Comprehensive Guide to SNUHI's Reconciliation Process

A Cornerstone for Accuracy, Transparency, and Trust

Snuhi’s reconciliation process serves as the backbone of data integrity and fairness on our platform. By employing robust methodologies and advanced technologies, Snuhi ensures a seamless alignment between survey completions and compensation while fostering a trusted ecosystem for suppliers and clients alike. Below, we delve into the intricate details of Snuhi's reconciliation process, its components, and the measures in place to maintain quality, transparency, and security.

1. Objective of Reconciliation:

Reconciliation is pivotal in identifying discrepancies, ensuring only valid completions are rewarded. This process eliminates fraudulent submissions, maintains data accuracy, and fosters a fair compensation system. By focusing on quality assurance and rigorous verification, Snuhi reinforces the trust between suppliers and clients.

1.1 Key Goals:

• Align survey completions with supplier reports and client expectations.
• Provide transparency through detailed discrepancy analysis and feedback.
• Prevent fraudulent activities and ensure data quality for actionable insights.

2. Types of Reconciliations:

Snuhi has developed a structured approach to reconciliation to cater to different phases and scenarios:

• Initial Reconciliation: Focuses on quality evaluation during the Test Launch phase with weekly reviews.
• Routine Reconciliation: Conducted biweekly for Full Launch suppliers, ensuring consistent data monitoring.
• Targeted Reconciliation: Addresses specific issues, such as spikes in invalid traffic or flagged activity patterns.
• End-of-Cycle Reconciliation: A comprehensive final review to close out campaigns or engagements accurately.

Reasons for Reconciliations After Survey Completion

Snuhi ensures the quality and integrity of survey data through a robust reconciliation process. Below are the reasons why a survey marked as completed may be subject to reconciliation or termination:

• Demographic Mismatch: The respondent's profile does not match the survey's target audience criteria.
• Inconsistent Responses: Contradictory answers within the survey, such as differing ages or incomes.
• Straight-Lining: Selecting the same answer repeatedly, indicating disengagement.
• Gibberish or Irrelevant Open-Ended Responses: Open-text answers that are nonsensical or irrelevant.
• Failure to Pass Attention Checks: Ignoring embedded instructions, such as "Select option B."
• Duplicate Responses: Multiple submissions from the same user or device, indicating potential fraud.
• Incomplete Responses: Missing required data despite the survey being marked as complete.
• Bot or Fraudulent Activity: Suspicious patterns, such as unusual IP addresses or automated behavior.
• Improper Use of Skip Logic: Incorrect navigation through the survey, leading to skipped or irrelevant sections.
• Quota Overlap or Errors: Responses fall into categories where quotas are full or not required.
• Device or Browser Incompatibility: Surveys completed on unsupported devices or browsers.
• Timing Irregularities: Completion times outside the allowed range, such as too fast or too slow.
• Survey Design Errors: Issues with the survey setup, such as broken logic paths or misprogrammed questions.
• Unethical Behavior: Attempts to manipulate the survey for incentives, such as providing false information.
• Non-Human Behavior Indicators: Automated behavior, like consistent completion times or unusual click patterns.
• Completion Time: Surveys must be completed within the allowed time range, typically a minimum of 12 minutes and a maximum of 45 minutes.
• Pause During Live Survey: A pause of more than 2 minutes during the survey may mark it as invalid.
• Speedy Completion: Completing the survey too quickly compared to the average time indicates low-quality responses.

Platform Average Reconciliations and Maximum Permitted Rates

Snuhi follows average reconciliation rates for each region, which may vary over time. Suppliers are advised to stay in touch with their account managers for updated rates. However, the average rates must not exceed the maximum permitted reconciliation rates. Low data quality flagged or high reconciliation rates may result in:

• Pausing traffic to specific markets.
• Temporary pause of traffic to all markets.
• Permanent pause of traffic and blacklisting of supplier accounts.

If flagged, payments will be reconciled no sooner than 90 days from the last survey completion, irrespective of any agreed terms.

Reconciliation Rates by Region

Region	Average Reconciliation Rate	Maximum Permitted Rate
North America	7%	20%
LATAM	10%	15%
MEA	10%	20%
APAC	10%	20%
India	12%	25%

3. Quality Standards and Verification:

Snuhi implements stringent quality controls to safeguard data integrity. These include:

• Traffic Monitoring: Verifying IP locations, device fingerprints, and traffic sources to ensure authenticity.
• Duplicate Detection: Screening for repeat entries using advanced algorithms and data fingerprinting.
• Survey Completion Validation: Analyzing completion times, response consistency, and engagement metrics.
• Behavioral Analysis: Identifying abnormal patterns, such as bots or incentivized traffic, using machine learning tools.

4. Security Measures and Fraud Prevention:

The security framework at Snuhi ensures that all data interactions meet the highest standards:

• IP and VPN Restrictions: Blocking flagged or suspicious IPs to prevent unauthorized access.
• Real-Time Anomaly Detection: Identifying and mitigating unusual activities during live campaigns.
• Multi-Layer Authentication: Ensuring that all platform interactions are secure and verifiable.

5. Reconciliation Timeline:

Snuhi tailors its reconciliation schedules to the supplier's lifecycle stage:

• Test Launch: Net60 reconciliations with weekly feedback to promptly address quality issues and ensure smooth onboarding.
• Full Launch: Net60 reconciliations with Biweekly feedback cycles allow for detailed quality checks and adjustments.
• Critical Reconciliations: Immediate reconciliations triggered by quality or performance concerns.

6. Reporting and Transparency:

Detailed reports are a cornerstone of Snuhi's reconciliation process, offering clarity and actionable insights:

• Completion Metrics: Breakdown of valid, invalid, and flagged completions.
• Discrepancy Insights: Detailed explanation of identified discrepancies and their impact on compensation.
• Supplier Feedback: Recommendations for improving completion rates and quality standards.
• Chargeback Overview: Transparent summary of adjustments applied to invalid responses.

7. Payment and Chargeback Policies:

Payments follow a Net60 timeline, promoting a balance between supplier needs and data validation. Chargebacks are applied for discrepancies caused by invalid responses, ensuring that compensation reflects actual performance and quality.

7.1 Guidelines for Chargebacks:

• Clear documentation provided for every adjustment.
• Disputed chargebacks reviewed through a transparent audit process.

8. Auditing Process:

Audits ensure that reconciliation processes remain fair and effective:

• Data Sampling: Randomly selected data entries reviewed for consistency and accuracy.
• Anomaly Review: Investigating discrepancies that deviate significantly from expected metrics.
• Corrective Action Plans: Providing actionable recommendations to address recurring quality issues.

9. Benefits of Snuhi's Reconciliation Process:

Snuhi’s reconciliation process delivers multiple benefits:

• Improved Data Quality: Ensures accurate and reliable insights for clients.
• Supplier Empowerment: Detailed feedback fosters growth and continuous improvement.
• Fraud Prevention: Proactively identifies and mitigates fraudulent activities.
• Transparency: Open reporting builds trust and long-term partnerships.

Conclusion:

Snuhi's reconciliation process exemplifies our commitment to excellence, fairness, and integrity. By employing advanced verification, detailed reporting, and regular audits, we ensure a trustworthy ecosystem that benefits suppliers, clients, and the broader survey community.