Differentiating Between First-Party and Third-Party Data

Are you ready for a cookieless future? 

Alphabet plans to phase out the use of third-party tracking cookies in its Google Chrome browser by late this year, while Apple began phasing them out of Safari a decade ago. Both moves are causing much hand wringing among digital advertisers who have long relied on third-party cookie data to track web activity.

But such methods are becoming obsolete as the demand for greater data privacy forces marketers to revise how they use data to identify and understand audiences. Marketers today need to understand the various data types, as well as the tactics and tools that can transform data into valuable relationships. Once they master the difference between first- and third-party data in particular, they can start winning the trust of audiences and begin building personalized experiences. 

So, while we’ll provide a broad overview of the different data types, we’ll focus on why first-party data has risen in importance and what the death of the third-party cookie means for your marketing. 

Understanding different types of data

Zero-, first-, second- and third-party data are classifications that refer to the institution responsible for collecting and distributing certain information. Each data type offers a different value when it comes to understanding a customer’s preferences, behaviors, and interactions with a brand. 

The collection of these data relies on cookies. Stored on your computer, cookies are files designed to hold a small, specific amount of data about a particular website or individual. The main purpose of a cookie is to identify the user so their web experience can be personalized. 

Similarly, a domain cookie is a cookie associated directly with the domain of origin. First-party cookies are stored under the same domain that a user is currently visiting, while third-party cookies are stored under a different domain than the one a user is currently interacting with. The host or domain owner of the cookie file determines which kind of data type is collected:

• Zero-party data is a more detailed version of first-party data (also often called declared data). It’s information willingly provided by a person, such as their communication preferences. For example, a customer who fills out a survey stating they’d like to receive a weekly newsletter has given an organization zero-party data.
• First-party data is gathered by tracking and observing user behavior on various digital properties and interpreted by marketers to build their segmentation and targeting initiatives. 
• Second-party data is information an organization collects directly from its audience and then sells to another company.
• Third-party data arises from outside an organization. It’s typically collected by web-cookie tracking from multiple sources, such as browsing and advertising.

What’s the difference between first-party and third-party data?

Above are quick definitions of the different data types, but it’s first- and third- party data that have most consumed marketers’ attention. Multiple concerns are driving the interest, including when third-party cookies will sunset, how well organizations collect first-party data, and adherence to data-privacy regulations.

It’s worth digging deeper into each data type to better understand their significance, but the easiest way to distinguish the two is to remember how each data type ends up in the hands of organizations.

More about first-party data

First-party data, for instance, is gathered through digital properties that an organization owns and maintains. Those properties range from websites and mobile apps to sign-in tablets at company events, monthly e-newsletters, and company surveys.

Examples of data that such sources produce include:

• Average time on page, user journey, and product pages of interest
• Demographic data, such as name and address
• Communication, product, or content preferences
   

Tools like Google Analytics, customer relationship management (CRM) solutions like Salesforce or HubSpot, and email marketing platforms like Mailchimp or MailerLite hold such information, so you can imagine the goldmine of data accessible to most organizations. This data can be used to:

• Learn how audiences navigate your site during the customer journey
• Discover which content resonates with customers, donors, or prospects
• Inform customer personas — who really buys your products or services?
   

If user consent has been obtained, then an organization may share this data with a partner, and it becomes second-party data, e.g., if an online travel agency’s data may be useful to an affiliate cruise line or hotel.

More about third-party data

Third-party data, on the other hand, is gathered by organizations other than your own — third parties that have no direct relationship with your audiences. These entities may include:

• Data processors like Acxiom that compile, process, and resell data from various sources
• Research firms like Statista
• Public sector aggregators and publishers like the U.S. Bureau of Labor Statistics
   

Organizations can combine this data with their own to:

• Understand audiences’ interests and hobbies. For example, if a shoe designer learns its customers often buy from or visit a certain dressmaker, then it might consider a collaboration with that brand.
• Learn the life events that precede an individual’s engagement with an organization’s products or services, e.g., the purchase of a car or a high school graduation.
• Improve ad-targeting efforts.
   

Keep in mind that third-party data can be static, so organizations may pay a lot for customer data that’s frozen in time.

