Machine Learning Analytics In Retail Industry: An Essay.

Key challenges faced by the retail industry and how Machine Learning can help

a) Retail Industry

In this case, Machine-learning analytics has been applied in the retail industry. The retail industry includes all the companies that are used to sell their goods as well as services to the consumers. Several types of retail sales are available worldwide, which include convenience, grocery, department stores, especially electrical, independents.

The retail industry seems to be undergoing a spontaneous and incessant evolution on its front. The potential problems experienced by the retail industry are the customers constantly changing their patterns of purchasing, and markets are moving rapidly toward the complex ecosystem (Jin et al., 2020). Apart from this, the advanced technologies are rapidly evolving and make the sector disruptive at a stunning pace. Hence, the shoppers are being bombarded with tempting offers and competing for their attention on several channels. Lastly, Customers are also choosing multi-channels to gain experience in purchasing.

In this case, Machine Learning is the analytics, which has been used and addresses the problems regarding business in the retail industry. The model of machine learning, in this case, helps the retailers to efficiently review, and break a huge volume of typical data of information into actionable insights (Pantano et al., 2018). This generally enables optimising the inventory management, understanding the ends of the customers with better segmentation, and accurately estimating the demands for the future. Hence, combining machine learning with the efforts in marketing, leads to the organisation achieving and enabling them to best use the consumer data.

The main challenges regarding Machine learning in the retail industry to achieve the objectives of the business are as follows:

• Requirements of Infrastructure for Experimentation and Testing- The retail industry is lacking in the appropriate infrastructure, which is known to be essential for reusability and data modelling (Schelter et al., 2018). In this case, a proper structure is required to check and monitor the testing of various tools.
• Data Security and Inaccessible Data-One of the common machine learning issues faced by the retail industry is the availability of information and data. Moreover, the collection of the data is not only a concern, but also it needs to be modelled and process the data to maintain the algorithms which will be used.

The recommendations to the stakeholders to adopt the applications of the machine learning for the business are as follows:

• Taking the sentiments of the customers on social media- Top brands have an active presence on popular channels. However, most of the brands use those channels as an official contact to provide and deliver customer care. In this case, Machine learning can be used to track the sentiment of the customers and the reputation of the brands in these channels.
• A custom-developed, according to Gated recurrent unit, Hybrid recommender software leverages the deep learning power to monitor and track the behaviour of site-purchasing and also provides recommendations to improve the experience of shopping.
• It also ensures a seamless supply chain.

Artificial Intelligence has been applied to the automotive industry. The automotive industry consists of a vast range of organizations and companies involved in the development, design, manufacturing and selling of cars and its components.

The business problems of automobile industries which need to be solved are basically, increasing the demand for the electric vehicles and automakers generally placing huge bets on the batteries and electric feet, shortage of semiconductors used to provide pressure in the supply chains of automotive, and the economy of fuel standards in the flux, quality control, etc.

By applying the AI, the automotive beginners will be able to produce modes which can mirror the aspects of the design of a vehicle under dynamic and realistic scenarios before its built (Tubaro et al., 2019). On the other hand, AI is also able to identify quality control, components and cars. It can also determine any internal defects 90 per cent more effective as compared to humans. It also helps in managing the logistics and supply chain.

Based on the case study, the main challenges of implementing Artificial Intelligence in the retail industry are lack of skilled personnel, level of complexity; the models of AI generally require huge training and require vast resources, data acquisition, security and proper storage (Borg et al., 2018). Data storage is one of the real problems faced by automotive industries.

Benefits of applying Machine Learning in the automotive industry

The recommendations to the stakeholders regarding adapting the applications of AI in automotive industries are

• Recommending the stakeholders implement Big Data in the automotive industry to market the motor vehicles to their customers (Grover et al.,2018). It can help them to identify and analyse the characteristics which they predict to purchase.
• Big Data in AI also helps the vehicle companies to use the insights such as online behaviour, and demographics to establish and develop communication in marketing as well as share important content.
• Lastly, Big Data analytics can allow the manufacturers of vehicles to design cars to meet the changing requirements and preferences of the customers

Operational analytics is analytics of a specific term that focuses on enhancing the existing operations (Bera, 2021). This is a type of analytics, that generally uses a different type of data aggregation as well as data mining tools to achieve more transparent data and information which can be used for effectively planning the business. It generally shifts the focus from understanding the data from different systems of software to putting the data in the tools to work to run the process of the business.

On the other hand, Discovery analytics is the best approach for the data analysts, it helps them to use to explore, and interact with the visualized data gathered from the voice of the customer, programs based on market research, and the voice of the employee.

Benefits of using operational analytics and how it is used to solve the business problems:

• Aids in making better cost-effective decisions- The best way to make an appropriate or big decision for any type of business, especially the retail industry, is using the data. In this case, it has been identified and determined that the operational analytics is good, cost-effective, and also comes through faster (Conboy et al., 2020). Using operational analytics helps to make vital decisions for the business hence ensues that the bottom path does not face any inefficiencies.
• Marketing industry to boost sales- Operational analytics also help the marketing managers to run various experiments regarding increasing the turnovers. It is easier for the organisations to nurture the techniques which generally work on termination of the ones which are not used to be effective when all the information is there.
• Supply chain management- In this case, operational analytics provides the employees or workers with entire relevant information and data to inspect and request for supplementary delivery. It can be the best approach for the retail industry to implement it and analyse their problems so that they can properly fix them.

