Ensuring data quality has become an ever greater concern as market researchers grapple with the need to distinguish between genuine consumer responses and automated or fraudulent inputs. Identifying and mitigating such data distortions are now essential to maintaining the reliability and effectiveness of market research efforts in an increasingly digital and interconnected environment.

Third Party Verification: Getwizer Defeats Bots and Non-Relevant Respondents

Confidence in our tools and tactics to ensure data quality is not only important for our clients, but also for us. That is why we used a third-party vendor to insert code into Getwizer’s recent surveys to track over 150 browser metrics from respondents. The results found the following:

Getwizer is able to achieve these results using its unique 3-layered data quality approach to ensure the highest levels of research data quality are maintained. This involves:

#1 Collaborating exclusively with top-tier panels

Getwizer only collaborates with top-tier panels with a proven track record of strict adherence to stringent security protocols. These panels are systematically monitored to ensure the highest levels of professional and security standards are consistently maintained. Once a panel is selected, we then use a comprehensive review process, encompassing both automated assessments and human evaluations. Our team of Wizers can then actively identify and block any panels or panelists that do not meet the established security and quality criteria.

#2 Systematically scrutinizing survey responses 

Getwizer’s approach to data cleansing involves employing a variety of techniques, including profile and location-based validation. This underlying strategy enables us to identify and disqualify fraudulent, dishonest, and ghost respondents in real-time. By implementing these multiple validation methods, we ensure the integrity and reliability of the data collected, providing a foundation for accurate and trustworthy market research insights.

#3 Real-time data analysis and cleansing

During the research, automated red flags are used to tag potential issues, such as a sample flowing in too fast, high drop-off rates from the survey, missing demographic profiles, and other problems that can raise concerns about the collected responses.

Getwizer’s multi-language, real-time cleansing algorithm automatically identifies gibberish, rectifies typos, recognizes name variants and comprehends slang. It also automatically disqualifies straight-liners, pattern drawers and speeders. Once these are eliminated, the outcome is a refined and usable dataset, achieved in a fraction of the time and cost associated with manual processing.

Getwizer also uses Natural Language Processing (NLP) to analyze text in their open-ended responses, ensuring relevance and readability.

Safeguarding the Integrity of Research Data

Maintaining data quality in market research is a significant challenge but one Getwizer meets head on. This is because we know poor or “dirty” data compromises the integrity of the entire research dataset and the insights derived from it. Getwizer addresses this issue through real-time automation of data cleansing, ensuring high quality, cost-effectiveness, eliminating the need for manual labor, and reducing the overall time spent in the field.

Discover how Getwizer can boost the quality of your research data by booking a demo today.