I just had my 13th anniversary at work a couple of weeks ago. I am always amazed at how many people keep up with that kind of trivia. I got emails, texts, and phone calls from quite a few people….many that I haven’t talked to since this time last year. Regardless, it has been a time to reminisce and talk about the good ole days. I remember, in my first few months at work I quickly realized that we needed to analyze our store performance differently. We basically looked at all 4,000+ stores as if they were exactly the same. Within chaos lies opportunity…right? I went to work on figuring out how to group stores with similar sales patterns, unit velocities and/or brand preferences.

I teamed up with our analytics team and after a lot of trial and error, data scrubbing, analysis, etc. we landed on a process and eventually had a tool built. The first clustering run took me a full week to run through the process as defined by hand. The clustering tool moved that time to within a couple of hours.

At this time, I’m going to pause the story and share that last Wednesday night Prof Ames described the almost identical process and math to us in less than an hour. I was blown away and more than a little humbled. Now to continue with the story….

The first category where we implemented store clusters was a small $600 million category that was running -6% year over year sales. The category had a new buyer and he was desperate for help. We implemented a 5 cluster set, created with a k-means algorithm and within weeks was running +10% sales growth on less inventory. Needless to say I was very excited and soon had more work than I could handle. This department now has a hundred or more analysts doing category analytics, including clustering….it is required.

The funniest thing was one night as I arrived home from work my young, 5 or so year old, daughter comes running out to me and asks….”Did you get to cluster something today daddy?” She thinks she is so funny! Of course I did!!

The time has come that I knew would eventually come. That point when family, school, and work, didn’t leave anytime for life. The problem is that I had to take a road trip. Maybe it was that life actually didn’t leave time for work or school. Regardless, my 7 month old grand-daughter needed to be visited by her grandpa. So, I am officially out of the running for parent or grand parent of the year, but that’s ok. I was cramming homework in every chance I had available. I must plan better.

I do enjoy the homework very much. I am learning exactly the things that I expected and hoped to learn, and would be painfully disappointed if this was easy. In short, I have never spent the time to learn to code. I hack around in VBA, and can usually make things work. I once hired a guy that his job, at the time was coding, bullet proofing, fixing, and removing the duct tape from many of my coding attempts. He was an amazing hire. We got along great. In the interview, it was super-interesting to hear him dance around how good he was at his job without throwing my coding skills under the bus. He waited until after he got the job to do that!

This week we are learning how to write macros in SAS. I am all about automating repetitive tasks, so this is fun. Coding and anticipating what the code needs to do is still very foreign to me, but this is how you learn. Last week we did a deep dive into SQL. I wish I had taken this class a long time ago. The SQL work is extremely applicable to my day job. Most interns I work with wish they would have studied SQL and Excel harder. That’s what makes the corporate world go round, sad but true. Keep it coming.

Week 3 has been a lot of fun. It has been challenging to say the least, but the “learnings” are absolutely worth the effort. At work my team teases me about how often I use standard deviations and Z-scores in our day to day work. It helps me understand not just the item or store metrics but how it fits in the distribution of items or stores metrics. They say it is my hammer, and “when you’re holding a hammer everything looks like a nail!” It seems like the basic “index” math is what makes “Wally World” go round.

The index shows the percent difference from the mean, using the mean as the denominator. This is a good, valid number, but it doesn’t show if that number is expected or is an outlier. For instance, what is a good index? We always look for an index of 120, meaning the number is 20% greater than the mean. Depending on the base that could be good, great or insignificant. When we look at demographics that buy a particular item. An index of the Caucasian customer at 120 is huge, because the mean is in the 70% range, which gives me an actual of 84%. An index of 120 for the Asian customer is different because the average is less than 5%, so the Asian demographic for that item would be 6%.

Therefore, I love Z-scores. A Z-score tells me how many Standard Deviations from the mean an item or store metric would be. The index of 120 might land with a Z-score of 3.5 for that Caucasian customer, meaning it is an outlier, and a Z of 0.5 for the Asian customer, where it is absolutely expected. Another benefit of Z-scores are the fact that they are real relative numbers that I can apply real math to. For instance, I can calculate Z-scores for dollar sales, unit sales, growth, share metrics etc., and then weight and sum them to create a valid cumulative standardized score that I can then rank and study.

