What Is Declared Data?
Part II: On our series on data collection for SaaS Marketers.
If you're interested, check out Part I here.
As a marketer, do you sometimes feel like you’re drowning in the “Big Data” that promised to revolutionize your lead generation efforts? Or perhaps that the data you’re using is a little more than underwhelming? If so, you’re not alone.
When it comes to various data sources, two-thirds of marketing executives report conflicting information. This stat even holds for first-party data, considered amongst the most accurate sources available.
What to do? It’s time to put declared data to work for your company.
Unlike other data (including first-party data) that relies on broad inferences for its usefulness, declared data results from direct interaction between the consumer and the brand. Keep reading for a full breakdown of declared data strategy and why you need it.
What Is Declared Data?
While declared data falls into the first-party data category, it is generated solely through information volunteered by customers to a brand. That means no third-party intermediary.
Because consumers explicitly share declared data, it differs vastly from other sources of consumer-related information. In turn, it provides marketers with a much better snapshot of who their customers are.
How does declared data compare to something such as zero-party data? That’s a fantastic question with a simple answer. They refer to the same type of data and often get used interchangeably.
The Perils of Making Assumptions
Inferred data comes with plenty of risks and hidden costs. What do I mean? Let’s consider one scenario, a tale of two consumers.
One is a grandmother buying a dress for her granddaughter to wear to prom. The other is a teenaged girl purchasing her prom dress.
Most first-party data would not be able to make the distinction between these two customers. Yet, I think we can agree there are significant differences when it comes to who they are and why they’re motivated to make their purchases.
If we allow faulty inferences and broad generalizations to affect how we address each of these consumers, then we stand to alienate one (if not both) of them.
How can I make a statement like this? Consider this.
In recent years, consumer sentiment has significantly shifted when it comes to privacy and data collection. As consumers become savvier about how marketers collect data about them, they’re also gaining awareness. As a result, they reject inaccurate targeting more than ever before.
Of course, nothing says bad targeting quite like marketing to a grandmother the way you would a 16-year-old prom-goer or visa versa.
The Advantages of Using Declared Data
Fortunately, with declared data, you can validate identifying data such as demographics (e.g., a consumer’s age, location, gender, etc.).
What’s more, you have the opportunity to access other sources, capturing “soft” attributes such as:
- Motivations
- Interests
- Preferences
- Aspirations
- Intentions
- And more
How do you determine what these “soft” attributes are? By asking your customers the right questions. Not by turning to third-party sources for educated guesses or by making those assumptions yourself.
Despite all of the advances that have occurred in marketing over the years, one fundamental rule remains unaltered. Know your customer.
Only then can you speak your customer’s language. Only then can you address their pain points and fulfill their wants and needs.
If you can achieve this, you’ll be amazed by what your ROI calculators start telling you.
Understanding Inferred Data
Now that you’ve got a better understanding of what declared data is, it’s time to dive more deeply into why you should care about it. This deep dive begins with a thorough examination of where inferred data goes right and wrong.
Most first-party data falls into the category of inferred data. That means data gathered during digital interaction between a brand and a consumer. Inferred data falls into two distinct camps:
- Behavioral data
- Transactional data
Behavioral data refers to information about a consumer’s behaviors or actions on an owned channel. For example, behavioral data could be pages visited by a customer or the emails that they’ve opened.
Transactional data is also commonly referred to as purchase data. No matter what you call it, this information relates to what a consumer bought from a brand and when.
In both cases, inferred data provide a powerful tool for learning more about your customers and how they interact with your brand. What’s more, this data proves relatively scalable with the right tech stack.
The benefits of inferred data don’t stop there, though. Unlike second- or third-party data, inferred data is also proprietary. It’s unique to your brand. In other words, your competitors can NEVER access it.
The Downside of Relying on Inferred Data
Of course, everything in life has a caveat. When it comes to inferred data, one of the most significant flaws is that it can paint the wrong picture when it comes to who your customer is and what drove them to purchase from you. As the name suggests, it relies on inference.
And the other name for inference? Guesswork.
Now, as we already discussed in our illustration of two different consumers buying a dress for prom, guessing can lead to plenty of errors.
Each of these customers leaves behind a trail of proprietary data with their purchase. Unfortunately, that trail looks identical without additional qualifying information.
As a result, relying solely on inferred data would lead to the same customer follow up. Despite the fact, you’re dealing with two vastly different consumers.
In other words, inferred data is the fast road to clumsy targeting — something you must avoid at all costs.
Consumers Have a Mobile Mindset
Why do consumers now have such high expectations when it comes to the brands that market to them? Because people spend more time on their mobile devices than ever before. As a result, they’ve developed a mobile mindset.
What does this mindset entail?
For one, it’s transformed consumer expectations. That means consumers now expect relevant, personalized messages and immediate value. This content also has to be immediately accessible.
