One of the largest retailers in the U.S. was in the process of re-evaluating how they measure and track their customers’ satisfaction across all departments and the entire experience while shopping. The number of attributes being considered needed to be measured and narrowed down to a feasible list for quarterly tracking. 11 different departments were assessed and upwards of 200 attributes for each department needed to be tested.
While traditional measures of importance such as ratings and rankings are subject to scale-use bias and would not be feasible given the number of attributes, another method needed to be applied. While derived importance using regression was an option the linearity assumptions for the data would weaken the model and also the survey length would not be feasible.
IDG designed 11 different Max/Diff (Maximum difference scaling) studies to measure the importance of the attributes and help to narrow them down to the most impactful for tracking. Additionally, selecting the final attributes simply by the most important ones does not provide the best mix of attributes to be tested due to possible duplication among the importance. By taking the Max/Diff data and running TURF (Total Unduplicated Reach & Frequency) optimizations, the ideal attributes for tracking were identified assuring maximum coverage regarding what was important to customers.
