Whether companies are looking to develop new products, discover new markets, or simply continue to drive customer acquisition and sales, their most important resource is data. Data can inform their strategies in all aspects of their business and support their decisions for development and growth. However, not all data is created, sourced, and leveraged equally. Brands and marketers should be fully aware of the similarities and distinctions between different types of data. Otherwise, their strategies will lack the full benefits of data-driven marketing, especially if they don’t take advantage of what first-party data has to offer. What is first-party, second-party, and third-party data, and how do they differ?

What is the difference between first-party, second-party, and third-party data?

The most basic commonality across all types of data is that they provide information and insights that companies need to understand themselves, their customers, their industry, and their competitive landscape. Brands use data to guide their strategies in every facet of their business, such as business and product development, marketing and sales, and supply chain management and logistics. Each type of data has their own distinct differences:

First-party data comes from a brand’s audience, which consists of their customers, visitors, and followers. Their audience provides this information because it’s necessary for engaging with a brand and its products and services. Examples of first-party data include user feedback, survey and poll results, purchasing history, website and app activity, social media profiles and interactions, and customer relationship management systems.
Second-party data comes from a brand’s trusted partners (i.e. other organizations). Their partners provide this information because it’s mutually beneficial for both businesses. Second-party data essentially consists of the same information as first-party data, with the only differences being where the information originates from and who it’s about.
Third-party data comes from outside sources and companies that aren’t directly related to a brand or its audience. External sources provide this information because it gives industry and market insights that brands can use to create well-informed strategies. Examples of third-party data include user feedback, website and app interactions, and interview and focus group responses.

As of recently, some elements of first-party data may now be considered zero-party data instead. Zero-party data is information that customers give intentionally and proactively to businesses so that brands are better positioned to fulfill their needs. Some examples of zero-party data include what was previously considered first-party data, such as preference centers, survey responses, and poll results.

First-Party Data

Benefits and challenges of first-party data

The biggest advantage of first-party data is that companies have complete ownership and control of their information. Brands can collect, manage, and use their first-party data without outside influences or biases. They don’t have to worry about their competitors having access to the same information, as the data is exclusively theirs.

Another primary benefit of first-party data is its compliance with data privacy and consumer protection laws, such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA). This ensures trust and transparency with users, governments, and policy makers, and reduces the legal and ethical risks associated with other types of data. Both consumers and companies can feel confident about first-party data, as they know exactly where, why, and how businesses collect and leverage user information.

First-party data is also more accurate and relevant than second-party and third-party data, as it comes directly from a business’s operations and customers. Brands can develop strategies based on how their audiences engage with them and their products and services. Lastly, first-party data is cost-effective and easy to collect. Unlike other types of data, which requires additional time, money, and resources to obtain, much of first-party data collection comes from a business’s pre-existing operations. Companies can gather large amounts of valuable information without extraneous effort from themselves or their users.

The main disadvantage of first-party data is its finite scope and scale. As first-party data is limited to a brand’s own audience, it can lack insights into people who haven’t engaged with them and their products and services. User feedback, social media and website analytics, and CRM data can only tell them so much about their competitors, their industry, and their potential customers. This makes it more difficult for companies to scale and grow their business. To circumvent this, brands should practice social monitoring and social listening to track conversations outside of their own networks. Social listening can help them monitor competitor performance, identify new leads, and develop business strategies.

How to collect and leverage first-party data

Businesses capture first-party data through sales software, customer relationship management platforms, user support and feedback systems, and website, app, and social media analytics tools. This helps them collect consumers' contact information, social profiles, purchase history, comments and reviews, and so on.

Once their customer data has been captured and analyzed, companies can then leverage their first-party data for two main purposes: segmentation and personalization. With segmentation, brands can use data to classify their users in a number of ways, such as by age, gender, location, and income. More complex categories can include their consumers’ values, interests, and where they are on their path to purchase.

These segments can then help marketers provide unique, customized brand experiences, which nurtures consumer retention and fosters customer loyalty. For example, companies can segment users by past purchases to send targeted product recommendations or by location to offer region-specific deals. Personalization makes customers feel more connected with brands, strengthening the business-consumer relationship and encouraging further engagement.

Registration forms are a great way for companies to capture contact details and user preferences at the beginning of their relationship with a new or potential customer. For example, ASOS, a British fashion retailer, asks users to either sign up with their social account or their email address. Then, they ask for their name, age, and gender. Finally, they ask what kinds of email content they’d like to receive, such as discounts and sales or new arrivals. This helps them segment their users by demographics and interests, giving them a more tailored brand experience.

Targeted messaging is one of the most common uses of segmentation in marketing. When brands categorize their consumers effectively, personalized email campaigns and digital ads can significantly increase conversions and sales. For example, Adidas often segments users by gender so they can recommend gender-specific products. Their Adidas Originals email campaign featured Rita Ora and Pharrell Williams in their respective campaigns for women and men.

Second-Party Data

Benefits and challenges of second-party data

The main advantage of second-party data is its ability to help businesses identify potential prospects and develop strategies for scaling their efforts to acquire them. Second-party data is essentially another company’s first-party data, meaning that brands can learn what worked for them and what to avoid. Since companies purchase this information directly from trusted partners, they can feel assured that they’re one of the few brands, if not the only other brand, with access to the same data.

Other benefits of second-party data include its precision and transparency. During the purchasing process, brands can be very specific about what kinds of information they’re looking for, such as website activity or product reviews. They can even ask for data on defined audiences and hierarchies. This also reduces costs, as they’re paying solely for information of relevance to them, rather than paying for an entire database. Second-party data also typically goes through a comprehensive vetting process, meaning that brands can trust in its legitimacy and accuracy.

