When somebody walks into my office with a spreadsheet, a chart, or a graph in their hand, my first response is to duck. In my gut, I think they’re coming after me…with evidence of where I screwed up.
When the offending reports are laid before me, my fear triggers defensive shields and I mentally prepare (in nanoseconds) for battle. Blood rushes to my cerebrum and I’m poised to find fault with the underlying data that would find fault with my performance. It doesn’t matter if the poor person on the other side of the desk is there to discuss optimal parking lot striping, I’m ready to dig into the numbers and do my best to understand the means, modes, and standard derivations thrust upon me. The ensuing conversation is not at all what was planned or hoped for.
In their book, “Switch: How to Change Things When Change Is Hard,” Chip and Dan Heath describe this issue with enough examples for me to tell I am not, in fact, a sociopath. They explain how people respond to levels of detail (especially numerical detail) vs. emotional pleas.
“Jim, given that we have 2,775 cars per hour trying to park in 2,547 perpendicular spaces with an average size of 9 feet by 10 feet, this graph shows that if we angled them, we could cut the size by .05 percent which lets us put in 87 additional spaces and makes it 6 percent easier for people to park.”
At this point, my brain is working so hard to catch up with the sheer number of numbers that my questions are all numeric:
How did we count the cars?
What about rush hour?
2,547 spaces doesn’t sound right. How were those counted?
Are all the spaces the same size?
I am focused on the details because the conversation started there. How much different it would be if the conversation began:
“Jim, we think we’ve found a way to increase sales by 7 percent on a daily basis just by re-striping the parking lot. We’ve done the math and compared it to some other case studies and it looks promising.”
If I can’t believe my ears, I’ll ask for the data. I won’t question the numbers, I’ll just want to see how it was figured out. I’ll only have two questions: At what cost? What is the impact on profits?
The next time your Web analytics and marketing metrics people start a meeting with a chart, a graph, and a spreadsheet, stop them. Ask them to tell you the story instead. Ask them to tell it backwards. Ask them to begin with the conclusion.
A/B testing can save us a lot of time and trouble.
Multivariate testing can make us a bundle.
A social media program for Product A will not be as valuable as an e-mail campaign, but will be twice as valuable for Product B.
If their conclusions sound right, confirm that they have done their homework and have the numbers to back it up, but please don’t get dragged down into the tar pit of numerical details. It’s not worth it.
On the other hand, “Switch” is very worthwhile and I thank my friend and fellow ClickZ columnist Bryan Eisenberg for insisting I read it.
Marketers need to know what’s in their data and trim out the filler to provide continuous, data-driven ROI for their brands.
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