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@@ -18,12 +18,28 @@ class Scenario:
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# ── Cost-side drivers ───────────────────────────────────────────
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voice_bot_deflection: float # share of voice volume deflected to bot
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voice_bot_avg_minutes: float # bot minutes per deflected call
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agentic_va_deflection: float # share of voice volume to agentic VA
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# Agentic VA deflection is INCREMENTAL — applied to the residual volume
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# after the voice bot has already handled its share (layered model).
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# Effective total deflection = bot_rate + (1 − bot_rate) × va_rate.
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agentic_va_deflection: float # share of RESIDUAL voice volume to agentic VA
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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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# completion_rate — share of "deflected" calls that don't escalate to an agent
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# mid-session (bot/VA fully handles the interaction).
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# labour_realization — staffing flexibility: deflected volume doesn't reduce
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# headcount 1:1 due to minimums, shrinkage, occupancy ceilings.
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# callback_discount — fraction of deflected calls that re-enter as repeat contacts
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# (poorly-handled deflections drive callbacks).
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# Combined realistic factor: 0.70 × 0.80 × (1 − 0.05) ≈ 0.53
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va_completion_rate: float = 0.70
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va_labour_realization: float = 0.80
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va_callback_discount: float = 0.05
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# year -> fraction of full benefit realized
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benefit_realization: dict[int, float] = field(default_factory=dict)
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@@ -59,10 +75,16 @@ 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": 0.30, "realistic": 0.30}, # × acceptance
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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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# Virtual Agent realization factors.
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# ``claim`` = 100% realization (original model assumption — no haircuts).
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# ``realistic`` = production-calibrated midpoints per the spec analysis.
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"va_completion_rate": {"claim": 1.00, "realistic": 0.70}, # 60-75% voice bot; 50-70% agentic VA Y1
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"va_labour_realization": {"claim": 1.00, "realistic": 0.80}, # 70-85% staffing flexibility
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"va_callback_discount": {"claim": 0.00, "realistic": 0.05}, # 5-10% deflected re-enter as repeat contacts
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}
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@@ -76,6 +98,11 @@ SCENARIOS: dict[str, Scenario] = {
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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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va_labour_realization=0.70,
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va_callback_discount=0.05,
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benefit_realization={1: 0.30, 2: 0.60, 3: 0.80},
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),
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"realistic": Scenario(
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@@ -87,6 +114,11 @@ SCENARIOS: dict[str, Scenario] = {
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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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va_labour_realization=0.80,
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va_callback_discount=0.05,
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benefit_realization={1: 0.50, 2: 0.80, 3: 0.95},
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),
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"stretch": Scenario(
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@@ -98,6 +130,11 @@ SCENARIOS: dict[str, Scenario] = {
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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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va_labour_realization=0.85,
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va_callback_discount=0.03,
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benefit_realization={1: 0.75, 2: 0.95, 3: 1.00},
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),
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}
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