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IAB Podcast Measurement Technical Guidelines Version 2.1
IAB Podcast Measurement Technical Guidelines Version 2.1Updated
Draft in Public Comment until December 31, 2020
Draft in Public Comment until February 12, 2021
Drafted by the Podcast Technical Working Group Please email audio@iabtechlab.com with any feedback.
Drafted by the Podcast Technical Working Group Please email audio@iabtechlab.com with any feedback.


The IAB Podcast Measurement Technical Guidelines document was developed by the IAB Tech Lab Podcast Technical Working Group, in partnership with the IAB Audio Committee.
The IAB Podcast Measurement Technical Guidelines document was developed by the IAB Tech Lab Podcast Technical Working Group, in partnership with the IAB Audio Committee. The following IAB Tech Lab member companies contributed to the creation of this document:
The following IAB Tech Lab member companies contributed to the creation of this document:
• Acast Stories USA
• Acast Stories USA
• AdGear Technologies, Inc.
• AdGear Technologies, Inc.
• AdLarge Media
• AdLarge Media
• Adswizz Inc
• Adswizz Inc
• Art19
• Art19
• Audible.com
• Audible.com
• BlogTalkRadio
• BlogTalkRadio
• CBS Local
• CBS Local
• Condé Nast
• Condé Nast
• Cox Media Group
• Cox Media Group
• Cyber Communications Inc.
• Cyber Communications Inc.
• Digital Advertising Consortium Inc.
• Digital Advertising Consortium Inc.
• DoubleVerify
• DoubleVerify
• ESPN.com
• ESPN.com
• Libsyn
• Libsyn
• Midroll Media
• Midroll Media
• Minnesota Public Radio • NPR
• Minnesota Public Radio • NPR
• New York Public Radio • Nielsen
• New York Public Radio • Nielsen
• Pacific Content • Pandora
• Pacific Content • Pandora
• PodcastOne
• PodcastOne
• Podtrac
• Podtrac
• RawVoice/Blubrry • RhythmOne
• RawVoice/Blubrry • RhythmOne
• Sizmek
• Sizmek
• Slate
• Slate
• Triton Digital
• Triton Digital
• Westwood One • WideOrbit
• Westwood One • WideOrbit
• Wondery
• Wondery
https://iabtechlab.com/working-groups/podcast-technical-working-group/
https://iabtechlab.com/working-groups/podcast-technical-working-group/
The IAB Tech Lab lead on this initiative was Mike Midden.
The IAB Tech Lab lead on this initiative was Mike Midden.
Please contact audio@iabtechlab.com if you have any questions or comments about this document.
Please contact audio@iabtechlab.com if you have any questions or comments about this document.
This document (and future updates) can be found on the IAB Tech Lab website at:
This document (and future updates) can be found on the IAB Tech Lab website at:
https://iabtechlab.com/standards/podcast-measurement-guidelines/
https://iabtechlab.com/standards/podcast-measurement-guidelines/


Table of Contents
Table of Contents
1. Executive Summary 3 Audience 3 About this Version 4
1. Executive Summary 3 Audience 3 About this Version 4
2. Overview 4 Podcast Player Market Share and Tracking Limitations 5 Scope 6
2. Overview 4 Podcast Player Market Share and Tracking Limitations 5 Scope 7
3. The Podcast Medium – Content Delivery 7 Downloaded Podcasts 7 Progressively Downloaded Podcasts 8 Raw Server Logs 8
3. The Podcast Medium – Content Delivery 7 Downloaded Podcasts 7 Progressively Downloaded Podcasts 8 Raw Server Logs 8
4. The Podcast Medium – Ad Delivery 8 Integrated Ads 8 Dynamically Inserted Ads 8
4. The Podcast Medium – Ad Delivery 8 Integrated Ads 8 Dynamically Inserted Ads 8
5. Podcast Measurement 9 Measurement with Server Logs 9 Recommended Process for Measurement 10 Podcast Content Metric Definitions 15 Podcast Delivery Metric Definitions 15 Podcast Audience Metric Definitions 15 Podcast Ad Metric Definitions 16 Higher Level Metrics 17
5. Podcast Measurement 9 Measurement with Server Logs 9 Recommended Process for Measurement 10 Podcast Content Metric Definitions 15 Podcast Delivery Metric Definitions 15 Podcast Audience Metric Definitions 15 Podcast Ad Metric Definitions 17 Higher Level Metrics 18
6. Summary 18
6. Summary 18 7. Appendix: Podcast Player Recommendations 19 User Agent Structure 20 Recommendations for Device Platforms and App Developers / Publishers 20 Automatically Triggered Downloads 21 Apple watchOS Duplicate Downloads: Filtering Guidance 21
7. Appendix: Podcast Player Recommendations 18 User Agent Structure 20 Recommendations for Device Platforms and App Developers / Publishers 20 Automatically Triggered Downloads 20 Apple watchOS Duplicate Downloads: Filtering Guidance 21


1. Executive Summary
1. Executive Summary
Podcast audiences represent a growing segment of effective marketable media. Podcast ad revenues are expected to reach over $1 Billion in 2020, a 14% increase from 2019, according to the IAB Podcast Advertising Revenue Study conducted by PwC US. Podcasts offer advertisers a hyper-focused audience that chooses to listen to that content. Podcast listeners have been shown to be the most loyal and engaged audience of any digital medium. However, measurement of content and ad consumption in podcasting has not been consistent so far, which sometimes limits participation of brand advertisers. This document provides an introduction to tracking ad delivery in a podcast and attempts to provide clarity in the marketplace by describing best practices for measuring downloads, audience size, and ad delivery.
