In general, the main difference between first, second, and third-party data is the trade-off between quality and reach. The quality characteristic refers to how close, rich, relevant or related the dataset is. CRM data may be of the finest quality for reaching or retargeting a current customer base but is extremely limited to reach new audiences.
First-party data is the data collected about customers or audience behaviours. It may be sourced from websites, mobile apps, CRM, customer feedback, in-store beacons, purchases, contact centre, point-of-sale communication, or any other information provided with the consent of users. It is a result of direct, trusted relationship and communication with a consumer, which makes it the most powerful form of targeting data.
First-party data also allows the owner to create their segments and profiles based on this unique consumer data. It is considered to be more valuable and relevant through its specificity and quality compared to second-party or third-party data.
However, first-party data has a major limitation, Reach. First-party data provides a detailed picture of a specific audience such as transaction history or behavioural data from sites and campaigns but these data may not have the scale required to make assumptions nor reach new audiences.
The broadness of first-party data is restricted only to the scope of operations – for a health-related app, the customer data will be restricted to the fitness or health interests of the customers and will not be sufficient to make precise assumptions and create an appropriate targeted campaign.
Second-party data is someone else’s first-party data that can be utilized for targeting. It represents a way to overcome the scale limitations of first-party data to expand the reach and increase the effectiveness of campaign targeting. It can be used to scale campaigns beyond existing customer bases and to drive acquisition. Access to a second-party can be obtained through an agreement between a first-party data owner and other entities such as a second-party data network. One example of obtaining a second-party data source might be a hotel booking website collaborating with an airline to mutually benefit from each other’s first-party data. Usually, such a relationship fosters collaboration and trust and is beneficial for both parties. But this is only possible when there is no conflict of interest between the two parties.
Another thing is that although more scalable, second-party data reach is again limited to the reach of your first-party data partners.
Third-party data is generally aggregated from many different sources and consists of rich behavioural or demographic data. It is often collected by an entity that doesn’t have a direct relationship with consumers. Third-party data is often inferred data, which means that it is based on past user behaviour and not on information, provided explicitly by the user. By collecting detailed behavioural profiles of users such as interests, patterns of browsing activities, hobbies or preferences third-party data provides excellent reach.
There is a trade-off between quality (first-party data) and reach (second/third-party data). An inherent weakness of third-party data is quality – third-party data is statistical aggregated data, it has not been derived from a direct relationship, which makes it harder to trace to a reliable data source. Furthermore, since third-party data is offered by large data aggregators, it is not exclusive and will inevitably be sold to other parties, including competitors.