Practice makes perfect, a famous quote. It’s a cliché, and many of us hate to admit it that it is true. Here is an interesting story that explains why it is so important to just roll up your sleeves and start experimenting. This story is from the book Art & Fear by David Bayles and Ted Orland.

The ceramics teacher announced on opening day that he was dividing the class into two groups. All those on the left side of the studio, he said, would be graded solely on the quantity of work they produced, all those on the right solely on its quality.

His procedure was simple: on the final day of class he would bring in his bathroom scales and weigh the work of the “quantity” group: fifty pound of pots rated an “A”, forty pounds a “B”, and so on. Those being graded on “quality”, however, needed to produce only one pot – albeit a perfect one – to get an “A”.

Well, came grading time and a curious fact emerged: the works of highest quality were all produced by the group being graded for quantity. It seems that while the “quantity” group was busily churning out piles of work – and learning from their mistakes – the “quality” group had sat theorizing about perfection, and in the end had little more to show for their efforts than grandiose theories and a pile of dead clay.

Fascinating experiment. Quantity leads to quality. So don’t try to make it perfect the first time. Even after many experiments, your A/B tests might not be perfect. That is OK, things change all the time: new insights, new tools, changing customer behaviour etcetera. In the end maybe practice does not make perfect but what is for sure: practice makes progress or in CXO terms: Velocity leads to value.

Practice does not make perfect, Practice makes progress

In CXO terms: Velocity leads to Value

This is the third part in our series why experimentation programs fail. It is inspired by a great article published by Go Group Digital. In the first part we talked about the lack of an overarching business purpose for CXO. The second part is about siloed teams. Part 4 is about how to get value from losing experiments.