In online research, sample quality is crucial for obtaining reliable and representative data. So ThinkNow has implemented rigorous security measures and a protocol for handling fraudulent samples to ensure data integrity. In this guide, we will explore the security measures we employ in our panel and how we address quality issues in completions.
Security Measures to Prevent Fraudulent Samples
Data integrity continues to be an ongoing concern within the online sample industry. However, some measures can be taken to effectively mitigate the risk of fraudulent samples, including:
- Fingerprinting: We employ fingerprinting technology to identify whether new registrations originate from distinct devices. This measure helps us effectively safeguard against the creation of multiple fraudulent accounts originating from the same device.
- Phone Validation: We randomly validate the phone numbers provided by panelists during the registration process. This enables us to verify the authenticity of the numbers and prevent fraudulent registrations.
- Email Verification: To confirm the authenticity of the email addresses provided during registration, we send a verification message to panelists. This measure helps us validate the genuineness of the email addresses and prevents registrations generated by bots or other fraudulent means.
- Geolocation: Using the geolocation of the IP addresses of new registrations enables us to determine the approximate location from which the registration is being made, helping us detect possible fraudulent or geographically inconsistent registrations.
Identifying and Handling Fraudulent Panelists
In addition to the aforementioned security measures, we have implemented a protocol for identifying and addressing fraudulent panelists in our sample, including the following:
- Ghost Completes: Panelists who attempt to modify tracking links or engage in fraudulent activities are quickly identified and removed. By encrypting panelist information and the ongoing survey, we prevent data manipulation and ensure the integrity of the panelists’ final status.
- Multiple Accounts from the Same IP: We closely monitor registrations and generate comprehensive reports to identify panelists who share or have shared the same IP address. This proactive measure enables us to promptly detect and address potentially fraudulent activities linked to multiple accounts originating from a single IP.
Protocol for Quality Issues
If samples exhibiting quality issues within certain completed surveys are delivered, we adhere to a strict reconciliation protocol in collaboration with the client to resolve the issue. Steps taken include:
- Identification of panelists with poor performance: We identify panelists exhibiting poor performance in terms of quality, such as providing inconsistent or invalid responses. Once identified, the appropriate actions are taken to address the situation.
- Removal of incentives associated with problematic completions: If a survey has performance issues, we remove the incentive granted to the panelist. By implementing this measure, we uphold the quality and reliability of our sample.
- Communication and feedback: We email the affected panelists, informing them of their survey performance and providing constructive feedback. Doing so encourages a high level of engagement among panelists and promotes continuous improvement and active involvement in future surveys.
ThinkNow takes pride in our commitment to ensuring the quality of the sample we provide our clients. We have implemented robust security measures like fingerprinting, phone validation, email verification, and geolocation which instill confidence in the authenticity and integrity of our data. In the rare event of a quality issue, our protocol enables us to promptly and effectively address challenges, safeguarding the quality of our sample. We are committed to providing reliable and representative results to our clients and continue to invest in the tools that enable us to do that.