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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...
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