via Broadcasting & Cable
(source: Kelly Abcarian – SVP, Nielsen Product Leadership & Molly Poppie – SVP, Nielsen Data Science)
The race is on to understand identity across both TV and digital, and when it comes to using big data to understand audiences, there is no such thing as perfect information. The biggest misconception today is that set-top box data represents the universe of actual TV viewing behavior. In reality, it’s far from it, and we must first ask ourselves if this data even represents true-person’s behavior.
Set-top box data is not census measurement. On average, a given measurement provider is only able to receive data from 40%-60% of a set-top-box provider’s footprint in a market. Our own research has shown that homes that return data and homes that do not return data also view television differently.
To overcome the bias, coverage gaps and inaccuracies of big data, set-top box data must be cleaned up. This means leveraging reliable, representative panel-based measurements to compare and contrast against data from set-top box providers and make necessary adjustments.
The first step in the cleanup effort is examining household characteristics. We know third parties can be very useful in assigning household characteristics to big data, but their data can be subject to inaccuracies. In an example, households with persons 18-34 were correctly identified 60% of the time, and the designation of a two-person household was only correct 30% of the time.
The second step is comparing the tuning records of set-top box data with panel measurement. Nielsen has found that across all markets more than 25% of time-shifted viewing in the raw set-top box data was assigned to the incorrect station/network.
Once household characteristics and tuning behaviors are compared, data deficiencies can be corrected and validated to reflect real audience estimates. This is not just a one-time effort—this is something that is critical and must be established as an ongoing process to ensure that the veracity of the ratings is never questioned.
For advertisers to feel completely confident in TV audience ratings or audience-based buys that use set-top box data, the household and tuning data coming from these devices must be validated against true-person’s behavior. Fixing errors and correcting for biases is imperative in delivering reliable and truly accurate measurement. In a $70 billion advertising ecosystem, accuracy counts.
This article has been edited – you can read it in its entirety here