One of the best comments I've received on this blog came yesterday from Graham Hill. Here is his comment about my rejection of the hypothesis that marketing bloggers are largely negative:
"But, as one statistician to another. Are you not in danger of making unfounded generalisations about blogging based upon a hugely inadequate number of observations."
The answer to Graham's question is "YES"! I'm absolutely in danger of doing this! Graham's comment is insightful and correct.
Early in my career, when it was my job to be a statistician, it was my job to be "right". I made sure that my work was perfect, that my conclusions were rock-solid and air-tight. I was given months to complete a project. Those were good times.
In 1998, I became Director of Circulation at Eddie Bauer. I was member of the "Catalog Business Team", a group of Directors and VPs responsible for meeting or exceeding budgeted sales and profit goals for the Catalog/Online division at Eddie Bauer.
We met as a team every Wednesday morning.
Questions would come up, questions that required rapid answers. For instance, the Merchandise executive might say "We're killing this business by running Mens merchandise in the first twenty pages of the catalog. Let's stop this practice, and run best-selling Womens merchandise in the first twenty pages."
Maybe we ran Mens merchandise in the front of the past two catalogs, and maybe those two catalogs were ten percent below our expectations, whereas the prior five or six catalogs met expectations. On the surface, the merchandising executive seemed to have a point.
As a statistician, you'd like to run a series of experiments, and prove that Mens merchandise was killing the performance of the book. However, these experiments required many folks in print production and creative to create various versions of the catalog. Once created, it would be close to two months before the print production process was completed, resulting in catalogs being mailed to customers. Another month needed to go by before a proper statistical analysis was completed.
So, sitting in this meeting, my choices were to recommend a three month process to test the hypothesis in just one catalog, or to quickly review as a team the past eight catalogs in an ad-hoc, unscientific manner, and make a decision as a team before leaving the room.
It requires a lot of patience to learn the balance between making ad-hoc, gut-feel decisions and doing a thorough, accurate statistical analysis. You never really perfect the balance, you make mistakes, and you make the right decisions.
The key factor in this is that you "make decisions". Decisions, positive or negative, move a business forward, increase accountability, and reduce red-tape.
I once met with my marketing Vice President, when I was a statistician. I wanted a lot of time to do an analysis "the right way". He told me that he'd rather make five decisions with 80% accuracy than make one decision with 100% accuracy, because at the end of the day, you'd make four correct and one incorrect decision, whereas the "right" approach yielded only one correct decision. He preferred to make four right and one wrong decision each day than making one right decision.
I've tried to balance his viewpoint with my statistical heritage of "being right". I've always admired the leader who is decisive, makes four right and one wrong decision, and takes accountability for the wrong decision.