Improving Consistency in Longitudinal Research

Using Synthetic Modeling to Fill Dropout Gaps and Maintain Tracking Integrity

The Challenge

Maintaining Data Integrity in Longitudinal Research
A global FMCG client partnered with DataDiggers to conduct a year-long longitudinal tracker focused on consumer attitudes toward sustainable packaging across 12 countries. While initial wave participation was strong, dropout rates began to rise steadily across subsequent waves—particularly in low-incidence regions and younger demographic cohorts.

Despite high panel quality and rigorous respondent validation via our MyVoice proprietary panels and partners, natural attrition is inevitable in multi-wave research. For this client, even a 15% dropout rate risked distorting time-series analysis and compromising cross-wave comparisons. Worse yet, re-contacting lost participants wasn't viable due to opt-outs and GDPR compliance restrictions.

The client's central concern:
"How can we maintain trend integrity and compare results meaningfully across waves, despite growing attrition?”

The Solution

Synthetic Modeling with Modeliq & Correlix to address the missing-data dilemma without sacrificing data validity, DataDiggers deployed a hybrid solution using its proprietary synthetic data engines: Modeliq and Correlix.

• Modeliq applied advanced synthetic modeling to simulate responses from lost participants, based on patterns in their past answers and adjacent peer groups.
• Correlix was used to calibrate and validate the synthetic outputs using bias correction and variance control mechanisms, ensuring alignment with real-world data distributions.

This approach followed a strict integrity framework:

• ✅ Only demographic and behavioral variables from verified waves were used for model training
• ✅ Bias checks ensured that synthetic data did not disproportionately reflect dominant segments
• ✅ Each imputed data point was marked and tracked to preserve transparency and allow auditability

By applying this method, we filled dropout-related data gaps while preserving the statistical structure of the original panel population across waves.

The Results

Improved Continuity, Valid Comparisons, Zero Compromise
The results exceeded expectations on multiple fronts:

• 90%+ trend line continuity was retained in key KPIs (e.g., awareness, purchase intent, packaging preferences)
• Synthetic-augmented data showed <3% variance when tested against actual sample refreshes in two pilot markets
• Client reporting cycles remained uninterrupted, and stakeholder confidence in the tracker remained high

Notably, the client appreciated that no artificial inflation or smoothing occurred—thanks to Correlix’s transparent, audit-ready data augmentation logic. By bridging the gaps responsibly, we avoided the common pitfall of overfitting or statistical distortion that plagues simpler imputation methods.

Why It Worked

The DataDiggers Difference
This case showcased the distinct advantages of DataDiggers’ integrated research ecosystem:

• Proprietary global panels (MyVoice) ensured high baseline data quality
• Brainactive platform enabled real-time monitoring of dropout trends
• Modeliq and Correlix delivered powerful, compliant, and trustworthy synthetic modeling at scale
• ISO 20252:2019 certification and GDPR adherence ensured full compliance and transparency

Most importantly, the solution was collaborative, explainable, and scalable. The client now plans to use the same synthetic modeling strategy for future trackers in even more challenging markets.

Final Thought

‍As longitudinal research becomes increasingly essential for tracking shifting consumer sentiment, attrition and panel fatigue remain persistent threats. With DataDiggers’ synthetic modeling capabilities, powered by Modeliq and Correlix, brands can overcome these challenges—without sacrificing data integrity or research credibility.

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Need to safeguard your tracking study against dropout gaps?
Let’s talk about how synthetic modeling can keep your trends reliable, your insights trustworthy, and your decision-making confident.