Unconventional Data Analysis: Exploring Google Trends

Measurement Talks

In this episode, the hosts explore the Q1 earnings season, focusing on Meta's unique standing as the only stock that dropped while others surged. They delve into Wall Street's repeated underestimate of Zuckerberg's strategies and pivot to a deeper analysis of Meta's ad metrics. Insights on the cost of impressions, daily and monthly active users, and ad pricing trends across different regions are shared, highlighting the sustained user growth on Meta platforms despite public skepticism. The discussion also covers the strategic implications of ad cost changes in North America, Europe, Asia Pacific, and worldwide, providing listeners with valuable data for planning international expansions and understanding market demands. Additionally, the episode touches on the importance of understanding profitability versus profitable growth, the significance of maintaining market presence, and innovative ways to utilize Google Trends for marketing strategy adjustments.

00:00 Diving Into Q1 Earnings: Meta's Unique Position

00:51 Deep Dive: Analyzing Ad Metrics and User Growth

01:40 The Impact of Ad Impressions and Cost Changes Across Regions

06:16 Understanding Consumer Sentiment and Marketing Strategies

06:58 The Art of Balancing Profitability and Growth in Marketing

12:04 Leveraging Google Trends for Strategic Marketing Insights

21:24 Exploring Channel Incrementality and Testing New Marketing Channels

34:08 Concluding Thoughts on Marketing Strategies and Channel Testing

Bryan was the Director of Efficacy at Kargo and directly helped answer this question for many brands through his work there. Now, he leads marketing for one of the fastest-growing, profitable DTC companies, Nood. 

Rishabh led new business at LiveRamp, the largest provider of online digital identity, and directly helped scale many measurement and attribution programs for the ad tech ecosystem. He is now the CEO of Fermat Commerce, a commerce platform that enables content-native shopping that has no attribution loss.

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