The countdown to a cookieless future has begun. Are you ready? Both Apple and Google have announced plans to phase out the use of third-party tracking cookies in their Safari and Google Chrome browsers. Digital advertisers have long relied on third-party cookie data to track web activity. However, these methods are becoming obsolete as the demand for greater data privacy forces marketers to adapt how they use data to identify and understand their audience.
If you’re unsure what the death of the third-party cookie means for your marketing and need help understanding how best to embrace a first-party data strategy, we’ve got you covered. Learning how to take control of your first-party data to win trust and build personalised experiences starts by understanding the difference between different data types as well as the tactics and tools needed to turn this data into valuable customer relationships.
Zero-Party vs. First-Party vs. Third-Party Data
Zero-, first-, second- and third-party data are classifications that refer to the institution responsible for collecting and distributing certain information. Each type of data offers a different value when it comes to understanding a customer’s preferences, behaviors and interactions with a brand.
- Cookies are files stored on your computer 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 personalised. 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 cookies that are stored under a different domain than the one that user is interacting with. The host or domain owner of the cookie file determines which kind of data type is collected. For example:
- Zero-Party Data: A more detailed version of first-party data (also often called declared data). This data is willingly provided by a person and addresses such things as communication preferences. For example, a customer may fill out a survey stating they’d like to receive a weekly newsletter.
- First-Party Data: Data gathered by tracking and observing user behaviour on a website and interpreted by marketers to build out segmentation and targeting effort.
- Second-Party Data: Data that an organisation collects directly from its audience and then sells on to another company.
- Third-Party Data: Data from outside your organisation and typically collected by web cookie tracking from multiple sources, such as browsing and advertising.
Customer Data Solutions and Technology
Executing a first-party data strategy requires data unification across the customer lifecycle, meaning that brands need a digital ecosystem that allows them complete visibility and control over all of their different data sources. However, not all data management solutions are created equal.
- Data Management Platform (DMP): A platform used for collecting and managing customer data. DMPs 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 Platform (CDP): A marketer-controlled system capable of unifying customer data, whatever the source, and creating a single view of the customer. CDPs also provide deep analytical insights that are easy for non-technical people to understand, and orchestrate data for action across channels.
- Content Management System (CMS): Software used to manage the creation and modification of digital content. CMSes are typically used for enterprise content management (ECM) and web content management (WCM).
- Digital Experience Platform (DXP): A digital experience platform (DXP) is a platform that serves as the connective tissue of digital experiences by integrating multiple products from multiple vendors together so they can work as one. It’s important to note that a digital experience platform can encompass both a customer data platform and content management system within its portfolio in order to optimise 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 in order 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 can be applied to marketing to analyse and activate customer data.
- Artificial Intelligence: The process of using machines and computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition and decision-making.
- Algorithm: 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 ABM campaign.
- Machine Learning: At its core, a way to quickly label and analyse huge data sets. Marketers can do this on their own, but a machine helps do it faster 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 data cohorts.
- Data Cohort: 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 are more effectively grouped and treated as one.
- Target Audience: Defined by looking at the preferences of each cohort at a more detailed level, the specific group of consumers most likely to want your product or service, and therefore the group of people you should target for your campaign. Audiences can be segmented by age, gender, income, location, interests and a number of other factors.
Having all of this data accessible can help marketers future-proof their data strategy through predictive analytics to assess the likelihood of an event happening in the future. With a predictive marketing strategy, companies can anticipate the next best action to take for each customer. Some tools that marketers can use to communicate more effectively with customers are:
- Customer Journey Mapping: A visualization of every customer interaction in context of a customer’s long-term relationship with your brand.
- Marketing Automation: Technology that helps marketers save time, energy and money by automating repetitive tasks such as sending personalised emails or text messages for each audience segment.
- Low-Code Tools: Templates or front-end technology that enables marketers to customise web pages and experiences without the help of a developer.
Composable Data and the Composable Enterprise
With so much data and insight into customer experiences at marketers’ fingertips, organisations must have a larger strategy in place for how to assemble all of these pieces into the unique experiences their 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.
- Composable Enterprise: An emerging concept from Gartner, who defines it as, “An organisation that delivers business outcomes and adapts to the pace of business change. It does this through the assembly and combination of packaged business capabilities.”
- Composable Commerce: A subset of the composable enterprise vision; 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.