9612 lines
259 KiB
Plaintext
9612 lines
259 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "758e4c36",
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"metadata": {},
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"source": [
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"# CTM Token Calculator — Genesys AI Cost & Business Case\n",
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"\n",
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"**Version 0.1.0** · interactive working analysis for Robert / CTM stakeholders / NTT delivery.\n",
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"\n",
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"Models Genesys Cloud **CX 3** platform + AI feature costs (published token meters,\n",
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"confidence-flagged) against pressure-tested benefit scenarios — replacing single-point\n",
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"vendor ROI outputs with **Floor / Realistic / Stretch** sensitivity-aware analysis.\n",
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"\n",
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"> ⚠️ **Planning tool.** List rates unless overridden; not contractual pricing.\n",
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"> Site data outside NAM is **estimated — confirm with CTM**.\n",
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"\n",
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"Same `tokencalc` library that powers the corrected business case notebook — serve either interactively with `mercury --working-dir notebooks/` —\n",
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"Run-All here produces identical headline numbers on default inputs."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "57efb206",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-07-13T19:32:57.711516Z",
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"iopub.status.busy": "2026-07-13T19:32:57.711205Z",
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"iopub.status.idle": "2026-07-13T19:32:58.206644Z",
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"shell.execute_reply": "2026-07-13T19:32:58.205709Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tokencalc loaded — 9 sites, 2,088 named users (contracted: 2,088)\n"
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]
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}
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],
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"source": [
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"# ── Setup ──────────────────────────────────────────────────────────\n",
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"import sys, pathlib\n",
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"_ROOT = pathlib.Path.cwd()\n",
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"if not (_ROOT / \"tokencalc\").exists(): # notebook lives in notebooks/\n",
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" _ROOT = _ROOT.parent\n",
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"sys.path.insert(0, str(_ROOT))\n",
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"\n",
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"import dataclasses\n",
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"import pandas as pd\n",
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"import plotly.express as px\n",
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"import plotly.graph_objects as go\n",
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"\n",
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"from tokencalc import *\n",
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"from tokencalc.scenarios import BENEFIT_PARAMS\n",
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"\n",
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"pd.options.display.float_format = \"{:,.0f}\".format\n",
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"print(f\"tokencalc loaded — {len(CTM_DEFAULT_SITES)} sites, \"\n",
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" f\"{sum(s.named_users for s in CTM_DEFAULT_SITES):,} named users \"\n",
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" f\"(contracted: {CONTRACTED_NAMED_USERS:,})\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cbf6851d",
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"metadata": {},
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"source": [
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"## Inputs\n",
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"\n",
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"Sites, cost takeouts, and feature scoping as Python objects (CTM defaults).\n",
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"To resume a saved scenario instead:\n",
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"`sites, takeouts, scopes = scenario_state_from_json(\"my_scenario.json\")`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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||
"id": "361bde24",
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||
"metadata": {
|
||
"execution": {
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||
"iopub.execute_input": "2026-07-13T19:32:58.210328Z",
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||
"iopub.status.busy": "2026-07-13T19:32:58.209921Z",
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"iopub.status.idle": "2026-07-13T19:32:58.234502Z",
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"shell.execute_reply": "2026-07-13T19:32:58.233799Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>site_name</th>\n",
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" <th>region_pricing</th>\n",
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" <th>agents</th>\n",
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" <th>supervisors</th>\n",
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" <th>voice_volume_monthly</th>\n",
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" <th>email_volume_monthly</th>\n",
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" <th>chat_volume_monthly</th>\n",
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" <th>sms_volume_monthly</th>\n",
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" <th>voice_aht_seconds</th>\n",
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" <th>email_aht_seconds</th>\n",
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" <th>chat_aht_seconds</th>\n",
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" <th>voice_acw_seconds</th>\n",
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" <th>fully_loaded_agent_cost_annual</th>\n",
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" <th>fully_loaded_supervisor_cost_annual</th>\n",
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" <th>licence_type</th>\n",
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" <th>languages</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>NAM</td>\n",
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" <td>US</td>\n",
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" <td>890</td>\n",
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" <td>60</td>\n",
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" <td>1214358</td>\n",
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" <td>275800</td>\n",
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" <td>110</td>\n",
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" <td>1040</td>\n",
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" <td>300</td>\n",
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||
" <td>600</td>\n",
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||
" <td>480</td>\n",
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||
" <td>60</td>\n",
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" <td>65000</td>\n",
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" <td>95000</td>\n",
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" <td>named</td>\n",
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" <td>English, French, Spanish</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>EMEA</td>\n",
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" <td>EU</td>\n",
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" <td>320</td>\n",
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" <td>25</td>\n",
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" <td>420000</td>\n",
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" <td>95000</td>\n",
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" <td>40</td>\n",
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" <td>400</td>\n",
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" <td>300</td>\n",
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||
" <td>600</td>\n",
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||
" <td>480</td>\n",
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" <td>60</td>\n",
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||
" <td>60000</td>\n",
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" <td>88000</td>\n",
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" <td>named</td>\n",
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" <td>English, French, German, Italian, Spanish</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>AUZ</td>\n",
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" <td>AU</td>\n",
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" <td>180</td>\n",
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" <td>15</td>\n",
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" <td>250000</td>\n",
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" <td>56000</td>\n",
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" <td>25</td>\n",
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" <td>250</td>\n",
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||
" <td>300</td>\n",
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||
" <td>600</td>\n",
|
||
" <td>480</td>\n",
|
||
" <td>60</td>\n",
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||
" <td>70000</td>\n",
