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The 3 Most Common Types of Data Behind Audience-Based Buying

A primer for understanding the fundamentals of data-driven TV advertising.

Jul 27, 2018

Planning a television media campaign is complicated. Marketers define their audience with specific segments, which are disconnected from the broad demos that are used to buy the ad inventory. Building the right approach sometimes feels like a series of compromises. Often the result is a plan that doesn’t quite match your original goals; you were hoping for apples, but you got oranges.

The emergence of advanced audiences makes the task of matching an audience to your marketing goals a bit easier. Advanced audiences use data from surveys, transactions, or a variety of other sources, which enable advertisers to create more precise customer profiles than age and sex.

Given that audience-based campaigns have shifted from experiments to dedicated components of TV buying, it’s time to figure out how it all works.

So where to begin? Like any other campaign, start with the basic questions:

  • Who is your audience?
  • What logic did you use to determine that audience?

There are no right or wrong responses. Instead, your answers will help determine the best source of data for your campaign and the best way to segment your audience.

Choosing the Right Data

There’s more available consumer data than ever before, but the best use of data is often situational, and not all sources are created equal. The table below gives an overview of three common types of data used for advanced TV buys—and some important considerations for working with each type. The good news: the best choice is often intuitive once you learn the value of each.


Types of Data Used in Audience-Based Buying

 

1. First-party data

Best used to locate an audience that has a history with a brand or product

If you’re primarily interested in an audience that has some history with a brand, first-party data is a good place to start. This typically includes the in-house data of a brand’s existing prospects or customers and is most often based on customer relationship management (CRM) data such as email newsletter subscribers.

Of course—as is often the case—the better the data, the smaller the scale. First-party data is great for advertisers looking to re-market to consumers who’ve already shown an interest in their product, but modeling is often required to build an audience that reflects the national population. A final consideration: first-party data often contains personally-identifiable information (PII). This means you’ll need to pay attention to data privacy, anonymity, and usage rights when applying first-party data to ad targeting.

2. Transactional data

Best used to target an audience based on past shopping habits

If your audience target relies on buying behaviors, or even general customer interests, transactional data is a good place to start. This type of data generally comes in two flavors: credit card data, which aggregates transactions from a large, nationally-representative population; or data from loyalty cards services that provide transaction-based rewards to the consumer. Each type has its pros and cons, but they are both based on specific, measurable behaviors, leaving little room for misinterpretation.

Credit card data offers store- or product-category level insights since card data vendors have information on nearly every transaction made by their customer base. It won’t give you SKU-level information—meaning you can’t see the items customers purchased—but you can reliably infer a lot about purchase patterns by looking at the data in aggregate.

Loyalty card data, by contrast, goes deep on frequently purchased items. If you’re managing a CPG buy, for example, this is a great way to reach consumers of owned, adjacent, and competitive products. Though not everyone shops at the same set of stores, there are aggregators who partner with regional retail, grocery, and big-box stores to create a large enough scale to model for U.S.-level representation.

3. Survey data

Best used when you are building a specific campaign that requires opinions, beliefs, or attitudinal insights

Survey tools are versatile enough to capture responses to questions at varying levels of specificity and granularity. In a well-designed survey, respondents answer questions about what they buy, what they’re interested in, what they like to do—nearly anything you can think of that may be relevant to a brand.

Survey data covers a broad spectrum of topics, enabling you to create a highly-specific audience using only one data source. But because it’s self-reported, the answers reflect how a person prefers to think about themselves or their behavior, which opens the possibility of unconscious bias in reported behavior. As Joshua Pisano, SVP of Operations & Development at Gfk MRI states, “Survey-based research is an excellent way to get a full picture of what drives a consumer. It goes beyond what is simply observed, and captures the opinions, attitudes, and beliefs which drive behavior.”

Survey-based information is great, but, like any data, it’s only useful when applied in a way that considers context. When you have an audience definition that requires a high degree of specificity, survey-based data collection often is your best choice.

Applying the Data to Find Your Audience

Advanced data solutions offer new possibilities for advertisers aiming to reach a specific audience on television. While there is a small learning curve, you won’t have to fly solo to figure out what data is best for your campaign. Publishers with advanced audience capabilities like Viacom can help you build advanced campaigns and find the right audience when applying advanced data to TV media buying.