Search Analytics: Conversations With Your Customers. But is it really a conversation? I mean we’d like it to be, but is it? I asked Lou the other day about the title of the book and it went a little something like this…
A conversation (usually) means that one person is doing the talking while another is doing the listening, and when it your turn you switch positions. Sometimes it’s good, sometimes it’s not, but it’s a two-way communication activity. What I see (not on my projects of course), but know that happens most often with search data s that no one, rarely if ever, even looks at the data, let alone take any action on it. Why I’ll never understand, but it’s true. Sad, but true.
Do you recall ever being lectured by you parents when you were young, or being in a lecture hall in college? Those weren’t conversations, they were one-way communications. They said something, maybe you listened, I recall them being quite like listening to Charlie Brown’s teacher, Mrs. Donovan, which sounded like a trombone with a plunger used as a mute. So, I ask you. Is SSA really a conversation, or is it a ‘confession’?
What is a confession really? Assuming your not a priest, it’s an acknowledgment, an avowal, an admission, and/or a disclosure from one person to another that doesn’t require the other person to respond. Written within all these confessions your users/customer/clients/etc. are telling you their needs, their desires, what they can’t find, what is frustrating them, etc. And the best and most juiciest part of every single confession is.. it is in their own language! No guessing, no paying for more research studies, it’s all right there in ‘black & white’ just waiting for you to absolve them of their issues.
Now, if you come full circle with these juicy morsels and apply these great insights you’ve discovered from those confessions , i.e. take action on them, then there might be grounds for calling it a conversation.
So what’s you take on this? A Conversation or a Confession?
As I’m sure most authors would be, I’m very excited to have an opportunity to tell you about the things that keep me up at night such as climate change, our current financial crisis, and of course SSA. Fortunately for you, my rants in this blog & book will be confined to the later and more importantly how it can help you become stronger in your practice and produce better work products, whether you are Visual Designer, IA, IxD, Strategist, and even a Web Analyst.
In addition, I am of course looking forward to working with Lou, well, because he’s Lou. And as he mentioned previously, I’m a “quant”. Meaning that I approach about everything in and out of work in terms of mathematics & probability. Yet, my years of work in the consulting and agency world have given me a common language with him and most of you – User Experience. The interesting thing about this shared language, is that I approach almost every problem and project from almost the opposite end of the spectrum, yet I strive to end up in and around the same space that you do. It’s like the difference between those that find religion and those that find a bottle – we are both trying to get the same place just through different methods. Lou, like most UX folks, is trained in or generally works with “small” data sets which are traditionally gathered via qualitative methods. While we “quant jocks” on the other hand, tend to deal with very large data sets (knowing that Excel breaks at 65,536 rows of data kind of data sets) that are gathered through web analytics, business intelligence, databases, or similar technologies. But at the end of the day our goal is the same – to produce the best and most effective User-Centered Design possible.
Like many things today the term “2.0” has gotten attached to almost everything and “Analytics 2.0” is no exception. Unlike those that are just marking hype, Analytics 2.0 has some very real shifts in how we fundamentally go about and think about doing analytics. The main difference is trying to understand the “why”. What I mean by that is quant data only tells half of the story, specifically “what” a user did. It cannot tell me “why” a user did something, (user behavior), I can at best only infer their intentions. In order to understand “why” a user did something you need qual data, and the best of both worlds collide when you combine them to provide a (more) complete picture of your users.
We expect this book to give you all that need to get started and more to better understand data and improve your work products. But what would happen if we could do more than just that? What if we could start a change in the industry that was so powerful it could change how people went about doing and thinking about their work. Those might be some pretty bold questions, but I seem to remember a particular “Polar Bear” book that did just that. Instead of simply applying or borrowing a few methods from one field to the other, what if we could bring them closer together to fill in the gaps that exist in each and could show you how they truly compliment one another? What if down the road these two fields began to truly merge? What if?
I welcome you thoughts, comments,etc. on this and anything else that is keeping you up at night 🙂
p.s. It’s not pajamas, but one my favorite t-shirts has a picture of Thomas Bayes on the back with Bayes’ theorem printed across the chest. (see post). Quant Jocks Unite!