Why Podcasting Advertising is a Treat in a Cookieless World

With third-party cookies crumbling in digital advertising, marketers are wondering how to adapt. 3P cookies have been essential for tracking users across the web, allowing targeted advertising with accurate attribution and reporting.

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So what’s an advertiser to do now there’s no more hands in the cookie jar? Amidst this shift, podcast advertising is rising to the occasion, serving up a feast of first-party data and contextual relevance that's leaving marketers hungry for more. 

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We've conducted an in-depth interview with Matt Drengler, Head of Revenue at Podscribe, a leading provider of measurement and attribution for podcast, streaming, YouTube, and CTV advertising. Matt's expertise sheds light on how podcast advertising isn't just adapted to the cookieless era— it's thriving in it.

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Join us as we uncover the reasons behind podcast advertising's success and its potential to reshape the future of digital marketing in a privacy-first world.

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Introduction to Podscribe

Can you give us a brief introduction to Podscribe and the services you offer in the podcast advertising space?

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We offer a suite of services designed to help advertisers, publishers, and agencies optimize their podcast ad campaigns and achieve their marketing goals. Our advanced measurement solutions include pixel-based attribution, incrementality and conversion lift testing, and data-backed media planning. Additionally, we provide automated airchecks and verification, along with competitive intelligence, research, planning, and discovery tools, ensuring accurate performance tracking and strategic insights for effective podcast advertising.

Podscribe doesn't just tell you what happened; we show you why it happened, providing actionable insights to improve your podcast advertising efforts. Whether you're looking to measure performance, verify delivery, or gain competitive intelligence, Podscribe has the tools and expertise to help you succeed in the podcast advertising space​​​​.

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Your product helps advertisers measure the impact of their podcast ad campaigns through pixel-based attribution. Can you explain how this technology works?

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Podscribe's pixel-based attribution technology measures the impact that podcast ad campaigns have on an advertiser’s website or app through the lens of  views, installs, signups, leads, and purchases. Here's how the technology works:

• Set Up Data Streams: Publishers integrate the Podscribe tag, and advertisers install tracking on their sites or apps through a pixel or a Mobile Measurement Partner (MMP)  integration.
• Collect Data: With the data streams set up, Podscribe collects IPs from the ad exposed listeners, and IPs plus other first party data on the purchasers on site. 
• Match Households: Podscribe matches those IPs and first-party data points to households so we can start to see which ads drove results.

This approach matches conversions to ad exposures, providing detailed, real-time reports with metrics familiar to digital advertisers.

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What challenges do advertisers typically face when measuring the impact of podcast ads, and how does pixel-based attribution address these challenges?

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• Advertisers face several challenges with traditional methods of podcast ad measurement primarily that these methods are indirect and reliant on users 
• User Dependent: Traditional methods rely on listener memory and willingness to participate, introducing bias and error.
• Promo Code and URL Flaws: Promo codes can be leaked and misused, and listeners may misremember or incorrectly enter vanity URLs, leading to tracking losses.
• Delayed Insights: Traditional methods lack real-time data, delaying campaign adjustments and capturing only final conversions, thus missing the full marketing funnel.
• Limited Accessibility: Not all advertisers can implement or benefit from these methods due to technological or business model constraints.Incomplete Tracking: These methods fail to monitor the complete listener journey from ad exposure to action, not considering other ads that might have influenced the purchase.

How Pixel-Based Attribution Addresses These Challenges:

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• Comprehensive Tracking: Captures web and in-app events such as views, installs, signups, and purchases, providing a fuller picture of engagement.
• IP Matching: Matches IP addresses from impressions to conversions, ensuring precise attribution and minimizing user-dependent errors.
• Device Graph Integration: Utilizes hashed email and MAIDs to link different IP conversions to household impressions, enhancing accuracy and completeness.
• Real-Time Data: Provides real-time insights, allowing for timely campaign adjustments and better understanding of the marketing funnel.
• Full Journey Monitoring: Tracks the entire listener journey from ad exposure to action, considering the influence of other ads and providing a more holistic view of the campaign's impact.
• First-Party Data Integration: Combines data from promo code redemptions, vanity URL uses, and post-purchase surveys to complete the performance picture, overcoming limitations of traditional methods​​​​.
• Pixel-based attribution offers a more accurate, reliable, and comprehensive method for measuring the impact of podcast ads, addressing the key challenges of traditional attribution techniques.

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The Shift to a Cookieless World

Why has the industry started phasing out mobile device IDs and 3rd party cookies, and what are the key drivers behind this shift?

