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For management purposes, it becomes necessary to make decisions. The alternatives are evaluated continuously, by the mangers, and decisions are made, for a wide range of matters. There are different decision-making styles and managerial styles. Uncertainty and risk are involved in decision making, and there are varying degrees of risk aversions. Quantitative and qualitative analyses are involved in making decisions, and decision makers prefer one type of analysis over another. Rational factors are not the only factors which are involved in making decisions, but also, non rational factors, like peer pressure, personality of the decision maker, the organizational situation, etc. affects in making decisions.
In tough times, we concentrate more on decreasing costs than on increasing costs. We need to know, whether predictive analytics can help us in such situations. You will spend less, if you filter high risk prospects. A customer is earned, if one retains customers more efficiently. To increase revenue and profit, predictive analytics is leveraged and acts more than a cost cutter. But, this technology helps a lot to improve efficiency, and driving decisions can be optimized more effectively. Predictive Analytics has many applications, which are positioned to reduce cost specifically.
Whether a customer will buy only if contacted can be predicted, and we prefer such predictions. But we should predict whether the customer will buy if they are contacted. So, now we are able to obtain the answer to the question that, whether the customer ought to be contacted or not. And, this is possible for the predictive score of the customer.
Predicting Marketing Influence:
For maximizing impact, we require data driven marketing decisions. Marketing influence is optimized by predicting it, and it is the only way to do it. For doing it, uplift modeling is the analytical method. By means of uplift modeling, customer behavior can be predicted well, and this is completely a different tool for doing so. The influence on customer behavior, which is gained by choosing marketing decisions, can be predicted by uplift modeling. ROI is going where it has never gone before, a case study says so. For any customer, one cannot observe marketing influence. Now the question is that if the influence is not observable, then how the model can predict it.