Part 2: Building an activation-ready marketing stack

In Part 1 of this series, we explored why MAP is necessary for modern marketing stacks. Now we’ll dive into how it works in real-world environments without replacing existing tools or imposing a rigid architecture.

To do that, we’ll take a look at how MAP enables composability and coordination across varied MarTech ecosystems. The key concept: MAP amplifies your CDP, your personalization engine, or your reporting tools.

As explained in Part 1, to understand the role MAP plays in the stack, it’s important to think in terms of two functional zones:

• Zone 1 is where MAP resides. This is the data foundation: ingesting data from all sources, resolving identity, applying business logic, and staging data for downstream activation.

• Zone 2 is where CDPs and real-time decision engines operate. These platforms consume the enriched data from MAP to drive segmentation, personalization, and customer journey orchestration.

MAP integrates with Zone 1 (data prep and enrichment) and supports seamless delivery into Zone 2 (CDPs and decisioning engines) without slowing teams down.

To achieve that, MAP operates across three core layers:

1. Ingestion: Accepts behavioral and transactional data through APIs, message queues, or cloud drops. Sources include web/app trackers, CRMs, partner feeds, IoT devices, and media platforms.

2. Processing: Cleans, normalizes, joins, and enriches data. Supports business logic like campaign taxonomy, identity resolution, incentive mapping, and vehicle configuration preferences.

3. Delivery: Routes enriched data to CDPs, AI/ML models, real-time decision engines, and reporting platforms with appropriate latency and granularity.

MAP keeps these tools in sync with cleaned, structured, identity-resolved data.

Use case: Activating experiences in a real-world scenario

We can illustrate this by returning to the use case of an EV battery supplier and automotive OEM, introduced in Part 1. The two entities are partnering to launch electric classic cars across a multi-channel retail and dealer network.

The OEM and battery supplier used MAP to support Zone 1 functions, serving as the intelligence hub ingesting dealership CRM data, web interactions, test drive bookings, incentive eligibility by region, and usage data from connected vehicles.

Enriched profiles were built with regional charging readiness, commute patterns, and preferred purchase methods. Once passed to the CDP in Zone 2, these profiles powered real-time test drive reminders, charger rebate offers, and personalized finance plans.

MAP deployments in this scenario included:

• Ingestion from retail APIs, third-party incentive data, and IoT signals

• Normalization and joining of customer behavior and infrastructure readiness

• Shared governance between the OEM and battery supplier for joint campaign insights

• Observability tools and version-controlled configuration for controlled rollouts

MAP made it possible to activate experiences that reflected not just the customer’s preferences, but also regional constraints and emerging infrastructure realities.

Read on in Part 3 of this series, where we shift from individual implementations to see how MAP supports a shared go-to-market strategy.

• Data
• Marketing Technology
• Marketing Technology Optimization
• Marketing Technology Strategy
• Data Analytics
• Data Architecture
• Data Strategy
• Marketing Analytics Platform (MAP)
• MarTech Stack
• Integrating data