Innovation, opportunity and growth all start with accurate insights. Those insights come from real people, engaged and ready to respond, delivering data you can trust. Dynata partners with clients to establish and implement data quality best practices. Data quality is managed at multiple levels. Have a look to see how we do it.
Welcome Survey – Real-time panelist behavior evaluation conducted via Dynata screening tools
(QualityScore). Captures key data and tests behavioral elements as well as passive elements (mouse movement, speed, copy and pasting) to assign a quality score before respondents touch a survey. Scores below 20 are removed from the panel immediately.
Building a digital identity – Dynata validates panelists at sign-up, using proprietary technology that combines Imperium ReleventID, RealAnswer and RealMail. Digital identity validation and reputation monitoring create a lifetime quality profile for each respondent.
Real-time source monitoring – Dynata leverages machine learning (ML) tools to identify and stop unnatural changes in traffic. Dynata automatically prioritizes sources delivering higher-quality responders.
QualityScore – Automated multi-point system utilizes ML model to evaluate 20-plus data points to determine if a responder should be included in the final data set when Dynata programs the survey.
Encrypted end links – Signed end links and server-to-server security allow us to securely pass panelists to and from survey platforms, while protecting against opportunistic bad actors attempting to manipulate the survey URL (available on Dynata’s platforms and other major platforms).
Quality control checks built into survey – Customized checks adhering to AAG best practices help mitigate fraud and identify low-quality responders. These can be tailored to your survey content, accounting for your audience.
Post-survey cleaning – Manual review of responses for suspicious patterns of response or fraud and removal of poor-quality verbatim responses. Removals, with reasons, are tracked to facilitate panel maintenance and improve the ML models based on actual, valid completes.
Two-factor phone authentication and claims delay process – Automated phone validation takes place before rewards can be claimed and immediate reward delivery for newly opened accounts is prevented.
Pre-redemption account review – Model-driven validation for suspect responders requires additional authentication before being able to claim rewards. This review continuously improves the model as the valid cases dataset continuously grows.