Solutions for managing customer data 

Given the stress and scrutiny that third-party data and its providers are under, organizations have begun crafting first-party data strategies for their marketing objectives. Executing one requires data unification across the customer lifecycle, which means brands need a digital ecosystem that gives them complete visibility and control over all their data sources. 

Not all data management solutions are created equally, though. Here’s a quick rundown of the types of technologies that organizations may mix and match in their marketing technology (martech) stack when considering how to manage their customer data.

• Data management platforms (DMPs) collect and manage customer data. They primarily focus on third-party data sources (such as cookie IDs and IP addresses) to help marketers more effectively target different customer segments with paid ad campaigns.
• Customer data platforms (CDPs) are a marketer-controlled system capable of unifying customer data whatever the source and creating a single view of each customer. CDPs also provide deep analytical insights that are easy for non-technical people to understand. In addition, CDPs orchestrate data for action across channels.
• Content management systems (CMSs) manage the creation and modification of digital content. They’re typically used for enterprise content management (ECM) and web content management (WCM).
• Digital experience platforms (DXPs) are the connective tissue of digital experiences. They integrate multiple products from multiple vendors so they work as one. A DXP can encompass both a customer data platform and content management system within its portfolio to optimize all sides of the customer experience. 

How to activate and gather insights from your customer data

No matter how much data a company collects, it doesn’t mean much without a way to transform that data into insights that inform actions and help achieve specific business goals. Today, many customer data management solutions need to leverage intelligent technology to detect patterns from customer data and create targeted, relevant marketing messages. 

Data-driven marketing often begins with understanding predictive marketing and how artificial intelligence (AI) can be applied to marketing to analyze and activate customer data. Here are broad overviews of the field and aspects of it:

• Artificial intelligence is the process of using machines and computer systems to perform tasks that normally require human intelligence, such as visual perception, speech recognition, and decision-making. 
• Algorithms are a sequence of steps or rules designed to produce a specific outcome from a set of inputs. In marketing, algorithms can be used to impact both smaller tasks like purchasing advertisements, as well as major strategic decisions like selecting a specific vertical for a long-term account-based marketing (ABM) campaign.
• Machine learning (ML) is a way to quickly label and analyze huge data sets. Marketers can do so on their own, of course, but a machine can do it more quickly and on a much larger scale.
   

Once machine learning technology has identified different trends in customer behavior, marketers can use these customer profiles to create audience segments, such as: 

• Data cohorts informed by specific experiences, events, or other factors shared by a group of consumers. These cohorts are used to identify and target segments of the market that can be more effectively grouped and treated as one. 
• Targeted audiences that are defined by looking at the preferences of each cohort at a more detailed level, the specific group of consumers who most likely want a product or service, and thus the group that your campaign should target. Audiences can be segmented by age, gender, income, location, interests, and numerous other factors.
   

With this data accessible, marketers can prepare a data strategy through predictive analytics and assess the likelihood that an event would occur. With a predictive marketing strategy, companies can anticipate the next best action to take for each customer. Some activities and technologies that marketers use to communicate more effectively with customers include:

• Customer journey mapping involves visualizing every customer interaction to understand their long-term relationship with your brand. 
• Marketing automation is technology that helps marketers save time, energy, and money by automating repetitive tasks, like sending personalized emails or text messages to each audience segment. 
• Low-code tools are templates or front-end technology that enable marketers to customize web pages and experiences without the help of a developer.

Building your data strategy 

With so much data and insight into customer experiences at marketers’ fingertips, organizations must develop a cohesive strategy for assembling their data and producing unique experiences that customers want. The flexibility, speed, and freedom required to shape these moments on demand has been referred to as the model of a composable enterprise. 

There’s now also composable commerce, a subset of the composable enterprise vision. It’s a commerce strategy that allows businesses to select best-in-class technology from various vendors rather than relying on a single vendor to provide a standard functionality.

But, to gain greater insight on the cookieless future ahead, check out our free e-book The Evolution of Digital Experience in a Cookieless World.

And, to learn more about the best technology for optimizing your first-party data, we’d be more than happy to walk you through a demo of the Acquia CDP — sign up here!