Predictive Analytics – Increasing your Competitiveness

Posted by | February 1, 2012 | Analytics">

Well I couldn’t have asked for a better article on Predictive Analytics for this months Blog.

Shawn really touches and speaks to the value of Predictive Analytics across verticals and processes.

Once Predictive Analytic models are developed and implemented within a company, the challenge remains “what are you going to do” with these insights? See the article on “Why companies fail in their retention efforts”

And now to the article by Shawn.

Predictive Analytics Is the Answer by Shawn Hessinger

Predictive analytics creates a remarkable solution for companies and organizations struggling to cut costs and boost profits in a global and connected economy– and Olivia Parr-Rud, a business intelligence expert, dug into how during a recent Webinar, “Drive Your Business with Predictive Analytics.”

Parr-Rud, author of Data Mining Cookbook: Modeling Data for Marketing, Risk, and Customer Relationship Management and Business Intelligence Success Factors: Tools for Aligning Your Business in the Global Economy, described an environment where a global and interconnected economy has created increased competition, and where the proliferation of social networking and Web use has given consumers a powerful voice and increased selection. She discussed how companies use big data and Predictive Analytics to create an advantage in this new environment.

Describing the volume, velocity, variety, and complexity of available information, both collected by companies and in unstructured form, Parr-Rud admitted she often finds it staggering that 90 percent of the world’s data has been created in the past two years. However, this huge amount of data creates an opportunity to learn more about customers, discovering ways to increase opportunities and decrease costs and risks. This is where the power of Predictive Analytics comes in.

For example, using a customer segmentation model, companies through the development of Predictive Analytic models can separate customers using predictions based on measurements of their past responses, potential revenue, and risk they represent. They can then standardize appropriate actions based on a customer’s value. So the most valuable customers might get a personal sales visit, the next most valuable a sales call, and the third most valuable a sales email.

Meanwhile, a loyalty, retention, attrition, or churn model would enable a company to predict, based on behavior, if and when a customer is likely to leave.  A company uses this information to retain customers or mitigate the cost of their departure.H3>

Naturally, business goals should dictate the predictive analytics model, said Parr-Rud.  Goals might range from attaining new customers to reducing risk or avoiding loss.  They might also include retaining customers or ascertaining their value as suggested above.”

In summary, we have seen successful implementations of Predictive Analytics in many verticals; Health Care, Insurance, Logistics and Supply Chain, Banks, Police departments and many more.

In fact Predictive Analytics can be seen almost everywhere.  The key is to ensure you develop the right Predictive Model and gain the right insights and that those insights become fully actionable.

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