Podcast audiences represent a growing segment of effective marketable media. Podcast ad revenues are expected to reach over $1 Billion in 2020, a 14% increase from 2019, according to the IAB Podcast Advertising Revenue Study conducted by PwC US. Podcasts offer advertisers a hyper-focused audience that chooses to listen to that content. Podcast listeners have been shown to be the most loyal and engaged audience of any digital medium. However, measurement of content and ad consumption in podcasting has not been consistent so far, which sometimes limits participation of brand advertisers. This document provides an introduction to tracking ad delivery in a podcast and attempts to provide clarity in the marketplace by describing best practices for measuring downloads, audience size, and ad delivery.
Podcasts are downloaded to a device for later listening or through progressive download listening. In most cases, the podcast file and any ads included with it are downloaded to a device that doesn't, or can't, send data about the consumption of the podcast and ads. This lack of data beyond ad delivery limits real-time measurement. In contrast, other media are consumed by reading an article and interacting with a site, playing a game, or streaming a video, all of which can be measured in real time. Even audio stations that offer music or news are streamed and measured in real time in today’s media marketplace.
Podcasts are downloaded to a device for later listening or through progressive download listening. In most cases the podcast file and any ads included with it are downloaded to a device that doesn't, or can't, send data about the consumption of the podcast and ads. This lack of data beyond ad delivery limits real-time measurement. In contrast, other media are consumed by reading an article and interacting with a site, playing a game, or streaming a video, all of which can be measured in real time. Even audio stations that offer music or news are streamed and measured in real time in today’s media marketplace.
Podcast listeners have the ability to download files to consume whenever and wherever they want and are not required to have an active internet connection to play back an episode. The medium, the distribution, and the platforms used to collect and listen, are built around the habit of downloading the file. Tracking content in this time-shifted medium involves filtering server logs to produce meaningful data for measurement. Since podcast technical teams analyze server logs differently, results vary across the industry.
Podcast listeners have the ability to download files to consume whenever and wherever they want and are not required to have an active internet connection to play back an episode. The medium, the distribution, and the platforms used to collect and listen are built around the habit of downloading the file. Tracking content in this time-shifted medium involves filtering server logs to produce meaningful data for measurement. Since podcast technical teams analyze server logs differently, results vary across the industry.
The challenge for podcast producers and distributors is to offer buyers a set of metrics that is consistently defined and measured equally across the podcast medium. The definitions in this document aim to reduce measurement discrepancies and present a set of recommended metrics and guidelines based on industry best practices. With a consistent set of podcast advertising metrics, buyers and sellers can engage in a conversation about campaign strategy with confidence.
The challenge for podcast producers and distributors is to offer buyers a set of metrics that is consistently defined and measured equally across the podcast medium. The definitions in this document aim to reduce measurement discrepancies and present a set of recommended metrics and guidelines based on industry best practices. With a consistent set of podcast advertising metrics, buyers and sellers can engage in a conversation about campaign strategy with confidence.
Audience
Audience
While all professionals in the podcast supply chain can benefit by being familiar with this document, metric definitions are primarily intended for podcast producers and distributors. Specifically, account managers should be familiar with and use metrics as defined in this document when negotiating ad packages with buyers. Additionally, podcast ad operations teams should use the metric definitions in this document to design or adjust the ad measurement technology they use to analyze server logs for podcast ad measurement.
While all professionals in the podcast supply chain can benefit by being familiar with this document, metric definitions are primarily intended for podcast producers and distributors. Specifically, account managers should be familiar with and use metrics as defined in this document when negotiating ad packages with buyers. Additionally, podcast ad operations teams should use the metric definitions in this document to design or adjust the ad measurement technology they use to analyze server logs for podcast ad measurement.
Buyers should also reference this document to better understand how ads in podcast content are counted. This document offers a set of metrics that establish a mutual understanding in podcast advertising negotiations.
Buyers should also reference this document to better understand how ads in podcast content are counted. This document offers a set of metrics that establish a mutual understanding in podcast advertising negotiations.
About this Version
About this Version
The first version of this document was released in September 2016, and additional significant updates were made with version 2.0 released in December 2017.
