01
Sep
08

Predictive vs Descriptive modeling – Understanding the difference

Many organizations use historical analytics data as a basis for forecasting future growth, and establishing performance goals and budgets. This applicaton for analytics data can blur the distinction between predictive and descriptive data. Understanding this difference is critical to an effective analytics program.

Predicitive modeling refers to a mathematical model that can accurately predict future outcomes. For instance, I know that if I apply sufficient heat to water, the water will reaach 100 degrees celsius and begin to boil (barring slight variations for altitude which are also predictable). The rate at which this happens and the amount of energy required can be mathematically described.

Descriptive modeling refers to a mathematical model that describes historical events, and the presumed or real relationship between between elements that created them. For instance, yesterday when I went to the store to buy milk, it cost me $1.00 a litre, last month it was 95 cents, last year it was 80 cents.. Based on historical events, I assume it will cost me roughly $1.05 to buy a litre of milk next month.

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At Cequity, we believe customer intelligence will be the biggest competitive advantage enterprises will have in the next decade or two. Successful enterprises of tomorrow will be the ones who can organize and leverage this information at speed to optimize their marketing performance, increase accountability, improve profit and deliver growth. Cequity insights will bring to you trends and insights in this area and it’s our way of sharing best practices so as to help you accelerate this culture and thinking in your organization.
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