You’re going to hear a lot more from me now that I’m graduated from college and I’ve found my home in digital analytics and marketing.
The semester wrapped up really well. I won’t go too in depth about the projects but I can tell you that the Twitter study didn’t turn up anything I considered valuable:
This is a distribution of 2 different word categories across a month on Twitter. One category contained substance-use/abuse-related words, while the other contained words with similar monthly volume drawn at random from an English dictionary. Individual users do not use either type of word in any significantly different way – notice that p-value. During my presentation I pointed this out and one of the attendees (a very well-regarded scientist and mathematician at ASU) gave me something to think about. He explained that these types of words may be used in a very different way across different social media platforms. The “stream of consciousness” manner in which Twitter is generally used makes it very different from the “thoughtfulness” of a Facebook or Google Plus post, or the “curated” feel of a LinkedIn post. It was a commonsense observation that was easy to miss while I was digging away at the code. And of course this becomes blatantly obvious once I am entrusted with creating some social media content for a few clients at the agency.
The part that is stuck firmly in my craw is that the type of research we were doing was cutting edge stuff, and there wasn’t much available yet in the way of academic comparisons between platforms. Perhaps someday that would be available as more than blog posts – and not in any way to diminish specialist bloggers in “social forensics”, it’s just difficult to justify that sort of thing in academic literature. I can’t cite Moz or KISSmetrics.
Nonetheless, I’ve moved on. Now I turn my attention to statistics in a much more applied way. Although my position at the agency does not directly require me to perform research on traffic, I do get to spend a lot of time with keyword research. Specifically, I’ve fallen in love with the long tail of the distribution, something that turns statistical analysis on its head and instead takes us a murky step forward into the sociological side of the Internet.
Here’s the fun question that can’t really be answered in one word: What are users really searching for? Try typing in something like “cars” and whatever comes back on that results page clearly indicates that a lot of businesses are trying to get in front of a word that obvious. So the obvious question should be – why is someone typing in “cars”? Probably because they want to buy a car, right? Maybe. Or maybe because they are:
- Interested in classic cars and want to look at some pictures but don’t know where to start,
- Middle-schoolers who have to do a report on the history of cars and want some fun facts,
- Car dealerships that are just entering the world of digital marketing and are taking the misguided step of seeing who ranks for a keyword with an estimated search volume of over 3 million,
- Looking for Cars.com but can’t actually remember the web address for it (it happens),
- Huge fans of the Pixar animated film, or perhaps watching it at that moment and trying to figure out the name of the washed-up comedian that did the voice of the tow truck,
- Looking to sell, get repairs for, talk about, find parts for, read maintenance tips about, read funny stories about, read creepy fan-fic about, learn the history or business behind, or find good places to wash their cars.
Oh, and also those users that are looking for a dealership to buy cars from. “cars” is one of those words that exist in what is termed the “fat head” of the distribution. And frankly, it’s a way to get whacked by Google. That’s Web 1.0 hogwash. Altavista-Era foolishness. Pre-Panda-poppycock! In fact, I’d say that “fat head” is the best name for it.
Let’s take a quick look at this using Google AdWords:
It should be pretty clear at this point, and hopefully very intuitive – the more specific the search term, the lower the results. That makes sense, right? Interestingly enough, many, many businesses do not understand the implications of this! They immediately attempt to get in front of those 3 million searches without realizing that this number may include everything below it in this list. They don’t know that they are rushing toward irrelevance! Couple that with interest in pay-per-click and the result is that a lot of companies that can’t afford to spend as much as they do on digital marketing are probably wasting the majority of it.
Now consider the impact of different posting types from social media. Depending on where your interested users are coming in from, who knows if you’re even targeting the right long tail keywords?
I never anticipated finding so many interesting challenges in this field, but here we are!