The first version of this document was released in September 2016, and additional significant updates were made with version 2.0 released in December 2017.
Updates in this version (2.1) includes language edits for more clarity, a new section with guidance on user agent structure, recommendations for IPv6 IP addresses, filtering guidance for Apple watchOS user agents, and a number of additional podcast player recommendations.
Updates in this version (2.1) includes language edits for more clarity, a new section with guidance on user agent structure, recommendations for IPv6 IP addresses, filtering guidance for Apple watchOS user agents, and a number of additional podcast player recommendations.
2. Overview
2. Overview
Podcast content is an on-demand media format that listeners either download to listen to later or consume online. Unlike the streaming format more common in video, podcasts continue to be downloaded because of the convenience offered by existing platform and application functionality.
Podcast content is an on-demand media format that listeners either download to listen to later or consume online. Unlike the streaming format more common in video, podcasts continue to be downloaded because of the convenience offered by existing platform and application functionality.
Despite the use of the word “streaming” in podcasting, "streamed" podcast files are progressively downloaded via the standard HTTP protocol. True streaming—typically reserved for live events—requires a specialized server and uses an entirely different protocol.
Despite the use of the word “streaming” in podcasting, "streamed" podcast files are progressively downloaded via the standard HTTP protocol. True streaming—typically reserved for live events—requires a specialized server and uses an entirely different protocol.
While "streaming" a podcast and true streaming formats appear exactly the same to end users, delivery of a streamed podcast is logged the same way as a downloaded file in the server logs. This important distinction impacts the ability to measure content and ad delivery in real-time without access to client-side analytics. Podcast publishers must work around this limitation and track metrics using server log data.
While "streaming" a podcast and true streaming formats appear exactly the same to end users, delivery of a streamed podcast is logged the same way as a downloaded file in the server logs. This important distinction impacts the ability to measure content and ad delivery in real-time without access to client-side analytics. Podcast publishers must work around this limitation and track metrics using server log data.
Because log-based measurement counts only the file downloads and not the actual listening, in the long run it might not represent the best possible measurement approach. New player- based measurement approaches recently announced by Apple and others show the potential for podcast measurement that’s in the client app and can track actual plays, pauses, and other listener behaviors. But it will take some time for any of these client-side approaches to be adopted by all the podcast players that exist,and it will depend on the extent of data made available. So, for the foreseeable future, log-based measurement is the only way to achieve comprehensive measurement of all podcast usage.
Because log-based measurement counts only the file downloads and not the actual listening, in the long run it might not represent the best possible measurement approach. New player- based measurement approaches recently announced by Apple and others show the potential for podcast measurement that’s in the client app and can track actual plays, pauses, and other listener behaviors. But it will take some time for any of these client-side approaches to be adopted by all the podcast players that exist, and will depend on the extent of data made available. So for the foreseeable future, log-based measurement is the only way to achieve comprehensive measurement of all podcast usage.
Media delivery via true streaming falls outside of the definition of a "podcast" and is therefore excluded from this document.
Media delivery via true streaming falls outside of the definition of a "podcast" and is therefore excluded from this document.
Podcast Player Market Share and Tracking Limitations
Podcast Player Market Share and Tracking Limitations
The ability to track podcast content and ad playback largely depends on the player requesting the file. The native players that operate on iOS systems, namely the Apple Podcasts App and iTunes currently do not offer technology to confirm that a podcast file was played. While this hopefully will change with Apple’s announcement of client-side metrics, at the time of publishing this document, this lack of client-side response prevents podcast distributors from measuring ad plays at the level expected in other digital media. Once Apple and other platforms start supporting client-side metrics, the Podcast Technical Working Group will work on an update to these guidelines to include those.
The ability to track podcast content and ad playback largely depends on the player requesting the file. The native players that operate on iOS systems, namely the Apple Podcasts App and iTunes currently do not offer technology to confirm that a podcast file was played. While this hopefully will change with Apple’s announcement of client-side metrics, at the time of publishing this document, this lack of client-side response prevents podcast distributors from measuring ad plays at the level expected in other digital media. Once Apple and other platforms start supporting client-side metrics, the Podcast Technical Working Group will work on an update to these guidelines to include those.
In order to provide insight into the limits on tracking podcast content, the IAB Podcast Measurement Working Group was asked to provide reports on the market share for platforms that request podcast files. Podtrac, Blubrry/RawVoice, WideOrbit, Libsyn, and PodcastOne submitted reports used to produce the following table, which aggregates the resulting market share percentages for the month of April 2016.
In order to provide insight into the limits on tracking podcast content, the IAB Podcast Measurement Working Group was asked to provide reports on the market share for platforms that request podcast files. Podtrac, NPR, Blubrry/RawVoice, Libsyn, and Triton submitted reports used to produce the following table, which aggregates the resulting market share percentages for 2020.
