Leveraging Big Data To Improve TV Targeting Precision

Published May 24, 2014   |   
Joe Abruzzo

Big data now represents the next step in improving television program selection and targeting precision. Merge, append, fuse and model are all terms that can bring increased precision for reaching high value prospects. These methods are discussed below:

This is is a great way to understand the television viewing preferences of current best customers. A customer database, such as a file containing frequent shopper data (first-party data) is merged on an exact name and address basis with set-top box household viewing data. The new enhanced data set provides a basis for understanding differences in between-segment viewing preferences — for example, active customers vs. high-value customers vs. purchasers of particular types of merchandise vs. lapsed customers who we need to re-activate.

To avoid privacy concerns, the customer data and the set-top-box data are merged by an independent third-party supplier. Following the data match, personally identifiable information is removed. The enhanced data set is then returned to the owner of the set-top box data, who works with the advertiser to access and analyze the resulting merged data set.

A shortcoming of merging data sets can be low match rates. If there is a 30% match — for example, the people in the advertiser’s loyalty card data file match 30% of the households in the set-top-box panel — we are then limited to using 30% of the set-top box viewing data. Nevertheless, a set-top-box panel of one million and a 30% match rate would provide viewing behavior for 300,000 households. We just have to trust that there is no systematic bias in the matched vs. non-matched records.

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