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The shift away from device IDs and third-party cookies is primarily driven by growing concerns over consumer privacy and the demand for greater user control over personal data. Here are the key drivers:

1. Privacy Concerns: Increasing awareness and concern about how personal data is collected, stored, and used have led to calls for more stringent privacy protections.
2. Regulatory Changes: Legislation such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the U.S. impose strict requirements on data collection and user consent, making third-party cookies less viable.
3. Consumer Choice: Consumers are demanding more control over their data, including who can access it and how it is used. This has pushed companies to adopt more transparent and user-friendly data practices.
4. Browser Policies: Major web browsers like Safari, Firefox, and Google Chrome are phasing out support for third-party cookies to enhance user privacy and security.
5. Trust and Transparency: Companies are recognizing that building trust through transparent data practices can lead to stronger customer relationships and loyalty.

This shift aims to create a more privacy-centric digital ecosystem, prioritizing consumer rights and trust over invasive tracking methods.

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Has the phasing out of device IDs and third-party cookies impacted digital advertising overall, and specifically podcast advertising?

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Yes, the phasing out of device IDs and third-party cookies has significantly affected digital advertising overall. Here are the main impacts:

1. Reduced Reliability and Accuracy: Without device IDs and third-party cookies, classic digital marketing platforms are finding that they can’t do the same things they used to do with third-party data, like retargeting, cross-platform contextual targeting, and 1:1 attribution.
2. Increased Dependency on First-Party Data: The rise in Retail Media Networks can be largely credited to the reduction of third-party data signals in digital advertising. It also has silo’d measurement results, at times making the jobs of digital marketers harder. 
3. Shift in Measurement Methodologies: The industry is moving away from the expectation of perfect 1:1 tracking and attribution, focusing more on household attribution, as well as tried and true measurement methodologies such as media mix modeling, match-market testing, and incrementality.

However, this shift has had a varied impact on podcast advertising:

• Limited Negative Impact: Podcast advertising is less affected because we have never had to rely on device IDs or third-party cookies. Podscribe, for instance, uses first-party data, IP matching, and device graphs to track and measure performance that will not be affected by the degradation of third-party signals.
• Future-Proof Methods: By employing first-party cookies and other identifiers such as hashed emails, podcast attribution remains robust despite the phasing out of third-party cookies.

The move away from third-party cookies has driven the ad-tech world to innovate and adopt new methods, ensuring continued effectiveness in digital and podcast advertising​​​​.

First-Party Data and Podscribe's Approach

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Not all cookies are the same, can you explain the difference between 3P cookies and 1P cookies?

• Third-Party Cookies (3P Cookies):
  • Scope: These cookies are set by domains other than the one the user is currently visiting.
  • Function: They can track a user's activity across multiple websites, linking behaviors from different sites. This ability allowed targeting companies to create intent-based audiences by observing users' journeys across the internet.
  • Usage: They provide extensive contextual information about users, which is useful for detailed tracking and targeted advertising. However, this can be seen as invasive and privacy-intrusive.

• First-Party Cookies (1P Cookies):
  • Scope: These cookies are set by the domain of the website the user is currently visiting.
  • Function: They track user behavior only within that specific website, without the ability to link actions across different sites.
  • Usage: They are primarily used to improve user experience on the website, such as remembering login details, preferences, and session information. They offer a privacy-friendly way to gather data as they do not track across the broader internet.

In summary, third-party cookies offer broader tracking capabilities across the web, often used for targeted advertising, but are seen as invasive. First-party cookies are limited to individual sites, enhancing user experience without crossing privacy boundaries​​​​.

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What’s the difference between a device ID and a cookie?

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A cookie is set by a web-browser, and can only be accessed by the web-browser that set the cookie. In contrast, a device ID is set by a mobile device’s operating system, and as long as the user has opted in, can be sent from in-app activities. A device ID can be linked across apps, functioning similarly to a third-party cookie but for in-app events rather than browser events.

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Why is first-party data more crucial in the advertising landscape?

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Here’s why it is becoming increasingly crucial in advertising:

1. Accuracy and Reliability: First-party data is considered more accurate and reliable because it comes directly from the interactions between the user and the company's platform. This direct collection ensures higher data quality and relevance.
2. Privacy Compliance: With growing privacy concerns and stricter regulations (e.g., GDPR, CCPA), first-party data is seen as more privacy-compliant. It is collected with user consent and provides transparency about data usage, thus building trust with customers.
3. Personalized Experiences: It allows companies to gain deep insights into how users interact with their specific site or app. This understanding helps in creating more personalized and relevant user experiences, improving customer satisfaction and engagement.
4. Independence from Third-Party Data: As third-party cookies are phased out, advertisers and marketers can no longer rely on cross-site tracking to gather user data. First-party data becomes essential for targeting and measuring advertising effectiveness within the confines of one’s own digital properties.
5. Better Data Control: Companies have full control over their first-party data, enabling them to manage, analyze, and utilize it according to their specific needs and strategies without depending on external data providers.

In summary, first-party data is vital in today’s advertising landscape because it is accurate, privacy-compliant, and allows for better personalization and control, especially as reliance on third-party cookies diminishes​​​​.

