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Man sitting on a bench and contemplating his next move on a giant chessboard in front of him: data strategy

Contemplating a Move: Data Strategy for Local TV Campaigns

October 12th, 2017   ||    by Monta Monaco Hernon   ||    No Comments

Big data is just that: big . . . and . . . data. An advertiser could have gigabytes (or reams, if you want to go old school) of the stuff from internal campaigns, local TV stations, CRM . . . the list goes on and on.

But just as in a well-played game of chess, knowing the rules and properly executing them are two very different things. An optimized television campaign requires not just information, but a data strategy crafted to make sure the right insights are applied.

By its very nature, local TV advertising offers geotargeting by region, city, town, etc. But by following a few guidelines, you can narrow down an audience even further to variables like income, purchase history, gender, and viewing preferences. Here are five moves to help set up a winning data strategy.

Define Your Target Audience

What demographic has typically been attracted to your product? Are you looking to aim your message at them, or are you seeking to reel in a new market segment? The dataset and algorithms you’ll choose depends on your answer to these questions.

Vet Your Data

No matter where your data comes from—even if it’s internal—there are several questions you should ask before considering it valid.

In an AdExchanger article, Oleg Korenfeld, executive vice president of ad tech and platforms at Spark Foundry, suggests finding out how up-to-date the data is: When was it collected? How often is it refreshed? How was a segment formed—probabilistically or deterministically?

Spend Money to Make Money

Unfortunately, there’s no way to know how well an audience pool will perform until it’s tested. In the same AdExchanger article, Rolf Olsen, chief data officer at Mindshare, said his firm trials “data nuggets” to determine how appropriate they are for an advertising segment.

He notes that although this experimentation is expensive, it’s also necessary to ensure the most efficient data, and it applies even if the data came from a high-tech source. Flashy doesn’t mean foolproof, after all.

For example, geolocation, which uses GPS to discover more about where a customer is and how he or she shops, has proven “fishy,” Olsen says. The example he uses to illustrate is that of a shopping mall. To have any significance, the technology would have to pinpoint not only where the shopper is directionally, but also on which floor and in front of what store.

Use Data from More Than One Source

To more accurately drill down to a target audience, combine data from different sets. Joining together information from a variety of sources paints a more complete picture of which consumers an advertiser can reach and where.

In the local arena, this cross-analysis could mean the ability to determine the demographics of each nightly news program, for example, or what type of audience is attracted to an early-morning show.

Local TV can be a huge advertising advantage . . . but the right kind of granular local targeting is just as crucial.

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