The biggest disadvantage of second-party data is its indirect connection to a company’s needs and strategies. While it helps businesses learn the ins and outs of another brand’s customers, their interests may not necessarily align, meaning the information won’t be as relevant as it could be. For example, second-party survey responses are only useful if the initial questions relate to a brand’s goals. Consumer purchasing habits may fail to correlate with their own sales. Second-party data can also be difficult to store and integrate, as data management methods may vary from company to company. Lastly, second-party data can be costly and risky if companies don’t take the necessary precautions. Businesses need to invest the requisite time and effort to purchase only the most essential information from reputable sources.

How to collect and leverage second-party data

Businesses collect second-party data directly from trusted organizations and partners or data marketplaces, which help buyers and sellers come to a mutual agreement. As mentioned previously, second-party data is another company’s first-party data, meaning businesses are buying and selling another brand’s customer contact information, purchase history, user feedback, and so on. Both parties must agree to what data is being sold, how much it’s been sold for, and how it will be passed from seller to buyer.

Once brands have purchased and accessed their second-party data, they can leverage it much the same way as they do with first-party data. They can create customer segments, develop personalization strategies, and nurture users on their path to purchase. Second-party data can also merge with first-party data to build predictive models. Comparing existing customers to potential leads and vice versa gives companies a better understanding of consumer behavior, such as purchase motivations and how they like to interact with brands.

Airbnb’s customer satisfaction survey is one example of user feedback that can be sold as second-party data to other organizations. In this survey, they ask customers why they chose Airbnb, whether they were satisfied with their accommodations, and so on. They also ask customers about their primary reasons for traveling, what activities they participated in during their trip, and if they visited any local businesses based on their host’s recommendations. This information can help businesses in various industries, including hospitality, food, retail, and recreation.

Netflix’s customer satisfaction survey is another example of user feedback that can be sold to trusted partners as second-party data. In this survey, they ask users some basic questions about their Netflix experience to start. Then, they ask more specific questions about the movies and shows they watch, such as their favorite scenes, their favorite actors, and whether they’ve watched it with other people. This information not only helps the creators and producers of Netflix’s original programming, but also helps the film and television studios whose content is available on Netflix.

Third-Party Data

Benefits and challenges of third-party data

The biggest advantage of third-party data is its large volume and broad scope. First-party and second-party data face some limitations in terms of the amount of information captured and who the information is about. With third-party data, organizations are obtaining aggregated datasets from other companies, giving them a significant amount of data about a significant number of people. This helps businesses reduce the time and resources they would otherwise need to invest in the data collection process themselves.

As with second-party data, third-party data also helps brands identify potential customers and develop strategies for acquiring them. Third-party data is typically already categorized into audience segments, making it easier to find relevant target markets and build buyer personas.

Despite its scope, scale, and convenience, third-party data has many disadvantages. To start, third-party data is of varying accuracy, reliability, and quality. The information gathered may come from unreliable or unknown sources, potentially causing marketers to draw the wrong conclusions. This also puts businesses at risk of breaching data protection policies, as they don’t know where their data is coming from or how it was originally collected. Governments and organizations are especially concerned about user privacy and data ownership; Google will be phasing out third-party cookies in the near future.

Third-party data’s broad scope can be seen as a limitation rather than a benefit, as much of the information provided is likely to be irrelevant to a brand’s strategies. For example, audience segments that don’t align with their existing customer base or user purchasing habits that don’t apply to their own industry. As a result, businesses may get a lower return on investment than expected. Third-party data is also available to everyone, meaning that a brand’s competitors have access to the same information. Lastly, third-party data shares a few similar challenges with second-party data. It can also be difficult to store and integrate into existing data management systems and methods. Like second-party data, third-party data also creates an impersonal disconnect between company and consumer.

How to collect and leverage third-party data

Businesses acquire third-party data from data marketplaces, data vendors and brokers, and DaaS (data-as-a-service) companies who have paid other businesses for their first-party data. Most third-party data originates from online activity, including social media interactions, search history, and online transactions. Unlike first-party and second-party data, third-party data sources typically have no direct connections or interactions with customers.

Like second-party data, third-party data can be used in combination with first-party data to build predictive models, identify potential leads, and nurture customer acquisition through segmentation and personalization. Third-party data is also leveraged in retargeting strategies. In retargeting, companies use third-party cookies to track users across different websites and serve relevant ads based on their browsing history. For example, a customer visits an electronics store’s website to look at a specific laptop model. Then, when they navigate to another website, they may see a banner ad for the same laptop from the same store.

Retargeting ads are typically seen on news websites, blogs, and other websites that allow external ads from Google Display Network and other similar platforms. They are also commonly seen on social media networks like Facebook and LinkedIn. In this example, a TripAdvisor user was looking for hotels in Bangkok. Then, after they left TripAdvisor’s website, they visited Facebook, where they saw sponsored posts and ads encouraging them to compare Bangkok hotels and book a room.

Capture and incorporate first-party data into your marketing strategy

Each type of data has its own advantages and disadvantages, and can be leveraged in a number of different ways. However, if companies want to connect with customers, comply with data protection laws, and create impactful strategies, they should focus on first-party data. Segmentation and personalization are crucial for customer retention and brand loyalty, which is only possible when businesses collect information from their own users.

3 tier logic’s PLATFORM³ helps brands nurture customer loyalty and capture valuable first-party data through sweepstakes, contests, loyalty programs, and more. The Data Capture & Analytics dashboard can give them insights into consumer behavior that informs and supports their future strategies. To learn more, book a demo with our team today.

What Is First-Party, Second-Party, and Third-Party Data? (Examples Included)