Quantified Search-Based Leading Indicators of Brand Health Across 7 Markets

The Setup

Brand marketers needed a higher-frequency way to monitor meaningful brand movement during high-spend campaigns. Survey-based brand trackers updated slowly (and often had low sample sizes), limiting the ability to make in-market adjustments. Data availability also introduced constraints: only ~3 years of history, staggered time series due to lags, and markets with quarterly or missing survey waves. Seasonality and trends required transformations to produce interpretable relationships.

Work Completed

Prepared and transformed daily search datasets for the brand and nine key competitors across seven global markets. Tested leading/lagging relationships using correlation analysis with multiple lags in both directions. Built and tuned multiple regression models to identify the most valuable search signals while accounting for seasonality/trend effects. Visualized and documented findings through an executive slide deck and a detailed R Markdown data story, including code delivery to enable long-term integration into existing internal models.

Results

Enabled a weekly review of meaningful brand-health movement (versus monthly or quarterly survey cycles) and introduced a practical leading indicator that marketers could use to adjust campaigns while in-market to improve spend efficiency. Established a predictive relationship among search, brand-health surveys, and commercial outcomes, allowing brand health to be used as an intermediate variable in marketing mix models—supporting more actionable understanding of drivers of both brand movement and sales.

Skills Exercised