Design teams who simply utilize web log data to extrapolate which items are used ‘most frequently’ often don’t realize their data may be suspect. For example, some users will just ‘pick’ items that are near the top of the list because they either do not know which item to pick, or don’t care.
As a case in point, while at Blue Cross I examined the most commonly picked ‘no-sale’ reason codes from the sales call-center’s non-sales reports. My goal was to use the no-sale reason code data to try to better predict which advertising sales audiences might provide better sources of leads, thus better sales.
The Agents had to choose a non-sale reason code from a list of over 40 items. They did this by using a drop down tool to select from this long list of codes. The listing order was by frequency of use, thus the most picked reasons were at the top. According to the Blue Cross non-sales reason code data, only the top 2 or 3 reasons were picked. It was very rare to find another code other than the top 2 or 3. Those top 2 reasons made up about 90% of all reason code responses. Why was there such a large discrepancy between the number of codes and the very few that were picked?
Instead of just taking the data at face value and using it to predict target audience potential sales activity, I decided to go and observe and interview the agents. I wanted to learn the ‘why’ of their selection behavior. In interviewing the agents, and conducting contextual observations, I learned Agents were extremely pressed for time. Thus they just picked one of the first reason codes in the list, instead of scrolling through the entire list. Furthermore, agents were not rewarded for the quality of the reason code response provided, just for providing a response. So picking any reason code was good enough, from their perspective.
Źródło: Jakob Nielsen Got It (Mostly) Wrong | Useful Usability.
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