Podsights is launching our Incremental Lift product today. What’s Incremental Lift? I’m glad you asked.
Incremental Lift separates what is native demand from the gain provided by advertising. In simpler terms, it measures the increase in conversions from podcast advertising vs. doing nothing at all. We use a control group of podcast listeners that have not been exposed to your podcast advertising and compare their activity to an exposed group.
You can think of it this way. No one buys podcasts alone. You, as a marketer, have a balanced portfolio of investments in the advertising space. You do some social, search, TV, digital, a few billboards, and, most importantly, some podcasting.
I, as a consumer, may have seen your message on one of these channels or many. I may already be a user of your platform. The question becomes, which channel did I come from? There are multi-touch attribution providers that try to solve this problem ingesting all the data from all the places in a digital ecosystem.
Podcasting is fundamentally different because it lacks these digital identifiers, which is a good thing. If you look at what’s happening in the digital space, all attribution is going to look like podcast attribution, but that’s a discussion for a different day.
How it Works
From a practical level, you will click a button in the Podsights Dashboard, and we will set up a time to chat and walk through the results with you. On the backend, we are doing a whole bunch more.
Podsights has three different ways of creating Control Groups.
- Podcast Baselines: When you set up a campaign, you can now mark a line item or episode as a baseline. Podsights will use these impressions as the control group.
- Publisher Control Group: Podsights will use a sampled group of impressions from the publisher’s network as long as they use Podsights RSS prefix.
- Household Control Group: Podsights will use a sample group of impressions from our household graph.
Unless the baselines are handpicked, Podsights will create multiple versions of the baseline and average them together to avoid bias in the control groups.
Much simpler, just the impressions where your ad appeared.
Podsights does Incremental Lift slightly different than the majority of providers in the space. We aren’t looking for just one number; we are looking for a return to the baseline.
For a given set of Control Group impressions, the odds that a household visited after day one or after day ten should be about the same. These households were not exposed to a podcast ad, and the site traffic is consistent week over week.
For the exposed group, we should see a natural decay in the effectiveness of the advertisement. If I’m exposed on day 0, the odds of me converting goes down over time. The result looks like so:
You can see a clear bump in the early weeks that declines over time. Lift is the area above the baseline average. Sometime the effects can last weeks, sometimes days depending on the show, format, etc.
These types of analyses help clients further understand their Podcast spend. Looking for Incremental Lift for your next podcast campaign, talk to us. If you are one of the hundreds of brands that already use Podsights, login, and request one.