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" <td>100000</td>\n",
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" <td>named</td>\n",
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" <td>English</td>\n",
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||
" </tr>\n",
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||
" <tr>\n",
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" <th>3</th>\n",
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" <td>APAC HK</td>\n",
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" <td>APAC</td>\n",
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" <td>120</td>\n",
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" <td>10</td>\n",
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" <td>160000</td>\n",
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" <td>38000</td>\n",
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" <td>15</td>\n",
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||
" <td>150</td>\n",
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||
" <td>300</td>\n",
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||
" <td>600</td>\n",
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" <td>480</td>\n",
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" <td>60</td>\n",
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" <td>55000</td>\n",
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" <td>80000</td>\n",
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" <td>named</td>\n",
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" <td>English, Cantonese, Mandarin</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>APAC SG</td>\n",
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" <td>APAC</td>\n",
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" <td>110</td>\n",
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" <td>10</td>\n",
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" <td>150000</td>\n",
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" <td>34000</td>\n",
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" <td>15</td>\n",
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" <td>120</td>\n",
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" <td>300</td>\n",
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" <td>600</td>\n",
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" <td>480</td>\n",
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" <td>60</td>\n",
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" <td>55000</td>\n",
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" <td>80000</td>\n",
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" <td>named</td>\n",
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" <td>English, Mandarin, Malay</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>APAC SH</td>\n",
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" <td>APAC</td>\n",
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" <td>130</td>\n",
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" <td>10</td>\n",
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" <td>175000</td>\n",
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" <td>40000</td>\n",
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" <td>15</td>\n",
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" <td>130</td>\n",
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" <td>300</td>\n",
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" <td>600</td>\n",
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" <td>480</td>\n",
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" <td>60</td>\n",
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" <td>35000</td>\n",
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" <td>55000</td>\n",
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" <td>named</td>\n",
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" <td>Mandarin</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>APAC GZ</td>\n",
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" <td>APAC</td>\n",
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" <td>90</td>\n",
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" <td>8</td>\n",
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" <td>120000</td>\n",
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" <td>28000</td>\n",
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||
" <td>10</td>\n",
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||
" <td>100</td>\n",
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||
" <td>300</td>\n",
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||
" <td>600</td>\n",
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||
" <td>480</td>\n",
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||
" <td>60</td>\n",
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||
" <td>35000</td>\n",
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" <td>55000</td>\n",
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" <td>named</td>\n",
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" <td>Mandarin, Cantonese</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>APAC JP</td>\n",
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" <td>APAC</td>\n",
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" <td>60</td>\n",
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||
" <td>6</td>\n",
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" <td>80000</td>\n",
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||
" <td>19000</td>\n",
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||
" <td>8</td>\n",
|
||
" <td>80</td>\n",
|
||
" <td>300</td>\n",
|
||
" <td>600</td>\n",
|
||
" <td>480</td>\n",
|
||
" <td>60</td>\n",
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||
" <td>60000</td>\n",
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" <td>85000</td>\n",
|
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" <td>named</td>\n",
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" <td>Japanese</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>APAC TW</td>\n",
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" <td>APAC</td>\n",
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" <td>40</td>\n",
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" <td>4</td>\n",
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" <td>54000</td>\n",
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" <td>12000</td>\n",
|
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" <td>5</td>\n",
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" <td>50</td>\n",
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" <td>300</td>\n",
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||
" <td>600</td>\n",
|
||
" <td>480</td>\n",
|
||
" <td>60</td>\n",
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" <td>40000</td>\n",
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" <td>60000</td>\n",
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" <td>named</td>\n",
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" <td>Mandarin</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" site_name region_pricing agents supervisors voice_volume_monthly \\\n",
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"0 NAM US 890 60 1214358 \n",
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"1 EMEA EU 320 25 420000 \n",
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"2 AUZ AU 180 15 250000 \n",
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"3 APAC HK APAC 120 10 160000 \n",
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"4 APAC SG APAC 110 10 150000 \n",
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"5 APAC SH APAC 130 10 175000 \n",
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"6 APAC GZ APAC 90 8 120000 \n",
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"7 APAC JP APAC 60 6 80000 \n",
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"8 APAC TW APAC 40 4 54000 \n",
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"\n",
|
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" email_volume_monthly chat_volume_monthly sms_volume_monthly \\\n",
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"0 275800 110 1040 \n",
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"1 95000 40 400 \n",
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"2 56000 25 250 \n",
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"3 38000 15 150 \n",
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"4 34000 15 120 \n",
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"5 40000 15 130 \n",
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"6 28000 10 100 \n",
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"7 19000 8 80 \n",
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"8 12000 5 50 \n",
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"\n",
|
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" voice_aht_seconds email_aht_seconds chat_aht_seconds voice_acw_seconds \\\n",
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"0 300 600 480 60 \n",
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"1 300 600 480 60 \n",
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"2 300 600 480 60 \n",
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"3 300 600 480 60 \n",
|
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"4 300 600 480 60 \n",
|
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"5 300 600 480 60 \n",
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"6 300 600 480 60 \n",
|
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"7 300 600 480 60 \n",
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"8 300 600 480 60 \n",
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"\n",
|
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" fully_loaded_agent_cost_annual fully_loaded_supervisor_cost_annual \\\n",
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"0 65000 95000 \n",
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"1 60000 88000 \n",
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"2 70000 100000 \n",
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"3 55000 80000 \n",
