MLR or Multiple Linear Regression, works by optimally combining all the predictor variables into a simple ‘weighted linear combination’. All that means is that we say our prediction (eg the chance a customer is going to switch away from you) is equal to a sum of all the predictors (purchase amounts, purchase dates, demographics etc) with an important weighting factor in front of each predictor. These weighting factors, which may be negative or positive, depend on the scaling of each variable (it’s range of minimum to maximum values) and, critically, how well it correlates with the predictor using historical data.
This is the base-line method of predictive analytics and will typically tell you how predictive your data is with some caveats. And it will usually be within 10-30% of the best prediction you will ever devise using more advanced methods.
MLR is what we do on day 1 after cleaning up your data. That way we don’t waste your time and budget with complex analyses if there is no predictivity in your data.
After that it’s time to bring out the big-guns of predicative analysis, the logistic analysis and the machine learning algorithms including artificial neural networks and the like. they’ll get you an extra 10-30% predictive accuracy which, to get to a bottom-line, can translate to 10-30% improved profits on top of whatever gains MLR got you.
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