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The work of interpreting data to help decision-makers goes back some 5,000 years to...
NHL franchises are slowly embracing the ‘Moneyball’ strategy of using analytics to assist in...
In the analytics space, it is quite common for many organizations to have a team of data miners who are now referred to as data scientists and a team of business users who are often referred to as value architects. It has been a common practice ever since the first direct marketing models were produced […]
Our prior article on this venue began outlining the business value for solving “the other churn” – employee attrition. We introduced the “quantitative scissors” with a simple model of employee costs, benefit, and breakeven points. The goal was to create a robust mental model for the cost of employee attrition. In this entry, we will […]
In part one, I described one problem with overfitting the data is that estimates of the target variable in regions without any training data can be unstable, whether those regions require the model to interpolate or extrapolate. Accuracy is a problem, but more precisely, the problems in interpolation and extrapolation are not revealed using any […]
Over the past 5 years there have been several trends that have changed the way retailers operate their businesses. Many of them have to do with how consumers use technology to make a purchase. Pure e-commerce retailers have gained momentum causing brick-and-mortar retailers to re-think and re-design their online channels. Meanwhile, mobile technology has surged […]
Much has been written about customer churn – predicting who, when, and why customers will stop buying, and how (or whether) to intervene. Employee churn is similar – we want to predict who, when, and why employees will terminate. In many ways, it is smarter to to focus inward on employees. For one thing, it […]
Arguably, the most important safeguard in building predictive models is complexity regularization to avoid overfitting the data. When models are overfit, their accuracy is lower on new data that wasn’t seen during training, and therefore when these models are deployed, they will disappoint, sometimes even leading decision makers to believe that predictive modeling “doesn’t work”. […]
The need to adopt sophisticated data analytics has become widely apparent to businesses recently, and the necessity of adopting “Big Data” analytics approaches is only becoming more evident. Gartner’s report on Big Data Adoption in 2013 found that 64 percent of organizations have already invested in or plan to invest in Big Data technology, and […]