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"4 55000 80000 \n",
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"5 35000 55000 \n",
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"6 35000 55000 \n",
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"7 60000 85000 \n",
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"8 40000 60000 \n",
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"\n",
|
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" licence_type languages \n",
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"0 named English, French, Spanish \n",
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"1 named English, French, German, Italian, Spanish \n",
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"2 named English \n",
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"3 named English, Cantonese, Mandarin \n",
|
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"4 named English, Mandarin, Malay \n",
|
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"5 named Mandarin \n",
|
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"6 named Mandarin, Cantonese \n",
|
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"7 named Japanese \n",
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"8 named Mandarin "
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]
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||
},
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||
"metadata": {},
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||
"output_type": "display_data"
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||
},
|
||
{
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"data": {
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"text/html": [
|
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"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>takeout</th>\n",
|
||
" <th>annual_cost</th>\n",
|
||
" <th>start_year</th>\n",
|
||
" <th>confidence</th>\n",
|
||
" <th>notes</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>NICE IEX (NAM)</td>\n",
|
||
" <td>1300000</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>🟡 estimated</td>\n",
|
||
" <td>Mid-band estimate; needs CTM contract confirma...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Legacy CC platform</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>🔴 unknown</td>\n",
|
||
" <td>Placeholder — populate once retirement scope i...</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" takeout annual_cost start_year confidence \\\n",
|
||
"0 NICE IEX (NAM) 1300000 1 🟡 estimated \n",
|
||
"1 Legacy CC platform 0 2 🔴 unknown \n",
|
||
"\n",
|
||
" notes \n",
|
||
"0 Mid-band estimate; needs CTM contract confirma... \n",
|
||
"1 Placeholder — populate once retirement scope i... "
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"sites = list(CTM_DEFAULT_SITES)\n",
|
||
"takeouts = list(CTM_DEFAULT_TAKEOUTS)\n",
|
||
"scopes = list(CTM_DEFAULT_FEATURE_SCOPES)\n",
|
||
"meters = dict(DEFAULT_METERS)\n",
|
||
"pricing = dict(DEFAULT_PRICING)\n",
|
||
"\n",
|
||
"total_users = sum(s.named_users for s in sites)\n",
|
||
"if total_users != CONTRACTED_NAMED_USERS:\n",
|
||
" print(f\"⚠️ Named users ({total_users:,}) ≠ contracted licences \"\n",
|
||
" f\"({CONTRACTED_NAMED_USERS:,})\")\n",
|
||
"\n",
|
||
"display(sites_dataframe(sites))\n",
|
||
"display(pd.DataFrame([{\"takeout\": t.name, \"annual_cost\": t.annual_cost,\n",
|
||
" \"start_year\": t.start_year,\n",
|
||
" \"confidence\": t.confidence.icon + \" \" + t.confidence.value,\n",
|
||
" \"notes\": t.notes} for t in takeouts]))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "51e84f75",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Token meters\n",
|
||
"\n",
|
||
"Source: [Genesys Cloud AI Experience metering details](https://help.genesys.cloud/articles/genesys-cloud-tokens-model/) (2026-06-07)\n",
|
||
"\n",
|
||
"🟢 confirmed (published Genesys rate) · 🟡 estimated · 🔴 unknown (working default, rate not yet published).\n",
|
||
"\n",
|
||
"**Licence variants** — Agent Copilot and Speech & Text Analytics each have two published rates:\n",
|
||
"- **[named]** — 40 tok/user/month (Copilot) · 30 tok/user/month (STA)\n",
|
||
"- **[concurrent]** — 60 tok/user/month (Copilot) · 45 tok/user/month (STA)\n",
|
||
"\n",
|
||
"CTM has **named** licences; the `[named]` variants are used in `CTM_DEFAULT_FEATURE_SCOPES`.\n",
|
||
"Override by swapping the feature name in `scopes` for sites with concurrent licences.\n",
|
||
"\n",
|
||
"**Copilot rule:** Agent Copilot includes interaction summarization, so\n",
|
||
"AI Summary & Insights is never billed at Copilot-enabled sites.\n",
|
||
"\n",
|
||
"**New in 2026-06-07 catalogue:** Digital Bot (51 sessions/token), AI Scoring\n",
|
||
"(20 evaluations/token), Predictive Routing (17 routes/token), Genesys Cloud Copilot\n",
|
||
"(20 AI actions/token), and AI Translate is now a confirmed consumption meter\n",
|
||
"(2 translations/token, previously UNKNOWN)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "72311955",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-07-13T19:32:58.236321Z",
|
||
"iopub.status.busy": "2026-07-13T19:32:58.236148Z",
|
||
"iopub.status.idle": "2026-07-13T19:32:58.246527Z",
|
||
"shell.execute_reply": "2026-07-13T19:32:58.245798Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>feature</th>\n",
|
||
" <th>meter_type</th>\n",
|
||
" <th>units_per_token</th>\n",
|
||
" <th>tokens_per_unit</th>\n",
|
||
" <th>confidence</th>\n",
|
||
" <th>notes</th>\n",
|
||
" <th>source</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Voice Bot</td>\n",
|
||
" <td>per_minute</td>\n",
|
||
" <td>17</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>🟢 confirmed</td>\n",
|
||
" <td>IVR self-service voice bot minutes; 17 min per...</td>\n",
|
||
" <td>https://help.genesys.cloud/articles/genesys-cl...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Digital Bot</td>\n",
|
||
" <td>per_interaction</td>\n",
|
||
" <td>51</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>🟢 confirmed</td>\n",
|
||
" <td>Digital (non-voice) bot sessions; 51 sessions ...</td>\n",
|
||
" <td>https://help.genesys.cloud/articles/genesys-cl...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Virtual Agent (legacy)</td>\n",
|
||
" <td>per_interaction</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>🟢 confirmed</td>\n",
|
||
" <td>Legacy (non-agentic) virtual agent; 0.5 tokens...</td>\n",
|
||
" <td>https://help.genesys.cloud/articles/genesys-cl...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Agentic Virtual Agent</td>\n",
|
||
" <td>per_interaction</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>🟢 confirmed</td>\n",
|
||
" <td>Agentic VA; 1.2 tokens per interaction.</td>\n",
|
||
" <td>https://help.genesys.cloud/articles/genesys-cl...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Agent Copilot [named]</td>\n",
|
||
" <td>per_user_per_month</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>40</td>\n",
|
||
" <td>🟢 confirmed</td>\n",
|
||
" <td>40 tokens per named user per month. Includes i...</td>\n",
|
||
" <td>https://help.genesys.cloud/articles/genesys-cl...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>Agent Copilot [concurrent]</td>\n",
|
||
" <td>per_user_per_month</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>60</td>\n",
|
||
" <td>🟢 confirmed</td>\n",
|
||
" <td>60 tokens per concurrent user per month. Inclu...</td>\n",
|
||
" <td>https://help.genesys.cloud/articles/genesys-cl...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>AI Scoring</td>\n",
|
||
" <td>per_interaction</td>\n",
|
||
" <td>20</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>🟢 confirmed</td>\n",
|
||
" <td>AI-scored quality evaluations; 20 evaluations ...</td>\n",
|
||
" <td>https://help.genesys.cloud/articles/genesys-cl...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>AI Summary & Insights</td>\n",
|
||
" <td>per_summary</td>\n",
|
||
" <td>50</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>🟢 confirmed</td>\n",
|
||
" <td>Supervisor standalone summarization; 50 summar...</td>\n",
|
||
" <td>https://help.genesys.cloud/articles/genesys-cl...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>Speech & Text Analytics [named]</td>\n",
|
||
" <td>per_user_per_month</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>30</td>\n",
|
||
" <td>🟢 confirmed</td>\n",
|
||
" <td>STA named licence; 30 tokens per named user pe...</td>\n",
|
||
" <td>https://help.genesys.cloud/articles/genesys-cl...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>Speech & Text Analytics [concurrent]</td>\n",
|
||
" <td>per_user_per_month</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>45</td>\n",
|
||
" <td>🟢 confirmed</td>\n",
|
||
" <td>STA concurrent licence; 45 tokens per concurre...</td>\n",
|
||
" <td>https://help.genesys.cloud/articles/genesys-cl...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>Predictive Routing</td>\n",
|
||
" <td>per_interaction</td>\n",
|
||
" <td>17</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>🟢 confirmed</td>\n",
|
||
" <td>Predictive routing; 17 routes per token.</td>\n",
|
||
" <td>https://help.genesys.cloud/articles/genesys-cl...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>Direct Messaging</td>\n",
|
||
" <td>per_message</td>\n",
|
||
" <td>400</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>🟢 confirmed</td>\n",
|
||
" <td>Apple Messages for Business, Facebook Messenge...</td>\n",
|
||
" <td>https://help.genesys.cloud/articles/genesys-cl...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>Social Listening</td>\n",
|
||
" <td>per_message</td>\n",
|
||
" <td>400</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>🟢 confirmed</td>\n",
|
||
" <td>Genesys Cloud Social; 400 social post ingestio...</td>\n",
|
||
" <td>https://help.genesys.cloud/articles/genesys-cl...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>Social Responses</td>\n",
|
||
" <td>per_message</td>\n",
|
||
" <td>400</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>🟢 confirmed</td>\n",
|
||
" <td>Social Post Responses; 400 outbound messages p...</td>\n",
|
||
