As big data things continue to grow in this modern era, today we can learn how to predict or assume anything that will happen in the future with data from the past. This studies known as Predictive Analytics. Predictive analytics combine methods from machine learning, data mining and statistics to find meaning or pattern from a huge volume of data. Tom H Davenport, a senior advisor at Deloitte Analytics has broken down three primer models on doing predictive analytics: the data, statistics, and assumptions.
The first model is by using data. We must make sure the data that we use is good and accurate. For example, when you want to predict what payment channels that the customer will likely to use in the future, you must know the data on what
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Particularly, regression analysis, a statistical process to estimate the connection among dependent and independent variables. Accordingly, by using regression analysis the analyst can create the score that produced by those variables to predict what company needs like customer purchase behavior.
The third and the last model is assumptions. Both data and statistics have assumptions to make a viewpoint and conclusion about the predictive data. Assumptions are holding the key to our predictive analytics results. Consequently, if an analyst makes invalid assumptions, then the models that used to predict customer behaviors may no longer be trusted.
So in order to make valid assumptions, there are two things that we must make sure. First, we must include the key variable that will not have been changed substantially. Moreover, the key variable itself hold significant impact of predictive analytics result. Take an example of the financial crisis that happens in 2008 – 2009. That crisis happened because the model forgets to include the variable about housing prices might befall.
Second, the time when we build the models has to be a lower elapsed time. For example, if we want to predict current customer behavior with the model that created several years ago, we have just made a big