Contextual CDPs: Building the bridge
Part 4 of 6: What connects today's CDPs to tomorrow’s agent-driven context
If the first three parts of this series explored what’s possible when context becomes the driving force behind customer data, then this one asks a more grounded question:
How do we actually get there?
I’ve spoken with teams who are genuinely excited by the idea of using memory, inference, and context to make their CDPs smarter. But many feel stuck between inspiration and implementation. They see the potential, but don’t quite know where to start.
Let’s be honest, the martech stack is already complex enough. The last thing anyone wants is to bolt on a new set of speculative features without knowing whether they’ll break the flow or muddy governance. So what does a practical path forward look like?
Think in layers, not replacements
You don’t need to reinvent your CDP. You need to extend its awareness.
Most current platforms do well at storing and activating structured data. But the moment you ask them to interpret or recall patterns over time, they fall short. This is where contextual add-ons come in. Think of them as small, composable services that sit just outside the core CDP and help it "think" a little more like a person.
Example: a lightweight memory service that summarizes support interactions and flags emotional tone. It doesn’t change the CDP’s schema. It simply offers context to the tools that need it, your support dashboard, your outbound campaign planner, your preference center.
The same goes for RAG-style retrieval services. Instead of forcing every profile query through a rigid API, you can set up a context agent that knows how to pull the right customer fragments depending on what the asking system needs.
Personalized email? Fetch behavior summaries.
Support ticket triage? Pull recent complaints and loyalty status.
Different tools, different context views.
Start with what you know (and own)
It’s tempting to chase external enrichment or LLM-powered features out of the gate, but the most valuable context often lives right under your nose. Your CRM notes, NPS responses, support call transcripts, live chat logs, all of that is context just waiting to be shaped.
And shaping doesn’t require massive AI investments. In many cases, simple embeddings and summary layers are enough to create a memory scaffold that gives other tools more awareness of the customer’s current state.
I’ve seen teams use this to improve everything from suppression logic to churn prediction, all without overhauling their stack. The trick is to make these memory and inference layers available where decisions are being made, not buried in a back-end data warehouse.
Real teams, real boundaries
Let’s talk about the human side for a moment. Most martech teams aren’t staffed to build and maintain full-scale contextual infrastructure. And that’s okay. The goal isn’t to go from zero to omniscient AI in a quarter. The goal is to make slightly better decisions, based on slightly better context.
That starts with shared language. Are your data, marketing, and engineering teams aligned on what "context" even means in your organization? Do they know what kind of memory would actually support your use cases? Are there already conversations happening where someone says, “If only the system remembered X...”?
Start there. Then ask what’s currently missing from the decision moment. That missing piece is often the first place a context protocol or memory layer can add value.
Context is an interface, too
The part that often gets missed is that context should be treated not only as data, but as an integral part of the user experience. If your internal tools or customer-facing systems don’t reflect a shared understanding of the customer, the context work stays invisible.
This doesn’t mean every UI needs a timeline or AI-generated summary, but it does mean giving operators and end-users the ability to adjust, annotate, or even push back on what the system believes about someone. That dialogue is where trust gets built, both internally and externally.
Some of the most interesting work I’ve seen involves internal agent tools that surface a real-time context panel beside every action. Not to tell the operator what to do, but to offer a sense of the moment. A customer’s last mood. Their likely goal. The patterns that matter.
When teams can see context, they start to build with it.
Looking ahead
The tools are catching up. The interest is there. What’s needed now is more imagination paired with more grounded experimentation. You don’t need permission to explore these ideas. You need a use case, a memory scaffold, and a question worth answering.
In Part 5, I’ll take a closer look at some speculative architectures and zero-in on how teams might structure these contextual overlays without creating a mess of one-off systems. Because composability is only valuable when it stays coherent.
For now, I’ll leave you with this: you don’t need a perfect plan to get started. You just need to believe that your customer data has more to say, if only your systems could listen better.
Part 3: Trust as a feature
Part 4: Composable, not chaotic
Part 6: Contextual Fluency