" <td>https://help.genesys.cloud/articles/genesys-cl...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>AI Translate</td>\n",
|
||
" <td>per_interaction</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>🟢 confirmed</td>\n",
|
||
" <td>AI translation; 2 translations per token.</td>\n",
|
||
" <td>https://help.genesys.cloud/articles/genesys-cl...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>Genesys Cloud Copilot</td>\n",
|
||
" <td>per_interaction</td>\n",
|
||
" <td>20</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>🟢 confirmed</td>\n",
|
||
" <td>20 AI actions per token; Genesys Cloud knowled...</td>\n",
|
||
" <td>https://help.genesys.cloud/articles/genesys-cl...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16</th>\n",
|
||
" <td>Email AI (Auto-Respond)</td>\n",
|
||
" <td>per_message</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>🔴 unknown</td>\n",
|
||
" <td>Feature not yet available; rate TBD.</td>\n",
|
||
" <td></td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" feature meter_type units_per_token \\\n",
|
||
"0 Voice Bot per_minute 17 \n",
|
||
"1 Digital Bot per_interaction 51 \n",
|
||
"2 Virtual Agent (legacy) per_interaction 2 \n",
|
||
"3 Agentic Virtual Agent per_interaction 1 \n",
|
||
"4 Agent Copilot [named] per_user_per_month NaN \n",
|
||
"5 Agent Copilot [concurrent] per_user_per_month NaN \n",
|
||
"6 AI Scoring per_interaction 20 \n",
|
||
"7 AI Summary & Insights per_summary 50 \n",
|
||
"8 Speech & Text Analytics [named] per_user_per_month NaN \n",
|
||
"9 Speech & Text Analytics [concurrent] per_user_per_month NaN \n",
|
||
"10 Predictive Routing per_interaction 17 \n",
|
||
"11 Direct Messaging per_message 400 \n",
|
||
"12 Social Listening per_message 400 \n",
|
||
"13 Social Responses per_message 400 \n",
|
||
"14 AI Translate per_interaction 2 \n",
|
||
"15 Genesys Cloud Copilot per_interaction 20 \n",
|
||
"16 Email AI (Auto-Respond) per_message NaN \n",
|
||
"\n",
|
||
" tokens_per_unit confidence \\\n",
|
||
"0 0 🟢 confirmed \n",
|
||
"1 0 🟢 confirmed \n",
|
||
"2 0 🟢 confirmed \n",
|
||
"3 1 🟢 confirmed \n",
|
||
"4 40 🟢 confirmed \n",
|
||
"5 60 🟢 confirmed \n",
|
||
"6 0 🟢 confirmed \n",
|
||
"7 0 🟢 confirmed \n",
|
||
"8 30 🟢 confirmed \n",
|
||
"9 45 🟢 confirmed \n",
|
||
"10 0 🟢 confirmed \n",
|
||
"11 0 🟢 confirmed \n",
|
||
"12 0 🟢 confirmed \n",
|
||
"13 0 🟢 confirmed \n",
|
||
"14 0 🟢 confirmed \n",
|
||
"15 0 🟢 confirmed \n",
|
||
"16 0 🔴 unknown \n",
|
||
"\n",
|
||
" notes \\\n",
|
||
"0 IVR self-service voice bot minutes; 17 min per... \n",
|
||
"1 Digital (non-voice) bot sessions; 51 sessions ... \n",
|
||
"2 Legacy (non-agentic) virtual agent; 0.5 tokens... \n",
|
||
"3 Agentic VA; 1.2 tokens per interaction. \n",
|
||
"4 40 tokens per named user per month. Includes i... \n",
|
||
"5 60 tokens per concurrent user per month. Inclu... \n",
|
||
"6 AI-scored quality evaluations; 20 evaluations ... \n",
|
||
"7 Supervisor standalone summarization; 50 summar... \n",
|
||
"8 STA named licence; 30 tokens per named user pe... \n",
|
||
"9 STA concurrent licence; 45 tokens per concurre... \n",
|
||
"10 Predictive routing; 17 routes per token. \n",
|
||
"11 Apple Messages for Business, Facebook Messenge... \n",
|
||
"12 Genesys Cloud Social; 400 social post ingestio... \n",
|
||
"13 Social Post Responses; 400 outbound messages p... \n",
|
||
"14 AI translation; 2 translations per token. \n",
|
||
"15 20 AI actions per token; Genesys Cloud knowled... \n",
|
||
"16 Feature not yet available; rate TBD. \n",
|
||
"\n",
|
||
" source \n",
|
||
"0 https://help.genesys.cloud/articles/genesys-cl... \n",
|
||
"1 https://help.genesys.cloud/articles/genesys-cl... \n",
|
||
"2 https://help.genesys.cloud/articles/genesys-cl... \n",
|
||
"3 https://help.genesys.cloud/articles/genesys-cl... \n",
|
||
"4 https://help.genesys.cloud/articles/genesys-cl... \n",
|
||
"5 https://help.genesys.cloud/articles/genesys-cl... \n",
|
||
"6 https://help.genesys.cloud/articles/genesys-cl... \n",
|
||
"7 https://help.genesys.cloud/articles/genesys-cl... \n",
|
||
"8 https://help.genesys.cloud/articles/genesys-cl... \n",
|
||
"9 https://help.genesys.cloud/articles/genesys-cl... \n",
|
||
"10 https://help.genesys.cloud/articles/genesys-cl... \n",
|
||
"11 https://help.genesys.cloud/articles/genesys-cl... \n",
|
||
"12 https://help.genesys.cloud/articles/genesys-cl... \n",
|
||
"13 https://help.genesys.cloud/articles/genesys-cl... \n",
|
||
"14 https://help.genesys.cloud/articles/genesys-cl... \n",
|
||
"15 https://help.genesys.cloud/articles/genesys-cl... \n",
|
||
"16 "
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"display(meters_dataframe(meters))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6c308d2f",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Controls\n",
|
||
"\n",
|
||
"Plain variables (always work). Run the optional widget cell below for sliders —\n",
|
||
"either way, re-run the calculation cells after changing anything."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "8b24676f",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-07-13T19:32:58.248518Z",
|
||
"iopub.status.busy": "2026-07-13T19:32:58.248332Z",
|
||
"iopub.status.idle": "2026-07-13T19:32:58.252173Z",
|
||
"shell.execute_reply": "2026-07-13T19:32:58.251375Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Scenario: realistic · Year 1 · benefit params: realistic\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"SCENARIO = \"realistic\" # \"floor\" | \"realistic\" | \"stretch\"\n",
|
||
"YEAR = 1 # 1 | 2 | 3\n",
|
||
"BENEFIT_PARAMS_MODE = \"realistic\" # \"realistic\" (pressure-tested) | \"claim\" (Genesys)\n",
|
||
"USE_CONTRACTED_RATES = False\n",
|
||
"IMPLEMENTATION_COST = 0.0 # one-off, amortized over 3 years\n",
|
||
"\n",
|
||
"scenario = get_scenario(SCENARIO)\n",
|
||
"print(f\"Scenario: {SCENARIO} · Year {YEAR} · benefit params: {BENEFIT_PARAMS_MODE}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "810e9dae",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-07-13T19:32:58.253831Z",
|
||
"iopub.status.busy": "2026-07-13T19:32:58.253676Z",
|
||
"iopub.status.idle": "2026-07-13T19:32:58.302237Z",
|
||
"shell.execute_reply": "2026-07-13T19:32:58.301248Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "69f65b8fab04496fa3f3b45116f421e5",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"ToggleButtons(description='Scenario', index=1, options=('floor', 'realistic', 'stretch'), value='realistic')"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "2b35efbfa83f4dfb994080acef7b28cc",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"ToggleButtons(description='Year', options=(1, 2, 3), value=1)"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Optional: ipywidgets controls (skip if ipywidgets unavailable)\n",
|
||
"try:\n",
|
||
" import ipywidgets as W\n",
|
||
" from IPython.display import display as _disp\n",
|
||
"\n",
|
||
" _scen = W.ToggleButtons(options=[\"floor\", \"realistic\", \"stretch\"],\n",
|
||
" value=SCENARIO, description=\"Scenario\")\n",
|
||
" _year = W.ToggleButtons(options=[1, 2, 3], value=YEAR, description=\"Year\")\n",
|
||
"\n",
|
||
" def _on_change(change):\n",
|
||
" global SCENARIO, YEAR, scenario\n",
|
||
" SCENARIO, YEAR = _scen.value, _year.value\n",
|
||
" scenario = get_scenario(SCENARIO)\n",
|
||
" print(f\"→ {SCENARIO}, year {YEAR} — re-run the cells below.\")\n",
|
||
"\n",
|
||
" _scen.observe(_on_change, \"value\"); _year.observe(_on_change, \"value\")\n",
|
||
" _disp(_scen, _year)\n",
|
||
"except ImportError:\n",
|
||
" print(\"ipywidgets not installed — use the plain variables above.\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5a590988",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Virtual Agents — deflection, costs & benefits\n",
|
||
"\n",
|
||
"**Both** voice volume cost drivers (token consumption) **and** benefit drivers\n",
|
||
"(agent labour avoided). Two mechanisms operate on the same call pool:\n",
|
||
"\n",
|
||
"| Layer | What it does | Cost meter | Benefit |\n",
|
||
"|---|---|---|---|\n",
|
||
"| **Voice Bot** | IVR self-service on the full call pool | per-minute (17 min/token) | labour avoided × completion |\n",
|
||
"| **Agentic Virtual Agent** | Handles a share of the **residual** (calls the bot did not deflect) | per-interaction (1.2 tok/interaction) | labour avoided × completion |\n",
|
||
"\n",
|
||
"### Layered (sequential) deflection model\n",
|
||
"\n",
|
||
"The two mechanisms are **substitutes**, not independent additive benefits:\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\text{effective deflection} = r_{bot} + (1 - r_{bot}) \\cdot r_{va}\n",
|
||
"$$\n",
|
||
"\n",
|
||
"Realistic scenario: 35% + 65% × 15% = **44.75%** (not the naive 50%).\n",
|
||
"\n",
|
||
"### Three realization haircuts on labour avoided\n",
|
||
"\n",
|
||
"Deflected volume does NOT translate 1:1 into labour savings:\n",
|
||
"\n",
|
||
"1. **Completion rate** — share of \"deflected\" calls the bot/VA fully handles\n",
|
||
" without escalating mid-session (60-75% voice bot, 50-70% agentic VA Y1).\n",
|
||
"2. **Labour realization** — staffing flexibility ceiling (minimums, shrinkage,\n",
|
||
" occupancy); 70-85% of deflected volume actually reduces FTE need.\n",
|
||
"3. **Callback discount** — poorly-handled deflections drive 5-10% repeat contacts.\n",
|
||
"\n",
|
||
"Combined realistic factor: 0.70 × 0.80 × (1 − 0.05) ≈ **0.53**\n",
|
||
"\n",
|
||
"So a naive \"$25M of labour avoided\" is realistically ~$13M. The variables below\n",
|
||
"override the scenario defaults — adjust them to test sensitivity, then re-run\n",
|
||
"the Cost & Benefit cells.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "65773422",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-07-13T19:32:58.304432Z",
|
||
"iopub.status.busy": "2026-07-13T19:32:58.304177Z",
|
||
"iopub.status.idle": "2026-07-13T19:32:58.312707Z",
|
||
"shell.execute_reply": "2026-07-13T19:32:58.311008Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Voice Bot: 35% of full pool · Agentic VA: 15% of residual\n",
|
||
"Effective deflection: 44.75% · Realization factor: 53.2% · Net labour avoidance: 23.8%\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# ── Virtual Agent inputs (override scenario defaults below) ──────────\n",
