation of machine learning.

Machine Learning Overview

Machine learning is one of the subfields of the Artificial Intelligence that aims at understanding the structure, trend, and behaviour of data and make suitable changes in the model as per the data in such a way that it can be easily utilised and understood by people. Machine learning applications are based on certain algorithms that train the computer for certain statistical data inputs so as to get the output within the desired range. Due to this, the decision-making in the organisations has turned automated. Organisations are rapidly shifting to the machine learning approaches in order to automate and increase the efficiency of their processes.

Machine Learning vs. Artificial Intelligence

Artificial Intelligence and Machine Learning are two terms usually used interchangeably by laymen. However, in the field of computer science, these two are different. Artificial Intelligence has consisted of "Artificial" and "Intelligence.” The former means something that is developed by humans from non-natural things and Intelligence means the ability to think and understand. Usually, people think that AI is a system which is a misconception. AI technique is incorporated into the system (Syam& Sharma, 2018). The aim of AI is to improve the success rate but not the accuracy. AI is programmed to carry out smart work. There is not learning from any data involved in this system.

On the other hand, machine learning is all about making a machine learn by its own without much of programming or specialised algorithms. It is a branch of AI that arms the system with an ability to learn automatically from past data, information, and trends. The aim of machine learning is to improve the accuracy of the system but does not care about the success rate. It is totally dependent on data feeds and maximise the performance of the machine. It does not make any decision whereas, AI is empowered to make decisions.

Companies Implemented Machine Learning

Before discussing the industries that have implemented the machine learning process, it is assumed that those companies are successful in achieving their motive and goals. Another assumption is that machine learning application is capable of handling multidimensional data related to a particular field (Syam& Sharma, 2018). Here are some of the sector that are implementing machine learning.

Automotive Industry : In the automotive industry, the application of machine learning is usually related to product innovation. For instance, the latest inventions and advancement in the automobile, such as self-driving cars, smart energy systems, lane change assistance, etc., are successfully implemented just because of the application of machine learning. In addition to this, ML is having a tremendous impact on the sales and marketing operations (Kaneko&Yada, 2016). Car companies are more connected with their customers now as they used to be in the past. In addition to this, the production unit is directly connected with and directed by the planning and inventory management team (Perlich, et.al, 2014). This controls the production of cars as per the demand in the market, thereby sustaining the profitability of the company.

Real Estate Industry :The global real estate sector is among the fastest-growing sector. The market is considerably controlled by the sellers and their demands. A successful company requires to analyse the demand and supply of the real estate sector correctly. This can be easily done by machine learning. In the real estate business, machine learning can carry out various operations simultaneously. For instance, development and operationalize the consumer app, machine learning-based application, chatbots for customer support (Cui, et.al, 2017). In addition to this, it can carry out automated property management, building automation system, analysis of demand and supply and customer preference analysis (Park&Bae, 2015).

Banking Sector : The global banking sector is dependent on customer satisfaction. Machine learning is a great way of dealing with clients. The finance sector is among those sectors that have complex operations run on data analytics. Machine learning can assist in sorting mails by making use of Natural Language Processing (Farquad, et.al, 2012). In addition to this, it can update the records and details of customers on a regular basis and serve as the best Customer Relation Management (CRM) solution.

 Adopting the Machine Learning Culture in JD

Majority of the business organisation are pretty much aware of the fact that AI alone cannot solve all of their issues. Therefore, implementing machine learning is the need of time. JD is one of the leading e-commerce businesses and understands very well that e-commerce business will surely be increasing that allows JD to collect the user data and shopping trends in order to improve customer experience. Machine learning can assist the company in doing so as it handles a large amount of data in a more efficient, easier, engaging, and easily adaptable manner.It is assumed that the machine learning application is designed for handling multivariate data as it deals in the different customer section. Adopting the Machine learning process or application is not a cakewalk as it requires proper planning from the management. First and foremost, it is important for JD to determine an area or a process in the current business operations that can add more value to the business with modern changes. In addition to this, the organisation has to define clearly what it can accomplish with the implementation of machine learning (Rodriguez&Laio, 2014). Once this is done, it is important to identify the right persons who will ensure the proper implementation of ML. It is important for JD to identify a process that is inefficient and complex and requires some serious changes and upgrades.

There are various operation fields where the JD can implement machine learning applications. Two of the most ineffective ones are discussed below:-

Customer Support: for e-commerce like JD, it is quintessential to have the best customer support department and application. Usually, customers face issues such as long waiting time, unqualified advice, re-explaining their issues multiple times, and much more. With the help of machine learning, the company can be able to automatize the customer support process through bots that can answer the phone calls and address the basic query of customers. The earlier process usually faces linguistic barriers due to which customers are not pretty much satisfied (Rodriguez&Laio, 2014).

Business Outlook: JD has a plethora of business operations running simultaneously. There are plenty of options available with the retailer.For example, ML can assist in deciding the customer segmentation of JD. This would require a great deal of data and make the marketing process more precise. In addition to this, product categorisation is another aspect that can be taken care of by the implementation of product categorisation. Machine learning can automatically sort the goods and products in JD in various categories for pacing up and improving inventory management and customer navigations. At last, ML can improve the inventory forecast of JD. This technological advancementcan help in making a more efficientprediction of the market demand based on the past data and trend analysis in no time.

Implementation of Machine learning has its own pros and cons. These are discussed below:-

Advantages of Machine Learning

• With the ML, the company can review a large amount of information and data and one can discover a particular pattern and strategy for a particular market.
• ML algorithms can be upgraded on a regular basis and this way the system or operations can be continuously improved with more accurate predictions.
• It arms JD with an ability to handle multi-dimensional and variety of data and discard the market ups and downs.

