docs: introduce Mercury Notebook Deliverable Pattern

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2026-07-08 13:43:12 -04:00
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"""
Scenario definitions — Floor / Realistic / Stretch.
Every scenario parameter the cost and benefit engines read lives here;
no magic numbers in the calculation modules. Ships with the spec
defaults; callers may construct custom :class:`Scenario` objects.
"""
from __future__ import annotations
from dataclasses import dataclass, field
@dataclass
class Scenario:
name: str
# ── Cost-side drivers ───────────────────────────────────────────
voice_bot_deflection: float # share of voice volume deflected to bot
voice_bot_avg_minutes: float # bot minutes per deflected call
# Agentic VA deflection is INCREMENTAL — applied to the residual volume
# after the voice bot has already handled its share (layered model).
# Effective total deflection = bot_rate + (1 bot_rate) × va_rate.
agentic_va_deflection: float # share of RESIDUAL voice volume to agentic VA
voice_summarization_eligibility: float
voice_knowledge_eligibility: float
email_auto_respond_rate: float # share of email auto-responded
# ── Virtual Agent benefit realization factors ───────────────────
# Applied to both Voice Bot and Agentic VA deflection benefits.
# completion_rate — share of "deflected" calls that don't escalate to an agent
# mid-session (bot/VA fully handles the interaction).
# labour_realization — staffing flexibility: deflected volume doesn't reduce
# headcount 1:1 due to minimums, shrinkage, occupancy ceilings.
# callback_discount — fraction of deflected calls that re-enter as repeat contacts
# (poorly-handled deflections drive callbacks).
# Combined realistic factor: 0.70 × 0.80 × (1 0.05) ≈ 0.53
va_completion_rate: float = 0.70
va_labour_realization: float = 0.80
va_callback_discount: float = 0.05
# year -> fraction of full benefit realized
benefit_realization: dict[int, float] = field(default_factory=dict)
# year -> fraction of steady-state consumption cost incurred.
# Per-user licenses are paid in full from day 1; consumption meters
# ramp with usage (default Y1 = 70%).
consumption_cost_realization: dict[int, float] = field(
default_factory=lambda: {1: 0.70, 2: 1.0, 3: 1.0}
)
def realization(self, year: int) -> float:
if year in self.benefit_realization:
return self.benefit_realization[year]
last = max(self.benefit_realization, default=0)
return self.benefit_realization.get(last, 1.0) if year > last else 0.0
def cost_realization(self, year: int) -> float:
if year in self.consumption_cost_realization:
return self.consumption_cost_realization[year]
last = max(self.consumption_cost_realization, default=0)
return (
self.consumption_cost_realization.get(last, 1.0) if year > last else 0.0
)
#: Benefit reduction parameters. ``claim`` = Genesys ROI-doc figure;
#: ``realistic`` = pressure-tested midpoint of the spec's Y1 range.
#: The benefit engine uses ``realistic`` by default; ``claim`` powers
#: the side-by-side comparison view.
BENEFIT_PARAMS: dict[str, dict[str, float]] = {
"voice_aht_knowledge_reduction": {"claim": 0.094, "realistic": 0.055}, # 4-7% Y1
"voice_acw_reduction": {"claim": 1.00, "realistic": 0.40}, # 30-50% Y1
"digital_aht_reduction": {"claim": 0.18, "realistic": 0.085}, # 5-12% Y1
"digital_acw_reduction": {"claim": 1.00, "realistic": 0.40}, # 30-50% Y1
"sta_aht_reduction": {"claim": 0.04, "realistic": 0.015}, # 1-2% Y1
# ESTIMATED lines (no Genesys claim published):
"supervisor_copilot_time_saving": {"claim": 0.10, "realistic": 0.05},
"predictive_routing_aht_reduction": {"claim": 0.04, "realistic": 0.02},
# Virtual Agent realization factors.
# ``claim`` = 100% realization (original model assumption — no haircuts).
# ``realistic`` = production-calibrated midpoints per the spec analysis.
"va_completion_rate": {"claim": 1.00, "realistic": 0.70}, # 60-75% voice bot; 50-70% agentic VA Y1
"va_labour_realization": {"claim": 1.00, "realistic": 0.80}, # 70-85% staffing flexibility
"va_callback_discount": {"claim": 0.00, "realistic": 0.05}, # 5-10% deflected re-enter as repeat contacts
}
SCENARIOS: dict[str, Scenario] = {
"floor": Scenario(
name="floor",
voice_bot_deflection=0.20,
voice_bot_avg_minutes=1.0,
agentic_va_deflection=0.05,
voice_summarization_eligibility=0.50,
voice_knowledge_eligibility=0.40,
email_auto_respond_rate=0.10,
# VA realization: conservative — low completion, limited staffing flex
# Combined: 0.60 × 0.70 × (1 0.05) ≈ 0.40
va_completion_rate=0.60,
va_labour_realization=0.70,
va_callback_discount=0.05,
benefit_realization={1: 0.30, 2: 0.60, 3: 0.80},
),
"realistic": Scenario(
name="realistic",
voice_bot_deflection=0.35,
voice_bot_avg_minutes=1.5,
agentic_va_deflection=0.15,
voice_summarization_eligibility=0.70,
voice_knowledge_eligibility=0.60,
email_auto_respond_rate=0.20,
# VA realization: production midpoints per spec analysis
# Combined: 0.70 × 0.80 × (1 0.05) ≈ 0.53
va_completion_rate=0.70,
va_labour_realization=0.80,
va_callback_discount=0.05,
benefit_realization={1: 0.50, 2: 0.80, 3: 0.95},
),
"stretch": Scenario(
name="stretch",
voice_bot_deflection=0.50,
voice_bot_avg_minutes=2.0,
agentic_va_deflection=0.25,
voice_summarization_eligibility=0.90,
voice_knowledge_eligibility=0.80,
email_auto_respond_rate=0.50,
# VA realization: optimistic — high completion, good staffing flexibility
# Combined: 0.75 × 0.85 × (1 0.03) ≈ 0.62
va_completion_rate=0.75,
va_labour_realization=0.85,
va_callback_discount=0.03,
benefit_realization={1: 0.75, 2: 0.95, 3: 1.00},
),
}
def get_scenario(name: str) -> Scenario:
try:
return SCENARIOS[name.lower()]
except KeyError as e:
raise KeyError(
f"Unknown scenario {name!r}. Valid: {sorted(SCENARIOS)}"
) from e