|
||
"# Cost & benefit drivers — applied as deflection_target overrides on the\n",
|
||
"# Voice Bot and Agentic VA feature scopes; realization haircuts mutate\n",
|
||
"# the scenario object so calculate_va_deflection_benefit picks them up.\n",
|
||
"\n",
|
||
"# Voice Bot — % of FULL voice volume deflected to the bot\n",
|
||
"VA_BOT_DEFLECTION = scenario.voice_bot_deflection # cost & benefit\n",
|
||
"VA_BOT_AVG_MINUTES = scenario.voice_bot_avg_minutes # cost only\n",
|
||
"\n",
|
||
"# Agentic VA — % of RESIDUAL (1 − bot) deflected to the agentic VA\n",
|
||
"VA_AGENTIC_DEFLECTION = scenario.agentic_va_deflection # cost & benefit\n",
|
||
"\n",
|
||
"# Realization haircuts (benefit-side only — never reduce token consumption)\n",
|
||
"VA_COMPLETION_RATE = scenario.va_completion_rate # bot/VA fully handles call\n",
|
||
"VA_LABOUR_REALIZATION = scenario.va_labour_realization # staffing flexibility\n",
|
||
"VA_CALLBACK_DISCOUNT = scenario.va_callback_discount # 5-10% repeat contacts\n",
|
||
"\n",
|
||
"\n",
|
||
"def _apply_va_inputs():\n",
|
||
" \"\"\"Push notebook variables into the scenario + scopes used downstream.\"\"\"\n",
|
||
" global scopes\n",
|
||
" # Mutate scenario (regular dataclass, not frozen) — picked up by\n",
|
||
" # calculate_va_deflection_benefit via _param() and direct attribute reads.\n",
|
||
" scenario.voice_bot_deflection = VA_BOT_DEFLECTION\n",
|
||
" scenario.voice_bot_avg_minutes = VA_BOT_AVG_MINUTES\n",
|
||
" scenario.agentic_va_deflection = VA_AGENTIC_DEFLECTION\n",
|
||
" scenario.va_completion_rate = VA_COMPLETION_RATE\n",
|
||
" scenario.va_labour_realization = VA_LABOUR_REALIZATION\n",
|
||
" scenario.va_callback_discount = VA_CALLBACK_DISCOUNT\n",
|
||
" # ALSO patch BENEFIT_PARAMS so the realistic path uses our overrides\n",
|
||
" # (calculate_va_deflection_benefit reads via _param()).\n",
|
||
" BENEFIT_PARAMS[\"va_completion_rate\"][\"realistic\"] = VA_COMPLETION_RATE\n",
|
||
" BENEFIT_PARAMS[\"va_labour_realization\"][\"realistic\"] = VA_LABOUR_REALIZATION\n",
|
||
" BENEFIT_PARAMS[\"va_callback_discount\"][\"realistic\"] = VA_CALLBACK_DISCOUNT\n",
|
||
" # Override deflection_target on the Voice Bot / Agentic VA scopes\n",
|
||
" scopes = [\n",
|
||
" dataclasses.replace(\n",
|
||
" s,\n",
|
||
" deflection_target=(\n",
|
||
" VA_BOT_DEFLECTION if s.feature == \"Voice Bot\"\n",
|
||
" else VA_AGENTIC_DEFLECTION if s.feature == \"Agentic Virtual Agent\"\n",
|
||
" else s.deflection_target\n",
|
||
" ),\n",
|
||
" )\n",
|
||
" for s in scopes\n",
|
||
" ]\n",
|
||
"\n",
|
||
"\n",
|
||
"_apply_va_inputs()\n",
|
||
"\n",
|
||
"bot_pct = VA_BOT_DEFLECTION * 100\n",
|
||
"va_pct = VA_AGENTIC_DEFLECTION * 100\n",
|
||
"combined = VA_BOT_DEFLECTION + (1 - VA_BOT_DEFLECTION) * VA_AGENTIC_DEFLECTION\n",
|
||
"real_factor = (\n",
|
||
" VA_COMPLETION_RATE * VA_LABOUR_REALIZATION * (1 - VA_CALLBACK_DISCOUNT)\n",
|
||
")\n",
|
||
"print(\n",
|
||
" f\"Voice Bot: {bot_pct:.0f}% of full pool · \"\n",
|
||
" f\"Agentic VA: {va_pct:.0f}% of residual\\n\"\n",
|
||
" f\"Effective deflection: {combined*100:.2f}% · \"\n",
|
||
" f\"Realization factor: {real_factor*100:.1f}% · \"\n",
|
||
" f\"Net labour avoidance: {combined*real_factor*100:.1f}%\"\n",
|
||
")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "872af216",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-07-13T19:32:58.315429Z",
|
||
"iopub.status.busy": "2026-07-13T19:32:58.315136Z",
|
||
"iopub.status.idle": "2026-07-13T19:32:58.329667Z",
|
||
"shell.execute_reply": "2026-07-13T19:32:58.328977Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "cf854dec89944ceca7119f9f177abfd5",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"VBox(children=(HBox(children=(FloatSlider(value=0.35, description='Bot %', max=0.7, readout_format='.0%', step…"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Optional: ipywidgets sliders for VA inputs (skip if ipywidgets unavailable)\n",
|
||
"try:\n",
|
||
" import ipywidgets as W\n",
|
||
" from IPython.display import display as _disp\n",
|
||
"\n",
|
||
" _w_bot = W.FloatSlider(value=VA_BOT_DEFLECTION, min=0, max=0.7,\n",
|
||
" step=0.01, description=\"Bot %\",\n",
|
||
" readout_format=\".0%\")\n",
|
||
" _w_botm = W.FloatSlider(value=VA_BOT_AVG_MINUTES, min=0.5, max=5.0,\n",
|
||
" step=0.1, description=\"Bot min\")\n",
|
||
" _w_va = W.FloatSlider(value=VA_AGENTIC_DEFLECTION, min=0, max=0.5,\n",
|
||
" step=0.01, description=\"VA %\",\n",
|
||
" readout_format=\".0%\")\n",
|
||
" _w_cmp = W.FloatSlider(value=VA_COMPLETION_RATE, min=0.3, max=1.0,\n",
|
||
" step=0.01, description=\"Completion\",\n",
|
||
" readout_format=\".0%\")\n",
|
||
" _w_lab = W.FloatSlider(value=VA_LABOUR_REALIZATION, min=0.3, max=1.0,\n",
|
||
" step=0.01, description=\"Labour real.\",\n",
|
||
" readout_format=\".0%\")\n",
|
||
" _w_cb = W.FloatSlider(value=VA_CALLBACK_DISCOUNT, min=0.0, max=0.30,\n",
|
||
" step=0.01, description=\"Callback\",\n",
|
||
" readout_format=\".0%\")\n",
|
||
"\n",
|
||
" def _on_va_change(change):\n",
|
||
" global VA_BOT_DEFLECTION, VA_BOT_AVG_MINUTES, VA_AGENTIC_DEFLECTION\n",
|
||
" global VA_COMPLETION_RATE, VA_LABOUR_REALIZATION, VA_CALLBACK_DISCOUNT\n",
|
||
" VA_BOT_DEFLECTION = _w_bot.value\n",
|
||
" VA_BOT_AVG_MINUTES = _w_botm.value\n",
|
||
" VA_AGENTIC_DEFLECTION = _w_va.value\n",
|
||
" VA_COMPLETION_RATE = _w_cmp.value\n",
|
||
" VA_LABOUR_REALIZATION = _w_lab.value\n",
|
||
" VA_CALLBACK_DISCOUNT = _w_cb.value\n",
|
||
" _apply_va_inputs()\n",
|
||
" eff = VA_BOT_DEFLECTION + (1-VA_BOT_DEFLECTION)*VA_AGENTIC_DEFLECTION\n",
|
||
" rf = VA_COMPLETION_RATE * VA_LABOUR_REALIZATION * (1-VA_CALLBACK_DISCOUNT)\n",
|
||
" print(f\"→ effective {eff*100:.2f}% · realization {rf*100:.1f}% \"\n",
|
||
" f\"· net {eff*rf*100:.1f}% — re-run cells below.\")\n",
|
||
"\n",
|
||
" for w in (_w_bot, _w_botm, _w_va, _w_cmp, _w_lab, _w_cb):\n",
|
||
" w.observe(_on_va_change, \"value\")\n",
|
||
" _disp(W.VBox([\n",
|
||
" W.HBox([_w_bot, _w_botm, _w_va]),\n",
|
||
" W.HBox([_w_cmp, _w_lab, _w_cb]),\n",
|
||
" ]))\n",
|
||
"except ImportError:\n",
|
||
" print(\"ipywidgets not installed — adjust the variables above.\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "07ed3a9e",
|
||
"metadata": {
|
||
"execution": {
|
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"iopub.execute_input": "2026-07-13T19:32:58.334260Z",
|
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"iopub.status.busy": "2026-07-13T19:32:58.334037Z",
|
||
"iopub.status.idle": "2026-07-13T19:32:59.377489Z",
|
||
"shell.execute_reply": "2026-07-13T19:32:59.376614Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.plotly.v1+json": {
|
||
"config": {
|
||
"plotlyServerURL": "https://plot.ly"
|
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},
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"data": [
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{
|
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"decreasing": {
|
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|
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|
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|
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|
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|
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"color": "#2E7D32"
|
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|
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|
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"measure": [
|
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"absolute",
|
||
"relative",
|
||
"relative",
|
||
"relative",
|
||
"total"
|
||
],
|
||
"name": "Call flow",
|
||
"orientation": "v",
|
||
"text": [
|
||
"100%",
|
||
"−35%",
|
||
"−15% of residual",
|
||
"",
|
||
"55.2%"
|
||
],
|
||
"textposition": "outside",
|
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"totals": {
|
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"marker": {
|
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"color": "#1565C0"
|
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}
|
||
},
|
||
"type": "waterfall",
|
||
"x": [
|
||
"Total calls<br>(14,572,296)",
|
||
"Voice Bot<br>(−5,100,304)",
|
||
"Agentic VA<br>(−1,420,799)",
|
||
"To agent<br>(8,051,194)",
|
||
"Net to agent"
|
||
],
|
||
"y": [
|
||
14572296,
|
||
-5100303.6,
|
||
-1420798.86,
|
||
0,
|
||
0
|
||
]
|
||
}
|
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],
|
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"layout": {
|
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"height": 420,
|
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"margin": {
|
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"b": 40,
|
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"l": 20,
|
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"r": 20,
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||
" <td>35.0%</td>\n",
|
||
" <td>5,100,304</td>\n",
|
||
" <td>13,282,041</td>\n",
|
||
" <td>7,066,046</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Agentic VA deflection (residual)</td>\n",
|
||
" <td>9.8%</td>\n",
|
||
" <td>1,420,799</td>\n",
|
||
" <td>3,699,997</td>\n",
|
||
" <td>1,968,398</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Combined (effective)</td>\n",
|
||
" <td>44.75%</td>\n",
|
||
" <td>6,521,102</td>\n",
|
||
" <td>16,982,038</td>\n",
|
||
" <td>9,034,444</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" layer % of total annual calls \\\n",
|
||
"0 Voice Bot deflection 35.0% 5,100,304 \n",
|
||
"1 Agentic VA deflection (residual) 9.8% 1,420,799 \n",
|
||
"2 Combined (effective) 44.75% 6,521,102 \n",
|
||
"\n",
|
||
" labour avoided (raw) labour avoided (realized) \n",
|
||
"0 13,282,041 7,066,046 \n",
|
||
"1 3,699,997 1,968,398 \n",
|
||
"2 16,982,038 9,034,444 "
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"NAM realization haircut: $16,982,038 raw → $9,034,444 realized (×0.532 = 70% completion × 80% labour × 95% retained)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Visualize the deflection cascade for the largest site (NAM) at the\n",
|
||
"# current scenario × year. Shows where calls go and how realization\n",
|
||
