CDPs as Tools Are Dead—But the Framework Lives On
Composable Decision Intelligence with Multi-Graph & Agentic AI: New Way to Tame Data Chaos
When I wrote about the death of CDPs some time ago, it stirred up quite a reaction among analysts and the broader ecosystem. Let me clarify: “CDP as a tool”—particularly as a narrow, VC-funded software category—may be on its way out, but “CDP as a framework” for unifying, analyzing, and activating customer data is more relevant than ever.
CDP Vendor Chaos Meets Enterprise Needs: Bridging the Gap
Recently, several well-known CDP vendors—such as mParticle, ActionIQ, and Lytics—have faced market pressures or undergone exits. This signals that the category became overly crowded. Meanwhile, enterprises are doubling down on what I call an “internal CDP 3.0” approach, leveraging a composable, data warehouse–first model.
Why the renewed interest?
First-party data is non-negotiable in today’s world.
Companies must own this data in a flexible architecture—especially if they want to leverage next-gen AI.
From my vantage point (both building iCustomer and guiding enterprises in their data strategies), I see three main camps:
Those Who “Get It” (AI-Native CDP 3.0 Builders)
They are building AI-ready insights engines using data warehouses like Databricks, GBQ, and Snowflake, while accelerating toward intelligence activation through composable decision intelligence platforms such as iCustomer.
Those Who Are Frustrated (DIY Rebooters)
They’ve tried traditional CDP vendors but found them too rigid. Now, they’re turning to their internal data lakes and warehouses, relying on consulting firms or in-house teams to build advanced AI from scratch. Phew!
Those Who Are Stuck (CRM/Marketing-Cloud-Only)
They rely on existing CRM and marketing cloud systems and integrations, with some “AI tinkering” in the name of agents. However, they lack a robust data foundation and modern revenue architecture—and are often postponing serious transformation.
The 5-Stage CDP Framework Maturity Model
After working with numerous high growth & enterprise organizations, I’ve noticed a typical progression in how they evolve along the CDP journey. Where majority is between 1-3 stage Here’s a simplified look:
Collect & Segment & Activate all-in-one Marketing Cloud
Use Case: Basic email or multi channel campaigns using siloed data.
Limitation: Little flexibility, non unified data; reliant on point solutions.
Layer: Single layer - All-in-One solution
Unify 1P & offer limited 3P Data API, Then Sync
Use Case: Unified profiles, self-service segmentation, some marketing automation.
Limitation: Still struggles with real-time, cross-channel intelligence.
Layer: Data Layer & Activation Layer
Unify 1P, Waterfall enrichment with 3P, Build an ID Graph
Use Case: Unified identity management across channels/devices.
Limitation: Good for the “who” but lacks deeper why or when context.
Layer: Data Layer & Activation Layer
Composable Data Warehouse + Scalable ID, Intent & Interest Data Graphs
Use Case: Combine structured and unstructured data to build holistic insights, factoring in ID, Intent, and Interest.
Value: You unify identities and the context of what each person watches, reads, or engages with—plus specific intent signals that matter to your business.
Layer: Decoupled Hybrid Data Layer, Intelligence Layer & Hybrid Orchestration Layer (Adtech & Martech)
Always-On Experimentation, Personalization with Reinforcement Learning
Use Case: Real-time connections across ID + Intent + Interest Graphs to understand each customer’s motivations and behaviors.
Value: True 1:1 personalization at scale, with continuous experimentation and self-learning from every interaction.
Layer: Decoupled Hybrid Data Layer, Intelligence Layer, Experimentation Layer & Hybrid Orchestration Layer (Adtech & Martech)
Why AI Readiness Matters (and Why Content Creation Isn’t Enough)
In marketing circles these days, “AI” is often conflated with content generation or quick-fix personalization. However, AI-driven marketing in a noisy world requires:
Constant iteration and experimentation
Continuous feedback loops
A composable architecture that leverages existing tools and data within a company for a scalable approach
Today’s customers shift preferences rapidly, making it impossible to deliver meaningful personalization—or effective testing—if your data foundation is a mess. That’s why a well-structured, composable CDP framework is essential. Only then can you deliver the right experience to the right audience, at the right time and on the right channel.
Key Point: Personalization is much more than inserting a first name in an email or making a single recommendation. You need an adaptive engine that learns over time—determining which offers, timing, and messaging resonate best with each individual.
ID Graph vs. Intent Graph vs. Interest Graph: The New Frontier
Most traditional CDPs handle ID Graphs in some way, uniting cookies, emails, device IDs—the “who”of first party data mostly. But modern AI-ready CDPs engines also require Intent and Interest layers to capture when and why people engage across social & community networks and also leverage unstructured data. Let’s break that down:
ID Graph: The “Who”
What It Does: Consolidates identifiers into a single profile (cookies, emails, activity & devices, etc.).
Why It Matters: You need a consistent 360° view or profile of each customer to avoid fragmented interactions.
Intent Graph: The “When”
What It Does: Picks up signals of near-term interest—browsing patterns, repeat visits, price checks, cart abandonment.
Why It Matters: You can’t miss the prime window when a prospect is ready to engage.
Interest Graph: The “Why”
What It Does: Maps passions, preferences, resonance or beliefs—like sustainability, wellness, fandom or sporting interests.
Why It Matters: It uncovers the deeper motivation behind actions, driving more authentic and long-lasting engagement.
The iCustomer Lens: Building a Composable, AI-Native Decision Intelligence Platform
At iCustomer, we’re committed to helping enterprises maximize their first-party data. Our approach aligns with these three core graphs—ID, Intent, and Interest—to create an Intelligence layer that’s truly AI-ready for activation or more.
