If you are searching for a tool that converses with consumers and then returns a digestible summary of the data—look no further than GroupSolver. Their feature, Idea Pool Manager (IPM), does all of this and more in real-time. This revolutionary AI survey technology adapts as data collects, so that it can provide a concise summary of the insights to researchers.
Put simply, as survey respondents answer an important open-ended question by submitting their free-text answers, the IPM learns the structure and granularity of the answers. While doing so, it adapts and interprets respondents’ language, grouping together answers that convey similar ideas. IPM then presents the most relevant respondent ideas to researchers, summarizing important answers from the data without being redundant.
Adrian Del Bosque, GroupSolver’s Research Team Lead, compares IPM to a car with an automatic transmission, “The algorithm will shift gears depending on the circumstances. Once the first set of responses is submitted, IPM will decide whether it needs to shift into a higher or lower gear in reaction to how steep a hill it is climbing. Depending on the complexity and similarity of the language respondents use to answer the question, IPM adjusts how hard it needs to work to summarize the data. In the end, this technology will serve researchers clearer final answers and a cleaned-up summary of the data.”
Rasto Ivanic, the co-founder and CEO of GroupSolver, adds, “With many text analytics tools, even if they use more advanced technology than simple frequency analysis, researchers still play a big role in deciding which answers should be combined into themes or excluded from the insights. These methods are subjective and take a lot of work. Our algorithm takes potentially biasing discretion out of the equation – it makes the process repeatable, predictable and quick.”
With the addition of Idea Pool Manager, GroupSolver’s AI powered survey automates the analyses needed to achieve the ‘Mutually Exclusive Collectively Exhaustive’ framework, lifting weight off researchers’ shoulders and allowing them to focus on what really matters – the story that their data is telling.