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How does Podscribe utilize first-party data to measure the impact of podcast advertising campaigns?

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First-party data such as coupon code redemptions, hashed emails, and vanity URL visits allow Podscribe to go beyond simple IP to IP address matching when measuring the effectiveness of a given campaign. Without this first-party data, we would rely on signals coming from households-only, undercounting the effectiveness of a podcast campaign. 

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You recommend that clients send first-party data, specifically hashed emails. How does this practice enhance the effectiveness of your measurement tools?

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Receiving hashed emails from clients significantly enhances the effectiveness of Podscribe’s measurement tools by improving data matching and attribution accuracy. Here’s how:

1. Precise User Matching: Hashed emails serve as unique identifiers attached to site conversions that can be matched against exposed household data collected from podcast ad impressions. This matching enables better measurement, meaning conversions can be accurately attributed to specific ad exposures, even when users switch devices or leave their homes.
2. Reduction of False Negatives: Hashed emails help Podscribe link user actions across multiple geographies. When mapping conversions to impressions using only IP addresses, it requires that both the exposure and conversion both happen on the same IP address. This means that both actions would need to take place while a listener is at home. Because of this, some methodologies will miss attributed conversions if they don’t use first-party data signals.
3. Reduction of False Positives: Utilizing hashed emails minimizes the risk of false positives in attribution. Unlike IP addresses, which can be shared by multiple users and can change over time, hashed emails provide a more stable and individualized identifier, leading to more accurate attribution.
4. Privacy-Compliant Data Handling: Hashed emails are anonymized, ensuring that user privacy is maintained while still providing valuable data for attribution. This approach aligns with privacy regulations and builds trust with users, as their personal information is protected.
5. Comprehensive Performance Insights: By combining hashed emails with other first-party data, such as promo code redemptions and vanity URL uses, Podscribe can offer a detailed and holistic view of campaign performance. This integration allows clients to understand the full impact of their podcast advertising efforts.

In summary, receiving hashed emails allows Podscribe to enhance the accuracy and reliability of its measurement tools, providing clients with more accurate results and comprehensive insights into the effectiveness of their podcast ad campaigns​​​​.

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Effectiveness

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Why is podcast advertising an effective strategy for businesses looking to reach audiences, within the context of 3rd party cookies being phased out?

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Podcasts can’t rely on third-party cookies for tracking ad exposures, as most podcast content is being listened to within an app environment. Because of this, Podscribe has never used third-party cookies in our methodology. We have been prepared for the phase-out of third-party cookies since the inception of podcast advertising.

Additionally, for Podscribe and other industry measurement providers who have never used third-party cookies, this means we are more prepared for the ‘death of the cookie.’ We have perfected attribution in a cookieless world from the beginning, ensuring accurate and reliable measurement of podcast advertising effectiveness without relying on third-party cookies​​​​.

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Future of Podcast Advertising

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What do you foresee as the future of podcast advertising measurement in a cookieless world?

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For Podscribe, there will be no changes, as we do not leverage third-party cookies. However, other measurement providers that rely on third-party cookies to build device graphs or link conversions back to households will face challenges. They will likely find that they can match fewer conversions to impressions, leading to less accurate results and lower reported effectiveness. 

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Final Thoughts

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What advice would you give to advertisers who are hesitant about investing in podcast advertising due to perceived difficulty in measurement and attribution?

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Pixel-based attribution for podcast advertising exists and is thriving. While traditional methods like promo codes, vanity URLs, and post-purchase surveys are still essential, the industry has evolved to provide comprehensive data, tracking, and measurement solutions that meet the expectations of performance and digital marketers.

Here are a few points to consider:

1. Advanced Measurement Solutions: Modern attribution tools, like those provided by Podscribe, offer detailed insights into podcast ad performance, similar to what you’d expect from digital channels like Facebook or Google Ads.
2. Unique Advantages: Podcast advertising offers unique benefits, such as reaching new, incremental audiences and blending direct response advertising with brand awareness. The trust and loyalty that listeners have towards podcast hosts can drive higher engagement and conversion rates.
3. Scalability: Although launching podcast ads might seem more complex initially, the potential for scaling your campaigns and reaching highly engaged audiences is significant.
4. Authority and Trust: Podcast hosts are seen as authoritative figures by their listeners, who often consider them friends. This relationship can enhance the effectiveness of your ads as listeners are more likely to trust recommendations from their favorite hosts.
5. Smooth Integration: Once you take the leap, you'll find that measuring podcast campaign performance aligns well with digital campaign performance metrics. The integration into your existing operations and procedures is seamless, making the onboarding process quick and efficient.

In summary, while podcast advertising may appear daunting at first, the advanced measurement capabilities and unique advantages it offers make it a worthwhile investment. With the right tools and strategies, you can achieve results comparable to, if not better than, traditional digital advertising channels​​​​.