Escaping The Walled Garden of Enterprise Analytics: Using R and Python For Data Analysis

In which an experienced analytics guy advises the younger generation to leave the walled garden of enterprise analytics tools and learn how to write code using a real programming language. Specifically advocating the use of R and Python for data analysis and related programming. But hey, I’m flexible on that point…

The use of COBOL cripples the mind; its teaching should, therefore, be regarded as a criminal offense.

– Dijkstra

I was taught a long time ago in some Management 101 course to sandwich constructive criticism between two compliments. So I’ll open with this statement:

SAS and the other BI vendors have done a nice job of bringing statistical computing techniques within the reach of the typical college graduate.

Now pull up a chair and grab yourself some popcorn, since I’m going to bite the hand that fed me for the first half of my career. I spent the first seven years of my career in roles involving significant usage of SAS and a variety of drag & drop query tools. The COBOL of the analytics world.

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The Online Marketing Sanity Test: Is Your Website A Waste of Money?

‘Cause only one thing counts in this world: get them to sign on the line which is dotted.

– Glengarry Glen Ross

Here’s one simple question that will identify about 85% of the completely worthless website and content development efforts out there:

Does Google show your site to people who don’t already know your name?

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Website Revenue Models: Real Revenue Statistics For Small Websites

Driven by a passionate desire to “scratch our own itch”, we released the first draft of a little project we’ve been working on this weekend. As our regular readers are aware, we built our first public website earlier this year. We started running ads earlier this summer (just to pay the server bills) and wanted some perspective on “what good looks like”. This is where things get furry: there aren’t any reliable and comprehensive public sources on revenue benchmarks for a small website. So we decided to build our own data set and create some benchmarks around how much a small website could earn. Here you go…

As you can see below, profit-per-visitor numbers vary widely. More after the jump…

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The Best Course I Ever Took: Combinatorics Problems

I sat quietly in the chair, number six or seven on George’s agenda of young hopefuls. The on campus recruiting process was a brutal cattle call. On the positive side, we had a large number of great companies coming to visit. Unfortunately, the vast majority of my 1500 classmates were tipped off about their arrival. Moo!

His eyes skimmed down my resume – and locked onto a phrase halfway down the page.

“Combinatorics Problems? What the heck is that?”

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The Addictive Nature of Real Time Analytics and Website Monitoring – Is Less More?

I spent most of my twenties managing direct marketing campaigns for the financial services industry, where we would release several multi-million dollar programs per year in the hope that they would generate enough customers to justify our salaries. Measuring and predicting the performance of these campaigns was a simple statistics problem – given X expectation and Y performance Z effective days since delivery into the campaign, estimate the difference between actual results and expected results.

The tricky part was getting Z (effective days since delivery) right early in the campaign, since you were trying to predict how fast a slug of bulk mail moves through the postal service system. This was subject to all kinds of minor disturbances such as holidays, weekend timing, and potentially even how your letter shop sorted the file for production. This was before the Internet-based tracking systems we have today, so we were very old school and did a lot of estimation.

As the program manager, I cultivated this Zen state for the first couple weeks of a major program. We knew there was a zone of uncertainty: the most rational response to any developments was “wait and see what happens next”. While we could make good predictions several weeks into a campaign about the outcome, it was actually better not to look at anything from a strategic perspective for the first couple of weeks.. There was just too much noise to get a good read on the signal.

Flashing forward to the present, we put up our first website (a little AJAX based word game solver)  a couple of months ago and had to keep tabs on what was happening.

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DevOps Eye for the Analytics Guy

We may not be deploying cloud servers at the drop of a hat, but the analytics community can take a few lessons from our friends in the DevOps movement. DevOps, short for Developer Operations, has grown around the art of scripting and automating the process of setting up your infrastructure (servers, databases, etc.).

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The Business of Fraud: What I Learned Tracking A Credit Card Fraud Ring

I made an abrupt career change about ten years ago during the first dot-com recession, moving from a marketing role to working on credit card fraud detection projects. At the time I thought I was basically treading water – my old client was going away and I needed a job that would let me stay in Atlanta while my girlfriend and I got married. They needed an analytics guy and I needed a job.

Before I started working on the credit card fraud team, I imagined the fraudsters were unsophisticated criminals – high tech purse snatchers. What I discovered that summer surprised me.

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Blog Comment Spam vs. Effective Marketing – Managing The Balance

Every day or so, I perform a little ritual on one of my projects that those of you who have blogs are well familiar with. I log into the admin console and moderate comments.

Which brings me face-to-face with THEM. Spammers, in overwhelming numbers. It has gotten to the point where I actually “grade” them on the quality of their blog comment spam. There is a definite hierarchy in terms of blog comment spam quality.

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Diving into R

Spent most of the past week working on my first “serious” R scripting project.

I’ve been using R for a couple of years, generally as a free minitab replacement (using the R Commander GUI interface) and adjunct to Python projects. Most of my analytics projects to date have been coded in Python, since they are generally heavy on data (eg. require good integration with our data warehouse, dynamic SQL) and require either custom statistics or heavy text parsing. The latest project is somewhat the reverse – involving a relatively small dataset (very painful to assemble, since it reconciles data from two different transaction systems, but small) that I’m going to run a bunch of prepackaged statistical studies and graphics again. Sounds like a perfect fit for R.

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Why Every Analyst Should Learn To Code

In recent years, I’ve seen a new breed of analyst emerge. Indeed, more than a few of my superiors have encouraged me to join their ranks. This new breed of analyst knows statistics and finance but prefers to leave the coding to others. They have “people skills”. They know how to sit in meetings and provide “thought leadership” and “design policy”. However, these analysts cannot pull the actual data to support their point of view!

This approach to the technical side of our profession is doing them a serious disservice. Here’s why:

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