Predictive Analytics Today - Literature review Example

?Running Head: PREDICTIVE ANALYTICS TODAY Predictive Analytics Today Predictive Analytics Today Introduction In 2007 Davenport and Harris (2007) wrote a book that explored the nature of predictive analytics and how they were used for competition in business. Since 2007, however, the world has changed dramatically and the ways in which predictive analytics were used then may not have the same results in this world. Although the economic concepts of six years ago are the same, the way in which different aspects of the world effect economics has changed and it is important to update the material to match the new parameters that must be taken into consideration. The interrelationships between what used to be an indicator of health or illness in the economy have changed dramatically and analytics must be changed to reflect those facts. An example of one of the ways that the world is very different is the health of the stock market as a reflective device about the wealth of the nation. While the stock market has been showing better numbers in recent months, the nation on the whole is not thriving at the same level with unemployment still high. As well, the level of CEO salaries are over 300% that of the average worker. It used to be that the level of salary of a CEO reflected the health of the salaries of the employees. In 1980 the ratio between the CEO and the worker was 42-1 (Carroll & Buchholtz, 2010. What this reflects is the continuing disparity of wealth in the United States. And while this existed in 2007, the economic downturn of 2008 established the destruction of the Middle class, creating a whole new way in which consumer predictions needed to be approached. The cost of living has gone up and working class people and the remnants of the middle class have very little disposable income. Davenport, Harris, and Morison (2010) discuss some of the reasons to not use analytics. One of these reasons is when history misleads the results of the analytics. Because of the changes that have occurred in the last six years, the historic interconnections of different indicators are not necessarily still meaningful. The example of the stock market and how it no longer indicates overall wealth is an example of how predictors can now be misleading for the future from today. Willis (2011) writes that in the last century the stock market has always been an indicator of overall wealth, but since the economic downturn that has changed. This example shows how a number of factors have changed in the new economy and in order to create a predictive analysis, these factors must be taken into consideration. What has not changed, however, is the power of distinction. People are still finding ways to buy items and distinction has created enough power for many companies to thrive in this stifled economy. One example of this is the iPad which launched in 2009 and sold over 25 million of the units in a few short years. Distinction has created the market for the iPad and its competitors have not come near to duplicating that success (Bell, 2011). It is the one that comes out first that will get the attention and this is how distinction is still a powerful factor. This can also be seen in the iPhone which has decent competitors, but all one has to do is watch the commercials to see that the competition is doing its best to diminish the cult status of the Apple phones. Through trying to insinuate that they are at the end of their life-cycle competitors like Galaxy and Microsoft are using a thin stick to strike a mighty mountain. In order to gain the power of predictive analytics, then, it is important to recognize what has changed in the last six years, but to realize that the most important part of business has not changed. When a new idea is good and has a great deal of consumer value the idea will succeed. Demand can be predicted through distinction, but where there is no distinction and an idea is being recycled or improved upon, the predictive analytics will have to take into consideration the real status of the economy. The following paper will examine the success of a variety of firms in order to evaluate the use of predictive analytics in the current economy. Through a literature review of relevant businesses, the topic of predictive analytics and their use in business will examined for the post-2008 economy. Literature Review One company that has a long history of innovative tactics in relationship to consumers is Ford Motor Company. Henry Ford was the first one to recognize that without disposable income to purchase products, the market was a limited place for a business. Through paying his workers a higher wage he created a working class that could afford luxuries. As the century progressed, the competition in the auto industry has required that companies seek more innovative ways in which to address the market. Through the use of statistical methods and data, the Ford Motor Company has been able to improve their performance and enhance their sales (Davenport, Harris, & Morison, 2010). The Ford Motor Company has not only used predictive analytics as a way to improve their business performance. Through using predictive analytics the company has established a method of detecting whether or not a driver is distracted or fatigued and cannot function well behind the well. The predictive analytics program that they have designed can accurately predict the level of attention the driver has for his driving tasks 86% of the time. Through the use of predictive analytics, not only does Ford improve their business performance, they have improved their on-board systems in their vehicles (Siegel, 2013). The act of insuring consumers is one in which predictive analytics have been used for decades in order to determine who is a good risk and who cannot be insured. Progressive Insurance is a company that sells insurance and as a result they categorize their customers through defining characteristics that