Aggregate Report on Podcast Player Market Share (April 2016)
Aggregate Report on Podcast Player Market Share (2020)
Platform requesting podcast file
Platformrequestingpodcastfile Averagemarketshare%
iTunes Browsers Stitcher Everything else
iOS - Apple Podcast App / iTunes / AppleCoreMedia
Range of market share %
59%
8-13% 6-14% 2-7% 12-30%
Spotify Browsers Everything else
iOS - Apple Podcast App
10% 5% 26%
45-52%

Reports for non-iOS and iTunes market share varied from one report to another, but the results reported for the iOS Apple Podcast app were consistent across all reports, with a mean of about 49%.
Reports for non-iOS (Apple) market share varied from one report to another, but the results reported for the iOS devices were consistent across all reports, with a mean of about 59%.
For about half of the podcast ads served to browsers (6-14%), some servers may be able to distinguish ad delivery from a probable ad “play.” Using browser plugins or other technology, a specialized tag used to request the ad file can indicate that the player accessed the ad. While this technique offers valuable tracking data, half of the 6-14% means that currently only about 3- 7% of podcast ads can be tracked this way.
Reports for Spotify were generally consistent as well, coming in at an average of about 10% of the market share. A major caveat for Spotify though is that the total market share may be under-represented, as the Spotify Passthrough feature which allows for the measurement of Spotify hosted podcasts has not been implemented by many small-medium sized podcast publishers resulting in partial visibility to Spotify’s footprint.
Another key insight for this report (not represented in the table) is that currently less than 3% of the market share enables client-side tracking as it exists in other forms of digital advertising. Only one of the five participants reported a small percentage (3%) of market share for host branded players (players owned by the podcast producers). Since the producer controls the player and the content, they can request ads and trigger tracking beacons based on ad play. Most podcast distributors see almost zero activity in this market. Even of the 3% of reported host-branded players, many may not be equipped to find tracking beacons and use them.
For about half of the podcast ads served to browsers (5%), some servers may be able to distinguish ad delivery from a probable ad “play.” Using browser plugins or other technology, a specialized tag used to request the ad file can indicate that the player accessed the ad. While this technique offers valuable tracking data, half of the 5% means that currently only about 2.5% of podcast ads can be tracked this way.
The data provided for this report shows that half of the market share for podcast players belongs to the Apple Podcasts app, which prevents any client-side tracking or even the ability to count a “play.” Podcast distributors must turn to server log analysis and report on ad delivery. Some distributors count an ad once it's been served. This count offers a valuable metric, but is out of scope for this document, because an ad served doesn’t indicate whether the ad file was downloaded.
Another key insight for this report (not represented in the table) is that only a very small portion of the market share enables client-side tracking as it exists in other forms of digital advertising. Host-branded players (players owned by the podcast producers) are a rarity in the industry today. In these instances, the producer controls the player and the content, allowing them to request ads and trigger tracking beacons based on ad play. Most podcast distributors see almost zero activity in this market. Even of the few host-branded players that exist today, many may not be equipped to find tracking beacons and use them. Future focus of the Podcast Technical Working group will explore expanding ways to access client-side data and the viability of applying measurement standards such as Open Measurement.
The data provided for this report shows that more than half of the market share for podcast players belongs to iOS devices, which prevents any client-side tracking or even the ability to count a “play.” Podcast distributors must turn to server log analysis and report on ad delivery. Some distributors count an ad once it's been served. This count offers a valuable metric, but is out of scope for this document because an ad served doesn’t indicate whether the ad file was downloaded.
Despite the limitations, podcast audiences are growing and offer valuable exposure for marketers. In order to offer this value to buyers, metrics must be consistently defined across the industry. IAB collaborated with members in the podcasting community to establish metric definitions that can be used consistently in the podcast marketplace.
Despite the limitations, podcast audiences are growing and offer valuable exposure for marketers. In order to offer this value to buyers, metrics must be consistently defined across the industry. IAB collaborated with members in the podcasting community to establish metric definitions that can be used consistently in the podcast marketplace.
Establishing consensus and clarity for podcast reporting metrics improves communication and establishes trust and accountability with buyers.
Establishing consensus and clarity for podcast reporting metrics improves communication and establishes trust and accountability with buyers.

Scope
Scope
This document defines content, ad, and audience metrics in the context of downloaded podcasts whether saved for later listening or listened to while being downloaded. In this context, both formats are typically pre-recorded and available on demand whenever the listener is ready to access the files.
This document defines content, ad, and audience metrics in the context of downloaded podcasts whether saved for later listening or listened to while being downloaded. In this context, both formats are typically pre-recorded and available on demand whenever the listener is ready to access the files.
