Declared data, or zero-party data, is a type of first-party consumer data that is willingly, explicitly volunteered by consumers. Here’s why that matters.
Digital marketing—data-driven marketing—rests on a foundation of (you guessed it) unprecedented amounts of data. When every digital interaction leaves behind a trail of information, businesses have happily sucked up as much consumer data as they can, creating vast data lakes in the hopes of turning that data into granular insights that power highly relevant and differentiated brand experiences.
Big data paints a rosy picture of what’s possible, but for most marketers, it doesn’t live up to its promise. More than two-thirds of marketing executives report conflicting information across various data sources. Even first-party data, widely considered to be the most accurate source of data, relies broadly on inferences for its usefulness. Layer in concerns of data privacy and increasing regulation, and all this data starts to look like a double-edged sword.
For marketers struggling to make heads or tails of their data, the answer isn’t more data—it’s uncovering the right data. Declared data can be the difference between meaningful consumer insights and messy assumptions.
Declared data is a type of first-party data, meaning that it is generated by a direct interaction between the consumer and the brand rather than purchased from or accessed through a third party. Unlike other kinds of first-party data, though, declared data is knowingly and explicitly volunteered by a consumer. It’s the difference between a consumer telling a brand that she is looking for clothes to wear to work and seeing that she has viewed several pencil skirts on your e-retail site.
Declared data can validate demographic or identifying data (a consumer’s age, gender, location, etc.) that marketers may have access to from other sources, but it can also capture “soft” attributes, like intentions, motivations, interests, preferences, or aspirations—things that, without explicitly asking the consumer about them, marketers can otherwise only make informed guesses about.
Forrester has recently popularized the term zero-party data in reference to information that is intentionally shared by consumers. If it sounds like declared data, that’s because it is—they are two different terms for the same thing.
The definition of declared data is fairly straightforward; why it matters is a more complicated question, one that has to do with the flaws inherent in other types of first-party data.
Most first-party data is inferred data, data produced in the course of a digital interaction between a brand and consumer. Inferred data is usually one of two types:
Inferred data offers plenty of benefits. With the right tech stack in place, it’s relatively scalable, and better yet, it’s proprietary—unlike second- or third-party data, it’s unique to a brand and can’t be accessed by their competitors.
Inferred data can paint a picture of the consumer, but as the name suggests, it relies on guesswork to paint that picture—and guesses can be wrong.
Take for instance a consumer who searches for skateboards on an online sporting goods store, then purchases one. They’ll leave behind a trail of data that will look exactly the same if the buyer is a fifteen-year-old kid who wants to be the next Tony Hawk or if the buyer is that fifteen-year-old kid’s grandma searching for the perfect birthday present. With inferred data alone, any remarketing would treat those two buyers exactly the same way, despite the fact that they’re vastly different consumers.
A single declared data point—whether they were buying the skateboard for themselves or as a gift—validates and contextualizes that incomplete picture of the consumer in a way that completely changes any subsequent messaging. Just a few more pieces of declared data, such as what additional accessories the teenager might be in the market for, or the grandmother’s relationship with the gift recipient and the date of their birthday, and the retailer can set in motion a personalized, highly relevant retention campaign.
It’s not only a question of opportunity, but of cost as well. A 2017 Accenture study put the cost to businesses of customers switching brands due to poor personalization at $756 billion annually. Jebbit’s own research on trust and consumer data shows that roughly a quarter of consumers say that the thing that makes them most distrust a brand with their data is creepy or inaccurate personalization—comparable to the number of respondents who identified a public data scandal as their top reason for mistrust. Personalization based on declared data, rather than assumptions, neatly sidesteps many of these issues.
Declared data, by virtue of the fact that it is volunteered by consumers, is by its nature consented—an important consideration given the steady drumbeat of data privacy laws, particularly GDPR in the EU, that restrict the collection and use of consumer data by brands.
While the degree and definition of consent varies across legislation (for instance, GDPR is much stricter than the California Consumer Privacy Act, its closest analog in the US), information that a consumer is consciously aware they are handing over to a brand consistently meets the standard, whereas scraped behavioral data often does not.
But even setting aside the legal necessity of compliance, consumer sentiment is shifting towards a growing desire for better data privacy and more control over their data. Marketing strategies that rely less on purchased and inferred data in favor of explicitly consented, declared data will be more defensible in response to both changing legislation and growing consumer awareness of data privacy issues.
When it comes to consumer data, if you didn’t ask the consumer for it, it’s just a guess.
Inferences about prospects and customers have their usefulness, but they also come with hidden costs that marketers don’t need to accept as the cost of doing business. Bad targeting isn’t only a missed opportunity, but a risk of customer annoyance, unsubscribes, lost business, and loss of trust.
Declared data provides the context that behavioral and transactional data are missing. It allows you to only show consumers the messaging, products, and offers that are directly relevant to them—because they’ve told you so.