12-05, 14:30–15:00 (UTC), General Track
The paid search landscape is undergoing a remarkable transformation, evolving from traditional keyword-centric strategies to a more nuanced approach that prioritizes audience targeting. This shift is not just a trend; it’s a response to the ever-increasing demand for precision and effectiveness in reaching potential customers in a crowded digital marketplace.
At the forefront of this evolution is our innovative automated system designed to identify high-intent users through sophisticated batch processing of their website behaviour. By harnessing the power of machine learning, we create a dynamic layer that curates smarter audiences those that closely resemble our most valuable converted customers. This enables us to execute precise retargeting campaigns that not only drive meaningful engagement but also optimize marketing budgets, resulting in enhanced audience selection and significantly higher conversion rates.
The paid search landscape is experiencing a seismic shift, moving away from conventional keyword-centric strategies to embrace a more sophisticated approach to audience targeting. This transformation is driven by the need for greater precision and effectiveness in reaching potential customers, particularly in an era where consumer behaviour is constantly evolving.
In this session, we will explore our groundbreaking method for identifying high-intent users based on their behaviour on our website. This approach transcends surface-level interactions, employing a dynamic learning paradigm that continuously refines audience profiles. By focusing on users whose behaviour closely mirrors that of our converted customers, we can enhance engagement and drive conversions more effectively.
Key Discussion Points:
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Data Sources: We will dive into the various data sources available for capturing user interaction events, enabling us to gain deeper insights into customer behaviour.
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Complexity in Data Preprocessing: Managing and processing millions of daily interactions presents significant challenges. We’ll share insights into how we navigate this complexity, employing advanced data preprocessing techniques to extract meaningful insights that inform our targeting strategies.
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Class Probabilities: We’ll focus on the significance of class probabilities over traditional classification labels. This innovative approach allows us to filter and identify the most promising audience segments, ensuring our campaigns are directed towards users with the highest potential for conversion.
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Automation Techniques: Discover practical strategies for automating the customization of audience targeting through the Google Ads API. We’ll demonstrate how automation streamlines campaign management, freeing up valuable time and resources while enhancing targeting precision.
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Prospective Targeting: Our approach doesn’t stop at retargeting; we’ll discuss how we expand our reach to capture new audiences. By leveraging insights gained from high-intent users, we can effectively broaden our targeting scope in search engine advertising.
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Advantages and Disadvantages: Finally, we’ll provide a balanced evaluation of the benefits and potential drawbacks of these advanced targeting strategies. Understanding the implications of our approach is essential for making informed decisions in the fast-paced world of digital marketing.
Previous knowledge expected
I am a Data Scientist at Sixt SE, where I specialize in marketing technology projects designed to optimize campaign return on ad spend (ROAS), identify high-value customers, and predict churn. With four years of professional experience in the field, I leverage advanced analytical techniques to drive data-informed decision-making and enhance marketing strategies. I hold a degree in Computer Science from the University of Passau, with a particular focus on applying transfer learning methodologies to the LegalTech sector. My interdisciplinary background equips me with a unique perspective on integrating data science principles into diverse business contexts.