Why Tanqory Treats Marketing Automation as a Decision System
Research blog — architecture, methods, and evidence for event-driven marketing automation
Executive Summary
Marketing automation fails when it is treated as a batch campaign engine. Tanqory treats it as a decision system: event-driven sensing, causal and predictive reasoning, policy-aware execution, and tight observability. Signals span behavior, context, consent, trust, and cost. Decisions consider fairness, latency, and privacy. This post details Tanqory's Marketing Decision Fabric (MDF) and Execution Mesh (EM), showing how telemetry becomes safe, fast, and auditable actions across channels.
Key points:
- Event-first ingest with schema, consent, and residency; avoids brittle batch dependencies.
- Causal estimators and synthetic controls separate signal from noise (Athey and Imbens, 2016; Abadie et al., 2010).
- Contextual bandits and planners act under fairness and latency constraints (Agarwal et al., 2014; Patil et al., 2021).
- Control-inspired regulators smooth exposure to prevent KPI whiplash (Box et al., 2015; Deng et al., 2013).
- Observability, privacy, and governance align with trust and regulatory expectations (Dwork and Roth, 2014; Kohavi et al., 2020).
1. Problem Framing: From Campaigns to Decisions
1.1 Traditional Automation Gaps
- Batch segmentation leads to stale targeting; intent shifts faster than lists refresh.
- Correlation-driven rules misread causality; confounding inflates uplift claims (Pearl, 2009).
- Fatigue and fairness risks emerge when exposure is unmanaged (Patil et al., 2021).
- Latency and reliability are invisible; failures harm trust (Gefen et al., 2003).
- Privacy and consent checks are bolted on, not enforced on-path (Dwork and Roth, 2014).
1.2 Consequences
- Over-messaging and churn due to fatigue.
- Missed demand windows when signals arrive late.
- Biased uplift that alienates cohorts or regions.
- Compliance risk from insufficient consent/residency enforcement.
1.3 Tanqory's Approach
MDF and EM operate as an event-driven, constraint-aware decision loop. Signals flow continuously; decisions are evaluated with causal and predictive lenses; actions respect budgets on fairness, latency, privacy, and cost; observability and auditability keep trust intact.
2. Design Principles
- Event-first: all meaningful state changes become events with schema contracts and consent/residency flags.
- Causal preference: uplift, synthetic controls, and natural experiments trump pure correlation (Athey and Imbens, 2016; Abadie et al., 2010).
- Constraint-aware: fairness, latency, privacy, and cost guardrails at execution time (Patil et al., 2021; Dwork and Roth, 2014).
- Human-in-the-loop: high-impact changes require review; explanations accompany recommendations (Lundberg and Lee, 2017).
- Multi-objective: optimize conversion, revenue, cost, trust, and fatigue jointly.
- Observability: trace events to actions to outcomes; exportable for auditors (Kohavi et al., 2020).
- Multi-region: per-region routing, localization, and fairness thresholds.
3. Architecture Overview
3.1 Marketing Decision Fabric (MDF)
- Ingest and Contracts: streaming bus; schema registry; consent/residency tagging; dedupe and watermarking (Kleppmann, 2017).
- Feature/Signal Store: online/offline parity; freshness SLAs (<15 minutes for critical behavior); lineage captured.
- Signal Quality Monitors: null thresholds, distribution drift (Kullback and Leibler, 1951), volume anomalies.
3.2 Execution Mesh (EM)
- Causal Services: uplift models, synthetic controls for non-experimental regions, IVs when natural experiments appear.
- Predictive Models: boosted trees for reliability (Chen and Guestrin, 2016); lightweight Transformers for sequence signals under latency caps (Vaswani et al., 2017).
- Policy Engine: contextual bandits with fairness and latency constraints (Agarwal et al., 2014; Patil et al., 2021); planners for multi-step flows (channel + timing + offer).
- Safety Layer: consent/residency checks, bias scans, p95/p99 latency guards, fatigue caps.
- Action Connectors: email, push, in-app, SMS, on-site modules, partner APIs; all with rollback paths.
4. Signal Taxonomy
4.1 Behavioral and Intent
- Search queries, reformulation depth; query entropy as focus proxy (Huang et al., 2020).
- Clickstreams, scroll depth, dwell, backtracks; cart edits, wishlists, abandons.
- Session context: device, locale, bandwidth, referral, recency.
4.2 Channel and Delivery
- Open/click/engagement across email, push, in-app, SMS; delivery errors.
- Send-time and frequency histories; fatigue markers.
- Latency and render metrics by channel and device.
4.3 Commerce and Operations
- Inventory exposure and promise accuracy; substitutions and stockouts.
- Pricing and discounts; elasticity probes and price-check loops.
- Payment retries and declines; fraud scores and issuer patterns.
- Support tickets, CSAT, refunds; trust posture (Gefen et al., 2003).
4.4 External and Macro
- Exchange rates, inflation, holidays, local events.
- Trend indices from search/social (Choi and Varian, 2012); guardrails against big-data traps (Lazer et al., 2014).
- Weather/mobility where delivery affects experience.
4.5 Data Quality
- Freshness coverage; alert <90% refreshed.
- Schema rejects, volume anomalies.
- Field-level drift via KL; lineage completeness for audit.
