We think the answer to this question is a resounding Yes. We put our hypothesis to the test recently with an extensive research-on-research project that pitted traditional market research data collection methods against new technology that is grounded in specific trust principles. We examined consumer behaviours in both ecosystems, and have a detailed paper on the results coming out in a few weeks.
In the meantime, we are offering a sneak preview of the study in an exclusive webinar. We hope you will join us on 29 July 2020 at 11am EDT for "Is Trust the Solution to Dirty Data?"
We already know that data quality issues in market research are persistent and pervasive. In the webinar we will explore a key question for the industry: "Have we been focusing on the wrong things to fix them?"
Technology has been applied and solutions have been developed to combat clear problems such as fraud, speedsters and professional respondents. It seems sometimes that as fast as we develop solutions, the challenges continue to grow, change and find ways around our mitigation techniques. Our research took on quality from a new perspective: consumer trust in market research.
We will provide an overview of our findings that show the right combination of technology and core trust principles can result in consumers behaving differently when they respond to data jobs. We believe that if the industry begins to shift its focus, there a chance that consumers and businesses can come to trust each other again.
During this webinar we will:
- Examine the impact on survey participation and behavior when introducing the trust principles of data sovereignty, privacy by design, fair reward and transparency.
- Dive into new research results that illustrate how respondent behavior changes in traditional vs trust-based environments.
- Understand how implementing trust principles can have far-reaching positive impacts on the market research industry and new data opportunities.
To register for the session, please visit: https://us02web.zoom.us/webinar/register/WN_ghMRIPBBRuOcDUnANd9GJw