9 Causes Of Data Misinterpretation

Aggregation Obscures Truth

Different levels of aggregation can tell a different story. Knowing that, it is common to vary the levels of aggregation to confirm the trend and to understand at which point the results diverge or reverse.

"It's a good strategy to see whether the trends at the aggregate level hold up," said University of Tennessee's Ken Gilbert. "A better strategy is to ask, before you start collecting or analyzing data, what are the potential sources of variation [such as] why would sales vary from location to location and month to month during times of promotion versus when we don't have a promotion? That way, you have a list of things you believe have an impact, and you make sure your data is grouped according to the different values of those variables."

Andrew Christopher, head of loyalty consulting in South America for marketing and loyalty analytics company Aimia, said in an interview that aggregation levels helped determine whether or not a pilot program was successful.

"We were trying to persuade older customers to move from telephone service to a much lower cost online channel. When we viewed the age group in the aggregate, the results were not encouraging," said Christopher. "By further segmenting the pilot group, we were able to identify some specific subgroups for whom the campaign had been highly successful in changing their behavior. With the results of this analysis, we were able to alter the targeting of the full campaign to include only those subgroups, and drive significant cost savings."

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