The solution for the third-party data challenge

With the demise of third-party cookies, third-party data itself has garnered significant attention. Tighter restrictions around third-party data can create challenges for the organizations that depend on it. Many companies may be employing third-party data without even realizing it. If they’re not using it within internal applications, they may be using an external vendor or partner application that does.

Now is the time to perform an audit to understand where your organization is at risk and institute a robust first-party data policy.

Let’s first dive into third-party data concepts. Third-party data is collected on a user but does not require a direct relationship to that user. The user may not be aware their information is being collected at all or it may be indirectly sourced. So why would someone want to use third-party data? Here are a couple of examples:

1. To help identify and target users across campaigns, messaging, and ads in marketing.
2. For personalization based on indirect behavior such as actions taken on other website
3. To help drive more relevant support experiences, particularly when they’re outsourced
4. For customer or user intelligence research and analytics

The third-party dilemma

Programmatic ads that retarget users unknowingly based on their behavior across websites is most likely the use case that impacts people the most. The biggest privacy concern is data sharing that includes personal identifiable information (PII). Users’ calls for privacy have been heard, driving regulations such as the California Consumer Privacy Act (CCPA) and the General Data Protection Regulation (GDPR). As a result of these regulations, there is now a cookie consent banner in virtually every site you visit. Companies like Apple and Google have responded to privacy regulations by incorporating this into their Safari and Chrome browsers.

How does first party-data work? 

First-party data is collected from users interacting directly with a brand. It is significantly more relevant and accurate as compared to third-party data and also addresses common concerns with third-party data. First-party data is something an organization most likely already has and can build more intelligence around when tying it together with data from other enterprise touchpoints.

First-party data will generally include information collected directly from the user in the form of signals, such as interactions, transactions, and behavioral data. Most organizations are already collecting this type of information. Incorporating this data into machine learning can virtually eliminate any of the reasons for choosing third-party data in the first place.

Here are a few examples of the types of first-party data that organizations can use to maintain and improve personalization:

Customer data and behavioral data such as clickstreams and transactions across all channels can be used to drive personalized experiences. Signals that collect intent such as search, browse, and result views are potent vehicles you can get from most analytics platforms.  Machine learning can be applied to learn and drive the optimal experiences accordingly across users and individually.              

Product or content attributes and user behavior data can drive granular 1:1 interactions based on user affinity to underlying product and content characteristics. This is then used to power more relevant content results, recommendations, and personalized discovery.                       

Connecting the knowledge base and first-party user profile with interactions such as chat, call center, ecommerce, and marketing can power more product experiences using customer transactions, context, and behaviors exhibited on other touchpoints. This is valuable for both employees and customers.

Five reasons to use first-party data

In summary, here are the five key reasons that first-party data is superior:

1. Control: Dictate what data you want to collect and how to collect it
2. Context: Enrich your data further with additional surrounding and situational data specific to your touchpoints
3. Freshness: Important for continuous and real-time 1:1 experiences across touchpoints
4. Accuracy: Collect directly from your customers versus third parties
5. Better Cost: No need to pay a third party for data

Deliver Personalization Despite Restrictions

Users expect personalized experience that understands who they are and what they like, but they don’t want their every move to be tracked across the web. Numerous research studies show that creating experiences based on first-party data and intent are significantly more effective than approaches based on third-party data.

Consumer privacy is paramount, and pairing first-party data with machine learning makes it possible to respect privacy while still delivering on a delightful, “you-know-me” type of experience. Invest in technology that captures implicit and explicit signals and transform it into insights to power a digital experience that keeps your users coming back for more.