Reading articles about market research and listening to what research buyers tell us they need, there continues to be a hunger for research to: 1. Capture authentic emotion 2. Deliver actionable diagnostic insight 3. Inspire the confidence to act with robust findings. MM-Eye has created a hybrid qual-quant research technique that does all 3: 1. Collecting emotionally rich qual data at scale 2. Converting it into quant data 3. Using a next-gen AI-powered text analytic approach we have custom designed for market research. It is called ThoughtScape and we would like to share our approach and the learnings we have gained along the way. ThoughtScape is theoretically robust, being grounded in the latest neuroscience understanding of how the brain works. It is also backed up by a decade of use, having been the core of the global brand research we undertake for Jaguar Land Rover. And to illustrate the unique insight ThoughtScape delivers we've conducted a demonstration study. The topic we have chosen is airline brands and the study directly contrasts two matched samples; one using traditional survey approaches, the other including ThoughtScape. And we'd like to share these results to demonstrate the proof of concept.
Sell-selected surveys on the Web are extremely cost-attractive however they lack a valid statistical inference. Similar to quota samples it is only the empirical validation that can give them some justification. The paper describes the empirical comparisons of the results from a self-selected Web survey and from a telephone survey that were performed simultaneously within Research on Internet in Slovenia (RIS) a national project. The analysis shows that the majority of variables are relatively robust however for certain topics the inference can be performed only within specific subgroups of Internet users and even there the available weighting adjustments have only a limited effect in correcting the estimates. The design issues of Web surveys are also discussed in the paper and the length of the questionnaire was found particularly important.