"# haircuts shrink raw deflection into actual labour savings.\n",
|
||
"\n",
|
||
"_site = next(s for s in sites if s.site_name == \"NAM\")\n",
|
||
"_annual_calls = _site.voice_volume_monthly * 12\n",
|
||
"_aht_seconds = _site.voice_aht_seconds\n",
|
||
"_labour_rate = _site.agent_cost_per_second\n",
|
||
"\n",
|
||
"bot_calls = _annual_calls * VA_BOT_DEFLECTION\n",
|
||
"residual_calls = _annual_calls * (1 - VA_BOT_DEFLECTION)\n",
|
||
"va_calls = residual_calls * VA_AGENTIC_DEFLECTION\n",
|
||
"to_agent_calls = residual_calls - va_calls\n",
|
||
"deflected_total = bot_calls + va_calls\n",
|
||
"\n",
|
||
"# Raw labour avoided (no haircuts)\n",
|
||
"raw_labour_avoided = deflected_total * _aht_seconds * _labour_rate\n",
|
||
"# After realization haircuts\n",
|
||
"real_labour_avoided = raw_labour_avoided * real_factor\n",
|
||
"\n",
|
||
"# Waterfall: starting at 100% of calls, show where they go\n",
|
||
"fig = go.Figure(go.Waterfall(\n",
|
||
" name=\"Call flow\",\n",
|
||
" orientation=\"v\",\n",
|
||
" measure=[\"absolute\", \"relative\", \"relative\", \"relative\", \"total\"],\n",
|
||
" x=[\n",
|
||
" f\"Total calls<br>({_annual_calls:,.0f})\",\n",
|
||
" f\"Voice Bot<br>(−{bot_calls:,.0f})\",\n",
|
||
" f\"Agentic VA<br>(−{va_calls:,.0f})\",\n",
|
||
" f\"To agent<br>({to_agent_calls:,.0f})\",\n",
|
||
" \"Net to agent\",\n",
|
||
" ],\n",
|
||
" y=[_annual_calls, -bot_calls, -va_calls, 0, 0],\n",
|
||
" text=[\n",
|
||
" f\"100%\",\n",
|
||
" f\"−{VA_BOT_DEFLECTION*100:.0f}%\",\n",
|
||
" f\"−{VA_AGENTIC_DEFLECTION*100:.0f}% of residual\",\n",
|
||
" \"\",\n",
|
||
" f\"{(to_agent_calls/_annual_calls)*100:.1f}%\",\n",
|
||
" ],\n",
|
||
" textposition=\"outside\",\n",
|
||
" increasing={\"marker\": {\"color\": \"#2E7D32\"}},\n",
|
||
" decreasing={\"marker\": {\"color\": \"#C62828\"}},\n",
|
||
" totals={\"marker\": {\"color\": \"#1565C0\"}},\n",
|
||
"))\n",
|
||
"fig.update_layout(\n",
|
||
" title=f\"NAM deflection cascade · {SCENARIO} scenario · \"\n",
|
||
" f\"{deflected_total/_annual_calls*100:.1f}% effective deflection\",\n",
|
||
" yaxis_title=\"Annual calls\",\n",
|
||
" height=420,\n",
|
||
" margin=dict(l=20, r=20, t=60, b=40),\n",
|
||
")\n",
|
||
"fig.show()\n",
|
||
"\n",
|
||
"# Summary table\n",
|
||
"summary = pd.DataFrame([\n",
|
||
" {\"layer\": \"Voice Bot deflection\",\n",
|
||
" \"% of total\": f\"{VA_BOT_DEFLECTION*100:.1f}%\",\n",
|
||
" \"annual calls\": bot_calls,\n",
|
||
" \"labour avoided (raw)\": bot_calls * _aht_seconds * _labour_rate,\n",
|
||
" \"labour avoided (realized)\": bot_calls * _aht_seconds * _labour_rate * real_factor},\n",
|
||
" {\"layer\": \"Agentic VA deflection (residual)\",\n",
|
||
" \"% of total\": f\"{(va_calls/_annual_calls)*100:.1f}%\",\n",
|
||
" \"annual calls\": va_calls,\n",
|
||
" \"labour avoided (raw)\": va_calls * _aht_seconds * _labour_rate,\n",
|
||
" \"labour avoided (realized)\": va_calls * _aht_seconds * _labour_rate * real_factor},\n",
|
||
" {\"layer\": \"Combined (effective)\",\n",
|
||
" \"% of total\": f\"{(deflected_total/_annual_calls)*100:.2f}%\",\n",
|
||
" \"annual calls\": deflected_total,\n",
|
||
" \"labour avoided (raw)\": raw_labour_avoided,\n",
|
||
" \"labour avoided (realized)\": real_labour_avoided},\n",
|
||
"])\n",
|
||
"display(summary)\n",
|
||
"print(f\"NAM realization haircut: ${raw_labour_avoided:,.0f} raw → \"\n",
|
||
" f\"${real_labour_avoided:,.0f} realized \"\n",
|
||
" f\"(×{real_factor:.3f} = {VA_COMPLETION_RATE:.0%} completion × \"\n",
|
||
" f\"{VA_LABOUR_REALIZATION:.0%} labour × {1-VA_CALLBACK_DISCOUNT:.0%} retained)\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "e7fc3a73",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-07-13T19:32:59.380017Z",
|
||
"iopub.status.busy": "2026-07-13T19:32:59.379684Z",
|
||
"iopub.status.idle": "2026-07-13T19:32:59.407404Z",
|
||
"shell.execute_reply": "2026-07-13T19:32:59.406552Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>input</th>\n",
|
||
" <th>value</th>\n",
|
||
" <th>drives</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Voice Bot deflection (full pool)</td>\n",
|
||
" <td>35%</td>\n",
|
||
" <td>Voice Bot tokens (cost) + bot deflection benefit</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Voice Bot avg minutes</td>\n",
|
||
" <td>1.5 min</td>\n",
|
||
" <td>Voice Bot tokens (cost only)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Agentic VA deflection (residual)</td>\n",
|
||
" <td>15%</td>\n",
|
||
" <td>Agentic VA tokens (cost) + VA deflection benefit</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Effective combined deflection</td>\n",
|
||
" <td>44.75%</td>\n",
|
||
" <td>Total share of voice calls deflected</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Realization factor</td>\n",
|
||
" <td>53.2%</td>\n",
|
||
" <td>Haircut on labour avoided (70% × 80% × 95%)</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" input value \\\n",
|
||
"0 Voice Bot deflection (full pool) 35% \n",
|
||
"1 Voice Bot avg minutes 1.5 min \n",
|
||
"2 Agentic VA deflection (residual) 15% \n",
|
||
"3 Effective combined deflection 44.75% \n",
|
||
"4 Realization factor 53.2% \n",
|
||
"\n",
|
||
" drives \n",
|
||
"0 Voice Bot tokens (cost) + bot deflection benefit \n",
|
||
"1 Voice Bot tokens (cost only) \n",
|
||
"2 Agentic VA tokens (cost) + VA deflection benefit \n",
|
||
"3 Total share of voice calls deflected \n",
|
||
"4 Haircut on labour avoided (70% × 80% × 95%) "
|
||
]
|
||
},
|
||
"metadata": {},
|
||
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|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>model</th>\n",
|
||
" <th>Y2 benefit</th>\n",
|
||
" <th>vs corrected</th>\n",
|
||
" <th>note</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Original (broken) — additive on full base, no ...</td>\n",
|
||
" <td>29,993,898</td>\n",
|
||
" <td></td>\n",
|
||
" <td>Counts bot + VA on the same call pool, 100% la...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Corrected — layered + 'claim' params (no hairc...</td>\n",
|
||
" <td>24,900,726</td>\n",
|
||
" <td>-17.0%</td>\n",
|
||
" <td>Layered model alone removes double-count</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Corrected — layered + realistic haircuts (curr...</td>\n",
|
||
" <td>13,247,186</td>\n",
|
||
" <td>-55.8%</td>\n",
|
||
" <td>Production-calibrated; this is what flows into...</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" model Y2 benefit vs corrected \\\n",
|
||
"0 Original (broken) — additive on full base, no ... 29,993,898 \n",
|
||
"1 Corrected — layered + 'claim' params (no hairc... 24,900,726 -17.0% \n",
|
||
"2 Corrected — layered + realistic haircuts (curr... 13,247,186 -55.8% \n",
|
||
"\n",
|
||
" note \n",
|
||
"0 Counts bot + VA on the same call pool, 100% la... \n",
|
||
"1 Layered model alone removes double-count \n",
|
||
"2 Production-calibrated; this is what flows into... "
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Reconciliation: how the inputs above flow into both the Cost Model and\n",
|
||
"# Benefit Model. Compare the corrected (layered + haircut) figures\n",
|
||
"# against the original Genesys ROI-doc 'claim' assumption to make the\n",
|
||
"# correction visible.\n",
|
||
"\n",
|
||
"from tokencalc.benefit_model import calculate_va_deflection_benefit\n",
|
||
"\n",
|
||
"# Use a year where both Voice Bot AND Agentic VA are active. In the\n",
|
||
"# default rollout that's year 2 onward (Agentic VA phase=2).\n",
|
||
"_RECON_YEAR = max(YEAR, 2)\n",
|
||
"\n",
|
||
"_bot_scope = next(s for s in scopes if s.feature == \"Voice Bot\")\n",
|
||
"_va_scope = next((s for s in scopes if s.feature == \"Agentic Virtual Agent\"), None)\n",
|
||
"\n",
|
||
"_bot_realistic = calculate_va_deflection_benefit(\n",
|
||
" sites, _bot_scope, scenario, _RECON_YEAR, params=\"realistic\"\n",
|
||
")[\"annual_value\"].sum()\n",
|
||
"_bot_claim = calculate_va_deflection_benefit(\n",
|
||
" sites, _bot_scope, scenario, _RECON_YEAR, params=\"claim\"\n",
|
||
")[\"annual_value\"].sum()\n",
|
||
"\n",
|
||
"if _va_scope is not None:\n",
|
||
" _va_realistic = calculate_va_deflection_benefit(\n",
|
||
" sites, _va_scope, scenario, _RECON_YEAR, params=\"realistic\"\n",
|
||
" )[\"annual_value\"].sum()\n",
|
||
" _va_claim = calculate_va_deflection_benefit(\n",
|
||
" sites, _va_scope, scenario, _RECON_YEAR, params=\"claim\"\n",
|
||
" )[\"annual_value\"].sum()\n",
|
||
"else:\n",
|
||
" _va_realistic = _va_claim = 0.0\n",
|
||
"\n",
|
||
"# What the (broken) additive model would have produced — naive sum on full base\n",
|
||
"_total_calls_yr = sum(s.voice_volume_monthly * 12 for s in sites)\n",
|
||
"_avg_labour = sum(\n",
|
||
" (s.voice_volume_monthly * 12) * s.voice_aht_seconds * s.agent_cost_per_second\n",
|
||
" for s in sites\n",
|
||
") / max(_total_calls_yr, 1)\n",
|
||
"_naive_additive_claim = (\n",
|
||
" _total_calls_yr * (VA_BOT_DEFLECTION + VA_AGENTIC_DEFLECTION) * _avg_labour\n",
|
||
" * scenario.realization(_RECON_YEAR)\n",
|
||
") # original error: rates added on full base, no haircuts\n",
|
||
"\n",
|
||
"_recon = pd.DataFrame([\n",
|
||
" {\"input\": \"Voice Bot deflection (full pool)\",\n",
|
||
" \"value\": f\"{VA_BOT_DEFLECTION:.0%}\",\n",
|
||
" \"drives\": \"Voice Bot tokens (cost) + bot deflection benefit\"},\n",
|
||
" {\"input\": \"Voice Bot avg minutes\",\n",
|
||
" \"value\": f\"{VA_BOT_AVG_MINUTES:.1f} min\",\n",
|
||
" \"drives\": \"Voice Bot tokens (cost only)\"},\n",
|
||
" {\"input\": \"Agentic VA deflection (residual)\",\n",
|
||
" \"value\": f\"{VA_AGENTIC_DEFLECTION:.0%}\",\n",
|
||
" \"drives\": \"Agentic VA tokens (cost) + VA deflection benefit\"},\n",
|
||
" {\"input\": \"Effective combined deflection\",\n",
|
||
" \"value\": f\"{(VA_BOT_DEFLECTION + (1-VA_BOT_DEFLECTION)*VA_AGENTIC_DEFLECTION):.2%}\",\n",
|
||
" \"drives\": \"Total share of voice calls deflected\"},\n",
|
||
" {\"input\": \"Realization factor\",\n",
|
||
" \"value\": f\"{real_factor:.1%}\",\n",
|
||
" \"drives\": \"Haircut on labour avoided \"\n",
|
||
" f\"({VA_COMPLETION_RATE:.0%} × {VA_LABOUR_REALIZATION:.0%} × \"\n",
|
||
" f\"{1-VA_CALLBACK_DISCOUNT:.0%})\"},\n",
|
||
"])\n",
|
||
"display(_recon)\n",
|
||
"\n",
|
||
