Consumable, the leader in audio advertising technology, explains the science behind PodIQ™, their podcast measurement product that solves long-standing addressability challenges in digital audio. In a new white paper released today, Consumable reveals how PodIQ uses probabilistic identity graphs to make podcasts fully addressable, and what that means for the audio industry.

"PodIQ represents a significant development in podcast measurement and addressability in the advertising ecosystem," said Jared Lapin, CSO of Consumable. “Historically, monetization in podcasting has been a challenge, especially in programmatic, but PodIQ paves the way for a new era of opportunity and growth."

To solve for addressability, Consumable uses advanced data science and machine learning to tag podcast ad spots with addressable identifiers, creating comprehensive user profiles. PodIQ enables more precise audience targeting, accurate impression tracking, and meaningful insights into listener behavior, unlocking the power of programmatic for podcasts. 

White Paper Highlights:

• How it Works: PodIQ collects anonymized listener data from multiple touchpoints, authenticates the signals in real time, and links the data to activity in programmatic marketplaces, connecting to an addressable identifier (MAIDs or cookies). 
• PodIQ Benefits: The new measurement solution offers enhanced audience targeting, privacy compliance, and scalability, benefiting both advertisers and podcasters. ​​
• No Integration Required: Advertisers and publishers can use their preferred DSP or SSP to conduct addressable podcast advertising transactions seamlessly.
• Increased Monetization: Podcasters can access a broader pool of ad dollars through programmatic, driving revenue and sustainable growth.
• Ecosystem Impact: Addressability in podcasting drives the expansion of the market, attracting more advertisers and investment, while promoting transparency and trust in podcast advertising. 

Read the full white paper: PodIQ™: Solving for Addressability in Podcasting