Growth Marketing 2.0 in the Cookieless World: The Prospect Data Platform

The landscape of growth marketing pre-iOS 14.5 looked considerably different than today, a world of heightened consumer privacy regulations and the beginning of a cookieless marketing environment. In the past, growth marketers could rely on ad platforms to identify new customers for their brands and scale their marketing spend efficiently. Read here if you are curious about what fundamentally changed in those capabilities. In anticipating this shift, brands have been hyper-focused on collecting valuable zero- and first-party data about their customers. Customer Data Platforms (CDPs) emerged as a new category to contain such data and create a unified customer profile. However, over this period, marketers have also realized the shortcomings of CDPs in relation to growth marketing, in particular paid acquisition marketing and on-platform personalization use cases. In this article, we explain those limitations and introduce the Prospect Data Platform: the newest, essential tool for growth marketers.

Understanding Customer Data Platform’s Limitations

CDPs collect and unify known customer data from various sources, including online and offline interactions, to create a 360-degree customer profile. This profile can then be used by marketers to gain insights into customer behavior, preferences and needs, allowing them to drive business growth by delivering personalized and relevant experiences across multiple channels. CDPs also connect to other platforms in marketers’ toolkits, such as customer relationship management software and customer engagement platforms for retention marketing and on-site personalization.

However popular they are, CDPs line of sight is restricted to users' existing interactions with the brand: their browsing behaviors online, and activities in-store and offline. While these interactions provide very valuable signals about who the customer is, they aren't a complete mosaic of the customer. In particular this is because the profile lacks more well rounded information such as the customer’s demographics, interests, and psychographic elements as well as purchase insights about what other brands the customer loves and spends money with.  

Due to this key limitation, CDPs use case for paid acquisition marketing has traditionally been limited to providing seed data to ad platforms, relying on the ad platform for any audience extension capabilities like lookalike modeling. Similarly, for retargeting and re-engagement campaigns, CDPs operate as a data management container with workflows to keep customer profiles up to date. However, their out-of-the-box AI and machine learning capabilities are fairly limited. Specifically, they struggle with building high dimensional ML models that incorporate first- and third-party attributes.

What is a Prospect Data Platform?

Prospect Data Platform, or PDP, is the newest category in Growth Tech, designed to,

  • Create predictive audiences (i.e. lookalike audiences) leveraging brand’s first- and zero-party data + a consumer graph and rich third-party feature set + machine learning models to identify the right communities for that brand. These high-propensity audiences can be tailored for a specific business or campaign objective;

  • Measure incremental performance of such predictive audiences across ad platforms and campaigns;

  • Generate actionable insights about the brand’s audience, illuminating the persona of their current and prospective customer for on- and off-platform personalization;

  • Generate predictive signals about these customers foreshadowing their future action (for example predicting lifetime value, next action, or churn risk, etc.) and push these predictions to paid marketing channels for campaign optimization as well as on-platform personalization;

  • Facilitate collaborative commerce and co-op marketing among participating brands and affiliate networks that are complementary and like-minded, in a privacy-safe and compliant manner.

How a Prospect Data Platform Differs from a Customer Data Platform

The Customer Data Platforms on the market today include a wide spectrum of applications with various functionality. Some act as a marketing cloud (multi-channel marketing hubs), as a smart hub (a single interface for orchestration and personalization) or as a data integration platform (event tracking, identity resolution, data governance, insights). Some perform multiple functions, such as the Customer Engagement Platform (CEP), a special case of CDP with elements of both marketing clouds and smart hubs.

The new Prospect Data Platform category is complementary to CDPs and CEPs, working in tandem with all of the functions performed by these tools and expanding beyond their limitations. Out of the box, PDP includes a third-party reference graph of 250M+ US adults, and appends predictive attributes to both current customer and prospective customer profiles. In contrast, CDPs only maintain zero- and first-party data that the brand actively ingests into the platform, and only for customers and leads but not all prospects.

Compared to Customer Engagement Platforms, PDPs offer a familiar canvas-style workflow to audience activation, especially within paid media where targeting is key to efficient spend. Again, like CDPs, CEPs only manage existing customer data whereas PDP manages both prospect and customer data.  

Additionally, PDP applies AI-powered machine learning models across the customer lifecycle, leveraging customer and prospect transaction data, while CEPs typically have light AI optimization for personalizing message content and timing based on marketing interactions.

Finally, PDPs offer brands the capability of building a partnership network. PDPs natively support audience collaboration and activation across participating brands and affiliate networks. This emulates a feature used in Data Management Platforms, which like CDPs are used for first-party audience management but also source relevant 3rd party audiences from marketplaces. Similarly, PDPs will enable brands to foster collaboration with digital and offline partnership marketing among participating brands and affiliate networks, while ensuring privacy and compliance. PDPs can choose to compare the communities of two or more participating brands to identify those with the most overlap, even if the brands have very little overlap among their transacting customers. This feature puts brands in control of the collaboration, so that like-minded brands can benefit from customer learnings and grow together.
Ultimately we’ve found that performance marketers are often not using their CDPs nor CEPs for new customer acquisition, upsell, and retention marketing because of these limitations, the larger concern of signal loss and the decline in marketing efficiency. Prospect Data Platforms, and specifically Angler AI, were created to be a complementary solution for performance marketers to fill these gaps and prepare for a cookieless world: Angler’s PDP doesn’t require cookies or Mobile Advertising IDs (MAIDs) and don't rely on click-path for attribution.

Angler AI is the category creator of the prospect data platform. Angler is purpose-built for direct-to-consumer, e-commerce and subscription brands in the U.S. Reach out for a demo today!

TLDR; Here is a Quick Breakdown of CDP vs. PDP Functions

AttributeCustomer Data PlatformProspect Data Platform
Customer Data ManagementComprehensive, unified, persistent view of known and anonymous customersUses known customers (PIIs) and a reference consumer graph for prospects.
Identity Resolution (i.e., Customer Matching)Rules-based, set by brands, only uses first party dataLeverages identity resolution engine with a reference consumer graph (with historical and current change records), a match waterfall and confidence grade.
AI & ML CapabilitiesLight AI optimization for personalizing message timing and content based on marketing interactionsRobust AI models across the customer lifecycle based on first-party (transactions, browsing), and third-party (demo, interest, shopper insight) attributes
Enrichment of Inferred DataDescriptive attributes only for customers, not prospectsAppends predictive attributes to both customer and prospect profiles
Audience CollaborationNot out of the boxNatively supports audience collaboration and activation across participating brands and affiliate networks
MeasurementNot out of the boxNatively supports incrementality measurement of activated audiences
InsightsNot out of the boxProvides actionable insights of audiences to power creative optimization, activation and personalization