Podcasts that use streaming technology to deliver the ad offer the ability to track activity in real time or near real time and one metric used to measure “client-confirmed ad delivery” is covered in this document. However, the percentage of market share for applications that support true streaming is currently too small to account for any meaningful campaign measurement. Additional measurement guidance for true streaming audio is covered in the MRC Audio Measurement Guidelines currently in development. Those guidelines also cover client-initiated podcast measurement, which is not the focus of this document.
Podcasts that use streaming technology to deliver the ad offer the ability to track activity in real-time or near real-time and one metric used to measure “client-confirmed ad delivery” is covered in this document. However, the percentage of market share for applications that support true streaming is currently too small to account for any meaningful campaign measurement. Additional measurement guidance for true streaming audio is covered in the MRC Audio Measurement Guidelines currently in development. Those guidelines also cover client-initiated podcast measurement, which is not the focus of this document.
Ad measurement in podcasting presents the industry with many challenges. For the sake of establishing common ground in tracking podcast ads, the definitions presented in this document address counting ad delivery. This count comes from analyzing server log files to determine what was actually delivered.
Ad measurement in podcasting presents the industry with many challenges. For the sake of establishing common ground in tracking podcast ads, the definitions presented in this document address counting ad delivery. This count comes from analyzing server log files to determine what was actually delivered.
3. The Podcast Medium – Content Delivery
3. The Podcast Medium – Content Delivery
Podcast listeners acquire podcast files in one of two ways: either by downloading the file for later listening (downloaded), or by listening while the file is downloaded (online listening). To a lesser degree, some podcasts may also be played while a persistent connection to the server is maintained (streamed), but the market share for applications that support this format is insignificant for campaign measurement and excluded from discussion here.
Podcast listeners acquire podcast files in one of two ways: either by downloading the file for later listening (downloaded), or by listening while the file is downloaded (online listening). To a lesser degree, some podcasts may also be played while a persistent connection to the server is maintained (streamed), but the market share for applications that support this format is insignificant for campaign measurement and excluded from discussion here.
Delivery methods for downloaded files, whether listened to later or during download, offer valuable inventory to advertisers, but content and ad delivery are handled differently in both environments. An overview of each format is explained below. Despite different tracking capabilities in each environment, a few baseline metrics should be able to offer similar reports for both podcast types.
Delivery methods for downloaded files, whether listened to later or during download, offer valuable inventory to advertisers, but content and ad delivery are handled differently in both environments. An overview of each format is explained below. Despite different tracking capabilities in each environment, a few baseline metrics should be able to offer similar reports for both podcast types.
Downloaded Podcasts
Downloaded Podcasts
Podcast downloading allows the audience to download full episodes of content that can be played at a later date and time. Listeners may subscribe to select programs, and platforms like the Apple Podcast App continue to support full downloads to a personal library for listening offline at any time in the future. The convenience of this system makes downloaded podcasts a continued preference among listeners.
Podcast downloading allows the audience to download full episodes of content that can be played at a later date and time. Listeners may subscribe to select programs, and platforms like the Apple Podcast App continue to support full downloads to a personal library for listening offline at any time in the future. The convenience of this system makes downloaded podcasts a continued preference among listeners.
Progressively Downloaded Podcasts
Progressively Downloaded Podcasts
These podcasts appear to be streamed, but the file is actually being downloaded while the listener is listening to the file. The downloaded file is stored in a temporary location rather than to a library as with a downloaded podcast. Since progressively downloaded files are typically downloaded the same way as the files stored for later listening, delivery for these two formats are recorded the same way in the server logs. The only difference between the two is whether the listener is actively playing the file as it is downloaded or being saved for later listening - which can only be discerned by the player.
These podcasts appear to be streamed, but the file is actually being downloaded while the listener is listening to the file. The downloaded file is stored in a temporary location rather than to a library as with a downloaded podcast. Since progressively downloaded files are typically downloaded the same way as the files stored for later listening, delivery for these two formats are recorded the same way in the server logs. The only difference between the two is whether the listener is actively playing the file as it is downloaded or being saved for later listening - which can only be discerned by the player.
Raw Server Logs
Raw Server Logs
In a downloaded file, segments of the file are collected on the listener’s device, or progressively downloaded. These progressively downloaded files result in a server log with several requests to the server, which must then be analyzed and filtered from other server requests in order to represent how many files were downloaded and to what audiences. When podcast publishers use a consistent process, metrics can be reported and trusted with a higher level of confidence.
In a downloaded file, segments of the file are collected on the listener’s device, or progressively downloaded. These progressively downloaded files result in a server log with several requests to the server, which must then be analyzed and filtered from other server requests in order to represent how many files were downloaded and to what audiences. When podcast publishers use a consistent process, metrics can be reported and trusted with a higher level of confidence.
4. The Podcast Medium – Ad Delivery
4. The Podcast Medium – Ad Delivery
Podcast ads can be delivered and tracked in a variety of different ways, but in general two different methods are used with variations on each.
Podcast ads can be delivered and tracked in a variety of different ways, but in general two different methods are used with variations on each.
