Interpreting Data: How to draw accurate conclusions

Let's suppose you've convinced your organization of the value of testing. Your marketing team has learned how to develop a proper research question; you now know how to design treatments and set up metrics to answer your test's research question. You even monitor for validity threats.

Congratulations! You've achieved a skill set that, frankly, many marketers do not have. (And it is evident in their low customer conversion rates.)

But it is at this level of achievement that organizations with a testing program in place often fall off the mountain. Data analysts neglect to focus on learning how to accurately interpret the results of all that careful testing.

Enter bias.

Often, we expect our test results to turn out a certain way, and this can make us unconsciously want a particular result, creating bias in our interpretation of the data. This costs us valuable time and money — and customers.

"We spend a lot of time in the planning phase and say 'if this metric is higher than that metric, then it means this.' So we try to eliminate that human bias beforehand through a lot of planning."

— Derrick Jackson, Director of Data Sciences, MECLABS Institute

In this excerpt from a live session of the University of Florida and MECLABS Institute graduate certificate program, Derrick Jackson, Director of Data Sciences, MECLABS Institute, shares the safeguards that his scientists and data analysts have in place so that human bias doesn't skew customer insight discoveries.

Does your marketing team have a data interpretation framework in place to protect against bias?

This excerpt is from the MMC 5422 Customer Research and the Fundamentals of Online Testing course, part of our graduate certificate program offered through UF. Click here to learn more about the program.

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