We create great customer experiences when we understand our customers, and we understand our customers with the help of data. There are several types of customer and audience data, each with its own set of benefits and challenges that guide our strategies. And while all types of customer data help us build better experiences to some degree, they are not all created equal. Let’s look at the types of data you can use and how they differ from each other.
Customer Data Here, There, Everywhere
Customer data comes in all shapes, sizes, and systems. The data that companies collect directly is first-party data. Other data comes from partners or is purchased, what we call second-party and third-party data. And then there’s the new one—zero-party data.
Here’s a summary of the four types of data:
|First-party Data||Second-party Data||Third-party Data||Zero-party data|
|Direct relationship with the customer||Indirect customer relationship||Indirect customer relationship||Direct relationship with the customer|
|Collected with consent||Collected with consent||Unknown if it’s collected with consent (depends on the data provider)||Collected with consent|
|Individual data||Individual data||Aggregate Data||Individual data|
|High accuracy and reliability||High accuracy and reliability||Low accuracy and reliability||High accuracy and reliability|
|Not shared||Shared only with trusted partners||Shared with many companies||Not shared|
|Examples:- Customer email- Phone number- Purchase history- Support history- Loyalty program info||Examples:- Website activity- Social media profiles- Customer feedback- Customer surveys||Examples:- Income – Age- Education- Websites visited- Survey responses||Examples:- Communication preferences- Product preferences- Customized account configurations|
First-party data is data you collect directly from interactions with your customers and audiences on your own channels, such as your website or your mobile application. Data comes from customer purchases, support and customer success programs, as well as marketing programs. Examples of first-party data include demographics, purchase history, website activity, email engagement, sales interactions, support calls, customer feedback programs, interests, and behaviors.
In terms of all data types, first-party data is the most valuable because you collect it directly and know it’s high quality, accurate, and relevant to your business.
It’s not hard to collect first-party data. All our customer-related systems collect some customer data. The challenge is that they all gather, store, and manage it differently, leading to inaccurate and inconsistent data in some systems. The best way to ensure your customer data is consistent across all your systems is to leverage a central platform, such as a customer data platform, to consolidate, standardize, and make it available to all systems regardless of where it was first collected.
Second-party data is data you acquire from a trusted partner. In most cases, you know the partner, which means you know the data quality and accuracy. You also know the data is relevant because it comes from a partner with whom you have a mutually beneficial relationship.
Equally important, your partner also complies with privacy regulations like the GDPR and the CCPA, so you can be confident the information was collected with the permission of the consumers in the dataset.
You can also buy second-party data by connecting with partners through data marketplaces. When you acquire data this way, you can discuss the data with the partner and select only the information you want. If you decide to go this route, you can be sure the marketplace is trustworthy, the partners you are connected with are reliable, and their data is collected and managed correctly.
In many ways, second-party data is identical to first-party data, because it’s collected in the same way, just by a partner.
There are a few benefits to using second-party data:
– It enables you to scale by connecting with new audiences that match your own audience data.
– You can combine it with your first-party data to build improved predictive models. This is especially true when you don’t have a lot of customers from which to develop predictive models.
– You can develop better audience insights by analyzing a more extensive audience group. Combining your first-party data with second-party data may help you find new ways to reach your audience or find new audiences to reach out to.
One example of second-party data is the data media publishers sell to advertisers. Another example is a grocery store selling its customer loyalty data to a credit card company.
Collecting second-party data is straightforward; you get it from the partner. Once you have it, you need to manage second-party data the same way you do first-party data, which means you need to store it securely and make it available through the same methods to your systems. You should also validate and clean your second-party data the same way you do your first-party data, to ensure it’s accurate and relevant.
Third-party data is data you acquire from a data aggregator. Data aggregators do not collect data directly but obtain it from other companies and compile it into a single dataset. As a result, the data can come from many different data sources, some large, others small, and there’s not always a clear definition of the audience that data comes from.
Most third-party data is purchased through a DSP (demand side platform) or a DMP (data management platform) for advertising. There are also many third-party data marketplaces, including Acxiom, Nielsen, Google, and OnAudience.
There are several reasons you might want to purchase third-party data:
– It helps you reach a broad audience for your advertising programs.
– When combined with your first-party data, it can help you improve targeting.
Third-party data is bought and sold programmatically, and it’s usually very large datasets. The biggest concern with this data is that you do not know where it came from, so you can’t ensure its reliability or accuracy. You also can’t be sure it was collected according to privacy regulations. Therefore, when you select a third-party data provider, you must do your research and understand where and how the data was collected.
What Is Zero-party Data?
We’ve been talking about first-party, second-party, and third-party data for a long time. But there’s a new data type that people are talking about—zero-party data. Unfortunately, zero-party data is confusing because it is the same as first-party data in many ways.
Coined by Forrester Research, zero-party data is defined as “data that a customer intentionally and proactively shares with a brand, which can include preference center data, purchase intentions, personal context, and how the individual wants the brand to recognize her.”
Examples of zero-party data include data a consumer explicitly provides, such as communication preferences or the types of information they want to receive. Interests are another example, with a consumer explicitly telling you what things they are interested in, such as craft beer, products for toddlers, or things to do on road trips.
Not everyone believes we need another data type, especially not one that suggests an even more direct source to the customer. Still, it’s a term we hear increasingly often, and therefore, it’s one you should understand.
Zero-party data is a component of first-party data and must follow all the rules around managing it. It also provides the same benefits as other data, including enabling you to create personalized, relevant experiences.
How Privacy and Going Cookieless are Changing the Way We Collect and Use Data
The way we collect and use customer data is evolving as customers become more informed about what information is collected, how it’s used, and their right to privacy.
Privacy Becomes Even More Important
Consumers are tired of being bombarded with irrelevant content and advertising. Many are choosing not to provide their data to companies because they don’t understand how their information is being used and if it’s properly (and securely) managed.
Privacy regulations like GDPR in the EU, CCPA in California, and many others in progress, mean that it is becoming increasingly important that companies collect customer data appropriately and are transparent on how that data will be used. They also need to follow through on how they use data to improve customer experiences.
Moving to a Cookieless World and the Impact on Data
Cookies have long been a primary tool for tracking and collecting consumer data on the internet. But times are changing. Google may have delayed its plans to block third-party cookies from its Chrome browser to 2023, but it will happen. Apple is already giving consumers the ability to opt-out of tracking and third-party cookies on its browser and in apps on iOS devices.
The writing is on the wall: Third-party cookies are going away, and companies need to find another way to track and personalize experiences. Start considering alternative approaches such as asking people to register and identify their preferences and interests, leveraging progressive profiling to slowly grow your customer dataset, creating highly targeted advertising on Facebook, LinkedIn, other social media platforms, and contextual ads.
Building Your Data Strategy
If you have a data strategy for your first-party data, you’re halfway there to a data strategy for all your data. The first step is to understand your requirements for customer experiences across the entire organization. Once you know what you want to do, you can figure out what types of data you need to implement those experiences.
Next, you need a way to bring all your data together—first-, second-, third-, and zero-party data—so it can be validated, cleansed, standardized, and compiled to make available to everyone who needs it.
It’s also time to start planning alternative approaches to data that you may soon have no access to. The key will be to develop new strategies to get first-party (and zero-party) data.
Finally, it’s critical to be testing and measuring the impact of your data constantly. This will never be a “set-it-and-forget it” situation. You will always need to think of new ways to collect and update data, stop collecting specific data, start collecting others, integrate new source systems, and more.