How mature is your architecture?

A five-to-seven-minute, vendor-neutral self-check across eight dimensions, spanning data, decisioning, and the operating model around them. You get a maturity tier and a focused place to start, not a sales pitch.

1 Data model & profile How unified and governed is your customer profile?

Data lives in silos; no shared profile A profile exists but is rebuilt in batches and partly trusted A governed profile most teams rely on Real-time, governed profile that powers every channel

2 Identity resolution How well do you resolve people across devices and channels?

Mostly channel-by-channel, little stitching Deterministic matching on known identifiers only A maintained identity graph with clear rules Consent-aware identity that travels across the stack

3 Real-time decisioning How real-time is your decisioning?

Batch segments on a schedule Some triggers, mostly scheduled sends Event-triggered journeys are the default Continuous decisioning that adapts within a session

4 Channel orchestration How coordinated is engagement across channels?

Each channel runs its own campaigns Some cross-channel coordination, manual Orchestrated journeys with global frequency rules Journeys run as control systems with holdouts and priority

5 Governance & consent How well is consent and governance built into the stack?

Consent handled ad hoc, per tool Consent captured but inconsistently enforced Centralised consent enforced across systems Consent and data governance are a designed-in layer

6 Measurement & feedback How well do outcomes feed back into decisioning?

Reporting only, after the fact Channel-level metrics, little incrementality Shared metrics and some incrementality testing Outcomes feed back into decisioning as signals

7 Agentic readiness How ready is your stack for AI-driven, autonomous engagement?

Rules only; no model-driven decisions Isolated AI features, not in the core loop Models in decisioning with human oversight Bounded autonomy with guardrails and clear accountability

8 Operating model & enablement How well do people, ownership, and ways of working support the stack?

Tools owned in silos; skills are thin and fragmented Some ownership, but delivery hinges on a few individuals Clear ownership and a repeatable operating model Cross-functional teams, strong enablement, continuous improvement