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Let the Data Preach

1.9.2016 Josh Schwartz
Data will tell us the good news of consumer preferences, if we'll shut up and listen.

If your data-driven marketing efforts aren’t bearing fruit, it may not be the data’s fault. To quote Ronald H. Coase from his Essays on Economics and Economists, "If you torture the data long enough, it will confess." It’s always tempting to project our own biases when we’re looking at data. Whether you call it selective hearing, rose-colored glasses, beer goggles, or plain ol’ pig-headed tomfoolery, when you start speaking for the data instead of letting it speak for itself, you’re headed for trouble.

The funny thing about the data revolution is that the data is still only as good as the human interpreting it. This affects our marketing when we fail to approach the information we’ve collected with humility and an open mind. If we aren’t careful it’s surprisingly easy to see what we want to see and go to a brainstorm or strategy session with dreams of sugarplums dancing in our heads. That’s a bad thing because data is a double-edged sword; that is to say, it’s a helpful tool, but it can hurt you if you don’t use it correctly. A campaign based on misinterpreted data is worse than a campaign that isn’t based on data at all, because the data can create a false sense of confidence in your conclusions and prompt you to go all-in on a bad strategy.

So, how do you avoid making this mistake? Having a trained data-analyst on staff doesn’t hurt, but here are a few questions you can ask yourself to reduce the odds of a misstep:

1. Do I have a personal or financial conflict of interest that makes me an unreliable interpreter of this data?

2. If not, what are all the possible conclusions I can reach from this data?

3. Of those conclusions, which is the most natural?

4. Why do I think my conclusion is correct?

5. Is there other data that supports or undermines my conclusion?

6. Do I have enough data to reach a reliable conclusion, or do I need to keep digging?

It’s not fun thinking about ourselves as potentially biased, but if we want to use data to create successful strategies that perform well, we need to let the data speak for itself.

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