Thursday, February 28, 2013

How to Think Like an Analyst

So I was talking to my Aunt a couple weekends ago. My Aunt explained that though she was happy for me and the work that I do, she didn’t understand any of it. I tried my best to explain in general terms what web analytics, and analytics as a whole, is and is all about.

Our conversation continued, and I further offered that though she may not understand exactly what it is I do, she could understand the spirit in which it is done - the way to think about analysis.

Not everyone is cut out to be an analyst. There are those who have always been good with numbers, and there are those describe themselves as 'the one who was always bad at math in high school’.

And that’s fine. Like I said, not everyone is cut out to be an analyst, not everyone wants to be, and not everyone can be. However, it is a firm belief of mine that everyone, everyone can think like an analyst.

And I’ll show you how.

The Questions You Need to Ask

True, you may not have the skill set necessary to be an analyst – you may, in fact, be one of those who was bad at math in high school, and when people mention spreadsheets you think about bedding not computer software.

But that doesn’t mean you can’t think like an analyst.

Part of being a good analyst is not just being able to do analysis, but being able to ask the right questions which lead to it.

All good analysis starts with a question. So all you have to do is ask the right questions.

And, in this humble author’s opinion, those two questions are how and what.

Question 1 – How (many)?

This is the simple question, and is one of measurement and descriptive statistics.

Thinking quantitatively is a key part of thinking like an analyst.

If you learn to think in this way you will find that ordinary, everyday situations can become part of ordinary, everyday analytics.

For instance, any time you are at some sort of gathering of people or social function you can think like an analyst by asking yourself the question - how many?

How many men are there in the room? How many women? How many are there proportionally?
How many people at the party are wearing glasses? How many are not?
How many people at the networking mixer are eating and drinking? How many are just eating? Just drinking?
How many people at the dinner party decided to have the chicken? How many did not? How many finished all their food and how many left food behind? How many plates did each person have?

And so on.

But as I said, the question of how many is simply one of describing the state of affairs. To really think like an analyst you also need to ask the second question.

Question 2 - What (is the relationship between....)?

The second question helps you to think like an analyst and go beyond simply describing things quantitatively and start thinking about possible relationships.

Here, to illustrate how thinking like an analyst is subject-matter independent, we can pick a topic, any topic. So let’s go with….. peanut butter. I like peanut butter.

The second question is what lately I find I'm asking myself all the time about almost everything (whether I like it or not). And that very important question you can ask yourself is - what is the relationship between......?

Pick properties of, or related to, your subject of analysis - some of which you may compare across or between, and others which may be measured. In technical terms these are known as dimensions and measures, respectively.

For example, using our randomly chosen topic of peanut butter, first we brainstorm all the things we could possibly think of related to peanut butter.

Type (chunky or smooth), brand, container (size, type, colour), price, sales, consumption, nutritional content,  location, time…

And so on. Let’s stop there.

Then we ask the question: What is the relationship between a and b? Where a is one of the things we brainstormed as a category, and b is one of the things we brainstormed as a measurement.

What is the relationship between the type of peanut butter and its nutritional content? (That is, how is chunky peanut butter different from smooth peanut butter in terms of calories and fat?)

What is the relationship between the brand of peanut butter and its sales? (That is, how do the total sales of different peanut butter brands compare? You could also add time and location dimensions – how do sales between brands compare this year? Last year? Worldwide? In Canada vs. the US? Per store in Ontario?)

What is the relationship between the container size and location? (That is, do different countries have different sized containers for peanut butter? What is the average container size per country? In each region? Or look at location in store – are all the containers in the same aisle or are the different sizes in different places (e.g. the bulk food aisle)? How is the distribution of container sizes broken down across different stores across the country?

And so forth. As you can see, there are so many questions you can ask by combining properties of a topic of interest in this way. And these are only questions with two properties – many more questions of greater complexity could be generated by combining multiple properties (e.g. What is the relationship between peanut butter sales and consumption and the brand and type?)

The Hard Question

There is one final question which I did not mention, which, if you really want to think like an analyst, is the most important question of all. In fact, I would go further and say that even if you are not thinking like an analyst, this is the most important question of all. And that ultimate question is why.

The question of why is the most important question, the hardest question, the question which drives all of the analysis that analysts the world over do.


Why has our new marketing initiative not resulted in increased sales in the third quarter? Why is the sky blue? Why does Amazon send me so many emails related to Home and Garden products? Why can’t I sleep at night? Why are there three million kinds of laundry detergent but only two kinds of baking powder? Why? Why? Why.

This is the question which drives all investigation, which drives all measurement, which drives all analysis.

And this is the question, whether you want to think like an analyst or not, you should always be asking yourself.

Saturday, February 9, 2013

Top 10 Super Bowl XLVII Commercials in Social TV (Respin)

So the Super Bowl is kind of a big deal.

Not just because there's a lot of football. And not just because it's a great excuse to get together with friends and drink a whole lot of beer and eat unhealthy foods. And not because it's a good excuse to shout at your new 72" flatscreen with home theater surround that you bought at Best Buy just for your Super Bowl party and are going to try to return the next day even though you're pretty sure now that they don't let you do that any more.

The Super Bowl is a big deal for marketers. For creatives. For 'social media gurus'. Because there's a lot of eyeballs watching those commercials. In fact, I'm pretty sure there's people going to Super Bowl parties who don't even like football and are just there for the commercials, that is if they've not decided to catch all the best ones after the fact on YouTube.

And also, you know, because if you're putting down $6 million for a minute of commercial airtime, you want to make sure that those dollars are well spent.

So Bluefin Labs is generating a lot of buzz lately as they were acquired by Twitter. TV is big, social media is big, so Social TV analytics must be even bigger, right? Right?

Anyhow Bluefin showed up recently in my Twitter feed for a different reason: their report on the Top 10 Super Bowl XLVII commercials in Social TV that they did for AdAge.

The report's pretty and all, but a little too pretty for my liking, so I thought I'd respin some of it.

Breakdown by Gender:

Superbowl XLVII Commercial Social Mentions by Gender

You can see that the male / female split is fairly even overall, with the exception of the NFL Network's ad and to a lesser extent the ad for Fast & Furious 6 which were more heavily mentioned proportionally by males. The Budweiser, Calvin Klein and Taco Bell spots had greater percentages of women commenting.


The Taco Bell, Dodge and Budweiser ads had the most mentions with positive sentiment. The NFL ad had a very large amount of neutral comments (74%), moreso than any other ad, proportionally. The Go Daddy ad had the most negative mentions, for good reason - it's gross and just kind of weird. It wouldn't be the Super Bowl if Go Daddy didn't air a commercial of questionable taste though, right?

Superbowl XLVII Commercial Sentiment Breakdown by Gender
Superbowl XLVII Commercial Sentiment Breakdown by Gender (Proportional)

Lastly, I am going to go against the grain here and say that the next big thing in football is most definitely going to be Leon Sandcastle.