What factors are contributing to the sudden 30% drop in data quality scores for Kantar's TGI consumer survey in the UK market?

Introduction

The sudden 30% drop in data quality scores for Kantar's TGI consumer survey in the UK market is a critical issue that demands immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term and long-term implications for Kantar's product and market position.

I'll approach this problem by first clarifying key details, ruling out external factors, and then diving deep into the product, metrics, and potential internal causes. We'll generate data-driven hypotheses, conduct root cause analysis, and develop a comprehensive plan to resolve the issue and prevent future occurrences.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking this could be related to recent changes. Has there been any significant update to the survey methodology or data collection process in the past month?

Why it matters: Recent changes often correlate with sudden metric shifts. Expected answer: Yes, we updated our online survey platform two weeks ago. Impact on approach: If confirmed, we'd focus on technical issues related to the new platform.

• Considering the magnitude of the drop, I'm wondering about data segmentation. Are we seeing this 30% drop consistently across all demographic segments, or is it more pronounced in specific groups?

Why it matters: Uneven impact could point to specific user experience issues. Expected answer: The drop is more significant in the 18-34 age group. Impact on approach: We'd investigate factors specifically affecting younger respondents.

• Given the nature of consumer surveys, I'm curious about seasonal effects. Is this 30% drop compared to the previous month, or is it year-over-year?

Why it matters: Seasonal variations can sometimes explain data quality fluctuations. Expected answer: This is a month-over-month comparison, but we've never seen such a drop before. Impact on approach: We'd rule out seasonality and focus on recent changes or issues.

• Thinking about data quality, I'm wondering about our validation processes. Have there been any changes to how we measure or define data quality in the past quarter?

Why it matters: Changes in measurement can sometimes be mistaken for actual metric changes. Expected answer: No, our data quality measurement process has remained consistent. Impact on approach: We'd focus on actual changes in data rather than measurement artifacts.