Bill James is by wide acclaim the smartest baseball thinker of the past fifty years. He’s the father of statistical analysis in our national past-time, and I think this excerpt from Michael Lewis’s Moneyball goes a long way in explaining why:
The statistics [traditional baseball fielding statistics] were not merely inadequate; they lied. And the lies they told led the people who ran major league baseball teams to misjudge their players, and mismanage their games. [Bill] James later reduced his complaint to a sentence: fielding statistics made sense only as numbers, not as language. Language, not numbers, were what interested him. Words, and the meaning they were designed to convey. “When the numbers acquire the significance of language,” he later wrote, “they acquire the power to do all of the things which language can do: to become fiction and drama and poetry….And it is not just baseball that these numbers, through a fractured mirror, describe. It is character. It is psychology, it is history, it is power, it is grace, glory, consistency, sacrifice, courage, it is success and failure, it is frustration and bad luck, it is ambition, it is overreaching, it is discipline. And it is victory and defeat, which is all that the idiot sub-conscious really understands.”
It’s remarkably easy to measure just about everything that happens on, in, through, and because of your website. I’m of the belief that the use analytics in marketing marks a true revolutionary moment for the industry. But is it possible that we’re getting distracted by the numbers? Let’s see what Avinash Kaushik has to say on this topic:
Like good little Reporting Squirrels we collect and stack metrics as if preparing for an imminent ice age. Rather than being a blessing that stack becomes a burden because we live in times of bright lovely spring and nothing succeeds like being agile and nimble about what we collect, what we give up, and what we deliberately choose to ignore.
The key to true glory is making the right choices.
In this case its making right choices about the web metrics we knight and sent to the battle to come back with insights for our beloved corporation to monetize.
A very simple test can allow you to figure out if the metric you are dutifully reporting (or absolutely in love with) is gold or mud.
It is called the Three Layers of So What test. It was a part of my first book, Web Analytics: An Hour A Day.
What’s this lovely test?
Simple really (occam’s razor!):
Ask every web metric you report the question “so what” three times.
Each question provides an answer that in turn raises another question (a “so what” again). If at the third “so what” you don’t get a recommendation for an action you should take, you have the wrong metric. Kill it.
Let’s check back in with Kaushik, to see an application of this line of questioning:
Here is how the “so what” test will work:
“The trend of repeat visitors for our website is up month to month.”
So what?
“This is fantastic because it shows that we are a more sticky website now.”
(At this point a true Analysis Ninjas would inquire how that conclusion was arrived at and ask for a definition of sticky, but I digress.)
So what?
“We should do more of xyz to leverage this trend.” (Or yxz or zxy – a specific action based on analysis of what caused the trend to go up.)
So what?
There is so much
If your answer to that last “so what” is: “I don’t know… isn’t that a good thing… the trend is going up… hmm… I am not sure there is anything we can do… but it is going up right?”
There is so much information available, literally at our fingertips. Much like what we saw with Facebook landing pages, analytics are an intriguing field of study, but they’re not of much use unless you have a plan to translate the numbers into profit or improvements or, well, an action of any kind. And it’s not just web analytics for which this is true–with the rise of phone metrics, the question of truly learning from your stats is becoming increasingly relevant to multiple marketing avenues. Like Bill James, it’s important not to get caught up in the numbers as numbers. Instead we need to ask ourselves, what story do these numbers tell?
What are the key metric points that you consider? How do you translate stats into action?
No related posts.
