Rep Data Adds Proprietary Hidden Variable Checks for Fraud Mitigation and Data Quality
Data collection firm for quantitative research adds to its already robust data quality measures with new exclusive method to screen out bots and automated scripts in surveys
Rep Data, a full-service data collection firm for quantitative primary research, has created a proprietary screening technique to further bolster its commitment to survey data quality. The new measure includes hidden text variables used during the respondent screening process. The variable, which appears as a possible answer in a multiple choice screening question, can only be accessed and viewed by bots. If selected, the bot will be terminated and removed from the respondent pool immediately.
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“We employ a layered approach to fraud mitigation at Rep Data that includes a mix of technology and human intervention,” said Patrick Stokes, CEO and Founder of Rep Data. “This latest layer is a new kind of ‘honey trap’ for bots and is one more way to ensure that every project we manage delivers the very best data quality to our clients.”
The new hidden text variables are enabled only in the background, and are not accessible or visible to actual respondents who are completing screening to enter a survey. Bots or other forms of automated scripts, which are created to scrape and automatically enter data, can access the hidden variable and are then screened out. This helps to reduce fraud.
In addition to this new proprietary technique, Rep Data employs a number of other fraud mitigation technologies:
- Digital Fingerprinting which examines potential respondents based on their past external activity before they engage in a survey.
- Text Analytics which measures and scores a respondent’s engagement in real-time by analyzing overall quality and thoughtfulness of open-end responses.
- Respondent Level Tracking which tracks a respondent’s rate of activity across the market research ecosystem, and is useful in flagging professional survey takers.
To ensure high-quality survey data these measures are layered with unbiased, efficient sourcing from multiple panels and sample suppliers to deliver more representative results; and expert project management for fieldwork. Rep Data conducted an in-depth research-on-research study last year and found that layering the technology driven techniques with sourcing broadly to minimize sample bias and close project monitoring resulted in significant quality improvements. The new proprietary hidden variable technique builds on these findings.
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