Centralize & Cleanse
Integrate 1P and 3P data into a composable data warehouse (e.g., Snowflake, Databricks).
Ensure it’s well-modeled and consistent—essential for everything from analytics to AI.
Establish ID, Intent & Interest Graphs
ID Graph (Who): A unified, 360° view of each customer profile.
Intent Graph (When): Signals that reveal purchase readiness or heightened interest.
Interest Graph (Why): Deep insights into motivations and passions.
Activate with Real-Time Intelligence
Feed these insights into every critical channel where an individual prefers or pays attention—email, SMS, in-app, social ads, chat etc.
Ongoing behavioral data (clicks, purchases, interactions) feeds back in, enabling reinforcement learning and continuous optimization.
Real-World Example: Reaching SMBs and Prosumers Through Social & Community Engagement
A leading cloud storage and collaboration provider set out to expand its user base among small businesses and prosumers—independent professionals, solo entrepreneurs, and power users—who actively share insights on social media and within niche communities. Here’s how we leveraged Who, When, and Why to create a social engagement strategy that really stuck.
1. Who (ID Graph)
Pinpointing SMBs & Prosumers
We gathered data from social platforms (e.g., Facebook Groups, LinkedIn communities), review sites (like G2, Capterra), and discussion forums (Slack channels, Reddit threads) to identify small business owners, freelancers, and tech-savvy professionals in need of robust storage solutions.Community-Focused Profiles
By unifying handles, membership details, and recurring content interests, we pieced together comprehensive profiles of each person’s online presence—clarifying precisely who our prime prospects were.
2. When (Intent Graph)
Monitoring Social Signals & Discussions
We tracked real-time conversations about “scalable storage,” “collaboration tool recommendations,” or frustration with existing solutions—particularly within popular small business and prosumer Facebook Groups, LinkedIn threads, and Slack channels.Business Trigger Announcements
Beyond direct complaints or recommendations, we also monitored key business updates such as funding announcements, new product launches, or plans to expand remote teams—public signals indicating a higher likelihood of needing upgraded storage and collaboration tools.Timely Engagement
Once we spotted these discussions or trigger events—be it a startup announcing a new round of funding or a solo-preneur hiring additional contractors—we presented targeted offers (like how-to guides, special discounts, or feature highlights) exactly when these users were most receptive.
3. Why (Interest Graph)
Uncovering Core Motivations
By analyzing user-generated posts, peer reviews, and Q&A threads, we identified the top reasons SMBs and prosumers sought a new solution: affordability, ease of use, and flexible collaboration features (like version control or large file-sharing).Tailoring the Value Proposition
Armed with these insights, we shaped our messaging around cost-effective pricing tiers, intuitive setup, and advanced collaboration capabilities—directly reflecting why these segments needed more robust solutions.
Activated Campaigns
Live Q&A & AMA Sessions
We hosted open forums in relevant LinkedIn/Facebook communities, inviting users to ask questions about data security, shared file management, or real-time collaboration. This established trust and visibility.Influencer & Ambassador Partnerships
We collaborated with micro-influencers known for tech reviews and productivity tips, as well as power users who showed a knack for guiding others through best practices—giving the brand a trusted voice in targeted circles.Promotional “Moment Marketing”
We timed special offers or product demos around those key “business triggers.” For instance, if a small agency posted about on-boarding new clients, we’d immediately share how easy it is to scale their storage plan.
Results
2.8× surge in sign-ups from SMB and prosumer audiences across social communities
+40% jump in trial-to-paid conversions tied to users who engaged with live Q&As or triggered offers
Significant increase in positive user reviews and word-of-mouth recommendations on platforms like G2 and Twitter
Why It Worked
By uniting the ID Graph (recognizing who the SMBs and prosumers are in various communities), the Intent Graph (knowing when they’re seeking new solutions or hitting growth milestones), and the Interest Graph (capturing why cost, simplicity, and collaboration are so critical), this cloud storage provider’s social engagement became a powerful acquisition engine. They reached the right people, at the right moments, with the right message—turning organic chatter into concrete adoption.
What’s Next? The Privacy-First, AI-Powered World
As third-party cookies still remain a challenge and data regulations tighten, first-party data becomes even more critical. But privacy and personalization can coexist when you build with a composable, AI-native architecture:
Decentralized Identity: Let customers control their data, yet still deliver personalized experiences.
LLM Enrichment: Use large language models to parse unstructured inputs (podcast transcripts, chat logs) for deeper insight into each user’s intent and interests.
Multi Agentic AI Architecture: Stateful Agents to leverage Ontology & semantics of business while keeping the data quality in place and get human approve the task or goal driven approach
Conclusion: The Future Belongs to the Composable, AI-Native Intelligence Layer
Despite the shakeups among CDP vendors, the CDP framework remains essential—especially one that integrates ID, Intent, and Interest Graphs into a single, flexible system. My experience with iCustomer has shown that a unified data foundation is crucial for true 1:1 personalization and continuous experimentation.
If you're frustrated with a legacy CDP or superficial personalization, now is the time to revisit your data strategy. Your current tool might be on life support, but with the rapid growth of data and the power of AI, a next-gen CDP framework—rooted in first-party data and real-time intelligence—can help you deliver the kind of experiences customers expect today.
Thanks for reading! If you have any questions on building a composable, graph-driven CDP framework, feel free to reach out. The AI era is here—let’s make sure your data is ready.