From blue sky to ground reality
Winnowing dozens of options down to a select few that hold the greatest market promise is daunting. Qbit's advanced statistical analysis capabilities, such as TURF, enable teams to expeditiously pinpoint the most promising set of candidates. Critically, Qbit also gives you confidence knowing each option has been considered by real, cognitively engaged human minds. Whether it’s determining the optimal set of guest services or beverage flavors, Qbit can help you quickly and smartly decide with total peace of mind data confidence.
Rapid yet thorough titration
After using Qbit to confidently identify your leading 5-6 candidates out of dozens, Qbit next supports rapid deep diving to identify the 'top box' strongest. Many companies at this stage engage traditional qualitative methods (e.g. central location tests, online focus groups, etc.) to understand respondent thinking and decision reasoning at a depth of understanding that typical surveys simply cannot provide. While qualitative insights are invaluable, traditional qualitative methods are fraught with challenges ranging from regional bias, small sample size bias, group bias, and facilitator bias (for more, see our blog post.) Fortunately, Qbit lets you instantly engage respondents anywhere in the country, eliminating these biases. You can ask the same open-ended questions as you would in-person and respondents and, in turn, they can respond back in that most natural and authentic of human capabilities – simply by speaking. Further, you can easily blend quantitative with the qualitative thereby creating a seamless experience for the respondent. Here's an example set of steps:
Quantitative | Rank order attributes
Qualitative | Verbally explain reasoning
Quantitative | Rate likelihood to recommend (NPS)
Qualitative | Verbalize likes & dislikes
Example: Unraveling Cognitive Reasoning
Interact | View or experience a lead candidate
Quantitative | Respond to pricing questions
Qbit supports both monadic (each respondent is shown one lead candidate at random), monadic sequential (each respondent is sequentially shown several candidates at random) and forced-choice (each respondent sees all the lead candidates.) In the example image above, you might first ask respondents to rank order key purchase motivations (i.e. quant) and second to explain their reasoning (i.e. qual). Following this, you could reveal the candidate through digital images, video, or physical interaction (usage, experience, taste, etc.) and gauge initial reaction in the form of sentiment rating or NPS. This can be followed by open-ended verbal questions that ask them to voice their likes & dislikes or explain why they would or would not recommend to others. Finally, a series of pricing sensitivity questions can be used to zero in on the optimal price point.
Qbit effortlessly captures all of this rich, real-time, open-ended feedback and automatically analyzes it to give it structure and meaning. The qualitative is then presented side-by-side with the quantitative in a highly interactive insights dashboard so you can rapidly identify which candidate is most likely to succeed and why.