Term Paper on "Using Predictive Analytic Tools to Gain Market Niche"

Term Paper 16 pages (4360 words) Sources: 12

[EXCERPT] . . . .

Predictive Analytic Tools to Gain Market Niche

Predictive analytics defined

The first step in reviewing the literature is that of assessing the meaning of predictive analytics. The academic community presents the reader with a multitude of definitions and explanations of predictive analytics, but at a most simplistic level, this refers to the analysis of the past and the present with the intent of predicting the future. Nathan Conz for instance argues that predictive analytics tools "allow organizations to leverage their massive depositories of data - historical and otherwise - to make better decisions going forward" (Conz, 2008).

Don Murphy (2002) on the other hand approaches the definition of predictive analysis as a comparison with descriptive analytics. Descriptive analytics refers to the ability to observe and understand all elements and actions present with a setting. Predictive analysis refers to the usage of specific tools in order to transform the observations gathered into an ability to predict the future behavior of the assessed setting.

John Goff (2004) also approaches the issue of consumer analytics and considers the topic one of the hottest developments in the recent years with respect to customer relationship management. The definition he offers of consumer analytics is simple and it states that the concept refers to "tools that dissect consumer-buying patterns, suss out preferences, and predict future behavior" (Goff, 2004).

John Zyskowski (2010) takes a more specific approach to predictive analytics and assesses it in light of technological advancements. Predictive analytics is presented as one of the top strategic tools and technologies of 2010 and Zyskowski argues that predictive analytics represents a new spin on data mining. This view is supported by others as well, who state that predictive analytics is in fact a combination of previous technologies and tools, which are now implemented at superior levels and they as such generate superior results. But in spite of the novelty or lack of novelty behind predictive analytics, fact remains that their implementation is expected to generate additional value and benefits within the communities.

Another belief of Zyskowski is that the new procedure is gaining popularity alike in the business community as well as within the technological community. Additionally, the author mentions that, due to the benefits it generates, predictive analytics would become an increasing presence within the federal field. At this level, predictive analytics could be used to improve the operations of fraud detection, waste management and apprehension of criminals.

Regardless of the source of information and of the form of presentation, the understanding of predictive analytics is the same in all instances -- the usage of customer information to predict their behavior. And the popularity of the practice has increased dramatically over the past years due to elements such as technological advancements, customer demands or higher levels of competition across industries. Ultimately, this popularity of predictive analytics is due to the ever changing nature of the environment and the consumers, and the constant need of economic agents to understand their clients. From this angle then, customer analytics emerges as an imperfect and ever changing domain (Goff, 2004).

Predictive analytic in the modern day business community

Thomas H. Davenport, Jeanne G. Harris and Robert Morison (2010) argue that predictive analytics has three distinct applications within the organizational context. First of all, it can be used to better understand the company customers; secondly, it can be used to improve the performances of the organizational operations and third, it can be used to support and improve the decision making processes. Today, more and more firms are beginning to implement predictive analytics as a means of increasing their chances of reaching the organizational goals.

But aside from the importance of analytics within the organizational context, the three authors mention that a broad approach and implementation is not sufficient. In this order of ideas, they argue that in order for the economic agent to become more successful and more powerful from the competitive standpoint, it has to select a specific domain and implement predictive analytics to a high extent at the level of this domain.

Davenport, Harris and Morison (2010) reveal several instances in which predictive analytics can be used and capitalized on within the business community. In this order of ideas, they argue that:

Within the financial services sector, predictive analytics can improve operations of credit scoring, detection of fraud, pricing strategies, or customer profitability

Within the retail industry, predictive analytics can enhance promotional efforts, shelf management operations, forecasting of products and/or service demand, inventory management and so on Within the manufacturing industry, predictive analytics can improve supply chain operations, inventory management, demand estimation, customization of products, the development of the items and so on Within transportation, predictive analytics can support operations of scheduling, routing or yield management

Within the healthcare industry, predictive analytics can improve operations related to drug interaction, management of diseases or early on diagnosis. As a parenthesis, a more in depth analysis of predictive analytics within the healthcare industry is conducted by Judith Lamont. Her findings indicate that the usage of predictive analytics within the sector is more and more popular and this is because of the benefits it generates, such as: advantages to the patient (improved medical services, earlier diagnosis, smarter health care and so on), reduced fraud in the medical system, creation and enforcement of more adequate rules, superior processing or centralized mapping (Lamont, 2010).

Within the hospitality industry, predictive analytics can support the development and implementation of pricing strategies or customer retention strategies

Within the energy industry, predictive analytics can support operations of trade, supply and demand estimations or legislative compliance

Within the communications industry, predictive analytics can improve pricing strategies, customer retention strategies, demand estimations, capacity analysis and planning, network optimization and so on Within the services industry, predictive analytics can improve operations related to customer service staffing, or the profitability of the organization

Within the federal sector, predictive analytics can improve operations of fraud detection, crime prevention or budget balancing

Within the virtual business community, predictive analytics can be used to improve the operations related to web metrics, website design or customer recommendations

Finally, within each and every one of the fields mentioned above, as well as within any other field, predictive analyses can successfully support increases in performance management (Davenport, Harris and Morison).

At the level of customer satisfaction, this is now the number one priority of economic agents. From a point in time at which customers would simply purchase whatever the companies would produce, the modern day clients have evolved to a stage at which they demand the producers what items to manufacture and sell. The progression of the customer role within the market has been marked by a series of economic, political or social modifications, but an important element is constituted by the globalization phenomenon. Through the opening of boundaries, firms increased their market penetration and this materialized in significantly higher levels of competition. Subsequently, a situation aroused in which organizational emphasis on customer satisfaction became pivotal. And this customer orientation was obvious even in the most unlikely industry sectors, such as the insurance industry.

In order to cope with the higher levels of modern day competition and incremental customer demands, economic entities developed and implemented a series of strategic courses of action. In support of the creation of the most adequate customer strategies, firms commenced to implement predictive analytics. Within the insurance industry for instance, predictive analytics was no longer implemented solely as a statistical tool by which insurance rates and damage compensations were estimated, but as a means of creating more value to the customer (Conz, 2008).

But the growing importance of predictive analytics tools within the organizational contexts has not only been due to increases in competition due to globalization, but also due to the advent of technology. In this order of ideas, customers were better able to gather product and service information and became as such more demanding. On the other hand however, the advent of technology also allowed companies to enhance the quality of the consumer experience, and this was possible -- among other things -- through the integration of predictive analytics. The direct result was that of analytic software, which emerged and became more and more popular within the totality of the business community. Software analytics is now present in manufacturing industries, in service sectors, in food market segments, consumer goods, financial markets and so on. They address aspects of inventory management, demand forecasting, estimation of labor force needs, pricing strategies and several other fields in which decision making is based on critical data. Peter Alpern (2010) argues that this situation was created as a result of evolution within the business community, or the result of increased business intelligence due to a higher access to information.

A mostly specific application of predictive analytics within the organizational context is observable at the level of the marketing efforts. The marketing operations draw tremendously on lists, databases and customer information. And predictive analytics has emerged to help marketing staffs better organize, assess and learn… READ MORE

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