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Engagement score

Client Engagement Intelligence Workspace

Monitor composite engagement across cohorts, validate AI pipeline quality, and activate agentic playbooks— all in a workspace designed for BrightNTech's regulated clients.

Live cohort metricsAI pipeline QAAgentic guidance
Engagement (4w avg)
67
Composite index
Conversion Δ
+20.0%
W08 vs W04
AOV Δ
+70.0%
W08 vs W01
Pipeline QA Pass
90%
Model + Agent
Engagement Over Time
Weekly composite index with AOV & conversion overlays
Model & Agent Insights
Transformer summaries, ML predictions, agentic playbooks
Transformer Summary

Semantic clustering shows 3 dominant intents: platform consolidation, AI governance, and omni-channel analytics. Content resonance +18% WoW.

ML Uplift Estimate

Propensity model (GBM) predicts +9–12% meeting conversion uplift for cohort B1 given tailored case studies and 14-day cadence.

Agentic Playbook

Orchestrator recommends a 3-step sequence: (1) diagnostic poll, (2) 30-min value mapping, (3) ROI simulator. SLA gate: security & compliance.

Pipeline Review QA
Pass/Fail by stage

Transformer QA checks prompt hygiene, factual grounding, and policy alignment. ML uplift focuses on calibrated propensity scores. The final agent verdict validates orchestration guardrails before any customer-facing action.

Adoption Ladder ↔ Engagement
4w Avg: 67 → Convinced

Thresholds — Not Aware (0–39), Aware (40–54), Enthusiast (55–64), Convinced (65–74), Ambassador (75+).

Opportunity Heatmap
Engagement × Revenue Potential
72
80
88
58
65
70
30
44
55

Top-left: High revenue potential × Low engagement → Prioritize targeted outreach.

  • 1. Send diagnostic poll to B1 cohort (security posture, use-cases, constraints).
  • 2. Book 30-min value mapping focused on ROI levers (time-to-value, risk, cost).
  • 3. Share tailored case studies (regulated industries). Track reply latency & depth.
Demo only. Wire this workspace to CRM (Salesforce/HubSpot), marketing automation, product analytics, and compliance telemetry. Use a feature store for computed metrics, then feed transformer summaries, ML uplift predictions, and agentic playbooks into these tiles.