are relevant to insurance and dividing people into groups who have the same risks attached. As an example, motor-cycle riders will get a lower rating if they are young than if they are older. Regression analysis is used in order to determine how the various factors might contribute to an accident. Through the use of analytics, insurance companies predict what they believe the risk is for insuring a customer. Through simulation software they prove their analysis and measure the implications of their belief systems (Syrett & Devine, 2012). With all the changes in regulations on behalf of the consumer, analytics are even more important as insurance companies work to mitigate as much risk as possible. Data warehouses are being used to create the analytic data needed to define risk, but this also means that the parameters given by the government must be taken into consideration. Denying insurance is a much more difficult process, although unreasonable premiums are one method to deny insurance to the high risk groups. Seeking patterns that create risk is an important part of the data mining that occurs during the process and it is in those patterns that the fates of the people that they represent are defined (Piety, 2013). Standard Chartered Bank uses analytics in order to create success through engaging the direct needs of their customers. Through creating a data base of characteristics of customers, the Bank can predict what services the customer will likely use in the future based on their history. The bank can then directly market the different products that they feel are relevant to the needs of the customer through the information that they have predicted will be relevant to them as they progress through life (Nasr & Draschen, 2013). Banking on the whole is no longer something that is done without the process of using predictive analytics. Analytics are used to define the success of different channels of use for the banking system. A bank relationship is now rarely defined by the experience in the branch, but through the experience of the ATM, online banking, and other forms of banking interactions so that banks can be dealt with through the most efficient ways. It is through analytics, as an example, that the success of taking a picture with a phone as a method of depositing a check was determined to be an application worth inventing. Through understanding the habits of consumers and their needs, future products that define and differentiate the brand are created (King, 2012). Another company that has had benefits from predictive analytics is John Deere & Company. Through looking at the available data for predicting the future needs of the company, they have determined the types of improvements they need in order to continue to compete. This meant making investments in operations that enhance the overall processes within the organization. Analytic tools are being used in order to optimize inventory as well as to create optimization for a variety of different operations. As a result, the company saved around a billion dollars that they attribute to the use of predictive analytics for their company (HBR, 2011). The John Deere & Company has also used predictive analytics to define the way in which they create the seats in their vehicles. Through the use of a human model who has the capacity to capture data on seat functions, the data is analyzed so that a more ergonomic seat can be developed. In addition to creating the seat, the use of business based predictive analytics will allow for an analysis of how the consumer will respond to the changes. Through multiple levels of use, the predictive analytics for John Deere & Company have use throughout the process of creating differentiation (Bordegoni & Rizzi, 2011). Logistics benefit from the use of predictive analytics. UPS has been using business analytics in its operations in order to establish a customer intelligence group which is used to determine the best methods of managing clients. Data patterns and complaints come together in order to create a process through which defections are understood. This gives them tools for creating reasons for regaining their lost customers. The organization has had a way to effectively compete with the emerging competition through discovering how customers are dissatisfied and what will bring them back (Luo, 2012). Proctor and Gamble is having more difficulty in the second decade of the 21st century in relationship to analytics. Davenport and Harris (2007) discussed their need to manage a variety of brands through the use of predictive analytics. The number of primary brands under the parent company has grown and this makes analyzing operations more difficult because different brands create different sets of data. Business intelligence has been more useful than analytics in attempting to put under control the data that is available for the various brands. Although analytics would be useful in creating competitive advantages, it is still a daunting task. Proctor and Gamble has not yet adjusted to the new economy and is still struggling with the problems that come from such a diverse number of products (Williams & Williams, 2011). Davenport and Harris (2007) begin their analysis of the use of predictive analysis by looking at the business Netflix. The company began through the owner taking a look at the exorbitant fees he had been charged for returning a DVD to Blockbuster after the due date. Combining that dissatisfaction with the idea of how a health club works through monthly memberships, the idea for Netflix was born. However, it was through predictive analytics that the business model was constructed. It does not end with that innovation. Soon, the idea behind Netflix was being overridden by the ease with which consumers could download or stream videos on the internet. Instead of sticking with their original plan, Netflix used more predictions through analytics in order to understand the changing market and make significant changes that