CTM Calculators: Add PS billing milestones, Adjust Genesys RAMP
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@@ -25,7 +25,6 @@ class Scenario:
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voice_summarization_eligibility: float
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voice_knowledge_eligibility: float
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email_auto_respond_rate: float # share of email auto-responded
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email_auto_suggest_acceptance: float
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# ── Virtual Agent benefit realization factors ───────────────────
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# Applied to both Voice Bot and Agentic VA deflection benefits.
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@@ -75,7 +74,6 @@ BENEFIT_PARAMS: dict[str, dict[str, float]] = {
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"digital_aht_reduction": {"claim": 0.18, "realistic": 0.085}, # 5-12% Y1
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"digital_acw_reduction": {"claim": 1.00, "realistic": 0.40}, # 30-50% Y1
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"sta_aht_reduction": {"claim": 0.04, "realistic": 0.015}, # 1-2% Y1
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"email_auto_suggest_time_saving": {"claim": 0.40, "realistic": 0.30}, # × acceptance; Genesys claims 40%
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# ESTIMATED lines (no Genesys claim published):
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"supervisor_copilot_time_saving": {"claim": 0.10, "realistic": 0.05},
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"predictive_routing_aht_reduction": {"claim": 0.04, "realistic": 0.02},
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@@ -97,7 +95,6 @@ SCENARIOS: dict[str, Scenario] = {
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voice_summarization_eligibility=0.50,
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voice_knowledge_eligibility=0.40,
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email_auto_respond_rate=0.10,
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email_auto_suggest_acceptance=0.25,
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# VA realization: conservative — low completion, limited staffing flex
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# Combined: 0.60 × 0.70 × (1 − 0.05) ≈ 0.40
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va_completion_rate=0.60,
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@@ -113,7 +110,6 @@ SCENARIOS: dict[str, Scenario] = {
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voice_summarization_eligibility=0.70,
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voice_knowledge_eligibility=0.60,
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email_auto_respond_rate=0.20,
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email_auto_suggest_acceptance=0.40,
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# VA realization: production midpoints per spec analysis
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# Combined: 0.70 × 0.80 × (1 − 0.05) ≈ 0.53
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va_completion_rate=0.70,
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@@ -129,7 +125,6 @@ SCENARIOS: dict[str, Scenario] = {
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voice_summarization_eligibility=0.90,
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voice_knowledge_eligibility=0.80,
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email_auto_respond_rate=0.50,
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email_auto_suggest_acceptance=0.60,
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# VA realization: optimistic — high completion, good staffing flexibility
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# Combined: 0.75 × 0.85 × (1 − 0.03) ≈ 0.62
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va_completion_rate=0.75,
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