October 15, 2020

Interpreting Data

In sports, each team has an analytics department. They analyze data "their" way, as it relates to "their" system. Via this style of analysis, two department can use completely different analytics to evaluate two similar players in very different ways.

Modern analytics/marketing is framed by the concept of "best practices" ... you use Google Analytics or Adobe or Tableau (yes, I'm exaggerating here) and you analyze customers they way they tell you to analyze customers, end of story (yes, I'm exaggerating again).

The secret to success, of course, is to do something wholly different. Different software, different reporting, different modeling, different application of tactics.

If your goal is to find a case study of how "Brand X" leveraged Tableau to create a life-altering graph of data by zip code, you can go ahead and do that.

In this COVID-infused marketing world, interpreting data is darn important. Given that a significant fraction of companies are posting +40% gains without doing anything special to generate the gains, one can easily misinterpret the findings and attribute success to marketing and/or merchandising. Or, one could play the role of Virtual Chief Performance Officer, and figure out what might be happening behind the scenes. Interpreting data becomes more important when the herd all moves in one direction, wouldn't you agree?

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