Truth and Accuracy Are Still Major Issues

To think we can ever achieve 100% truth and accuracy in any research is of course pure folly, but there are ways we can minimize the fraud and noise of the participants and resilience and qualitative value of the insights.

Audience fraud within Panel Platforms

2020 saw its largest spike in research fraud with some surveys reporting fraud levels greater than 30 per cent.

What if we can turn our passionate audiences into panels providing a funnel of qualitative insight on a quantitative scale? What if we could provide a social and smart environment for the consumer that is lightyears from the presently austere survey experience or chaos of legacy social media?

Create a Panel You Understand, Trust, and Control on an Industrial Scale

Social Listening Analytics

Quality of input will always affect the accuracy of results obtained, and historically, scraping social media never really provides social listening platforms with the resilience and qualitative data to provide operationally relevant insights in the now.

At the same time relying on just NLP/AI to analyze is always fraught with the chance of misconstrued. The solution is to focus on the consumer and help them supply as much insight as possible within a framework that encourages transparency and collaboration with the brand/researcher.

Using Qutee’s platform, we ask participants to augment their comments via intent tagging ( can be sentiment-based or what ever descriptors optimize the research ) then we apply the NLP engine.

This provides with resilient analysis of the comment overcoming the issue of ambiguities and issues that occur with lengthy, acronym led comments.

At the same, our AI pulls representative comments for each of the intent tags using a proprietary methodology.

Combine these compelling insights with polls and NPS within an automated report and you are saving teams weeks of analysis and creation.


Qutee’s passionate audiences overcome the issue of anonymous and non-resilient insights and fraud.

The combination of human intent tagging and natural language processing results in accuracy that far exceeds any Social Listening sentiment analysis, which means automated reporting removes the need for any long form human analysis.