We want to measure the impact of a podcast campaign.
To do this, we must somehow match the podcast ad impressions to the desired actions, such as a page view, purchase, or app install.
We do this on a household level, by matching the IP address the impression occurred on to the IP address the view or purchase was made on.
As an example, imagine that last Saturday morning, Steven in his house in Southern California, downloads an episode of Tim Ferriss with an Athletic Greens ad on it.
We (Podscribe) log that his IP 1.2.3.4 downloaded an ad last Saturday.
Then, today - Steven visits athleticgreens.com to make a purchase. We see that his same IP 1.2.3.4 visited the site, and then purchased.
We count this as a conversion because it was the same household (same IP), within the default conversion window of 30 days. Ta-da!
You may wonder why don’t we do this on an individual and not a household level? Or what if he purchases outside of his home on a different IP address? The weeds!
You must first understand that podcasting is poorer than other channels, such as digital, in terms of available device or person identifiers. Podcast impressions only come with two identifying data points - the IP address and the user-agent.