A simple score you can probably build in Excel

Guest post by Peter B. Wylie

 

In the evolving world of analysis for higher ed and non-profits, it’s apparent that a gap is widening: Many well-resourced shops are acquiring analytics talent comfortable with statistics and programming, but many others are unable to make investments in specialized talent.

 

Today’s guest post is a paper by Peter Wylie that addresses the latter group, the ones at risk of being left behind. Download his paper here: Simple_Score_in_Excel_Wylie

 

In this piece he uses data from two schools to show you something you can try with your own data, building a very simple predictive score using nothing but Excel.

 

Some level of data analysis ought to be accessible at some level to every organization, regardless of technical proficiency or tools. And in fact, shops that move too quickly to automate predictive scoring with black-box-like methods risk passing over the insights available to the exploratory analyst using more manual, time-consuming methods.

 

We hope you enjoy, and above all, that you try this with your own data. The download link again: Simple_Score_in_Excel_Wylie

 

Regular-season passing yardage and the NFL playoffs

Guest post by Peter B. Wylie, with John Sammis

 

How much is regular-season passing yardage related to success in the NFL playoffs? (Click link to download .PDF: Passing yardage in the NFL.)

 

Peter was really interested in finding out how strong the relationship might be between an NFL team’s passing during the regular season and its performance in the playoffs. There’s been plenty of talk about this relationship, but he wanted to see for himself.

 

A bit of a departure for CoolData, but still all about data and analysis … hope you enjoy!

 

A fresh look at RFM scoring

Guest post by Peter B. Wylie and John Sammis

Back in February and March, Kevin MacDonell published a couple of posts about RFM for this blog (Automate RFM scoring of your donors with this Python script and An all-SQL way to automate RFM scoring). If you’ve read these, you know Kevin was talking about a quick way to amass the data you need to compute measures of RECENCY, FREQUENCY, and MONETARY AMOUNT for a particular set of donors over the last five fiscal years.

But how useful, really, is RFM? This short paper highlights some key issues with RFM scoring, but ends on a positive note. Rather than chucking it out the window, we suggest a new twist that goes beyond RFM to something potentially much more useful.

Download the PDF here: Why We Are Not in Love With RFM