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CTM Calculators: Add PS billing milestones, Adjust Genesys RAMP

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This commit is contained in:
Robert Helewka
2026-07-08 11:24:57 -04:00
parent b5af65891c
commit a991879061
13 changed files with 4264 additions and 2712 deletions
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59
studies/202512_GenesysCX/ctm-token-calculator/tokencalc/appendix4.py
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@@ -107,6 +107,26 @@ TCO_CONTRACTED: dict[str, float] = {
"ccaas_annual": 3_200_000, # signed licence run-rate (deck pitched $4.3M/yr)
}
#: NTT professional services — SOW billing milestones (🟢 contractual).
#: The deck's verbatim anchor books $2.4M PS in year 1; the signed SOW
#: bills $2,025,446.48 in four milestones split 50/50 across 2026-27.
PS_MILESTONES: list[dict] = [
{"name": "SOW Effective Date", "date": dt.date(2026, 3, 15),
"share": 0.30, "amount": 607_633.94},
{"name": "Start of client UAT (first region)", "date": dt.date(2026, 9, 30),
"share": 0.20, "amount": 405_089.30},
{"name": "Start of client UAT (last region)", "date": dt.date(2027, 6, 30),
"share": 0.30, "amount": 607_633.94},
{"name": "Completion of last go-live migration", "date": dt.date(2027, 9, 30),
"share": 0.20, "amount": 405_089.30},
]
PS_CONTRACTED_TOTAL = sum(m["amount"] for m in PS_MILESTONES)
#: NTT managed services — commences billing at MCX go-live (🟢 contractual).
#: Not in the deck's TCO at all; an ongoing run-rate cost thereafter.
MANAGED_SERVICES_ANNUAL = 410_918.40
MCX_GO_LIVE = dt.date(2026, 9, 30)
def tco(key: str) -> float:
"""Contracted value where one exists, else the deck's verbatim anchor."""
@@ -120,7 +140,7 @@ REALIZE_MONTH = {r: m + BENEFIT_LAG_MONTHS for r, m in IMPL_MONTH.items()}
#: NA Gantt exception: Email implemented Jan 2027, realizes Apr 2027.
NA_EMAIL_IMPL_MONTH = 13
DEFAULT_RAMP_MONTHS = 12 # Genesys ramp programme
DEFAULT_RAMP_MONTHS = 6 # Genesys ramp programme (🟢 order form)
DEFAULT_TERMINATION = dt.date(2027, 12, 31) # current-platform term contracts
# ── Region ⇄ site mapping ────────────────────────────────────────────
@@ -275,12 +295,42 @@ def licence_costs_by_year(
for y in YEARS}
def ps_costs_by_year() -> dict[int, float]:
"""Base professional services + training — verbatim, year 1 only."""
return {2026: TCO_VERBATIM["prof_services_y1"] + TCO_VERBATIM["training_y1"],
def ps_costs_by_year(contracted: bool = False) -> dict[int, float]:
"""Base professional services + training.
Verbatim: the deck's $2.4M PS lump plus training, all in year 1.
Contracted: PS phased on the SOW billing milestones (50/50 across
2026-27, $2.03M total); training stays the verbatim year-1 line —
the SOW milestones don't itemize it separately.
"""
training = TCO_VERBATIM["training_y1"]
if contracted:
ps = {y: 0.0 for y in YEARS}
for m in PS_MILESTONES:
ps[m["date"].year] += m["amount"]
return {y: ps[y] + (training if y == 2026 else 0.0) for y in YEARS}
return {2026: TCO_VERBATIM["prof_services_y1"] + training,
2027: 0.0, 2028: 0.0}
def ps_milestones_dataframe() -> pd.DataFrame:
"""The SOW billing milestones as a display table."""
return pd.DataFrame(PS_MILESTONES)
def managed_services_by_year(
annual: float = MANAGED_SERVICES_ANNUAL, start: dt.date = MCX_GO_LIVE
) -> dict[int, float]:
"""Managed services bill from the month after go-live (Sep 30 → Oct),
then run at the full annual rate — an ongoing cost with no end date
inside the model window."""
def _months(y: int) -> int:
if y < start.year:
return 0
return 12 if y > start.year else 12 - start.month
return {y: annual * _months(y) / 12 for y in YEARS}
# ── Token consumption (missing cost #1) ──────────────────────────────
@@ -293,7 +343,6 @@ def claim_scenario(email_auto_respond_rate: float = 0.255) -> Scenario:
voice_summarization_eligibility=0.0,
voice_knowledge_eligibility=0.0, # unused by the Appendix-4 scope set
email_auto_respond_rate=email_auto_respond_rate,
email_auto_suggest_acceptance=0.0, # Auto-Suggest is inside Copilot (V2 #1)
consumption_cost_realization={1: 1.0, 2: 1.0, 3: 1.0},
)

