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Mobile Dev Memo Podcast
Season 2, Episode 5: How to measure a marketing campaign (with Olivia Kory)
Dec 6, 2023
Olivia Kory, Head of Go-To-Market at Haus and former senior marketing roles at Netflix, Quibi, and Sonos, discusses the role of causal inference in marketing measurement, skill set gaps in transition to probabilistic measurement, challenges faced by marketing teams, and the concept of probabilistic measurement for non-marketing teams. They also explore challenges in measuring marketing campaigns, understanding app store search prioritization and ad targeting, and the shift to probabilistic attribution in marketing. Olivia shares her experience transitioning to a measurement company and the impact of leading on incrementality work.
43:40
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Quick takeaways
- Causal inference allows marketers to determine the true impact of their interventions and understand the effectiveness of their marketing efforts in terms of cost per acquisition and channel performance.
- Transitioning to probabilistic measurement requires overcoming skill set gaps, educating finance teams about the benefits of probabilistic measurement, and having data science and analytical resources in place.
Deep dives
The Role of Causal Inference in Marketing Measurement
Causal inference is a scientific method used in marketing measurement. It aims to establish a cause-effect relationship by creating conditions for experiments. Unlike traditional attribution models based on correlation, causal methods focus on determining the actual impact of interventions or treatments on desired outcomes, such as sign-ups or app downloads. Causal inference provides insights into the effectiveness of marketing efforts, revealing the true cost per acquisition and uncovering the channels that deliver the best results. It helps businesses understand the true value of their marketing efforts and make more informed decisions.
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