Location data connect dots between fast food customers and their at-home TV habits
NinthDecimal and TiVo determined which shows and cable networks the customers of the top fast-food chains were watching.
Location data is being used in expanding and creative ways. Store visitation, offline attribution and audience segmentation are the use cases that have been most discussed; however, location data can also be used to assist marketers with media buying and planning.
A case in point comes from new data released by NinthDecimal connecting cable TV viewing to fast food restaurant visitation. For its research, the company (in conjunction with TiVo) determined which TV shows and networks the customers of the top five fast food restaurants preferred. It also captured daypart information.
The most popular show among the customers of four of the top five fast-food chains was “Designated Survivor” on ABC. After that come “Grey’s Anatomy” and “Dancing with the Stars.”
The study also captured the most-watched cable networks and the most concentrated dayparts (programming slots) for these customers.
According to the data, “MSNBC beats Fox News as the No. 1 cable network for 4 out of the top 5 QSRs. Outside of news networks, HGTV is the most-watched cable network, especially on weekends.”
There are immediate and obvious ad-buying implications. Fast food chain A could concentrate ad buys on its customers’ favorite programs to introduce a new menu item. It could also go after chain B’s customers. For example, Subway could advertise to McDonald’s customers during Fox News programming or “Grey’s Anatomy” in prime time.
These are superficial examples, but they illustrate the possibilities. And this type of analysis can be done for any brand or offline audience: what programs do Best Buy customers prefer vs. Target shoppers? Offline purchase data could be factored in as well. So, rather than relying on demographic reporting on network audiences, actual behavioral data can help frame media buying decisions.
Consumer identity is established by matching mobile IDs with other identifiers known to the set-top box or streaming service in the same house. Those data are then “anonymized” and rolled up into large audience segments.