Sales is more science than art. Yet it’s important to pick the right data set before you apply the formulas. Here is sales management made simple. 

In recent times Data has gained a lot of attention. Technology tools and AI have really been an advantage to mine all kinds of data. In 2020, people created 1.7 MB of data every second! By the end of 2020, 44 zettabytes made up the entire digital universe.

So while data is important and I love data and its usage specifically in sales situations, I do feel excess of anything is bad. Here are 3 elements I want to highlight (read rant about) to you as you start to use data in your conversations.

Firstly, Data is important, but what is more important is the analysis of data. Data pretty much means nothing unless it has a narrative with it. “Data are just summaries of thousands of stories – tell a few of those stories to help make the data meaningful.” said Chip & Dan Heath. Let’s take the most often heard objection from customers in a sales process – “you are too expensive”. This is a great opportunity to present data and relevant data can overcome this objection. What are you comparing with? What’s the criteria used to create the comparison? How does this impact the results / outcomes? Is there a thresh-hold we need to keep in mind?

In the second point I want to share this story that I love using to illustrate the importance of analyzing data intelligently. During World War II, fighter planes would come back from battle with bullet holes. The Allies found the areas that were most commonly hit by enemy fire. They sought to strengthen the most commonly damaged parts of the planes to reduce the number that was shot down.

A mathematician, Abraham Wald, pointed out that perhaps there was another way to look at the data. Perhaps the reason certain areas of the planes weren’t covered in bullet holes was that planes that were shot in those areas did not return. This insight led to the armor being re-enforced on the parts of the plane where there were no bullet holes. The story behind the data is arguably more important than the data itself. Or more precisely, the reason behind why we are missing certain pieces of data may be more meaningful than the data we have.

Finally, another of my favorite quotes is by Ronald Coase that says “Torture the data, and it will confess to anything.” I am a firm believer that if you look for data you will find it. This is where the ethics in managing data become so important. The best example is how almost all new channels will say they are the Leading Channel based on viewership! The fine print then shows which hour and for which viewer segment are they actually the leader!! So do you fit data into labels or create labels to fit the data?

I may have said this before but I really don’t worry about Artificial Intelligence specifically as it churns out data, but I do worry about Human Stupidity many times over!