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Conventional Predictive Analytics: The Good and the Bad

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Data preparation is a necessary thing for conventional predictive analytics program. Through educated guess the users make predictions of the risk variable that are required for the purpose of analyzing. Most of the matters are left unknown and only a part can be analyzed. The reason for this is limited time and cost.

The contact strategies and the effective collection are achieved with the help of predictive analytics and this also optimizes allocation of collection resources. In predictive analytics and forecasting the operations can be improved through SAS. Also optimization of efficiency, productivity and decision making can be achieved. Statistics, statistical modeling, statistical techniques, proprietary models, data mining tools, complex calculation are involved in predictive analytics. Also a few experts are there to deal with such problems and having such skills. So it is expensive for hiring them. The person who is involved with business data acts intermediately with statistical experts.

Uplift Modeling

Enterprise decisions are optimized through predictive analytics and a predictive model is taken into consideration for this purpose. It is with the help of relevant data that a predictive model is optimized. On the basis of the likelihood of behavior like defection or response a standard predictive model assigns scores to every customer or other element of the organization. On the basis of per- customer, operational decisions are made through predictive scores. Thus predictive analytics is often required in business.

Standard Predictive Analytics

The Good

Though essentially limited prediction regarding the behavior of the customers can be performed through conventional analytical models yet it has the potentiality of delivering significant business value. The campaign ROI is spiked through saving of the cost of contacting the customers who will not respond .This is possible because the predictive model gives scores of the likelihood of the customers to respond. For example when a direct mail is received a decision can be made about the customer's likelihood. Conventional prediction analytics gives an exact introduction and value of business application can be overviewed.

The Bad

  • Direct marketing should be targeted.

  • Retention Efforts should also be targeted.

Attrition Probability

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