First-party data

What is first-party data?

First-party data, also called 1P data or 1st-party data, is information an organization collects from a prospect or customer directly through their activity and brand interactions. 

First-party data includes everything from a customer’s name and contact information to insights about their shopping preferences and what they look for in a product or service. 

How is first-party data collected?

Organizations collect first-party data from offline and online channels throughout a customer’s journey, including an organization’s:

• Call center
• Email marketing
• Mobile application
• Point of sale
• Social media channels
• Website

First-party data helps marketers better understand individual customers. This allows them to refine their personas and deliver personalized experiences — which can increase customer retention, improve cross-selling and upselling success, and ultimately help the business achieve its goals. 

Customer data is typically stored in an organization’s data warehouse. A first-party data platform connects to the data warehouse to activate the data across marketing tools without copying or transferring it.

A first-party data strategy is designed to maximize an organization’s ability to capture customer first-party data and optimize its customer data management to deliver cohesive omnichannel experiences. 

How to use first-party data

First-party data enriches any customer outreach. For example:

• If a prospect identifies a specific business challenge during a call with a sales representative, the marketing team can start delivering content that speaks to how their organization solves that business challenge. 
• If a customer frequently buys products in a specific category, marketers can send targeted emails that promote similar products. 
• If a customer logs into a retailer’s app and browses specific products or services, marketing efforts can include tailored social media advertisements featuring those products or services. 

Why is first-party data important?

First-party data is often the most accurate and reliable type of customer data because it typically comes directly from the customer. Organizations also have complete control over this data. 

As privacy regulations continue to evolve and tech companies and advertising platforms adjust their third-party cookie policies — which have been commonly used to observe and track user behavior across the web — 1P data is a critical component of a resilient marketing strategy. 

Organizations have prioritized first-party data collection in recent years to get ahead of third-party cookie deprecation and create better audiences and journeys for them. Two key challenges in this process, however, are data visibility and data governance, which require teams to break down data silos and achieve customer 360. 

For more background on why first-party data is essential in a privacy-focused era, read this MartechView article from GrowthLoop’s Chief Data Strategy Officer Anthony Rotio.   

First-party data vs. second-party data

Unlike 1st-party data, organizations gain second-party data from sources that have a direct relationship with the customer or prospect. These sources typically include partner organizations or businesses that reach an overlapping audience with complementary products or services. 

• An airline shares its customer data with a hotel chain partner so they can promote complementary services based on the customer purchase history and typical travel behaviors, such as highlighting premium rooms for first-class passengers. 
• A concert ticket platform shares ticket sales and attendee data with entertainment venue owners so they can optimize their scheduling and find the best promotion strategies to reach buyers. 

Second-party data is valuable for combining data for better targeting, however, the acquired data may need to be cleaned, and the acquiring organization has no direct control over second-party data. 

A data clean room is often used to support second-party data sharing. Data clean rooms  provide a secure location to share, access, and analyze data while adhering to data privacy regulations. 

First-party data vs. third-party data

Organizations acquire third-party data from a source that has no direct relationship with the prospect or customer, such as a data broker. Third-party data is often unreliable for organizations, given data quality concerns and compliance risks. 

• A streaming service is approached by a data broker offering data it gleaned through third-party cookies. The streaming service can purchase the data and use it to refine its audience personas and potentially access new prospects who may be interested in their service.
• A gaming company could engage an advertising network to purchase third-party data that will help it optimize its in-game advertising and tailor promotions across digital marketing channels.

Organizations can access solutions or engage partners to enrich third-party party data, which can be especially helpful for optimizing advertising spend and increasing audience reach. 

What is zero-party data?

Zero-party data is any data a customer provides an organization proactively, such as filling out a survey, completing a feedback form, or communicating their needs on a customer service call or other support channel. 

Zero-party data is very similar to and complements 1P data, often providing deeper emotional insights into individual customers. 

For example: 

• An internet provider could learn a customer is interested in a higher bandwidth plan because they browsed upgrade options on the provider’s website. 
• The provider can send a customer survey to understand their current satisfaction with their service and their internet needs. 
• The customer could then proactively share feedback about how they have a large family who streams on multiple devices.
• This 1P data (in-app browsing history) coupled with zero-party data (survey feedback) empowers the marketing team to promote relevant solutions. 

First-party data examples

1P data collection and activation are essential for organizations to optimize the customer experience. A first-party data strategy can help marketing teams accomplish a range of goals, spanning new customer acquisition, increasing customer lifetime value, and reducing churn.

Retail 1P data example

Retail loyalty programs provide a solid foundation for 1P data collection. When customers create an account with an organization, their purchases and website behavior can be easily tracked and associated with them. This data helps marketers personalize their outreach to this customer, which can include sending personalized product recommendations and highlighting location-specific offerings. 

Financial services 1P data example

A financial institution can collect information about how customers typically use their online services, including which services are most popular with specific customers. This data allows the institution to deliver proactive customer reminders and targeted cross-sells to promote complementary services (like offering a bonus for new checking accounts opened by existing credit card holders). 

Media 1P data data example

Streaming providers can leverage customer watching and browsing history to tailor their streaming experience and deliver recommendations based on the genres or categories they watch most frequently. Information such as a customer’s address or IP address could also suggest if there are multiple profiles under one household, which could create an opportunity to promote a discounted family plan. 

Steps in activating first-party data 

First-party data activation involves a few common steps, each with specific tools to support the process. The primary steps include:

Data collection and ingestion

Organizations must identify their customer data sources and how they will collect data from those sources. Data ingestion tools will help move data from its source to a destination, such as from an eCommerce site to the organization’s data warehouse. 

Data organization and cleaning

Once data is moved into the warehouse, several processes take place to review the data and format it so it can be used across systems. Data modeling and data modeling tools enable organizations to effectively map and transform data so it is ready for use. 

Identity resolution is also an ongoing necessity, in which teams reconcile different customer records to build a single customer view. 

Data storage

A data warehouse stores customer data, which is continually collected and updated as the organization gains new customer insights. 

Data activation

The final step is customer data activation, in which the organization uses its data to conduct customized outreach to customers and customer personas across channels. Solutions like a composable customer data platform (CDP) orchestrate this process and empower marketing teams to create audience segments and launch cross-channel customer journeys that leverage accurate and up-to-date audience insights. 

Common mistakes with first-party data

Organizations make several common mistakes when developing a first-party data strategy and activating their customer data. Avoiding the following hurdles can accelerate an organization’s success with its 1P data:

• Be transparent about data collection and use - Customers should trust an organization is being responsible with its data collection and use. Marketers can proactively remind customers about how their data is used and provide opportunities for them to share more information or correct potential misconceptions the brand may have about them.
• Collect only the necessary data - Organizations can collect any information from customers, but most information is not helpful for tailoring their user experiences. Identify the essential 1st-party data types and ignore the rest. In the worst case, customers may feel like an organization is being too invasive by using accurate but irrelevant information in their communications. 
• Be aware of martech platform limits - Many organizations attempt to load their customer data warehouse into their martech platforms, however, this introduces significant scalability and cost issues and can hamper data modeling attempts. Activation layers like a composable customer data platform (CDP) create a centralized customer view in a cloud data warehouse without requiring data to be copied or moved.
• Implement automations - Relying entirely on manual efforts for campaigns is likely an inefficient use of a team’s resources. Review the existing marketing tech stack and capabilities within each tool to leverage automations or AI-powered features that can help the team save time and achieve greater success.

GrowthLoop co-CEO David Joosten explains more about what not to do with first-party data in this MarTech Zone article.