21
studies/202512_GenesysCX/ctm-token-calculator/tokencalc/benefit_model.py
View File

@@ -126,11 +126,12 @@ def calculate_email_ai_benefit(
params: str = "realistic",
rollout: RolloutPlan | None = None,
) -> pd.DataFrame:
"""Email Auto-Respond (full displacement at the respond rate) plus
Auto-Suggest (time saving × acceptance on the remainder)."""
"""Email Auto-Respond — full displacement at the respond rate.
(Email Auto-Suggest is not a separate benefit line: it is included
in Agent Copilot, whose per-user meter carries the drafting help.)"""
sc = get_scenario(scenario) if isinstance(scenario, str) else scenario
ro = rollout or NO_ROLLOUT
suggest_saving = _param("email_auto_suggest_time_saving", params)
realization = sc.realization(year)
rows = []
for s in sites:
@@ -145,11 +146,6 @@ def calculate_email_ai_benefit(
respond_seconds = (
annual_emails * respond_rate * s.email_aht_seconds * realization
)
suggest_seconds = (
annual_emails * (1 - respond_rate)
* sc.email_auto_suggest_acceptance * s.email_aht_seconds
* suggest_saving * realization
)
rate = s.agent_cost_per_second
rows.append(
{
@@ -160,15 +156,6 @@ def calculate_email_ai_benefit(
"confidence": Confidence.UNKNOWN.value, # meter rate unsourced
}
)
rows.append(
{
"benefit_line": "Email Auto-Suggest (drafting time)",
"scope": s.site_name,
"annual_value": suggest_seconds * rate
* ro.fraction_live(s.site_name, year),
"confidence": Confidence.UNKNOWN.value,
}
)
return _df(rows)

3
studies/202512_GenesysCX/ctm-token-calculator/tokencalc/cost_model.py
View File

@@ -86,8 +86,7 @@ def calculate_per_user_ai_cost(
use_contracted: bool = False,
rollout: RolloutPlan | None = None,
) -> pd.DataFrame:
"""Per-user-per-month AI features (STA, Agent Copilot, AI Translate,
Email Auto-Suggest).
"""Per-user-per-month AI features (STA, Agent Copilot, AI Translate).
No adoption ramp and no rounding (users × tokens/user/month is
exact) — but token usage only starts at site go-live, so the year

