Zero-party: The next frontier in consumer data

Companies have used loyalty programs as a primary vehicle for establishing ongoing touch points with their customers for decades. Of course, this has meant harnessing loyalty programs as a means of capturing first-party (1P) data from consumers. Complemented by second- (2P) and third-party (3P) data secured externally, these programs have given brands valuable insights into consumer behavior, preferences and intentions — all of which could be used to improve personalization.

In addition to enabling the highly personalized targeting of “known” loyalty members across marketing channels, loyalty programs provide a base of valuable data and insights that companies can use to build “look-alike” audiences they can also target. Companies with robust loyalty data use advanced analytics to identify common characteristics among customer cohorts, then build models for prospecting and re-engaging “unknown” consumers who could fit in known consumer groups.

This leads to generally higher click-through and conversion rates among the look-alike cohort, driving significant value for loyalty and CRM investments. In one EY-Parthenon program effectiveness study, the CRM-enabled loyalty program of a leading global beauty conglomerate generated 35% to 50% of its value from the precision-targeting of look-alike consumers.

Investments by companies in areas such as CRM, data enrichment, identity graphs and journey orchestration enabled by artificial intelligence (AI) have brought companies even closer to achieving intimacy with their customers.

External data, like demographic, socioeconomic and attitudinal data, has increasingly enriched the 1P data from loyalty programs to provide more clarity about customer decision journeys beyond companies’ “owned” properties, helping companies better understand their customers (and look-alike prospects) to reach new levels of intimacy, loyalty and advocacy. The integrated data has also improved the accuracy of segmentation efforts, enabled companies to better anticipate customer needs and even helped shape product and service portfolio and distribution strategies. This has resulted in improvements to the top line and increased internal efficiencies in many cases.

However, consumers’ privacy concerns, an increasingly stringent regulatory environment, technical infrastructure complexity and data issues such as incomplete, inaccurate or fake data have kept companies from seizing the full potential of more intelligent interactions with consumers.

As brands march toward a digitally enabled future, company leaders must remember that this journey fundamentally relies upon consumer data, which will fuel the improved predictions, recommendations and decisions they aim to make.

This is especially important as the customer experience (CX) becomes an increasingly critical business priority. According to the EY-Parthenon 2022 Realizing Strategy Survey, 75% of executives believe that the customer experience is more important than more traditional strategy elements, such as where to play. Consequently, with consumer data being a must for successful CX efforts, companies need a clear, coherent strategy to collect the right consumer data in a regulatory-compliant manner.

Companies also need the right tools and capabilities to make sense of collected data and apply it across use cases and channels — in a manner that is secure and legally compliant yet sufficiently personalized to deliver an enriching experience to the right consumer at the right moment. While most companies are still determining how to do the latter efficiently and at scale, some have had time to perfect how they acquire consumer data.

Nevertheless, customer-facing companies — even those that have historically underinvested in consumer data — will have an opportunity in the months and years ahead to establish a robust foundation for a strategy that may completely reshape how they exchange value with consumers.

Despite the myriad benefits associated with having a robust consumer data strategy, creating the requisite data “ecosystems” has been fraught with complexity and risk.

Consumer privacy concerns, exacerbated by high-profile data leaks, have led to consumer demand for not only greater transparency into how their data is used, but also more sovereignty and control over their data. In response to these and other concerns, third-party cookies have mostly been eliminated from internet browsers.

Addressing consumer concerns about privacy, governments across the globe have also passed legislation to protect people’s personal data. In addition to increasingly stringent regulations surrounding the collection and use of consumer data, the technical complexity and costs associated with integrating different technologies for the desired consumer data architecture have kept most companies from forming truly robust consumer data platforms. Technologies like CRM, data management platform and customer data platform systems coexist to integrate 1P, 2P and 3P data, but each of these platforms has its own unique configurations, features and capabilities.

Integrating, using and maintaining multiple platforms can be costly, both in terms of the technology infrastructure required and the resources needed to manage the systems. Companies also need to seamlessly integrate these systems with technology to enable capabilities like AI-powered personalization, customer segmentation and dynamic pricing. The increased complexity in both the data sourcing and processing comes at a cost that grows with the number of platforms in use.

High-level technology architecture supporting experience personalization today

A new data strategy approach seems necessary to alleviate some of the complexity and concerns companies face (e.g., bad or fake data) with the current models surrounding consumer data.

Zero-party (0P) data can help address the challenges of traditional consumer data models. Unlike 1P data that companies collect from their interactions with customers or 3P data that is obtained from external sources, 0P data is explicitly and proactively provided by customers themselves while providing the customer with transparency on how it will be used. This newly emerged standard for data strategy has the potential to significantly reshape the data ecosystem in the shift toward a smarter, safer digital future for consumers.

Traditionally, 0P data refers to data that the consumer voluntarily and proactively provides, ensuring that the data collected is consistent, accurate and relevant to the individual’s needs and preferences. It can be collected through forms, surveys or interactive experiences and can include information such as preferences (e.g., product categories, price points), personal context (e.g., household size, composition) and purchase intentions (e.g., propensity to purchase certain items or services). Yelp and Sephora are prominent examples of brands that leverage zero-party data in their operations. Yelp offers its customers the ability to customize their preferences to enhance their experience and receive more accurate results and recommendations for restaurants and delivery services. For its part, Sephora offers through its loyalty program a multifaceted experience to customers, encompassing the provision of personal data for loyalty points and exclusive offers, along with the opportunity to construct a comprehensive beauty profile. This enables customers to receive meticulously curated, personalized product recommendations that align precisely with their unique needs and interests. In this context, the 0P data companies collect differs from 1P data, as it is directly provided by the customer, not inferred from their behavior. Contrary to the approach taken by social media networks, which partially monetize consumer behavior by selling those insights to third parties, 0P data platforms provide transparency to the consumer into how their shared data is used and often compensate the user for sharing that data.

In practice, a company could collect 0P data by presenting its customers with a short in-app question about their product or service preferences. Their response would then trigger personalized content and perhaps a discount.

This may not seem like a revolutionary way to personalize experiences — after all, companies were probably already collecting 0P data before the term was first coined. However, it does pave the way for an evolution of the concept of data ownership that gives consumers ongoing sovereignty over their data and exactly how it gets used.

Mutually beneficial exchanges

There has been a rise in dedicated 0P data platforms (i.e., 0P data “intermediaries”), which serve as user-friendly hubs that permit customers to toggle the data that gets onboarded onto an app (in an anonymized form) and directly control the data and insights that are shared with brands. These platforms create “digital identities” of consumers so they can choose to share their preferences by answering surveys and connecting their favorite apps.

For example, an individual can choose to share what kind of leisure activities they enjoy. Individuals can also choose to share their TV viewing preferences by linking their streaming service accounts. From there, they can then decide to share this data with their favorite brands, which in turn can incentivize consumers with certain perks and benefits. In this new paradigm, brands benefit from highly accurate data that increases their intimacy with consumers who intentionally share their data with them, while consumers in turn reap the benefit of more highly personalized and targeted experiences that also happen to be reliably secure and legally compliant.

The resulting 0P paradigm also creates opportunities for more strategic collaboration among value chain partners. One possible scenario can be the emergence of turnkey, dynamic, multi-brand loyalty ecosystems. In practice, given consumer consent, 0P data collected by companies can be fed to partner brands, enabling them to understand a consumer’s complete shopping journey. This will allow brands to know when, where and how customers shop in partners’ channels — an insight that, while valuable, has generally been unclear for companies.

Possible use cases enabled by zero-party data