"_compare = pd.DataFrame([\n",
|
||
" {\"model\": \"Original (broken) — additive on full base, no haircuts\",\n",
|
||
" f\"Y{_RECON_YEAR} benefit\": _naive_additive_claim,\n",
|
||
" \"vs corrected\": \"\",\n",
|
||
" \"note\": \"Counts bot + VA on the same call pool, 100% labour avoidance\"},\n",
|
||
" {\"model\": \"Corrected — layered + 'claim' params (no haircuts)\",\n",
|
||
" f\"Y{_RECON_YEAR} benefit\": _bot_claim + _va_claim,\n",
|
||
" \"vs corrected\": f\"{((_bot_claim+_va_claim)/_naive_additive_claim - 1)*100:+.1f}%\",\n",
|
||
" \"note\": \"Layered model alone removes double-count\"},\n",
|
||
" {\"model\": \"Corrected — layered + realistic haircuts (current)\",\n",
|
||
" f\"Y{_RECON_YEAR} benefit\": _bot_realistic + _va_realistic,\n",
|
||
" \"vs corrected\": f\"{((_bot_realistic+_va_realistic)/_naive_additive_claim - 1)*100:+.1f}%\",\n",
|
||
" \"note\": \"Production-calibrated; this is what flows into the business case\"},\n",
|
||
"])\n",
|
||
"display(_compare)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3079316e",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Cost model"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "3ac4fee1",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-07-13T19:32:59.410682Z",
|
||
"iopub.status.busy": "2026-07-13T19:32:59.410375Z",
|
||
"iopub.status.idle": "2026-07-13T19:32:59.435201Z",
|
||
"shell.execute_reply": "2026-07-13T19:32:59.432035Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
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" }\n",
|
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"\n",
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" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
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" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>cost_line</th>\n",
|
||
" <th>scope</th>\n",
|
||
" <th>annual_cost</th>\n",
|
||
" <th>confidence</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Genesys CX 3 platform licences</td>\n",
|
||
" <td>all sites</td>\n",
|
||
" <td>2,788,232</td>\n",
|
||
" <td>confirmed</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Agent Copilot [named]</td>\n",
|
||
" <td>NAM, EMEA, AUZ, APAC HK, APAC SG, APAC SH, APA...</td>\n",
|
||
" <td>1,002,240</td>\n",
|
||
" <td>confirmed</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Speech & Text Analytics [named]</td>\n",
|
||
" <td>NAM, EMEA, AUZ, APAC HK, APAC SG, APAC SH, APA...</td>\n",
|
||
" <td>751,680</td>\n",
|
||
" <td>confirmed</td>\n",
|
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" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
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" <td>Voice Bot</td>\n",
|
||
" <td>NAM, EMEA, AUZ, APAC HK, APAC SG, APAC SH, APA...</td>\n",
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" <td>680,592</td>\n",
|
||
" <td>confirmed</td>\n",
|
||
" </tr>\n",
|
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" <tr>\n",
|
||
" <th>6</th>\n",
|
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" <td>Direct Messaging</td>\n",
|
||
" <td>NAM, EMEA, AUZ, APAC HK, APAC SG, APAC SH, APA...</td>\n",
|
||
" <td>132</td>\n",
|
||
" <td>confirmed</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Agentic Virtual Agent</td>\n",
|
||
" <td>—</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>confirmed</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>AI Summary & Insights</td>\n",
|
||
" <td>—</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>confirmed</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>7</th>\n",
|
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" <td>AI Translate</td>\n",
|
||
" <td>—</td>\n",
|
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" <td>0</td>\n",
|
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" <td>confirmed</td>\n",
|
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
|
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"text/plain": [
|
||
" cost_line \\\n",
|
||
"0 Genesys CX 3 platform licences \n",
|
||
"4 Agent Copilot [named] \n",
|
||
"3 Speech & Text Analytics [named] \n",
|
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"1 Voice Bot \n",
|
||
"6 Direct Messaging \n",
|
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"2 Agentic Virtual Agent \n",
|
||
"5 AI Summary & Insights \n",
|
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"7 AI Translate \n",
|
||
"\n",
|
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" scope annual_cost confidence \n",
|
||
"0 all sites 2,788,232 confirmed \n",
|
||
"4 NAM, EMEA, AUZ, APAC HK, APAC SG, APAC SH, APA... 1,002,240 confirmed \n",
|
||
"3 NAM, EMEA, AUZ, APAC HK, APAC SG, APAC SH, APA... 751,680 confirmed \n",
|
||
"1 NAM, EMEA, AUZ, APAC HK, APAC SG, APAC SH, APA... 680,592 confirmed \n",
|
||
"6 NAM, EMEA, AUZ, APAC HK, APAC SG, APAC SH, APA... 132 confirmed \n",
|
||
"2 — 0 confirmed \n",
|
||
"5 — 0 confirmed \n",
|
||
"7 — 0 confirmed "
|
||
]
|
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},
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"metadata": {},
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"output_type": "display_data"
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},
|
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{
|
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"name": "stdout",
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"output_type": "stream",
|
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"text": [
|
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"Year 1 total: $5,222,876 (of which 🔴 unknown-rate features: $0)\n"
|
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]
|
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}
|
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],
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"source": [
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"cost_df = calculate_total_cost(sites, scopes, meters, pricing, scenario, YEAR,\n",
|
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" use_contracted=USE_CONTRACTED_RATES)\n",
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"display(cost_df.sort_values(\"annual_cost\", ascending=False))\n",
|
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"unknown_cost = cost_df[cost_df.confidence == \"unknown\"][\"annual_cost\"].sum()\n",
|
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"print(f\"Year {YEAR} total: ${cost_df['annual_cost'].sum():,.0f}\"\n",
|
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" f\" (of which 🔴 unknown-rate features: ${unknown_cost:,.0f})\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "fa4c9cdc",
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"metadata": {
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"execution": {
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}
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},
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"source": [
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||
"# Stacked bar — cost by feature, one bar per year\n",
|
||
"frames = []\n",
|
||
"for y in (1, 2, 3):\n",
|
||
" d = calculate_total_cost(sites, scopes, meters, pricing, scenario, y,\n",
|
||
" use_contracted=USE_CONTRACTED_RATES)\n",
|
||
" d[\"year\"] = f\"Y{y}\"\n",
|
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" frames.append(d)\n",
|
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"cost_3y = pd.concat(frames, ignore_index=True)\n",
|
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"px.bar(cost_3y, x=\"year\", y=\"annual_cost\", color=\"cost_line\",\n",
|
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" title=f\"Cost breakdown by feature — {SCENARIO}\",\n",
|
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" labels={\"annual_cost\": \"$/yr\"})"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
||
"## Benefit model"
|
||
]
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||
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{
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"execution_count": 12,
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"execution": {
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"shell.execute_reply": "2026-07-13T19:32:59.926937Z"
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}
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{
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"data": {
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" .dataframe tbody tr th {\n",
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" }\n",
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"</style>\n",
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" <th></th>\n",
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" <th>benefit_line</th>\n",
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" <th>scope</th>\n",
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" <th>annual_value</th>\n",
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" <th>confidence</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>Voice Bot deflection (labour avoided)</td>\n",
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" <td>APAC GZ, APAC HK, APAC JP, APAC SG, APAC SH, A...</td>\n",
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" <td>6,981,080</td>\n",
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" <td>estimated</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>Voice ACW (summarization)</td>\n",
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" <td>APAC GZ, APAC HK, APAC JP, APAC SG, APAC SH, A...</td>\n",
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" <td>2,099,573</td>\n",
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" <td>estimated</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Voice AHT (knowledge surfacing)</td>\n",
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" <td>APAC GZ, APAC HK, APAC JP, APAC SG, APAC SH, A...</td>\n",
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" <td>1,237,248</td>\n",
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" <td>estimated</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>STA coaching (AHT)</td>\n",
