Predictive Analytic Tools to Gain Term Paper

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As Christopher Hosford (2009) best put it, "to marketers, lists are still important but the uses to which they are put seem to be in transition."

With the aid of predictive analytics, marketing activities make a transition from the simple identification and attraction of customers, to more complex endeavors of customer loyalty and retention, generation of sustainable revenues as well as other elements of marketing ROI (return on investment). This trend has had a direct impact on the secondary industry sector of list construction in the meaning of an increasing popularity and demand for these specific services. This in turn led to an increasing offer of listing services and the increase of competition within the field.

But given the complexities and requirements of the modern day business environment and social community, a necessity arises in the continuous assessment and upgrading of the databases and the information they contain. Examples of how the databases could be maintained and upgraded include the sustained gathering of new information, the more in depth segmentation of the consumers or the identification of current and emergent responsibilities (Hosford, 2009).

Predictive analytics and safety

Craig Lawson (2010) assesses the issue of predictive analytics from a rather intriguing standpoint -- the well-being of the equipment. He argues that through the use of predictive analytics, organizational leaders can identify errors and other problems with equipments early on. This would not only increase staff, operation and consumer safety, but would also reveal an ability to reduce maintenance costs by identifying and repairing damages early on, rather than waiting until the equipment problem is major and requires major investments to fix.

In order for predictive analytics to increase equipment safety and as such generate the adjacent benefits, they need to be supported by technologies -- both software as well as hardware. One such application is the SmartSignal Predictive Analytics software applications which is able to identify rod drop problems when other traditional applications cannot detect the problems that early on. When companies do not wish to invest tremendous sums of money into the development of their own predictive analytics tools and methodologies, they are presented with the opportunity of hiring third party firms which offer the respective services. Through extrapolation, this could constitute a specific case of penetration into a specific market of equipment safety.

Lawson's study is conducted within the pipeline industry, but the findings are relevant and can be extrapolated to include the entire manufacturing industry where increased usage of manufacturing equipments is made. Here, organizational leaders can predict problems and resolve them early on, without delays in operational processes or even without the incurrence of casualties. "This innovative technology allows you to migrate your maintenance strategy to a predictive, proactive strategy, where the morning water-cooler talk is about how to prevent an identified problem from happening, not how to fix one that already has occurred" (Lawson, 2010). Also as an extrapolation, predictive analytics can be used to penetrate the niche market of safety tools and equipments.

Predictive analytics to create customer value

A first application of predictive analytics is that it allows firms to better understand the customers. Traditionally, it was believed that customers could be stimulated solely through price reductions. The collection and processing of customer information with the use of predictive analytics however has revealed that not all customers are price sensitive, but that a large portion of them are driven by other elements.

Don Murphy (2002) argues that at the level of the marketing department, predictive analytics are presented as customer relationship management (CRM) analytics. Through them, companies collect customer data and then synthesize this data. The result is an increased ability to understand and predict customer behavior, which in turn can materialize in organizational success. Murphy explains: "With the right tools and a little intuition, organizations can increase profitability by leveraging customer information to anticipate customers' needs and influence their behavior. By consolidating customer information from within and beyond an enterprise, organizations establish the groundwork for modeling and predicting customer behavior. Quality information gathered from numerous customer-facing and third-party systems can help companies define and select customer populations for building predictive models. Companies can the use these models to segment and target customers for marketing campaigns, service programs and customer loyalty and churn analyses."

A relevant example is offered by the hospitality industry, where companies offer services rather than material products. This virtually means that the success of the respective entity -- hotel, entertainment facility and so on -- depends directly on its very ability to understand and serve its customer. These two goals are best achieved through intense communications with the customers and through the implementation of a wide array of strategies that generate and support customer loyalty.

In order to use predictive analytics to generate more customer value, Davenport, Harris and Morison (2010) have identified the necessity for the implementation of a six stage model. This model resembles a ladder of marketing predictive analytics and it is achieved as follows:

The construction and maintenance of an adequate customer database which includes information on all organizational customers

The segmentation of the market into target audiences based on a series of demographic and otherwise criteria. At this stage, it is important to understand the differences between various consumer categories in order to present them with different offers based on the elements which satisfy their specific needs.

The development of organizational strategies which create points of difference. At this stage, it is pivotal to respond specifically to the actions, decisions, needs and so on of the customers.

The management of the marketing campaign in an adequate means to promote the company, the product and to attract and retain the customers. At this level, it is necessary to keep a faithful track of all previous customer decisions in order to be able to personalize the product / service offering.

The creation of predictive models based on the observation of the past events. Throughout this stage, the company strives to foresee the future actions of the customer based on the observation of his past actions and decisions.

Finally, at the sixth and last stage, the company is expected to make a real time offer in the meaning that it is expected to create and offer a customized product / service for the customer (Davenport, Harris and Morison, 2010).

Don Murphy (2002) takes his analysis one step further and reveals the three specific steps companies need to undertake in order to predict the behavior of customers. In this order of ideas, at a primary level, it is required that the economic agents profile their customers. In order to create the profile, company members assess the behavior of their customers in specific circumstances -- especially those of interest to them. They use a multitude of sources of information, such as internal customer data previously collected, purchase information, call center information, performance indicators or other third party data.

At a secondary level, the company is modeling the customer data. "By using data mining on the profile information, analysts can uncover the most relevant characteristics of the customer segment being analyzed" (Murphy, 2002). Finally, at the third level, the economic agent engages in scoring operations. "Analysts use predictive analytics to score existing customers by comparing them to the model. Those most closely matching the characteristics included in the model are most likely to exhibit the targeted behavior" (Murphy, 2002).

While not referring specifically to predictive analytics, Alexei Sarnevitz (2008) mentions that a successful relationship with the customer is only achievable if both of two necessary conditions are met. The first of these conditions is that of segmentation, which has already been discussed and will not be focused on at this stage. The second condition is that of automation, which, in Sarnevitz's view, is the key to organizational success. And by the key to organizational success, he refers to the high degree of knowing the customer up to a point at which a long-term bond is created between the parties. "The ultimate goal is more than having the right product in stock at the right price; it's about tailoring the entire shopping experience to create an emotional bond with the customer. In effect, this means turning today's multi-channel retail enterprise -- in a consumer's eyes -- from "the store" to "my store." [And this goal is achieved through automation]. Automation is the key to enabling the delivery of just the right offer to each consumer, deploying exactly the right assortment to each store and optimizing the regular price for millions of SKUs. Examples include the latest marketing automation solutions that can target each consumer with relevant personalized messages or revenue optimization applications that determine optimal pricing for each item in every store" (Sarnevitz, 2008).

Predictive analytics to create customer value -- the case of the insurance industry

When predictive analytics was first implemented within the insurance industry as a means of creating customer value, its applications were limited and the immediate results were at least questionable in terms of…[continue]

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