could support the new environment and technologies. Still having advantages for being distinct, the level of competition in recent years has made it more difficult to keep changing their model, however, leading to diminished memberships (Rosenfeld, 2011). Netflix uses a number of different methods towards creating their data. One of the primary methods is through click analysis. As an example, Lost was 10th most frequently searched program on Netflix, but it was only put into the consumer’s cue 4.03% of the time. There had to be a problem that was occurring on a frequent enough basis to hold back the consumer from getting what they wanted. Failures in the search could be determined by the most predictable results of the searches done for the program which led to finding a way in which to create a resolution. The core of the Netflix experience is in the way in which the technology is used. The technology is recreated on a continuing basis through the use of analytics. Methods Secondary research was used in order to construct this study. Literature about the various different businesses that use predictive analytics was assessed in order to find different types of businesses that showed how predictive analytics could be used for a variety of functions in the business environment. The results of the search were narrowed down until there were businesses that had connecting themes that could create a story about the use of predictive analytics. These were placed into a literature review that was then evaluated for reoccurring themes. Results While competition is an important use for predictive analytics, it is not the only use for this valuable set of tools. Most companies not only use predictive analytics to produce consumer behavior models, but also to predict how consumers will react to new innovations. They are also used in order to define how new ideas will perform. In 2007 when Davenport and Harris wrote their book there were some fairly dependable economic factors that could be used to predict consumer behavior. In 2013 these factors are not all relevant to the effects on business. Predictive analytics require the use of human evaluation in order to assess if they are being used in a way that is modern and relevant. As in the Netflix example, the business model may need to change in order to accommodate current trends. Predictive analytics provide a framework, but they will never be a guarantee of how trends will turn and move. As well, as in the example of the insurance industry, the way in which social policy changes will have an effect on how predictive analytics are used. Proctor and Gamble are the example of how analytics may not be properly relevant to the best advantages of the business. In this case, business intelligence is a much better model through which to create business decisions. Analytics would be too complex to create any true answers. However, as the 21st century moves forward Proctor and Gamble may find that they are not in step with their competition that has fewer products per company and a better ability to use analytics. Conclusion Predictive analytics are a powerful tool and have remained so into the period post-2008. The economic downturn has changed a number of different factors and has made some predicting factors less powerful. The changes in the economy have provided for a new world through which competition has become more intense. Differentiation and providing something new is about the only truly valuable factor in predicting the outcome of a product. However, creating differentiation within a product, as has Netflix due to the information they discovered during their use of analytics, can lead to new forms of differentiation. Companies that use predictive analytics for more than just competition have a greater advantage for the success of their products. In the 21st century predictive analytics will have more importance, but will be less successful strictly on a economic competitive basis. Consumer activities will have the most power in creating data that can give a company the best advantage. Resources Bell, D. (15 July 2011). What the iPad’s success says about us. C/net. Retrieved from http://reviews.cnet.com/8301-31747_7-20079841-243/what-the-ipads-success-says-about-us/ (Accessed 25 May 2013). Bordegoni, M., & Rizzi, C.(2011). Innovation in product design: From CAD to virtual prototyping. London: Springer. Carroll, A. B., & Buchholtz, A. K. (2010). Business & society: Ethics and stakeholder management. Mason, OH: CL-South-Western Cengage Learning. Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: the new science of winning. Boston, Mass.: Harvard Business School Press. Davenport, T. H., Harris, J. G., & Morison, R. (2010). Analytics at work: smarter decisions, better results. Boston, Mass.: Harvard Business Press. Harvard business review on aligning technology with strategy. (2011). Boston, Mass.: Harvard Business Review Press. King, B. (2012). Bank 3.0: Why Banking Is No Longer Somewhere You Go But Something You Do. New York: Wiley. Luo, Z. (2012). Advanced analytics for green and sustainable economic development: Supply chain models and financial technologies. Hershey, PA: Business Science Reference. Nasr, M. S., & Drachen, A. (2013). Game Analytics Maximizing the Value of Player Data.. Dordrecht: Springer. Piety, P. J. (2013). Assessing the educational data movement. New York: Teachers College Press. Rosenfeld, L. (2011). Search Analytics for Your Site. Sebastopol: Rosenfeld Media. Siegel, E. (2013). Predictive analytics: The power to predict who will click, buy, lie, or die. Hoboken, N.J: Wiley. Syrett, M., & Devine, M. (2012). Managing uncertainty: strategies for surviving and thriving in turbulent times. Hoboken, NJ: John Wiley & Sons, Inc. Williams, S., & Williams, N. (2011). The profit impact of business intelligence. Amsterdam: Elsevier/Morgan Kaufmann. Willis, R. (2011). Cognitive investing: The key to making better investment decisions. New York: Author House. Read More