Marketers have never had more data at their disposal than they do today.
But as the technology used to achieve marketing goals gets more sophisticated, the terms and definitions associated with data-driven marketing can become increasingly confusing.
To help it all make sense, we’ve put together a glossary that defines common industry terminology and explains how brands and marketers can use these different data and audience types:
Definition: Personal or specific information that an individual willingly shares by filling out a form, completing an online sale or taking another purposeful action.
How marketers can use it: “Declared” data is often considered high-quality data because it is directly reported from the consumer. It also implies permission for future use of their information, such as for an email campaign. Generally, this data contains details about demographics, interests and purchase behavior. Declared data forms the foundation of content personalization and product recommendations popularized by Amazon and now used by most e-commerce sites.
Definition: Data and characteristics assigned to a person based on their activities and behaviors online, often based around content consumption.
How marketers can use it: Marketers can assign a classification, lifestyle or data to an individual depending on what they searched, read, watched or bought. This can be paired with declared data to build a richer customer profile. A brand could learn that a particular customer prefers to consume video content instead of text, or is interested in reading about particular topics, like fashion; this can help them tailor their messaging, advertisements or experience to fit the preferences of the individual.
Definition: Data based on a person’s engagement with a very specific category of content or product.
How marketers can use it: With observed data, marketers receive information about a customer that is more specific—and often lower in the purchase funnel—than inferred data. Although the individual did not purchase or fill out a form, as with declared data, the person did spend time visiting pages about a specific product category or product(s). Observed data can serve as the basis for a retargeting ad campaign or email program that persuades a potential customer to return to the site with the promise of a desired product, content or deal.
Definition: Data about a consumer’s interests based on the subject matter consumed via content websites.
How marketers can use it: This data enables a marketer to target someone based on interest, with the presumption he or she would be a likely customer for products related to that interest. Interest data can be useful for targeted display advertising campaigns, aimed at building awareness of a brand, a product or both. For example, a retailer could target their latest apparel line to young women who read fashion blogs.
Definition: Data about a person whose online actions express intent to purchase a specific product or service.
How marketers can use it: Ads and other marketing techniques can target customers who have identified themselves as interested in buying a product. This self-identification can take many forms, such as searching for certain terms, comparing pricing options or adding a specific product to their shopping cart. Intent data provides the basis for search engine marketing by identifying keywords and terms that will target high-intent buyers. It also can be used as the basis of a retargeting campaign for prospects who may have abandoned a shopping cart, compared products or received a price quote on another site.
Definition: Data about someone who has engaged with a product page, made a specific purchase, or taken another designated action.
How marketers can use it: This audience can be targeted with a high degree of certainty because they have an actual track record of a past purchase or high intent to purchase.Observed audience data can fuel an ad or email campaign aimed at a set of people who you know are further down the sales funnel and will have the highest propensity to purchase.
Definition: Takes a sample set of people who have purchased a product (or a similar product) and models a new audience of hopefully high-propensity buyers.
How marketers can use it: By modeling an audience (sometimes called “look-alike modeling”), you are creating a new and expanded audience of people who have similar attributes, behaviors and demographics to your actual buyers. The idea is that people with like attributes to your ideal customers will exhibit similar buying behavior. Modeled audience data can help expand a limited amount of verified buyers to a bigger audience or new geographic area.
Definition: Data based on actual behaviors and demographics used for building a picture of an audience.
How marketers can use it: This data provides raw facts about a person or group of people, such as how often they visit a certain site, what content they read, and their demographic information, such as gender, age and income. Descriptive attributes are the fundamentals when it comes to getting a basic understanding of an audience. For example, if you know that buyers of a certain product tend to be female and have high household income, you can start to make some inferences about how to target them with marketing messages and ads.
Definition: Data based on inferences or predictions of behavior about a person or group of people. It is derived by applying statistical testing or an algorithm to descriptive statistics as described above.
How marketers can use it: Based on a set of created rules and theories, you can attempt to predict behavior or possible action based on how a known set of people act. This technique can be valuable when looking for new sales prospects. For example, from statistical testing, you know that men under 40 from urban centers who make multiple visits to your site and engage with video content are the most likely to convert. You can create a program with an aggressive call to action for prospects that fit those criteria to attempt to accelerate them through the sales funnel.