CASE STUDY 6
Running head: CASE STUDY 1
Case Study
Student’s Name:
Institution Affiliation:
Case Study
Question 1:
Implications of the Statement “Zynga is an analytics company masquerading as a games company.”
It is evident from the case study that Zynga is the prominent developer of social network games. The company has prioritized data analytics which has enabled it to become the market leader in social gaming as well as having the capacity to offer critical data of its clientele in the social network. Data analytics is one of the essential gears that monitor the management in its expanding business model and the decision making of the firm (Laudon & Laudon, 2014). Zynga largely depends on the data analytics to enhance its user retention and to improve association among its gamers.
The company has further utilized the key metrics that that influence the economics of its social gaming encompassing social graph analysis. As a result of the new approach, Zynga has successfully enhanced its player experience and facilitated profitability of each aspect of the game. The data analytics has helped the company acquire meaning information that would enable it to observe how its player reacts to the game and therefore, designing the products to suit the customers need. The massive data analytics that Zynga uses has enabled it to disrupt the video industry. Unlike the conventional video game companies that only vend games to its clients, Zynga provides casual games and creates proceeds by vending virtual goods in-game.
Question 2:
Role of business intelligence in Zynga’s business model
The technology from Vertica Systems which has been incorporated in Zynga new business intelligence has facilitated magical improvement to its business model. This has been contributed by the company’s high churn rates, low in period for the game produced, low area of efficiency in sustaining its existing players, and too much spamming. The new technology from Vertica Systems has also enabled Zynga to solve its earlier problem in the context that Vertica’s data hayloft can function with compressed data that enhances performance by minimizing memory, processor demand as well as reducing disk input/out processing phase (Sharda, Delen & Turban, 2014). This new business Intelligence has enabled the firm to have rates of performance which are now 50 to 100 times faster compared to the data warehouses utilized by other firms. Hence, the management at Zynga is able to make more timely and accurate decisions in its business model. Zynga can now have increased accuracy in the production reports that provide tangible commendations for business improvements on its social graph data. The firm can also be able to determine the features that can work effectively in the game section (Laudon & Laudon, 2014).
Question 3:
Examples of three kinds of decisions supported by Zynga’s business intelligence
The first example of decision supported by business intelligence is that Zynga has the ability to enhance the relevancy of each of their project particularly in the context of offering gifts in their game to effectually upturn the extent of interaction between the active players. Also, Zynga has successfully reduced spam to their passive players. As a result, the firms have now been able to categorize groups of customers with similar behavior to get the clear-cut targeting of the game associated activities and promotions.
The second example is that the notion of providing free games that are channeled towards bigger and more casual gaming is part of the business model offered by Zynga. The company can generate income through retailing virtual goods in the game. The daily analytics graph created by the Zynga system can interpret the terabytes of data.
Lastly, Zynga’s Facebook software had the capacity to send direct messages to the Facebook clients, but the firm had restricted the feature due to spam grievances. The vertical software has in turn been used to access and manage the large collection of data quickly and accurately (Dunn, 2010).
Question 4:
The competitive advantage provided by business intelligence at Zynga
The business intelligence at Zynga facilitated an increase in revenue for the company. Zynga’s competitive advantage has been seen from its capacity to integrate the Vertica System, analytic teams, and Social Graph Analysis. The firm provides business intelligence which enables it to have strong competence in analytic and intelligence capacities in their business model. Also, Zynga uses the Vertica System to manipulate data 50-100 times faster which supports its business model particularly in analyzing huge data quickly and precisely.
The social network platform such as Facebook can enable Zynga to examine their gamer’s activities on the real-time and day-to-day basis. As a result, the firm can produce a custom-made user experience that is unique in the gaming industry (Giamanco & Gregoire, 2012). Another competitive advantage is based on Zynga’s human resource since they have established two analytics teams which include the analytics and reporting team. Having the teams implies that the firm has strong and competent internal resources that can offer excellent views on the path that the management should choose in the social gaming industry.
Question 5:
Problems that Zynga Business intelligence can solve
Zynga’s analyzing system is distinct from its hardware and as a result of the system allows the user to change from computers to mobiles or others. Also, the business intelligence helps the firm identify the features and trend of the user quickly making it easier to react and make changes (Laudon & Laudon, 2014).
Problems that Zynga Business Intelligence can’t solve
Zynga business model largely depends on the Facebook database, and if any problem arises in the database, Zynga system would not be in a position to address. This implies that of the Facebook system goes down then the firm’s system won’t be functional. Consequently, Zynga business intelligence cannot build loyalty among the customers in order to maintain them on their gaming products (Laudon & Laudon, 2014).
References
Dunn, B. J. (2010). Best Buy’s CEO on learning to love social media. Harvard Business Review,
88(12), 43-48.
Giamanco, B., & Gregoire, K. (2012). Tweet me, friend me, make me buy. Harvard Business
Review, 90(7), 89-93.
Laudon, K., & Laudon, J. (2014). Management information systems: Managing the Digital Firm
(p. 482). Boston: Pearson.
Sharda, R., Delen, D., & Turban, E. (2014). Business intelligence: A Managerial Perspective on
Analytics (pp. 159-213). Boston, Mass.: Pearson.