Data 360 Series | Chapter 5 — Data without action is just storage

Segmentation is where business logic meets data, and activation is where Data 360 reaches out and touches every channel your customer interacts with.

We’ve ingested data, harmonized it, and built unified profiles. Now comes the payoff: using that data to drive real business outcomes. Segmentation and activation are where Data 360 earns its ROI.

Four segment types — know when to use each

The architect guide defines four distinct segment types, each designed for a different latency and use-case profile:

STANDARD SEGMENT — Scheduled batch, 12–24 hrs The primary segment type. Uses the drag-and-drop filter canvas with AND/OR logic. Standard cadence is 12–24 hours; Rapid Publish runs every 1–4 hours for recent engagement data.

REAL-TIME SEGMENT — On-demand, milliseconds Completes instantly based on current profile data and recent events. Highly optimized for immediate personalization. Cannot use exclusion criteria or nested segments — purpose-built for speed over complexity.

EINSTEIN SEGMENTS — Natural language → filters Describe the audience in plain English. Einstein Segment Creation translates it to filter logic — not just syntax translation, but AI reasoning grounded in your actual unified Data 360 profile data. The result reflects what genuinely exists in your data model, not a generic interpretation. No SQL needed — built for marketing analysts.

NESTED & WATERFALL — Reuse & prioritize Nested: use an existing segment as a filter within a new one — saves credits, enforces consistent criteria. Waterfall: hierarchical priority order ensures each customer lands in exactly one, mutually exclusive audience.

Calculated Insights: enriching profiles before segmentation

Before you segment, you often need to compute summary metrics — lifetime value, purchase frequency, days since last purchase. Calculated Insights (CIs) are SQL-based aggregations that run on your DMOs and attach the results back to the Unified Profile. Available in both batch (scheduled) and streaming (near real-time) modes.

Activation: where your data reaches the world

Activation is the process of publishing segment members — along with their supporting profile attributes — to a configured destination. Data 360 supports three distinct delivery methods, each suited to a different use case and architectural pattern:

BATCH ACTIVATION — Scheduled, high-volume Data is staged to Secure Internal Buckets (cloud object storage) or via Secure File Transfer, then ingested by the target system. Supports incremental refresh to reduce volumes. Best for large-scale email campaigns and advertising audience refreshes.

STREAMING ACTIVATION — Near real-time, event-driven Direct API calls sent to the destination endpoint as events occur. Best for automation triggers, real-time personalization, and any scenario where seconds matter rather than hours.

ACTIVATION-TRIGGERED FLOWS — No-code, platform-native On completion, Data 360 populates an Audience DMO which triggers a High Scale Salesforce Flow. The Flow uses External Services and MuleSoft destinations to reach API-based targets. Fastest way to onboard new activation endpoints without code.

Communication governance — consent at the core

Before any activation fires, Data 360 enforces a communication governance layer. A centralized consent model stores all customer preferences — global opt-outs, channel-specific consent, purpose-specific consent — on the Unified Individual Profile.

During activation execution, the platform automatically removes non-consenting individuals from the payload via exclusion filtering. It also applies contact point selection rules to ensure only the single most compliant, preferred contact point is used for the intended channel. Dynamic data masking protects sensitive fields throughout.

This built-in consent enforcement is not optional — it’s how Data 360 ensures GDPR, CCPA, and similar compliance without custom code.

Credit alert: Segmentation is the #1 credit consumer in Data 360. The default lookback period for engagement data is 2 years — meaning every publish processes 2 years of events unless you set a filter. Always specify the lookback period you actually need. And stagger your publish schedules — running too many segments simultaneously causes failures.