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Qualtrics Fuses Complex Data to ID New Business Objectives. A Rating
February 28, 2014

Qualtrics Fuses Complex Data to ID New Business Objectives. A Rating

Blaine Schultz | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User

Modules Used

  • Research Suite
  • Qualtrics 360
  • Research Suite
  • Qualtrics 360

Overall Satisfaction

Qualtrics was used for the analysis of marketing surveys conducted to determine brand positioning and strategy for a new online organization looking to optimize their current marketing and advertising well identifying new target markets on a global scale.
  • Qualtrics excels at correlating data points across various metrics to find important statistical data points such as group means, medians and outliers.
  • Qualtrics is an excellent tool for comparing disparate survey questions to gain meaningful insight, such as psychographic to demographic.
  • The visualization capabilities help users to find meaningful insight and patterns in a new way.
  • Loading information is user friendly and can easily be scaled across an organization to allow multiple users to become experts quickly.
  • The user interface is complex, it could be made simpler.
  • If data is not correctly correlated, there is no warning system to tell a user he may have created inconclusive results.
  • There is no recovery is the program crashes during use, everything is lost. Save often!
  • Helped ID new markets.
  • Positive ROI
  • Fast Implementation and reporting
Survey monkey was another option. It could not create new insights for us, Qualtrics blew it out of the water.
If I ever need it again,I will use it but for now my role has changed.
Qualtrics is very well suited for survey analysis. Some good use cases include identifying key demographics, finding the means within a data series, and eliminating outliers from reporting. These can then be fused to create interesting new relationships between metrics, such as your target markets mean age and their prime geographic location. A key question would be, will this be used to create new meaningful relationships between data sets? are you looking to fuse disparate data together?