Making decisions with data and not your gut

Mark Wood-Collier
17 November 2020

Recently I was enjoying an al fresco breakfast with a friend in the Burleigh Laneways, our shorts still wet from our traditional post-run dip at Tallebudgera Beach. We eagerly awaited our food order, having burned over 1000 calories before breakfast. Fortunately, our coffee was rich and strong and our discussion was just as warm and upbeat.

We were chatting about the rewarding feeling my friend, Simon, felt about the progress of his relatively new tiling business. His workflow had been strong and leads were coming in thick and fast. A nice position to be in, of course.

He then mentioned there was a job coming up that week, which “felt like it wasn’t worth doing.” I was interested in his comment, and asked him to elaborate.

Experience trumps knowledge, but data-based decisions beat gut-feel hunches.

He was kind and spared me the technical details, “It’s just an average job because of the area and type of work.” I asked “What is the right area for that type of work?” He considered my question for a moment and replied “Good question, I don’t really know.”

His response indicated to me that he might be relying purely on his past experience when making decisions on which jobs to prioritise, or a price to quote. This type of decision making has its place; consider the number of decisions one has to make each day. In business, key decisions need to be supported by good data. Experience trumps knowledge, but data-based decisions beat gut-feel hunches.

Our hearty meals arrived and the steam coming off the deliciously presented breakfast reminded us of the freshness of the morning. There was no standing on ceremony.

Keen to help Simon, I continued my casual line of inquiry. I asked questions to help me understand his approach to initial job assessment and the type of past job information he may have available, like: “how do you price a job?”

The next obvious question was: “how do you know what is a good job or a bad job?”

Unsurprisingly, in the tiling game, quotes are on a square metre basis. However, it was interesting to me that there wasn’t much process behind tracking his costs. “But, how do you know what your margin is?” I asked. The answer was, he didn’t have a clear view. He had a good feel for it, which may well be broadly correct, but there was no data consistently recorded on each job to confirm his intuition.

The next obvious question was: “how do you know what is a good job or a bad job?” A job’s margin is only one of many factors, which influence the quality of the job from a business’ perspective. Again, there was no easily accessible data to support Simon’s recollections.

I suggested I develop a template for him to start tracking his jobs. The template had some dummy job data, which drove a set of outputs like tables, charts and analysis. This importantly illustrated the type of detailed reporting he could expect to access; all based on less than ten key data points from each job.

Having those simple job-level metrics immediately put the spotlight on which jobs in his pipeline were high quality and should be prioritised. It also drew a stark comparison to lower quality jobs, including identifying a few surprise donkeys.

He also took a lot of value from seeing how his annual profit target could be reduced to a few simple metrics, which he could keep front of mind while doing his daily’s. Staring down the barrel of the annual profit goal isn’t going to make it happen on a day to day basis. However, if you can break it down to a few simple actions and behaviours which you can perform each day, the end goal will take care of itself.

-MWC