First-party data: why and how to collect it effectively?

Lucile Blain is a Senior Consultant at Converteo. Her areas of expertise revolve around relationship marketing. She shares her insights on implementing a first-party data collection strategy that is both relevant and effective.

Key takeaways:

• The distinction between zero-party data and first-party data does not add significant value; the priority is to ensure the quality of the data at the time of collection.
• A relevant data collection strategy begins with defining the target use cases.
• Data collection should be gradual and occur at the right moment in the user journey to be maximized.
• Collection efforts are futile if the data is not reconciled into a unified profile.

With the gradual phasing out of third-party cookies, the focus has shifted to first-party data, sparking growing interest beyond its traditional use by CRM teams.

Its diverse applications include enhancing customer insights, personalizing offers, improving customer experience, and predicting behaviors. Media teams also leverage this data to refine targeting in campaigns and measure performance, as detailed in our white paper “The Usage of 1st Party Data.”

Beyond these uses, first-party data presents an opportunity for additional revenue generation through monetization. This trend is evident in the development of dedicated retail media platforms by retailers (such as Retailink for Fnac-Darty or Carrefour Links) and the establishment of partnerships that govern data sharing between affinity advertisers, particularly through Data Clean Rooms.

Regardless of the objective, several imperatives remain: collecting reliable and high-quality data through a tailored collection strategy, developing customer insights by processing and analyzing the data effectively to extract actionable insights, and ultimately making it exploitable.

Zero and first-party data: what’s the difference?

Within the realm of “proprietary data” directly collected by a company from its customers and prospects, a distinction is sometimes made between zero and first-party data. It’s important to note that there is no consensus on their definitions.

Some consider zero-party data as optional information that customers voluntarily provide through surveys or questionnaires, while first-party data encompasses other behavioral and declarative data collected by the advertiser. Others see a division between declarative data (zero-party) and behavioral data (first-party).

In both cases, zero-party data, actively and willingly provided by the customer, is considered more reliable. However, the quality also heavily depends on the conditions under which it is collected. For instance, mandatory fields in a form do not guarantee the reliability of the information collected if the customer does not see the value in sharing it. The timing and method of collection are therefore crucial to ensuring data reliability and enhancing customer insights. Additionally, the GDPR makes no distinction between zero and first-party data; thus, differentiating between them seems unnecessary.

Data Collection Strategy: How to Make It Effective?

Collecting reliable and relevant first-party data hinges on a strategy tailored to the company’s needs and the perceived value for the user. Practically, this involves incorporating several best practices:

• Start with Use Cases: The key to maximizing benefits for the company lies in defining the use cases to be implemented. In other words, instead of adapting use cases to the available data, a strategy should be built to collect the essential data for implementing the identified use cases. The goal is to collect only the necessary data, aligning the collection process with the user’s journey.
• Consider the Value of “Preference Data”: The activation of use cases will depend on the user’s consent. In the era of permission marketing, data collected through preference centers becomes crucial as it offers the opportunity to better personalize communications and maintain customer relationships by focusing on the channels, topics, and/or frequency the user has chosen and declared.
• Prioritize Quality Over Quantity: The effectiveness of data collection is not dependent on the volume of data but on its quality. Without guarantees of reliability, the data cannot be used effectively and may even go unused. Thus, the intrinsic value of the collected information lies in its accuracy and reliability.
• Simplify and Contextualize Collection Forms: When it comes to collecting declarative data from users, less is often more. Limiting the number of mandatory fields to those that are legitimate in the user’s eyes and relevant to the company’s goals ensures better data quality and a higher completion rate.
• Gradually Qualify Profiles: To match the user experience, it’s important to collect more precise data gradually throughout their journey. This allows new use cases to be activated as the relationship develops, considering that users may not be willing to provide all their data upfront without seeing an immediate benefit to their current experience.
• Clearly Define and Communicate the Benefit to Users of Providing Their Data and How It Will Be Used: Recent studies show that users are increasingly aware of the value of their personal data and the commercial motives behind its collection. Consequently, users expect to receive a benefit (advantage, content, exclusivity, etc.) in exchange for their data and want reassurance about how it will be used.
• Consider Alternative Collection Methods to the Classic Form When Use Cases Allow: Experience shows that data collected through scenarios like chatbots is not only twice as voluminous but also more qualitative, particularly because the information is gathered throughout the conversation in a contextualized and motivated manner.

What is the Importance of Reconciliation?

Collecting high-quality data is only part of the potential value. What truly matters is the company’s ability to effectively utilize this data by integrating it into a comprehensive and unified view of its customers.

The primary goal is to consolidate information gathered from various touchpoints to create unified user profiles. This 360° view is crucial for accurate analysis, customer evaluation, database segmentation, and ultimately, understanding user behaviors and needs, delivering high-quality personalized content, and potentially generating revenue from high-value proprietary data.