Integrated Ads
Integrated Ads
Historically, podcasting ad campaigns often involve ads that are read by the podcast host or a familiar voice. A static ad or jingle may be also included as part of the file. These ads are part of the content and included, or “baked-in,” with the file that is downloaded. Targeting is limited because everyone who downloads the file gets the same ads.
Historically, podcasting ad campaigns often involve ads that are read by the podcast host or a familiar voice. A static ad or jingle may be also included as part of the file. These ads are part of the content and included, or “baked-in,” with the file that is downloaded. Targeting is limited because everyone who downloads the file gets the same ads.
Dynamically Inserted Ads
Dynamically Inserted Ads
In recent years, ad technology has allowed for ads to be targeted and dynamically inserted at the time of file request. The ad server determines the best ad to serve to the listener at the time of request. In a podcast consumed online, ads may be inserted into a file that is being progressively downloaded at designated ad breaks. Some publishers may count this dynamic ad serve as an “impression” without confirming ad delivery. The metrics in this document focus on confirming that the ad was delivered. Server logs can confirm that the entire ad file was downloaded, but the process for counting a served ad can only determine that ad file was sent.
In recent years, ad technology has allowed for ads to be targeted and dynamically inserted at the time of file request. The ad server determines the best ad to serve to the listener at the time of request. In a podcast consumed online, ads may be inserted into a file that is being progressively downloaded at designated ad breaks. Some publishers may count this dynamic ad serve as an “impression” without confirming ad delivery. The metrics in this document focus on confirming that the ad was delivered. Server logs can confirm that the entire ad file was downloaded, but the process for counting a served ad can only determine that ad file was sent.
5. Podcast Measurement
5. Podcast Measurement
In digital display advertising, ad tracking is performed using beacons that are triggered in the web browser, or client, which verifies that the ad was presented and at least had an opportunity to be viewed. In podcasting, client-side tracking is usually only possible when the client player passes tracking data back to podcast producer or distributor. In this set-up, the player is programmed to notify the server when an ad has been played. While this set-up offers the most accurate ad delivery counts, it currently represents a very small percentage of the podcast industry–less than 3% according to member reports on market share in the industry (see table 1).
In digital display advertising, ad tracking is performed using beacons that are triggered in the web browser, or client, which verifies that the ad was presented and at least had an opportunity to be viewed. In podcasting, client-side tracking is usually only possible when the client player passes tracking data back to podcast producer or distributor. In this set-up, the player is programmed to notify the server when an ad has been played. While this set-up offers the most accurate ad delivery counts, it currently represents a very small percentage of the podcast industry–less than 3% according to member reports on market share in the industry (see table 1).
Measurement with Server Logs
Measurement with Server Logs
In order to produce accurate counts for podcast downloads and ads, technical staff in publisher and podcast ad operations must analyze server logs. These server logs may include file requests for a combination of downloaded podcast files, dynamically inserted ads, and any content requested by the web page or application hosting the player. A number of factors are used to analyze log files.
In order to produce accurate counts for podcast downloads and ads, technical staff in publisher and podcast ad operations must analyze server logs. These server logs may include file requests for a combination of downloaded podcast files, dynamically inserted ads, and any content requested by the web page or application hosting the player. A number of factors are used to analyze log files.
HTTP GET requests may be processed that contain the following data.
HTTP GET requests may be processed that contain the following data.
● IP Address - The IP address is one of the factors that may be used to determine if the
● IP Address - The IP address is one of the factors that may be used to determine if the
request is unique or a duplicate. (Exceptions are shared locations such as corporate offices, dorms etc., that have a large number of people sharing the external IP Address) It may also be used to determine geographical information of the media consumer. This applies to both IPv4 and IPv6 IP formats.
request is unique or a duplicate. (Exceptions are shared locations such as corporate offices, dorms etc., that have a large number of people sharing the external IP Address) It may also be used to determine geographical information of the media consumer. This applies to both IPv4 and IPv6 IP formats.
● Time Stamp - The date and time may be used to determine if the request should be counted.
● Time Stamp - The date and time may be used to determine if the request should be counted.
● HTTP Status Code - The appropriate HTTP status code is examined to determine if the request should be counted.
● HTTP Status Code - The appropriate HTTP status code is examined to determine if the request should be counted.
● Bytes Served - The value may be used to determine if the media was completely downloaded or if not, how much was downloaded. (Note: This information is only available from native server log files.)
● Bytes Served - The value may be used to determine if the media was completely downloaded or if not, how much was downloaded. (Note: This information is only available from native server log files.)
● Referer - The origin of the download may be used to determine if the request should be counted. e.g. media that is auto played upon loading a web page may be removed or reported.
● Referer - The origin of the download may be used to determine if the request should be counted. e.g. media that is auto played upon loading a web page may be removed or reported.
● User Agent - The identifier of the application or service consuming the media may be analyzed to determine if the request is unique.
