Netflix is ranked as among the leading companies that drive one-third of the internet traffic in the United States. Following a large number of subscribers, the company has an opportunity to collect a wide array of data through which they can predict consumer trends and determine the best offerings for their clients. Primarily, this data analytics technique is regarded as predictive data analytics. As a business tool, predictive data analytics can be used in the decision making, planning, management, and extraction of information from sets of data (Kohtamäki, 2017). Aside from examining previous and current data sets to determine consumer trends, predictive analytics are likewise tools for identifying potential opportunities and risks. The tool generates forecasts for data by integrating data machine learning, mining, and statistical modeling among other technological data models. Predictive data analytics has several benefits. The tool appears in several forms namely optimization and decision analysis, predictive modeling, predictive search and transaction profiling (Kohtamäki, 2017). For Netflix, the company uses an integration of the different forms of recommendation engines for future program offerings. Using the data mined from the tool and its various forms, the company uses the tool to make decisions and increase revenue.
Even though the technique has proven to be effective in the past, critics argue that it has several drawbacks. Foremost, while it uses algorithms to predict human behavior, it does not put into consideration several variables. Notable variables in human behavior include changing moods, preferences in taste, relationships, and financial status of individuals (Barker & Wiatrowski, 2017). All these variables seemingly have an impact on consumer purchasing power. Time also significantly influences the effectiveness of the tool. In this regard, while consumers might show preference to a certain program, changing time might likewise be accompanied by changes in consumer viewership. Hence, the program must be regularly updated to meet the changing times and demand for consumer behavior. For instance, a sudden period of the financial crisis might lead to low viewership and subscriptions while previous predictions might have shown an increase in viewership and subscriptions. In an interview by an industry watch blog, the company cloud persistence engineer and senior software engineer revealed that while Netflix benefits from existing data, it also wonders about the data that is not been collected by its system (Shaw, 2017). This automatically creates an appetite for the collection of more data. The need for additional data calls for the company to break from the existing system forcing Netflix to search for alternative analytics data systems. Alternative forms of data are seemingly accompanied by increased costs of research, implementation, and training of personnel on how to manage the system.
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With the increased need for data by several industries and companies, the marketplace has witnessed a surge in business intelligence system vendors. For this current paper, I will examine three vendors namely: QlikTech, Logi analytics, and Quadbase systems. QlikTech Inc. is a BIS vendor that operates on a platform referred to as Qlikview to analyze sales performance. In comparison to Netflix, the company offers several benefits in data analytics. Foremost, the QlikView platform prides itself in the analysis of data that dates back to twenty-five years’ worth of data to forecast demand, trends, and provide on the spot recommendations to the global clientele. By availing information that dates back twenty-five years, the company is able to form conclusive reports on consumer trends and demands by observing previous trends and demands (Walker, 2017). Also, analysts can be able to detect recurring trends among consumers. Spot on recommendations are likewise an advantage for Netflix, which is competing among other entertainment firms. This will ensure that the firm constantly stays ahead of competitors in program offerings. Lastly, QlikTech has developed a competitive advantage over rivals through the smart visualization analytics, which facilitates several functions including the spot on recommendations. With the accompanying benefits, the system might be costly for Netflix to set up or integrate into its already existing system.
Logi analytics is another BIS vendor that offers its services in two forms: visual analytics and business analytics. While the business analytics is developed to help developers create an analytics application, the visual analytics is designed for group cooperation. Overall, the system enables users to perform several functions including score carding, dash boarding, track performance, reporting, searching, asking queries, and data discovery (Shaw, 2017). The functions of the company are similar to those of Netflix predictive analytics, statistical modeling, data visualization, and big data analytics. This allows the company to forecast future trends of consumers and to avail programs that are currently trending. Unlike Netflix, however, it allows users to choose individual sources of data for analysis. While this might appear advantageous, it is likewise disadvantageous especially for Netflix since varied sources might not provide a conclusive report. Besides, as an entertainment company, use of multiple sources might prove cumbersome for study. The benefit, in this case, is yet beneficial since users can access and use sources that are easily available to them. Like the QlikTech system, this is likewise costly for the company to implement. Quadbase system Company like Logi Analytics avails several services to consumers including reporting, dashboarding, and data visualization. Recently the company launched a web-based platform that allows non-programmers for deployment and dashboard development (Kohtamäki, 2017). For Netflix, this is advantageous since it provides the company with a wide array of data to analyze since consumers from across the globe can access the site and make contributions to trending programs and movies. The platform while, beneficial, can easily be hacked by specialists, hence, producing false data.
In relation to Netflix, I choose four decision models namely customer analysis, behavior analysis, revenue generation, and sales channel analytics. Customer analysis applies several characteristics of the consumer including personalization, profiling, customer satisfaction, loyalty, lifetime value, and collaborative filtering. By identifying these traits of customers and linking them to the viewership of certain programs, Netflix can easily predict program needs and demands that are specific to certain types of clients. This technique is long lasting since information on past consumers of a similar trait to new consumers allows the company to easily offer new consumers programs that are suitable for their viewership based on information of past consumers of similar traits. Behavior analysis bases decisions on the following aspects: social network analysis, web activity, purchasing trends, sentiment analysis, and customer attrition. Behavioral patterns of consumers have over the years been the easiest tool used to forecast demand and trends. This is suitable for Netflix since the company can rely on the behavior of consumers based on the information of their current and potential clients that is available through social networks and web activity. Revenue generation as a decision-making model is based on several factors including cross-selling and up-selling, loyalty management, target marketing, location and geographic analytics, and market development (Barker & Wiatrowski, 2017). Obviously, this model is geared at generating revenues for the company using the traditional targeted marketing. By focusing on the needs of certain consumers and customizing marketing efforts to meet their needs, Netflix is guaranteed success. Lastly, sales channel analytics is founded on sales performance, marketing, and sales pipeline. This is similar to the revenue generation decision model. The difference, however, is that the company will rely on previous records of sales to predict future viewership by clients.
- Barker, C., & Wiatrowski, M. (2017). The age of Netflix : critical essays on streaming media, digital delivery and instant access. Jefferson: McFarland & Company, Inc.
- Kohtamäki, M. (2017). Real-time strategy and business intelligence : digitizing practices and systems (3rd ed.). Cham: Palgrave Macmillan.
- Shaw, R. B. (2017). Extreme Teams : Why Pixar, Netflix, Airbnb, and Other Cutting-Edge Companies Succeed Where Most Fail. New York: AMACOM.
- Walker, R. (2017). Netflix leading with data : the emergence of data-driven video. Evanston: Kellogg School of Management.