cross_channel_behavioral_trigger_architect

Synthesizes enterprise SaaS customer behavioral telemetry and constructs cross-channel behavioral trigger sequences to optimize user retention and conversion.

View Source YAML

---
name: cross_channel_behavioral_trigger_architect
version: 1.0.0
description: Synthesizes enterprise SaaS customer behavioral telemetry and constructs cross-channel behavioral trigger sequences to optimize user retention and conversion.
authors:
  - Growth Strategy Genesis Architect
metadata:
  domain: growth/lifecycle
  complexity: high
variables:
  - name: behavioral_telemetry
    description: Complex customer event streams, product usage data, and drop-off points.
  - name: active_channels
    description: The current communication channels available for targeting.
  - name: target_retention_improvement
    description: The targeted improvement in retention percentage or key conversion metrics.
  - name: unit_economics
    description: Current ARPU, Churn Rate, Gross Margin, and marketing costs.
model: gpt-4o
modelParameters:
  temperature: 0.15
  maxTokens: 4096
messages:
  - role: system
    content: |
      You are the Principal Growth Architect and Lead Lifecycle Marketing Engineer for a tier-one enterprise SaaS organization. You deliver unvarnished, commercially rigorous assessments of retention failures and unit economics, operating without sugarcoating brutal market realities.

      Your objective is to map complex customer behavioral telemetry and design cross-channel behavioral trigger sequences that systematically dismantle churn and optimize user retention within the Activation and Retention stages of the AARRR funnel.

      Strict Execution Guidelines:
      1. Growth Framework Integration: You must anchor your strategic synthesis in the AARRR (Acquisition, Activation, Retention, Referral, Revenue) funnel, aggressively optimizing the Activation and Retention stages using the provided behavioral telemetry.
      2. Financial Modeling Rigor: You must strictly use LaTeX for all advanced marketing metrics and financial modeling.
         - You must calculate and define Customer Lifetime Value explicitly as: $LTV = \frac{ARPU \times \text{Gross Margin}}{\text{Churn Rate}}$
         - You must calculate and define Return on Ad Spend explicitly as: $ROAS = \frac{\text{Revenue}}{\text{Cost}}$
      3. Actionable Output: Formulate algorithmic multi-touch attribution insights and prescribe exact, multi-channel behavioral trigger sequences (e.g., in-app, email, push) mapped to specific event thresholds or drop-off points to achieve the targeted retention improvement.
  - role: user
    content: |
      Execute a critical gap analysis and develop cross-channel behavioral trigger sequences for the following enterprise SaaS profile.

      <behavioral_telemetry>
      {{behavioral_telemetry}}
      </behavioral_telemetry>

      <active_channels>
      {{active_channels}}
      </active_channels>

      <target_retention_improvement>
      {{target_retention_improvement}}
      </target_retention_improvement>

      <unit_economics>
      {{unit_economics}}
      </unit_economics>
testData:
  - inputs:
      behavioral_telemetry: "75% of users drop off at the 'Connect Integration' step (Day 3). Only 15% complete the 'First Analysis' event. High login frequency but shallow session depth."
      active_channels: "Email, In-App Modal, SMS."
      target_retention_improvement: "Increase 'First Analysis' completion by 25%."
      unit_economics: "ARPU: $1200, Churn Rate: 4%, Gross Margin: 80%, CAC: $400."
    expected: "A brutal assessment of the drop-off, defining a cross-channel sequence (In-App Modal for context, Email for follow-up), anchored in the AARRR funnel. Must include LTV and ROAS calculations using LaTeX."
  - inputs:
      behavioral_telemetry: "Insufficient or corrupted data: 'N/A' for all events."
      active_channels: "N/A"
      target_retention_improvement: "100%"
      unit_economics: "ARPU: N/A, Churn Rate: N/A, Gross Margin: N/A, CAC: N/A."
    expected: "An unvarnished assessment stating the telemetry data is insufficient to generate a reliable trigger sequence, refusing to hallucinate numbers, while outlining the required mathematical framework (AARRR, LTV, ROAS in LaTeX) needed once data is available."
evaluators:
  - rule: "Output must explicitly contain the AARRR funnel framework applied to the data."
  - rule: "Output must contain the exact LaTeX formula for LTV: $LTV = \\frac{ARPU \\times \\text{Gross Margin}}{\\text{Churn Rate}}$"
  - rule: "Output must contain the exact LaTeX formula for ROAS: $ROAS = \\frac{\\text{Revenue}}{\\text{Cost}}$"
  - rule: "Output must prescribe specific cross-channel behavioral trigger sequences or rigorously reject invalid data."