● User Agent - The identifier of the application or service consuming the media may be analyzed to determine if the request is unique.
● Byte Range - The range of bytes requested in a given request may be used to determine what portion of the media is requested.
● Byte Range - The range of bytes requested in a given request may be used to determine what portion of the media is requested.
When analyzed across multiple requests, the information may offer statistics that represent podcast downloads, audience and ad delivery. Since media technology is always changing, no specific combination of factors or techniques will offer the most accurate count indefinitely. However, meeting some minimum requirements and following some best practices will help produce more consistent results. The next section will go over some best practices for generating server-side metrics.
When analyzed across multiple requests, the information may offer statistics that represent podcast downloads, audience and ad delivery. Since media technology is always changing, no specific combination of factors or techniques will offer the most accurate count indefinitely. However, meeting some minimum requirements and following some best practices will help produce more consistent results. The next section will go over some best practices for generating server side metrics.
The document then defines a few metrics for podcast content measurement, audience measurement and ad measurement. Podcast producers and distributors may include additional metrics beyond the ones defined here, but such additional metrics should be labeled separately from the core list of metrics described in this document.
The document then defines a few metrics for podcast content measurement, audience measurement and ad measurement. Podcast producers and distributors may include additional metrics beyond the ones defined here, but such additional metrics should be labeled separately from the core list of metrics described in this document.
Recommended Process for Measurement
Recommended Process for Measurement
This section lists best practices based on the experiences of the members of the Podcast Technical Working Group. While we have made the effort to be specific, publishers and distributors will have to look at the various options available and select the best for their particular circumstances. Metrics providers should support the process below or a process with a similar or more stringent level of analysis sophistication, disclose the options selected, disclose where they diverge from the recommendations, and provide the rationale/circumstances that drove those decisions.
This section lists best practices based on the experiences of the members of the Podcast Technical Working Group. While we have made the effort to be specific, publishers and distributors will have to look at the various options available and select the best for their particular circumstances. Metrics providers should support the process below or a process with a similar or more stringent level of analysis sophistication, disclose the options selected, disclose where they diverge from the recommendations, and provide the rationale/circumstances that drove those decisions.
To claim compliance with these guidelines, an organization must go through the IAB certification process and get listed on the IAB Tech Lab website:
To claim compliance with these guidelines, an organization must go through the IAB certification process and get listed on the IAB Tech Lab website:
https://iabtechlab.com/compliance-programs/compliant-companies/#podcast
https://iabtechlab.com/compliance-programs/compliant-companies/#podcast
We recommend a five-step process to generating metrics using server side log analysis.
We recommend a 5-step process to generating metrics using server side log analysis.
1. Apply filtering logic
1. Apply filtering logic
2. Apply file threshold logic
2. Apply file threshold logic
3. Identify and aggregate uniques
3. Identify and aggregate uniques
4. Generate metrics
4. Generate metrics
5. Audit the process (feedback loop)
5. Audit the process (feedback loop)
The recommendations assume a calendar day 24-hour window, in the time zone as chosen by the org for calculating the metrics. No window is perfect, but shorter windows open up a risk of double counting requests and so should be done with care. Conversely, longer windows risk undercounting delivery via recycled mobile IPs and true multiple listens. Companies are allowed to use more sophisticated mechanisms (like a rolling 24-hour window), but we are not mandating that because of the level of complexity that could introduce, with limited benefits.
The recommendations assume a calendar day 24-hour window, in the time zone as chosen by the org for calculating the metrics. No window is perfect, but shorter windows open up a risk of double counting requests and so should be done with care. Conversely, longer windows risk undercounting delivery via recycled mobile IPs and true multiple listens. Companies are allowed to use more sophisticated mechanisms (like a rolling 24-hour window), but we are not mandating that because of the level of complexity that could introduce, with limited benefits.
Step 1. Filtering
Step 1. Filtering
All requests that should not be counted for any reason should be filtered out up front. The criteria we have identified for filtering are listed below.
All requests that should not be counted for any reason should be filtered out up front. The criteria we have identified for filtering are listed below.
1. Eliminate Pre-Load Requests
1. Eliminate Pre-Load Requests
Pre-loading of podcasts directly results in podcast downloads being counted when they should not. There are two possible solutions to handle this.
Pre-loading of podcasts directly results in podcast downloads being counted when they should not. There are two possible solutions to handle this.
1. Policy put in place to not allow pre-loading in players and on websites (e.g. preload=none for HTML5)
1. Policy put in place to not allow pre-loading in players and on websites (e.g. preload=none for HTML5)
2. Use a download threshold based on ID3 header payload plus 1 minute of recording time to determine if request was for a play/download or for pre-loading (see Step 2 “Apply file threshold levels” below)
2. Use a download threshold based on ID3 header payload plus 1 minute of recording time to determine if request was for a play/download or for pre-loading (see Step 2 “Apply file threshold levels” below)
2. Eliminate Potential Bots and Bogus Requests
2. Eliminate Potential Bots and Bogus Requests
There are a number of scenarios where the raw requests include requests that should not be counted because they likely come from bots or from products that behave in ways that make them look like real downloads. We recommend that metrics providers look for the following to filter out.
