How Spiketrap’s AI solution helped Rival accurately analyze large amounts of data while reducing costs and saving time

The study had over a thousand participants across the USA, which generated over ten thousand responses. Undertaking a manual data analysis task with thousands of responses is extremely time consuming, expensive, error-prone, and hard to scope (as human input is not straight forward to determine up front). As a result, these datasets are often left unanalyzed, and if they are, it is done quite poorly and offers little insight.

This is where Spiketrap came in. Spiketrap’s data processing capabilities allowed Rival to extrapolate core themes for all the open-ended data collected. These themes were then automatically matched with corresponding statements paired with sentiment. This automatic process produced faster, better & cheaper results than any manual effort would have returned. Additionally, the software's NLP capabilities allowed for a deeper, more robust level of sentiment analysis that is not possible when using basic Likert scales. 

Overall, Spiketrap’s AI/ML solution was able to accurately analyze the open-ended responses at scale, while reducing resources needed and ultimately streamlining the data analysis process.