Robert Helewka
64fb83257d
- Add 202512_GenesysCX TEI study (config, seed data, notebooks, README) with NPV $10.8M / ROI 266% including AI-token cost line - Add explicit `key` parameter to all chart wrappers in app/components to prevent StreamlitDuplicateElementId errors when the same figure type renders across Summary/Benefits/Costs tabs - Render benefits bar and cost pie charts on their respective tabs - Add benefits_vs_costs_by_year chart wrapper
113 lines
4.4 KiB
Python
113 lines
4.4 KiB
Python
"""
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Scenario definitions — Floor / Realistic / Stretch.
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Every scenario parameter the cost and benefit engines read lives here;
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no magic numbers in the calculation modules. Ships with the spec
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defaults; callers may construct custom :class:`Scenario` objects.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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@dataclass
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class Scenario:
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name: str
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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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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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# year -> fraction of full benefit realized
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benefit_realization: dict[int, float] = field(default_factory=dict)
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# year -> fraction of steady-state consumption cost incurred.
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# Per-user licenses are paid in full from day 1; consumption meters
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# ramp with usage (default Y1 = 70%).
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consumption_cost_realization: dict[int, float] = field(
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default_factory=lambda: {1: 0.70, 2: 1.0, 3: 1.0}
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)
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def realization(self, year: int) -> float:
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if year in self.benefit_realization:
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return self.benefit_realization[year]
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last = max(self.benefit_realization, default=0)
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return self.benefit_realization.get(last, 1.0) if year > last else 0.0
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def cost_realization(self, year: int) -> float:
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if year in self.consumption_cost_realization:
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return self.consumption_cost_realization[year]
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last = max(self.consumption_cost_realization, default=0)
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return (
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self.consumption_cost_realization.get(last, 1.0) if year > last else 0.0
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)
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#: Benefit reduction parameters. ``claim`` = Genesys ROI-doc figure;
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#: ``realistic`` = pressure-tested midpoint of the spec's Y1 range.
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#: The benefit engine uses ``realistic`` by default; ``claim`` powers
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#: the side-by-side comparison view.
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BENEFIT_PARAMS: dict[str, dict[str, float]] = {
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"voice_aht_knowledge_reduction": {"claim": 0.094, "realistic": 0.055}, # 4-7% Y1
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"voice_acw_reduction": {"claim": 1.00, "realistic": 0.40}, # 30-50% Y1
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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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# 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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}
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SCENARIOS: dict[str, Scenario] = {
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"floor": Scenario(
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name="floor",
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voice_bot_deflection=0.20,
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voice_bot_avg_minutes=1.0,
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agentic_va_deflection=0.05,
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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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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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name="realistic",
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voice_bot_deflection=0.35,
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voice_bot_avg_minutes=1.5,
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agentic_va_deflection=0.15,
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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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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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name="stretch",
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voice_bot_deflection=0.50,
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voice_bot_avg_minutes=2.0,
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agentic_va_deflection=0.25,
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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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benefit_realization={1: 0.75, 2: 0.95, 3: 1.00},
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),
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}
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def get_scenario(name: str) -> Scenario:
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try:
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return SCENARIOS[name.lower()]
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except KeyError as e:
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raise KeyError(
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f"Unknown scenario {name!r}. Valid: {sorted(SCENARIOS)}"
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) from e
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