Benefits of using discovery analytics and how it is used to solve the business problems:

• Improve the productivity- In this case; the retail industry can solve their business problem through the help of discovery analytics, which can help in allowing the organisation to view the drawbacks from the voice of the customers or employees (Amalina et al.,2019). In this case, a business can be able to process the operations by depending on the data.
• Identify the trends and patterns- One of the best benefits of discovery analytics not only in the retail industry but also in several industries, is the ability to make well designed and informed decisions. It generally helps to identify the patterns based on the market research programs and from the customers as well as employees side. It can identify the fact which technology is required to lead the optimisation of processes and resources of the industry.
• Drive the revenue and performance level- It is one of the key techniques in driving the revenue and performance of the industry by tackling the turnover of the industry.

Cloud computing is the most valuable and useful technology for communication as well as networking provides a base company or organisations in the era. Software as a service allows the clients to access software algorithms or programs from anywhere and at any time. Sometimes this SaaS is known as "Software on-demand". As mentioned, organisations spanned their business in Southeast Asia and Latin America. Based on this case, this paper also aims to the decision-making process, benefits, and analytics capabilities across the organisations and the analytics talent for the given organisations.

Cloud computing is the modern technique in this era. This technology is helping to grow the business to the next level. Cloud computing technology and 5G networking business are fully based on the communication system or telecommunication industry. Some famous cloud service providers with the best security policy companies are Google, IBM, Amazon, and Azure. Some famous cloud computing policies or packages are (SaaS), software as a service, (PaaS), Platform as a service as well as (IaaS) Infrastructure as a Service. Based on this paper, it has been discussed that the software as a service based on cloud computing implementation technique and analytics capabilities across the organisation based on the study case.

Based on the case study topics, the chief analytics officer faces low earnings segmentation and furious investments in the upcoming generation of products and services. This multinational corporation company is working in South Asia and Latin America. Based on these regions, if the chief analytics officer chooses the software as a service from the cloud computing techniques to overcome the given problems with the best business benefits, that's also a great point for the company because the cloud computing service-based companies provide a customisable software service based in the given problems (Alouffi et al., 2021).

The study report mentioned that multinational corporations are working with telecommunication or networking community sectors. Therefore, cloud computing technology is the best concept for this industry to improve high performance and deliver communication or networking services. A good cloud computing service with the software as a service policy is helping to make an automation process for this company based on real-time or daily life data or information. They can use the Google cloud services with the software (Lakshminarayan et al., 2013). This Google cloud platform is built with the high-level productivity of innovation, which means Google updates this Platform every week. Their updated or latest functionalities are easy to assume. Therefore, the organization and its staff also learn the whole techniques easily (Bittencourt et al., 2018).

Operational Analytics versus Discovery Analytics: A comparison

Moreover, the Google cloud platform provides the remote access concept. For this reason, any staffs of this company easily maintain or controls the whole software from anywhere and at any time with the top-class security patch. On the other hand, Google also provides the automation process with the cloud computing service that helps grow the business progress and find out the risks and future outcomes. In this concern, this company can provide the best continuous service for their customers and any condition (Mentsiev et al., 2021).

Based on the Fithri (2020) report, Software as a service (SaaS) is a subscription-based model. In this model, users can easily customise the service based on their requirements. Implementation of this software as a service has several processes, such as comparing the services based on the requirement, first needing to decide and find out the requirement, then fixing the implementation costs and the capacity of data storage based on the requirements. This cloud computation SaaS is flexible and cost elasticity service (Fithri et al., 2020). Therefore, the organisations can easily calculate the costs and maintain their services.

Unity or teamwork is the most important thing for organizations. For this reason, need to encourage all employees to adopt this cloud computing technology within the organization. After this step, the most important part of implementing connectivity is to fix the hosting server and the bandwidth for getting a smooth service (Alzakholi et al., 2020). Moreover, the other most important consideration point in SaaS is the Platform's performance and ensuring the data security and the data management policy. Using this SaaS service, this company can easily elaborate and get the data information and the insights of data that helps to find out the outcomes and find the insights the outcomes.

Analysts with industry experience are even more difficult to come by. For reflecting this reality in the sourcing, development, and acknowledgement of Analytics talent, these businesses must alter their talent management practices. The parameters of these analytics talents are data insights, Outcome measurement, information and data management, and the talent management and structure of the management and ability of the development or implementation techniques.

Conclusion

This report provides a high degree overview of cloud computing and the SaaS concept, which helps make a long and automated business for the mentioned organizations. This paper also discussed production management and the smooth service management with the Google cloud platform (GCP) with the best quality security and exact cost elasticity (Cardellini et al. 2018). Moreover, this study report concerns the analytics talent deployed and the benefits of using cloud computing technology and the benefits of software as a service in a communication organization.

References

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