Unfortunately, many marketers have a long way to go when it comes to catching up with these expectations, and inferred data isn’t helping them get there.
To succeed when it comes to this new mobile mindset, marketers must provide educational, interactive, valuable, and relevant content and experiences. You can’t do that with guesswork and assumptions.
What’s more, trying to do so can alienate your customers, and alienated customers don’t buy products.
Why Declared Data Matters
How does declared data fit into all of this? It validates and contextualizes the incomplete picture that gets painted with inferred data.
The result? A personalized path to messaging, custom-tailored to different customer demographics, and, ultimately, a road to increased sales.
Best of all, you only need a few pieces of declared data to achieve these results. What might this data look like?
In the case of the prom dress buyers, it might be as simple as finding out whether they’re purchasing the gown for themself or someone else. It could also include a simple question about whether or not the customer might be interested in finding out more about accessories that would complement their purchase.
Two questions and you’ve got a treasure trove of data.
With this information in hand, you have what you need to target each customer with a retention campaign that’s personalized and highly relevant.
Just a couple pieces of declared data and you go from sloppy targeting with the potential of alienating customers to the building blocks of a winning campaign that will convert leads. You’ve also saved your company countless dollars.
How? By avoiding a common reaction to bad targeting — customers shift away from your company to the competition.
Companies lose big when it comes to poor personalization. How big? Roughly $756 billion each year as a result of customers moving from one company to the competition.
How Bad Targeting Destroys Customer Faith
If your head is still reeling from that last statistic, you’re not alone. After all, inaccurate personalization doesn’t seem like a cardinal sin, right?
Why do customers respond so negatively to it? Because customers react to inaccurate or creepy personalization with distrust.
Studies have even shown that for 25 percent of all consumers, no other single factor makes them feel greater distrust for a brand than lousy targeting. This figure equates with the percentage of people who’ve identified a public data scandal as their top reason for mistrust.
How do you avoid triggering this costly sense of mistrust in consumers? One of the easiest ways to do this is through the use of declared data. So, stop making assumptions and start asking your customers the right questions.
After all, when you consider your customer acquisition cost, it only makes sense to do everything you can to retain consumers who have chosen your brand.
The Changing Face of Data Privacy Regulations
If your company has ignored data privacy laws up to this point, it’s time to get with the game. After all, the steady drumbeat of these regulations shows no sign of slowing.
The definition and degree of consent for such initiatives vary by region, but they all represent legislative attempts to address real consumer concerns.
In the EU, for example, the General Data Protection Regulation (GDPR) has restricted the collection and use of consumer data. Brazil’s version is known as the General Data Protection Law (LGPD) and attempts to unify all legislation governing personal data.
The California Consumer Privacy Act (CCPA) remains the closest analog to the GDPR in the US. While the CCPA proves less strict than the GDPR, you must still understand how these regulations impact digital marketing, especially when it comes to targeting.
Navigating Customer Relations in the Age of Data Privacy Concerns
Fortunately, declared data does not fall into a category to be limited or restricted by regulations such as the GDPR or the CCPA. Why? Because a consumer freely gives this information to your company.
As long as a customer is consciously aware that they are volunteering information to your organization, it meets the standards of the GDPR and the CCPA. That said, scraped behavioral data, in most cases, does not.
Besides navigating these regulatory considerations, it remains in your company’s best interest to demonstrate your commitment to data privacy. After all, customer sentiment continues to shift towards more significant concern about data collected from users online.
By complementing inferred data with declared data to contextualize it, you take steps to avoid alienating your customers. You also remain proactive when it comes to data privacy regulations as they evolve.
Stop Assumption Marketing in Its Tracks
When you don’t ask your customer for the data you receive about them, then you’re indulging in a guessing game that stands to get your company nowhere.
Assumptions come with some payoffs, of course. They also carry with them a hefty price, however. This cost is one that marketers don’t need to accept as an inevitable part of doing business.
After all, poor targeting comes with a lot more than customer annoyance. It also accounts for countless unsubscribes, loss of trust, and, as a result, loss of business. Don’t make this risk a part of your strategy.
Instead, get onboard with declared data. It provides the context that transactional and behavioral data can’t, and it permits you to provide customers with the offers, products, and messaging that relates directly to them.
How do you know? Because they’ve told you so.
Declared Data-Driven Marketing
As consumers continue to spend more time on electronics, particularly mobile devices, they now have much higher expectations when it comes to the brand experiences that they have. They’re also much savvier and aware of issues such as data privacy. As a result, marketers have to adjust to new ways of reaching them.
Fortunately, declared data provides an excellent solution. It contextualizes inferred data so that you can reach out to customers in a more personalized, relevant way. One that won’t create discord or distrust.
This data also complies with regulations about data collection because you receive this information straight from the horse’s mouth. It’s a win-win for your company and your customers.
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