Demystifying Pixel-Based Attribution Webinar
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Introduction: Hello everyone, thank you for joining today's webinar on pixel-based attribution for podcast advertising. This session is part of our Modern Measurement webinar series, and today, we aim to demystify how pixel-based attribution works. Whether you're familiar with it or new to the concept, we'll break down its methodology, mechanisms, challenges, and more.
What is Pixel-Based Attribution? Pixel-based attribution involves tracking user interactions through a small piece of code called a pixel. This pixel is embedded in both the website and the ad, allowing marketers to measure interactions like promo views, purchases, leads, and site visits. For digital platforms like Facebook, Google, TikTok, and Pinterest, this is essential to track ad effectiveness.
High-Level Overview: For podcast advertising, pixel-based attribution matches the IP address of the listener or downloader with the IP address of the purchaser. This method helps attribute conversions from podcast ads, despite the inherent offline nature of podcasts.
Challenges in Podcast Attribution: Unlike digital channels, podcasts are considered offline channels. When you listen to a podcast, the audio file is downloaded locally to your device via an RSS feed. This offline nature limits data and tracking capabilities, making it challenging to gather engagement data and user tracking information.
Traditional Methods: Historically, attribution for podcasts relied on promo codes, vanity URLs, post-purchase surveys, and show audience surveys. These methods, while useful, are indirect, delayed, and often result in limited accessibility and bottom-of-the-funnel metrics.
Pixel-Based Attribution Process:
Capture the Listener IP:
There are two types of pixels used: prefix and campaign (or impression) pixels. The prefix pixel captures the IPs of all episode impressions, while the campaign pixel is used for impression-based buys and captures IPs for each ad impression.
Capture the Conversion IP:
This involves installing a pixel on the advertiser’s site, which tracks user interactions like site visits, purchases, and more. This step is similar to other digital marketing channels.
Match the IPs:
The IP addresses of the downloads and conversions are matched to attribute the conversions.
Handling Noisy IPs:
Static vs. Noisy IPs:
Static IPs are fixed and easier to match, while noisy IPs are shared among multiple users (e.g., public WiFi, cellular networks).
Modeling:
To handle noisy IPs, a conversion rate from static IPs is applied to the number of noisy IPs, creating a modeled performance estimate.
Device Graphs: Device graphs link individuals to their personal devices, improving match rates for cross-device conversions. Podscribe uses Tapad, part of Experian, to enhance matching accuracy by linking IPs across different devices.
Flexibility and Control: Podscribe offers flexibility and control in attribution settings. You can adjust attribution windows, select different attribution models, and exclude certain data sources. This ensures the data is tailored to your specific needs and more accurate for your brand.
Assessing Accuracy: Accuracy can be assessed by triangulating data from different sources like promo codes, vanity URLs, and post-purchase surveys. Comparing these with pixel-based conversions helps ensure a comprehensive understanding of ad performance.
Choosing an Attribution Provider: When selecting an attribution provider, consider factors like IAB certification, publisher coverage, independence, flexibility, control, and real-time reporting capabilities.
Impact of Cookie Deprecation: Pixel-based attribution for podcast advertising is not affected by cookie deprecation, as it relies on IP addresses rather than cookies. Podscribe’s pixel uses first-party cookies and hashed emails to maintain accuracy.
Future Developments: Upcoming advancements include improved device and identity graphs, enhanced incrementality testing, and new integrations with multimedia platforms. Podscribe is also exploring attribution for terrestrial and local radio.
Conclusion: Pixel-based attribution for podcast advertising involves capturing and matching IPs, handling noisy IPs through modeling, and utilizing device graphs for cross-device matching. It offers a flexible, controlled, and accurate way to measure podcast ad performance. By understanding this methodology, advertisers can better leverage podcasts for their marketing strategies.
Thank you for joining this webinar. If you have any questions or need further assistance, feel free to reach out. Keep an eye out for future webinars in our Modern Measurement series.