Predictive Analytics is a business intelligence technology and here are the top 4 reasons you need Predictive Analytics..
A predictive model distinguishes the microsegments of customers who choose your company from those who defer or defect to a competitor. In this way, your organization identifies exactly where your competitor falls short, its weaknesses.
Predicting direct marketing response, the most established business application of predictive analytics, delivers tremendous return. The value proposition is straightforward. By suppressing those customers less likely to respond, costs are slashed, so profit goes up.
Churn modeling may be the hottest business application of predictive analytics. While retaining customers is a top objective of many organizations, effective retention incentives, such as a discount offer, can be quite costly. The gain comes when such an offer is targeted only to those customers most likely to leave. With targeted retention, the growth rate of your customer base increases and compounds.
With predictive analytics, the enterprise learns from its cumulative experience (data), and takes action to apply what’s been learned. The data from which predictive modeling learns includes the negative as well as the positive examples, both the successes and the inevitable “mistakes.” Each of these two kinds of experience provides important cases from which to learn.
Even if the training data contains many more of one than the other – such as with direct mail, which often exhibits only a small percent of positive response – analytical methods can leverage 100% of the data in order to learn from all the outcomes an organization has experienced. At each step, the predictive scores foresee where a “blunder” may incur unnecessary risk, thereby guiding the organization to avoid it, hence delivering a complete data-driven system for risk management.
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