Disadvantages of Machine Learning

• Machine learning is effective for organisations that deal with a humungous amount of data. Companies have to sometime wait for the generation of such an amount of data.
• Machine learning requires enough time and training to excelling the software. In addition to this, it requires a complete change in the current operations and a great deal of resources to function well.
• High susceptibility for biases induced by the corrupted data. This may cost JD a great fortune.

Ethical, Social, and Legal Issues Associated with Machine Learning

As Machine learning is a part of technological advancement, there involve many ethical, legal, and social issues. Machine learning applications make use of big data and a large amount of user information (Char, et.al, 2018). Therefore, there is always a concern for theft or illicit use of data and personal information of clients and customers. In this section, three aspects of machine learning are discussed that can make it disadvantageous for the companies that are aiming to implement it in their operations (Witten, et.al, 2016).

The first and foremost issue is inducing biases in data. Machine Learning applications make use of data that is susceptible to biases. There have been many cases recorded in the past of racial, demographic, or other biases associated with machine learning. All these can be done just by changing the algorithm of the application. The second issue identified is that application based on machine learning gets started automatically without the consent of the user. This is against the principles of the safe use of technology. There are many instances where people could not be able to turn such application off (Char, et.al, 2018). Another issue is that machine learning applications are built on closed source licenses. This means that the algorithms and the source code of program are not available for the users to see. This makes the applications a bit complicated to be used in every field. Users usually want to see how a particular application based on machine learning works. This increases their confidence and trust in the results of the app or system (Rodriguez&Laio, 2014).

Another issue identified related to the machine learning system which is the most serious issue from the perspective of the e-commerce industry is illegal to use of personal information of users and customers without their consent and knowledge. No doubt the end-user license agreements clearly states about how the data of users would be used, but many machine learning applications do not abide by the agreement. This is a serious offense and can result in a severe penalty on the company (Char, et.al, 2018). Furthermore, Machine learning helps the e-commerce industry in understanding the trend and behaviour of the user. The machine learning apps recommend certain products or discount deals that might be fake or shoddy. These are certain issues associated with the use of technological advancement like machine learning.

Conclusion

Before ending on a high note, it is concluded that if JD could possibly implement machine learning successfully, then there is no stopping for the company to attain a competitive position in the market. It was determined that machine learning would help the company in understanding the ever-changing needs and preferences in a better way. In addition to this, it was revealed that the organisation should first identify the sector that requires serious changes and improvements. Furthermore, the organisation has to define clearly what it can accomplish with the implementation of machine learning. Once this is done, it is important to identify the right persons who will ensure the proper implementation of ML. The case studies discussed in the report would help in understanding the other usage of the application. It was revealed that operationalized work can be improved more effectively with the help of machine learning. Another point that was made in the report is that implementing machine learning is the need of the time. JD is one of the leading e-commerce businesses and understands very well that e-commerce business will surely be increasing that allows JD to collect the user data and shopping trends in order to improve customer experience.

Recommendations

Here are some ways through which JD can implement machine learning in its operations:-

1. Machine learning can be beneficial for the business in Price Optimisation. JD can develop a pricing engine that can deal with a large amount of information on current trends, customer preference, shopper's profile, competitor prices, product price, and much more. This would help JD in dealing with hundreds of customers with varying shopping taste and optimise the prices on products, thereby gaining a massive competitive edge (Syam& Sharma, 2018).
2. JD can retarget its visitors may not always end by purchasing anything. People usually browse and add items to their shopping cart and leave without completing the purchase. Through machine learning, JD can retarget its customers. It has been seen that Facebook, Instagram ads may be a part of a machine learning process (Cui, et.al, 2017).
3. JD can build a giant and incredibly effective recommendation engine that should be based on machine learning. This would help JD in analysing past shopping behaviour and develop useful information regarding trends and patterns. All these are done by improving the algorithm of the system (Rodriguez&Laio, 2014).

References

Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care—addressing ethical challenges. The New England journal of medicine, 378(11), 981.

Cui, L., Huang, S., Wei, F., Tan, C., Duan, C., & Zhou, M. (2017, July). Superagent: A customer service chatbot for e-commerce websites. In Proceedings of ACL 2017, System Demonstrations (pp. 97-102).

Farquad, M. A. H., Ravi, V., &Raju, S. B. (2012). Analytical CRM in banking and finance using SVM: a modified active learning-based rule extraction approach. International Journal of Electronic Customer Relationship Management, 6(1), 48-73.

Kaneko, Y., &Yada, K. (2016, December). A deep learning approach for the prediction of retail store sales. In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) (pp. 531-537). IEEE.

Kietzmann, J., Paschen, J., &Treen, E. (2018). Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey. Journal of Advertising Research, 58(3), 263-267.

Park, B., &Bae, J. K. (2015). Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data. Expert Systems with Applications, 42(6), 2928-2934.

Perlich, C., Dalessandro, B., Raeder, T., Stitelman, O., & Provost, F. (2014). Machine learning for targeted display advertising: Transfer learning in action. Machine learning, 95(1), 103-127.

Rodriguez, A., &Laio, A. (2014). Machine learning. Clustering by fast search and find of density peaks. Science (New York, N.Y.), 344(6191), 1492-1496.

Sayed-Mouchaweh, M., &Lughofer, E. (Eds.). (2012). Learning in non-stationary environments: methods and applications. Springer Science & Business Media.

Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 69, 135-146.

Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.