Data is just a pile of numbers until you figure out what they mean. We devise all sorts of metrics and KPIs to find truth in our numbers. But even smart people can unknowingly deceive themselves by trying to see what they want to see in their data. So how is it that smart people can misinterpret their data? How do they get it wrong?
It’s human nature. The problem with humans is that they are…human. They will make mistakes, not all of them consciously. T. Maxwell, owner of digital marketing agency eMaximize and a member of the Forbes Agency Council, said that “most mistakes are made by people not peeling back the onion”. For instance, they look at their Monthly Visitors and use it as a key metric to measure growth, but when they dig a little deeper they might notice that 40% of those visitors are from India and Russia and are most likely bots.
Erica Magnotto, director of SEM at Accelerated Digital Media, said that “interpreting data is more about relinquishing those thought processes”. Mark Stouse, chairman and CEO of Proof Analytics, added that bias is “usually the result of thinking too narrowly”, so it’s important to “enlarge the circle in terms of what you think is relevant to a decision”. He believes that diversity and inclusion can help to do this, as it enlarges your perspective and guards against something important being missed.
When measuring data, there are many ways to do it. But that doesn’t mean that what you are measuring helps create greater understanding. Some metrics are meaningful, others not so much. Maxwell said that it’s the job of the agency and the owner to choose which metrics are important. Magnotto echoed that, saying that both the client and marketer need to agree on the platform that is considered the source of truth for tracking primary KPIs and other performance.
Stouse said that “data is always the numerator in the equation, not the denominator”, and that the question or decision dictates the model. He also notes that the unaided human brain cannot handle more than three to four variables, so it’s important to manage the short game while understanding the causality that’s driving the overall situation. It’s also important to allow for the unpredictable to happen and optimize to keep a campaign on track.
In conclusion, smart people can misinterpret their data due to human nature and bias. It’s important to choose the right metrics, agree on the source of truth, and understand the causality that’s driving the overall situation. Allowance must also be made for unpredictable events, and optimization is needed to keep a campaign on track.
Originally reported by Martech: https://martech.org/dont-apply-wishful-thinking-to-your-data/
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