13
studies/202512_GenesysCX/ctm-token-calculator/tokencalc/defaults.py
View File

@@ -210,15 +210,9 @@ DEFAULT_METERS: dict[str, TokenMeter] = {
),
source_url=_GENESYS_TOKEN_METERS,
),
# ── Email AI (rates not yet published) ────────────────────────
TokenMeter(
feature="Email AI (Auto-Suggest)",
meter_type=MeterType.PER_USER_PER_MONTH,
units_per_token=0.0,
tokens_per_unit=0.0,
confidence=Confidence.UNKNOWN,
notes="Requires Agent Copilot. Token rate not yet published.",
),
# ── Email AI (rate not yet published) ─────────────────────────
# Email Auto-Suggest is included in Agent Copilot's per-user
# meter (no standalone SKU) — only Auto-Respond is listed here.
TokenMeter(
feature="Email AI (Auto-Respond)",
meter_type=MeterType.PER_MESSAGE,
@@ -423,7 +417,6 @@ CTM_DEFAULT_FEATURE_SCOPES: list[FeatureScope] = [
FeatureScope("AI Summary & Insights", ALL_SITE_NAMES, phase=1,
adoption_curve=_RAMP),
FeatureScope("Direct Messaging", ALL_SITE_NAMES, phase=1, adoption_curve=_RAMP),
FeatureScope("Email AI (Auto-Suggest)", ["NAM", "EMEA"], phase=2),
FeatureScope("AI Translate",
["APAC HK", "APAC SG", "APAC SH", "APAC GZ", "APAC JP", "APAC TW"],
phase=3),

9
studies/202512_GenesysCX/ctm-token-calculator/tokencalc/migration_wfm.py
View File

@@ -57,13 +57,18 @@ def runrate_saving_annual(
licence_annual: float | None = None,
regions: list[str] | None = None,
baseline_annual: float | None = None,
managed_annual: float = 0.0,
) -> float:
"""Steady-state annual saving once term contracts end and the ramp
is over: (baseline − licence run-rate) + scoped WFM annual values.
is over: (baseline − licence run-rate − managed services) + scoped
WFM annual values.
Defaults are the deck frame (deck licence rate, no managed
services); the contracted frame passes ``a4.MANAGED_SERVICES_ANNUAL``.
"""
lic = a4.TCO_VERBATIM["ccaas_annual"] if licence_annual is None else licence_annual
base = a4.TCO_VERBATIM["current_annual"] if baseline_annual is None else baseline_annual
return (base - lic) + wfm_annual_runrate(regions)
return (base - lic - managed_annual) + wfm_annual_runrate(regions)
def runrate_breakeven_label(

5
studies/202512_GenesysCX/ctm-token-calculator/tokencalc/scenarios.py
View File

@@ -25,7 +25,6 @@ class Scenario:
voice_summarization_eligibility: float
voice_knowledge_eligibility: float
email_auto_respond_rate: float # share of email auto-responded
email_auto_suggest_acceptance: float
# ── Virtual Agent benefit realization factors ───────────────────
# Applied to both Voice Bot and Agentic VA deflection benefits.
@@ -75,7 +74,6 @@ BENEFIT_PARAMS: dict[str, dict[str, float]] = {
"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
"email_auto_suggest_time_saving": {"claim": 0.40, "realistic": 0.30}, # × acceptance; Genesys claims 40%
# 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},
@@ -97,7 +95,6 @@ SCENARIOS: dict[str, Scenario] = {
voice_summarization_eligibility=0.50,
voice_knowledge_eligibility=0.40,
email_auto_respond_rate=0.10,
email_auto_suggest_acceptance=0.25,
# VA realization: conservative — low completion, limited staffing flex
# Combined: 0.60 × 0.70 × (1 − 0.05) ≈ 0.40
va_completion_rate=0.60,
@@ -113,7 +110,6 @@ SCENARIOS: dict[str, Scenario] = {
voice_summarization_eligibility=0.70,
voice_knowledge_eligibility=0.60,
email_auto_respond_rate=0.20,
email_auto_suggest_acceptance=0.40,
# VA realization: production midpoints per spec analysis
# Combined: 0.70 × 0.80 × (1 − 0.05) ≈ 0.53
va_completion_rate=0.70,
@@ -129,7 +125,6 @@ SCENARIOS: dict[str, Scenario] = {
voice_summarization_eligibility=0.90,
voice_knowledge_eligibility=0.80,
email_auto_respond_rate=0.50,
email_auto_suggest_acceptance=0.60,
# VA realization: optimistic — high completion, good staffing flexibility
# Combined: 0.75 × 0.85 × (1 − 0.03) ≈ 0.62
va_completion_rate=0.75,