DATA4000: Introduction To Business Analytics Case Study

Part A: Case Study Analysis

Netflix Predictive Analysis

Industry

This article is based on the video streaming sector and technical sector or industry.

Business problem

Analyzing the case study, it has been identified that The entertainment industry is very highly competitive and faces several types of issues related to customer acquisition or retention. Therefore the application of proper technique can be effective to manage the issues of improving customer engagement by increasing the number of the subscriber on the platform (Engati, 2020)

Type of analytics used for mitigating the potential problem

For managing the issue Netflix has used predictive analytics for identifying the major preferences of customers and providing proper personalized recommendations for increasing the subscription and engagement rate (Engati, 2020). This type of solution is majorly used in the machine learning algorithm for analyzing all types of customer data including their search queries and different types of shows or streaming. This analytical tool health organization by predicting the customers who might like to watch next and provide the proper recommendation based on their preferences.

Challenge in using the type of analytics

One of the primary issues that have been faced by Netflix in using the break to analytics is the accuracy of the presented data or predictions. The success of this recommendation only depends upon the accuracy of the data gathered by the organization (Engati, 2020). Sometimes due to the less quantity or quality of data, it can be a top priority for Netflix to use great analysis to obtain accurate predictions.

Recommendation

For assisting the stakeholders with adopting the application of analysis the following recommendations can be effective for the organization to get proper support.

• Identification of relevant business problems is very helpful in improving customer engagement and producing more successful shows.
• Obtaining sufficient data from the customers can also be effective for the analytics to predict different support to the organization.
• Training the employees can be effective in managing overall productivity by using predictive analysis in a better environment.

Coca-cola vs Pepsi

Industry

This article is based on discussing the uses of different data analytics in the soft drink industry of various companies including Coca-cola and Pepsi.

Business problem

One of the primary issues faced by both of the companies is to maintain their proper revenue and market share. The soft drink market is very much comparative and most companies are required to understand all the preferences and consumer behaviour to stay ahead of their competition (Mixson, 2022). Therefore the potential business problem is very much required to be solved for gaining the proper inside about the customer's preference and behaviour.

Type of analytics used for mitigating the potential problem

The famous company named Coca-cola has used machine learning for optimizing their beverage dispensing machines' placement to maximize their overall sales. This company also used regional analytics for determining the best location for they are different machines based on the sales data and customer traffic (Mixson, 2022).

On the other hand, Pepsi also used data analytics and machine learning algorithm to analyze the customer's identity and data to maintain the most effective marketing channels and provide messages to every customer segment. Both organizations are using different types of channels such as loyalty programs surveys and social media to collect data on customers.

Challenge in using the type of analytics

One of the major issues that can create a huge amount of negative impact on the organization is related to privacy concerns about customer data along with prediction accuracy. Due to the leakage of customer data, a huge amount of negative impact can happen on the business operations of the organization (Mixson, 2022).

Recommendation

Identification of the right business problem and ensuring that availability and data quality are the prime factors that can be helpful for both companies to maintain a proper prediction by the use of machine learning algorithms. Providing proper privacy to every customer's data can also help the organization to increase its brand image in front of the customers. Along with that, offering different training programs to the employees along with the workers can be helpful to predict the overall customer demand and preferences for soft drinks.

Part B. The Role of Analytics in solving business problems

Case study 1: The application of predictive maintenance using the machine learning algorithm

Proper analysis of different types of analytical tools provides a huge amount of support to the management to gather a clear idea about all kinds of operations in business problem-solving. The application of this analytical tool has been used in various sectors. In this analysis, the uses of this data analytics tool in the aviation sector have been described below.

Creative maintenance is one of the primary types of Data analytics that use machine learning algorithms for predicting different types of equipment failure or schedule maintenance before the failure has occurred. General electric aviation or GE Aviation has used the machine learning algorithm to monitor the performance of the aircraft engines to check their future. The type of data analytics platform used by general electric aviation is known as Prefix which helps to collect different types of data with the help of different engines and uses the machine learning algorithm to analyze and predict when the proper maintenance is required (Thapar, 2019). This type of platform also provides a proper inside about the overall operation of the engine which helps the airline to optimize its overall operations and reduce the cost of the organization.

With the help of this type of predictive analytics, this aviation can reduce different types of unstitched maintenance by up to around 60%. This type of factor also creates a leading factor for significant cost savings for the airlines (Thapar, 2019). This platform also allowed GE Aviation to identify different types of new services along with the customers such as predictive maintenance as their major service.

Case Study 2: Social media analytics for the marketing

Social media is one of the major platforms that most organizations are using for different types of promotion purposes and to analyze the data for gaining information about customers' behaviour and preference. Among the major companies, social media analytics has been used by the Coca-Cola group for understanding the oval impact of the customers. Interact with different brands through the application of social media platforms to evaluate their data.

Coca-cola used the social media analytics platform called Sprinklr for collecting various types of data through the application of different social media channels including Twitter, Facebook and Instagram (Telefonica, 2021). This platform provided a proper inside of the process of customers engaging with different social media content (Sastry, 2020). Through the application of this technology, the management of Coca-Cola understands the consumer's statement about the company their brand and its requirements for the future. Along with that, the Coca-cola group has used various types of AI technologies that assist the organization to understand the insights of the customers. Similarly, AI also assists in managing various functionalities which offer the management to get clear ideas about various changing requirements of customers and others. Using different types of insights collected from social media, this organization was able to develop a better effective social media strategy and create more in getting content for increasing customer satisfaction. This type of opportunity helps the customers to get proper feedback and provide their preferences about the different products offered by the Coca-Cola group.

