Brooklyn

March 5-6, 2019

Brooklyn
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How AT&T Gained Accuracy and Reduced the Respondent Burden in New Product Research

Room 2 | 4:00-4:30 | Wednesday, March 6, 2019

AT&T’s John Voda, in conjunction with KS&R, will detail the workings of a technique to improve accuracy and make the task of researching new products and services less cumbersome for respondents. The methodology is a two-step approach that combines discrete choice and Q-sort to yield a stronger result when a long list of features needs to be tested than forcing everything into a potentially burdensome discrete choice exercise.

There have been numerous ways proposed to augment discrete choice data when it involves an attribute with many levels such as a list of promotional items or possible features. These include progressive build tasks, partial profiles, tournament tasks and data augmentation using a max-diff or Q-sort exercise. The presenters will propose an alternative solution to this problem which lies in breaking the standard choice task into two simple tasks, a positive Q-sort exercise (or max-diff) and a reduced set of discrete choice scenarios with fixed sets of features. This approach relieves the strain on the respondent and results in a more accurate assessment of each feature at the individual level.


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