ORCH_PROTOCOL_02 // PROTOCOL_SPEC
Audience Translation Spec
Convert account-level cohorts into channel-specific audiences so every platform activates the same strategy—even when targeting mechanics differ.
Audience Drift Across Platforms
An account list rarely translates cleanly across platforms. LinkedIn uses titles, DSPs use domains, and retargeting uses cookies. Without translation logic, your audiences diverge, causing fragmented messaging and wasted reach.
CRITICAL FAILURE MODES
- ✕ LinkedIn targets the wrong personas because job titles weren't normalized
- ✕ Programmatic expands beyond ICP through uncontrolled lookalike modeling
- ✕ Audience sizes vary wildly between channels, breaking sequencing logic
Audience Translation Logic
SOURCE INPUTS
Account Cohort Lists
Persona Role Definitions
Domain / Company Data
Platform Targeting Capabilities
→
CHANNEL OUTPUT
Channel-Specific Audiences
Logic: [Account Cohort] + [Persona Role] → Platform Targeting Rules
PLATFORM ON (ABM Platforms)
We push cohort definitions into each platform using native targeting structures and automated API syncs to ensure real-time parity.
Tier1_Eval → LinkedIn Job Titles + DSP Domain List
PLATFORM OFF (Audience Manifest)
We generate channel-specific manifests for media partners to manually upload and verify against a master match-rate baseline.
Manifest: Domain list + Title clusters + Exclusions
Artifacts You Receive
- ✓Audience Translation Matrix (Platform Mapping)
- ✓Channel-Specific Audience Manifests
- ✓Persona Targeting Definition master
- ✓Platform Targeting Capability Audit
Implementation Steps
WK 1
Define account cohorts and persona targets within the central OS schema.
WK 2
Translate master audiences into channel-specific targeting logic (Title vs Domain).
WK 3
Deploy and validate audience consistency and match rates across all platforms.