The science and art of predictive analytics has gathered a lot of momentum recently due in part to the vast amount of customer and market data and the advance of analytic tools. Predictive analytics in a nutshell is about better understanding of patterns and behaviors of customers in various stages of buying cycle. This understanding in turn helps companies to better align their strategy, increase sales and lower costs. Predictive analytics can increase efficiency and help identify those parameters that can impact the bottom line.

One such application of predictive analytics is response modeling in direct marketing. The general idea is predict marketing response to marketing campaigns based on previous campaigns. For example it can be used to generate a list of contacts that are likely to respond to a future campaign. By targeting those contacts that are more likely to respond, not only we can lower costs but also achieve higher ROI. The chart below illustrates the concept by plotting results of a direct marketing campaign with and without  predictive analytics.

The higher the curve is above the dotted line, the higher the “lift”, and the higher the ROI.  Look for the horizontal distance from the diagonal. Let’s say you have a list of 1000 contacts for a direct marketing campaign and no predictive model to score and grade the list. The list in essence is a random list of contacts. A typical response rate for this list is 10% or 100 respondents. With predictive analytics we could achieve a significantly higher response rate from contacts that are predicted to response favorably.

Contact list: 1000
Response rate: 100 or 10%  without predictive analyticspredictive-analytics

Predictive analytics identified 500 contacts out of 1000 to be of high value. Our campaign contacted 125 contacts with the highest “favorable” score to generate 10% response rate.

This illustration though very general is meant to show the power of predictive analytics to increase sales and lower costs. Needless to say the effectiveness of the prediction is dependent on a number of factors that need to be looked at carefully when analyzing and data.

As you can see this tool can be significant asset when used properly in business. It can be used to predict the likelihood of sales, the behavior of customer segments, and many more.

You can contact us at:

Satoba Technology Marketing Inc.
Irvine, Ca, 92620
www.satoba.com
949-864-6526

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Nick Rakhshani