There are a number of scenarios where the raw requests include requests that should not be counted because they likely come from bots or from products that behave in ways that make them look like real downloads. We recommend that metrics providers look for the following to filter out.
1. IP addresses that cannot originate to actual users (for e.g., known servers)
1. IP addresses that cannot originate to actual users (for e.g., known servers)
2. IP addresses that account for a large number of downloads should be examined for
2. IP addresses that account for a large number of downloads should be examined for
potential fraud. (But also look at the safe IP addresses note below.)
potential fraud. (But also look at the safe IP addresses note below.)
3. IP address that are on a service like AWS.
3. IP addresses that are on a service like AWS.
4. Erroneous referrer data
4. Erroneous referrer data
5. Bogus user agents, e.g. Firefox 3.06
5. Bogus user agents, e.g. Firefox 3.06
6. User Agents that identify to be from sources that are not actual users (e.g. bots that
6. User Agents that identify to be from sources that are not actual users (e.g. bots that
selfidentify as being bots)
selfidentify as being bots)
7. Similarly, referer data that implies that the sources are not actual users.
7. Similarly, referer data that implies that the sources are not actual users.
8. Apple clients – Official Apple iOS Podcast app performs a 2 byte range (Range: 0-1)
8. Apple clients – Official Apple iOS Podcast app performs a 2 byte range (Range: 0-1) request that should always be excluded from processing. This request is made by Apple to check that the media file can be downloaded using byte range requests and is immediately followed by 1 or more additional range requests. Best practice is to ignore 0-1 byte range requests.
request that should always be excluded from processing. This request is made by Apple to check that the media file can be downloaded using byte range requests and is immediately followed by 1 or more additional range requests. Best practice is to ignore 0-1 byte range requests.
Note - Known “safe” IP Addresses (dorms, corporations etc.) should be maintained in a inclusion-list and be allowed through. These likely need to be re-validated frequently (say every 30/90 days) since IP addresses may not be static.
Note - Known “safe” IP Addresses (dorms, corporations etc.) should be maintained in a whitelist and be allowed through. These likely need to be re-validated frequently (say every 30/90 days) since IP addresses may not be static.
Note – The members of the Podcast Technical Working Group also suggested that we should start a project to share an inclusion-list of “safe” IP addresses that represent dorms/corporations and other known NAT situations. Similarly, we likely need an exclusion- list of known bad IP ranges. We will look into these after publication of this document. These lists should not be public and only accessible to members so as to prevent the bad players from adapting quickly based on that information. We are not developing these lists right now, and leaving it to the measurement platforms to manage them themselves. We might pursue these efforts in the future if there is sufficient interest.
Note – The members of the Podcast Technical Working Group also suggested that we should start a project to share a whitelist of “safe” IP addresses that represent dorms/corporations and other known NAT situations. Similarly, we likely need a blacklist of known bad IP ranges. We will look into these after publication of this document. These lists should not be public and only accessible to members so as to prevent the bad players from adapting quickly based on that information. We are not developing these lists right now, and leaving it to the measurement platforms to manage them themselves. We might pursue these efforts in the future if there is sufficient interest.
3. Handling HTTP Requests
3. Handling HTTP Requests
This section covers the correct handling of the logs based on the various types of HTTP requests.
This section covers the correct handling of the logs based on the various types of HTTP requests.
1. HEAD requests - these should not be counted because this is typically used to check for changes because no data is transferred in a HEAD request.
1. HEAD requests - these should not be counted because this is typically used to check for changes because no data is transferred in a HEAD request.
2. GET requests –
2. GET requests –
a. 200 (ok request) should be counted
a. 200 (ok request) should be counted
b. 206 (partial request) A partial request should only be counted if the download
b. 206 (partial request) A partial request should only be counted if the download
covers the 1 minute rule, and de-duplication based on IP Address/UA is being done to cover cases where the user might be skipping ahead. Determining whether the requests cover the 1-minute requirement might require reassembling of the requests.
covers the 1 minute rule, and de-duplication based on IP Address/UA is being done to cover cases where the user might be skipping ahead. Determining whether the requests cover the 1-minute requirement might require reas
c. 304 (not modified request) -> signal that user has existing file and wants to see if it changed.
3. There may also be platform specific quirks to watch for. For example, Akamai uses a HTTP code of 000 for 206 requests that ended prematurely.
Step 2. Apply File Threshold Levels
Downloads below a certain size are unlikely to result in human consumption because too little of the file was received to listen to any content. The following rules help eliminate the downloads that are too small to be counted.
1. To count as a