Keep it simple

Is analytics all about actionable insights? Go and check out some of the websites of companies that offers analytics as a service. Chances are extremely high that you will come across either “actionable” or “actionable insights”.

But the truth is a lot of the work done at Analytics companies is not actionable at all. A lot of the daily or regular requirements will be the “good to know” numbers or information, or what many analysts will derogatorily refer to as Reporting work.

To be honest, when I just started my Analytics career I had the same biased or uninformed opinions. Predictive Analytics or Modeling was the only “cool” thing in Analytics. As they say, much water has flowed under the bridge and below are some of the things I have learned along the way, over the years.

All numbers and insights need not be actionable

When working with a new department or analyzing a new customer base, most of the analyses will start with understanding the business or the customers. The simple reports or the exploratory data analysis is going to be a very useful and important guide for future business plans and strategies.

If the results of your analysis can answer a business question, that’s good. And sometimes, it can be very good.

Averages are sometimes the best

I learned this all over again recently. The client wanted to see how different their 2 groups of customers were. As the initial discussion was focused on the purchase behavior or purchase life cycles of these two groups, I jumped into analyzing the customers’ monthly transactions since their acquisition dates - trying to see if these two groups have different buying patterns across their tenures.

When their overall sales didn’t throw up any surprises, I went into sales within specific product categories. By the end of the week, I found a few interesting patterns. But during the second meeting with the client next week, as I was going through the slides one by one (about 7-8 slides) – both my client and I realized that though the buying patterns were very similar, there was a big gap between the lines. Instead of analyzing how the behavior spiked or dipped or flattened out month on month, the biggest and most important analysis would have been a single slide on the averages of the two customer groups over their 1 year tenure. The differences in their average sales, basket size, number of trips, etc. was clearly seen – one single slide, one table – that was what I should have done when my hypothesis or what we all wanted to see was proved wrong by the data.

Understand the drivers – is modeling really required?

When clients say they want to understand the drivers of customer attrition or response, the first thing many analysts will do is to develop a model. But wait a minute, have some patience and ask a few more questions. A model has to be built if the client has a marketing plan or strategy because you will need to score customers for targeting.

What if all the client wants is to understand the drivers? Maybe all she needs is to identify and understand who these attriters or responders are. And to answer that, you don’t need to develop a model that will take a lot of time and money.

All you need is EDA. Means, frequencies and cross tabs based on the target variable (example, responded or not) will reveal things like – 70% of the responders visited the store in the last 3 months, 80% of the responders live within 5 miles of the store, 65% of the responders use a Credit Card, etc. And this will very much answer your client’s questions.

Signing off with:

Karma police, arrest this man
He talks in maths
He buzzes like a fridge
He's like a detuned radio
-- Karma Police by Radiohead