Corporate Legal Workflow for Personalized Digital Advertising
Interactive workflow showing data → process → risk stages for corporate legal teams.
This workflow was created with the assistance of ChatGPT.
Stage 1 — Data Acquisition
🗂️ Data Sources
- Behavioral tracking signals
- Mobile & web identifiers
- Location & purchase data
⚙️ Collection Mechanisms
- Cookies & pixels
- SDK & API integrations
- Data broker aggregations
⚠️ Structural Risks
- Non-consensual data capture
- Lack of transparency
- Cross-device linkage risks
Stage 2 — Aggregation & Profiling
🗂️ Profile Construction
- Behavioral profiles
- Segmentation & clusters
- Predictive attributes
⚙️ Data Enrichment
- 3rd-party integration
- CRM linkage
- Offline data matching
⚠️ Structural Risks
- Profiling opacity
- Bias in segmentation
- Inaccuracy implications
Stage 3 — Algorithmic Targeting
🗂️ Targeting Inputs
- Audience segments
- Predictive scoring
- Behavioral signals
⚙️ Decision Systems
- ML optimization
- Dynamic bidding logic
- Automated decisions
⚠️ Structural Risks
- Algorithmic opacity
- Unexplainable outputs
- Discriminatory risk
Stage 4 — Personalized Ad Delivery
🗂️ Ad Infrastructure
- Ad exchanges & DSPs
- SSPs & marketplaces
- Programmatic routing
⚙️ Delivery Mechanisms
- Targeted display ads
- Cross-platform delivery
- Dynamic creative systems
⚠️ Structural Risks
- Dark pattern targeting
- Manipulative personalization
- Transparency gaps
Stage 5 — Feedback & Optimization
🗂️ Feedback Signals
- Clicks & conversions
- Engagement metrics
- View-through tracking
⚙️ Optimization Systems
- Audience refinement
- Retraining logic
- Automated adjustments
⚠️ Structural Risks
- Feedback loops opacity
- Performance bias
- Governance gaps