This past week we created analysis to measure performance of a stock portfolio vs individual stock performance. The key metric used was the Sharpe Ratio. I knew nothing about this metric, and knowing I was going to have to write a report on my results, I dug in and researched how the math worked and what the metric truly told me. Imagine my surprise when I realized that the Sharpe ratio is simply a slightly modified Z-score. The math is the difference of the mean of the daily return less the return of a risk-free security, then divided by the standard deviation of the daily mean returns. In other words how many standard deviations from the mean is my net return. Just as my example above, the Sharpe Ratio helps me understand the return in terms of the volatility. This was simple and awesome!

I absolutely gloated at work the next day at how a Nobel prize winner used Z-scores as part of his analysis and how it is still used everyday. I should change my name to Thor.

This has been an exciting week. I have written my first SAS program. I have also learned some really cool tricks in manipulating data in SAS. I wish we used this at work. I have re-learned a ton about probabilities, and brushed up on some statistics.

The data wrangling process in SAS is amazingly straight forward. It works in steps, just like I normally think about transforming data. If I were working in Excel, I would basically apply the same logic and transformation process. Once I get the syntax down this is going to be fun. I am again amazed how SAS processes one line at a time. I use monstrous data sets at work and our biggest limiters are environmental issues with processing. I can’t wait to try some things that I do at work in the SAS environment

I made this hard on myself. The material is really very organized and straight forward, and I’m feeling pretty good about what I’ve accomplished this week. As an overview, I learned how to build this blog, and wrote my first SAS program. I had very good instructions and plenty of resources to complete both, but I still feel pretty good about my accomplishments.

How many times can you forget to add a semi-colon to the end of a SAS statement. I’m sure I’m in the triple digits. Also, spelling counts when you are writing a program. AAPL is not the same as APPL. I spent an embarrassing amount of time chasing that one down. Other, than self-inflicted wounds it was really pretty painless, and I’m starting to catch the vision of how powerful SAS can be….with a trained pilot flying her. Importing and exporting were pretty straight forward. The data transformation and calculation abilities of SAS are off the chart. The Lag function is awesome! Being able to do math on data from a defined number of records back is powerful. That’s not to bad to do in Excel, but really tough in databases. It’s even easier in SAS. Here’s a link to their website if you would like to know more.

The point of attack changes as the play evolves.”

-Nick Saban

This is the first post on my new blog. I hope to share with you my educational adventure, as I attempt to evolve along with the technology and processes of Business Analytics. I’m just getting this new blog going, so stay tuned for more. Subscribe below to get notified when I post new updates.

I really enjoy solving problems. It is very therapeutic to fully immerse in a problem, and then extremely rewarding to find a solution.

I worked with my Grandfather for several years in my early career. He was a Navy trained Electronic Engineer. He could fix anything and he loved puzzles. I believe he loved more than anything stumping me with a puzzle, especially when the solution was easy and I “over-thought” it. I do that sometimes. However, I also believe these teasing challenges developed my love for logical, problem solving, and I am forever grateful.

I have worked in retail for 25 years. I have had a rewarding career helping the world’s largest retailer better serve their customers. I have always been able to solve big problems with “scrappy” tools and processes, but that is changing. Data is getting bigger and customers are getting more complicated. Today’s shoppers grew up in a tech enabled world. They aren’t confined to “brick and mortar” stores. They expect their goods and shopping experience to be customized for them. Everything is changing very fast, and small scrappy tools aren’t keeping up.

So, Why am I Here?

• I need to continue to sharpen my skills and evolve, to continue to exceed our customer’s expectations, and stay out in front of consumer trends and insight mining.
• I want to eventually evolve into a teaching role. I enjoy this work and want to be able to share my experiences. I hope to challenge others to “think and solve” as I was taught by my Grandfather.

I am writing this blog to share my educational journey as I pursue a MS in Business Analytics at Wake Forest University. I expect this to be an interesting journey as I finished my undergrad degree 28 years ago. I am counting on being able to learn some new tricks.

Over the next two years, I hope to share my thoughts, struggles, accomplishments and feelings of an old guy going back to school. I promise to be honest about my experience, so you can either laugh at me, cry with me, or decide if a “late career reboot” would be something you might be interested in as well.