Part C

Ingrain analytics into the organisation’s decision-making processes

The given case study highlights about different business analytics of Accenture. Accenture possesses specialized teams that provide services and expertise in the field of Analytics. This approach enables organizations to disseminate these scarce competencies throughout the enterprise, thereby enhancing the efficacy of existing business procedures. The issue at hand pertains to the inadequacy of constructing a sustainable analytics solution that can cater to the entire enterprise. Organizations are leveraging business analytics to expedite decision-making processes and rely on empirical evidence. An instance of their approach involves the utilization of granular data to enhance the personalization of goods and services, alongside the scaling of digital platforms to align buyers and sellers (Kelleher et al. 2020).

Organize and coordinate analytics capabilities across the organization

Accenture can maximize the performance of its analytics department in a number of ways. The structure processes, and values of a corporation may all be gleaned from this model. It's crucial that everything works together in a way that benefits the company and efficiently completes tasks.

There is no single optimal structure for an analytics team to operate under. According to Accenture, it doesn't matter how an analytics project is structured, cooperation and information exchange are essential to its success. 

The analytics team would develop an analytics strategy, offer guidance to the rest of the organization, and execute larger initiatives at the expense of the company's departments and divisions.

When there is a company-wide data strategy and a standard set of procedures for putting that goal into action, the centralized approach shines. Both are crucial to Accenture's analytical project explanation and execution. The formulation of an analytics strategy is imperative in order to effectively operationalize a data strategy. The formulation of a data strategy ought to encompass considerations of organizational objectives, desired business outcomes, stakeholder education, and the establishment of a plan for implementation. In light of the increased availability of data, it is crucial for the organization to establish a strategy that facilitates precise decision-making (Amarasinghe et al. 2020)

Source, train, and deploy analytics talent

Businesses are hiring a growing number of data scientists, analysts, and specialists in artificial intelligence; yet, there is no consensus on the skills required for each of these professions. There is a possibility that data scientists will concentrate a significant amount of focus on statistics, open-source technology, or the utilization of data to address business difficulties. Finding a data scientist who has all of these skills at the very highest level was never a possibility that could be considered practical. As a result of the increasing popularity and demand for the role, an increasing number of professionals are now using it to explain the obligations that they are responsible for. In response to the growing demand, educational institutions such as colleges and universities have launched hundreds of new programs in data science and analytics. However, the skills that are taught in these programs vary widely, and some educational institutions offer a wide variety of courses with a variety of emphases. It is highly improbable that titles such as data scientist and quantitative analyst effectively represent the knowledge of a person, regardless of how long they have been employed or how recently they have been hired (Ferraris et al. 2021)

Although they are still in the early stages, measures are being taken across companies to standardize the many jobs and necessary skills for data and analytics. Despite the fact that they are still in the early stages, this standardization is extremely important. Even while the initiatives themselves are an excellent idea, the process of developing new standards could take quite some time. Employers should prioritize classifying and credentialing the several types of analytical labor that they already have and will need in the near future. The growing gap between the demand for and supply of Analytics Talent has made it very hard for businesses in all kinds of fields to find the right people. This is because university graduates in this field are not well-equipped to meet the matured in-house capability needs of big companies. As a result, they tend to go into the service and banking industries. Large companies are trying to find people with analytics skills, so it's best for them to focus on hiring recent graduates or professionals with a wide range of skills and knowledge. This will help the company build cross-functional and organizational analytics skills, rather than just focusing on individual units. The way upscaling is going now makes it easier to find skilled analytics professionals and offer appealing ways to move up in your career and educational tools. This program meets the company's needs for attracting, developing, and keeping talent. It also helps graduates and professionals advance in their careers and grow as people by paying for their training and education.

References

Amarasinghe, S.L., Su, S., Dong, X., Zappia, L., Ritchie, M.E. and Gouil, Q., 2020. Opportunities and challenges in long-read sequencing data analysis. Genome biology, 21(1), pp.1-16.

Engati, 2020. Netflix Predictive Analytics: Journey to 220Mn+ subscribers. Engati.Available at:https://www.engati.com/blog/predictive-analytics[Accessed on 13rd April, 2023]

Ferraris, A., Mazzoleni, A., Devalle, A. and Couturier, J., 2019. Big data analytics capabilities and knowledge management: impact on firm performance. Management Decision, 57(8), pp.1923-1936.

Kelleher, J.D., Mac Namee, B. and D'arcy, A., 2020. Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. MIT press.

Mixson, E. 2022. Coca-Cola vs. Pepsi: The Sweet Fight For Data-Driven Supremacy. AI data analytics. Available at: https://www.aidataanalytics.network/data-science-ai/articles/coca-cola-vs-pepsi-the-sweet-fight-for-data-driven-supremacy[Accessed on 13rd April, 2023]

Sastry, V. V. L. N. (2020). Current Technologies Employed in E-Commerce Customer Service by Leading Players. Idea Publishing.

Telefonica, 2021. Coca-Cola’s use of AI to stay at the top of the drinks market. Telefonica tech. Available at: https://business.blogthinkbig.com/coca-colas-use-of-ai-to-stay-at-the-top-of-the-drinks-market/[Accessed on 13rd April, 2023]

Thapar. V, 2019. GE BRINGS AI INTO PREVENTIVE MAINTENANCE TO REDUCE JET ENGINE FAILURE BY ONE-THIRD. SPs aviation. Available at: https://www.sps-aviation.com/story/?id=2646&h=GE-brings-AI-into-preventive-maintenance-to-reduce-jet-engine-failure-by-one-third[Accessed on 13rd April, 2023]

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