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" <td>APAC GZ, APAC HK, APAC JP, APAC SG, APAC SH, A...</td>\n",
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" <td>562,386</td>\n",
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" <td>estimated</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>Supervisor time (AI summaries/insights)</td>\n",
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" <td>APAC GZ, APAC HK, APAC JP, APAC SG, APAC SH, A...</td>\n",
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" <td>318,500</td>\n",
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" <td>estimated</td>\n",
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" </tr>\n",
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"</div>"
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],
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"text/plain": [
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" benefit_line \\\n",
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"0 Voice Bot deflection (labour avoided) \n",
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"3 Voice ACW (summarization) \n",
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"2 Voice AHT (knowledge surfacing) \n",
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"1 STA coaching (AHT) \n",
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"\n",
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" scope annual_value confidence \n",
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"0 APAC GZ, APAC HK, APAC JP, APAC SG, APAC SH, A... 6,981,080 estimated \n",
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"3 APAC GZ, APAC HK, APAC JP, APAC SG, APAC SH, A... 2,099,573 estimated \n",
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"2 APAC GZ, APAC HK, APAC JP, APAC SG, APAC SH, A... 1,237,248 estimated \n",
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"1 APAC GZ, APAC HK, APAC JP, APAC SG, APAC SH, A... 562,386 estimated \n",
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"4 APAC GZ, APAC HK, APAC JP, APAC SG, APAC SH, A... 318,500 estimated "
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]
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},
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},
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{
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"name": "stdout",
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"text": [
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"Year 1 total benefit: $11,198,787\n"
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]
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}
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],
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"source": [
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"benefit_df = calculate_total_benefit(sites, scopes, scenario, YEAR,\n",
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" params=BENEFIT_PARAMS_MODE)\n",
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"display(benefit_df.sort_values(\"annual_value\", ascending=False))\n",
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"print(f\"Year {YEAR} total benefit: ${benefit_df['annual_value'].sum():,.0f}\")"
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]
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},
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{
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|
||
}
|
||
}
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Stacked bar by source per year + Genesys claim vs pressure-tested comparison\n",
|
||
"frames = []\n",
|
||
"for y in (1, 2, 3):\n",
|
||
" d = calculate_total_benefit(sites, scopes, scenario, y, params=BENEFIT_PARAMS_MODE)\n",
|
||
" d[\"year\"] = f\"Y{y}\"\n",
|
||
" frames.append(d)\n",
|
||
"ben_3y = pd.concat(frames, ignore_index=True)\n",
|
||
"fig = px.bar(ben_3y, x=\"year\", y=\"annual_value\", color=\"benefit_line\",\n",
|
||
" title=f\"Benefit breakdown by source — {SCENARIO}\",\n",
|
||
" labels={\"annual_value\": \"$/yr\"})\n",
|
||
"fig.show()\n",
|
||
"\n",
|
||
"claim = calculate_total_benefit(sites, scopes, scenario, YEAR, params=\"claim\")\n",
|
||
"comp = pd.merge(\n",
|
||
" claim[[\"benefit_line\", \"annual_value\"]].rename(columns={\"annual_value\": \"Genesys claim\"}),\n",
|
||
" benefit_df[[\"benefit_line\", \"annual_value\"]].rename(columns={\"annual_value\": \"Pressure-tested\"}),\n",
|
||
" on=\"benefit_line\", how=\"outer\",\n",
|
||
").fillna(0)\n",
|
||
"go.Figure([\n",
|
||
" go.Bar(name=\"Genesys claim\", x=comp.benefit_line, y=comp[\"Genesys claim\"]),\n",
|
||
" go.Bar(name=\"Pressure-tested realistic\", x=comp.benefit_line, y=comp[\"Pressure-tested\"]),\n",
|
||
"]).update_layout(barmode=\"group\", title=f\"Claim vs pressure-tested — Year {YEAR}\",\n",
|
||
" yaxis_tickformat=\"$,.0f\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "33e2dc31",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Business case — 3-year P&L, NPV @ 8%, payback, ROI"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "7bd6456e",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-07-13T19:33:00.025440Z",
|
||
"iopub.status.busy": "2026-07-13T19:33:00.025068Z",
|
||
"iopub.status.idle": "2026-07-13T19:33:00.089596Z",
|
||
"shell.execute_reply": "2026-07-13T19:33:00.088897Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>line</th>\n",
|
||
" <th>Y1</th>\n",
|
||
" <th>Y2</th>\n",
|
||
" <th>Y3</th>\n",
|
||
" <th>3-yr Total</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Genesys CX 3 platform licences</td>\n",
|
||
" <td>2,788,232</td>\n",
|
||
" <td>2,788,232</td>\n",
|
||
" <td>2,788,232</td>\n",
|
||
" <td>8,364,695</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Voice Bot</td>\n",
|
||
" <td>680,592</td>\n",
|
||
" <td>972,240</td>\n",
|
||
" <td>972,240</td>\n",
|
||
" <td>2,625,072</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Agentic Virtual Agent</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1,606,248</td>\n",
|
||
" <td>2,294,640</td>\n",
|
||
" <td>3,900,888</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Speech & Text Analytics [named]</td>\n",
|
||
" <td>751,680</td>\n",
|
||
" <td>751,680</td>\n",
|
||
" <td>751,680</td>\n",
|
||
" <td>2,255,040</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Agent Copilot [named]</td>\n",
|
||
" <td>1,002,240</td>\n",
|
||
" <td>1,002,240</td>\n",
|
||
" <td>1,002,240</td>\n",
|
||
" <td>3,006,720</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>AI Summary & Insights</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>Direct Messaging</td>\n",
|
||
" <td>132</td>\n",
|
||
" <td>144</td>\n",
|
||
" <td>144</td>\n",
|
||
" <td>420</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>AI Translate</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>4,434,000</td>\n",
|
||
" <td>4,434,000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>NICE IEX (NAM)</td>\n",
|
||
" <td>650,000</td>\n",
|
||
" <td>1,300,000</td>\n",
|
||
" <td>1,300,000</td>\n",
|
||
" <td>3,250,000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>Legacy CC platform</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>Voice Bot deflection (labour avoided)</td>\n",
|
||
" <td>6,981,080</td>\n",
|
||
" <td>11,169,728</td>\n",
|
||
" <td>13,264,052</td>\n",
|
||
" <td>31,414,859</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>STA coaching (AHT)</td>\n",
|
||
" <td>562,386</td>\n",
|
||
" <td>899,817</td>\n",
|
||
" <td>1,068,533</td>\n",
|
||
" <td>2,530,735</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>Voice AHT (knowledge surfacing)</td>\n",
|
||
" <td>1,237,248</td>\n",
|
||
" <td>1,979,597</td>\n",
|
||
" <td>2,350,772</td>\n",
|
||
" <td>5,567,617</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>Voice ACW (summarization)</td>\n",
|
||
" <td>2,099,573</td>\n",
|
||
" <td>3,359,317</td>\n",
|
||
" <td>3,989,188</td>\n",
|
||
" <td>9,448,078</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>Supervisor time (AI summaries/insights)</td>\n",
|
||
" <td>318,500</td>\n",
|
||
" <td>509,600</td>\n",
|
||
" <td>605,150</td>\n",
|
||
" <td>1,433,250</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>Agentic Virtual Agent deflection (labour avoided)</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2,077,459</td>\n",
|
||
" <td>2,466,982</td>\n",
|
||
" <td>4,544,441</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16</th>\n",
|
||
" <td>TOTAL COSTS</td>\n",
|
||
" <td>5,222,876</td>\n",
|
||
" <td>7,120,784</td>\n",
|
||
" <td>12,243,176</td>\n",
|
||
" <td>24,586,835</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17</th>\n",
|
||
" <td>TOTAL TAKEOUTS</td>\n",
|
||
" <td>650,000</td>\n",
|
||
" <td>1,300,000</td>\n",
|
||
" <td>1,300,000</td>\n",
|
||
" <td>3,250,000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>18</th>\n",
|
||
" <td>TOTAL BENEFITS</td>\n",
|
||
" <td>11,198,787</td>\n",
|
||
" <td>19,995,517</td>\n",
|
||
" <td>23,744,677</td>\n",
|
||
" <td>54,938,980</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>19</th>\n",
|
||
" <td>NET</td>\n",
|
||
" <td>6,625,911</td>\n",
|
||
" <td>14,174,734</td>\n",
|
||
" <td>12,801,501</td>\n",
|
||
" <td>33,602,145</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>20</th>\n",
|
||
" <td>Cumulative net</td>\n",
|
||
" <td>6,625,911</td>\n",
|
||
" <td>20,800,644</td>\n",
|
||
" <td>33,602,145</td>\n",
|
||
" <td>61,028,701</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" line Y1 Y2 \\\n",
|
||
"0 Genesys CX 3 platform licences 2,788,232 2,788,232 \n",
|
||
"1 Voice Bot 680,592 972,240 \n",
|
||
"2 Agentic Virtual Agent 0 1,606,248 \n",
|
||
"3 Speech & Text Analytics [named] 751,680 751,680 \n",
|
||
"4 Agent Copilot [named] 1,002,240 1,002,240 \n",
|
||
"5 AI Summary & Insights 0 0 \n",
|
||
"6 Direct Messaging 132 144 \n",
|
||
"7 AI Translate 0 0 \n",
|
||
"8 NICE IEX (NAM) 650,000 1,300,000 \n",
|
||
"9 Legacy CC platform 0 0 \n",
|
||
"10 Voice Bot deflection (labour avoided) 6,981,080 11,169,728 \n",
|
||
"11 STA coaching (AHT) 562,386 899,817 \n",
|
||
"12 Voice AHT (knowledge surfacing) 1,237,248 1,979,597 \n",
|
||
"13 Voice ACW (summarization) 2,099,573 3,359,317 \n",
|
||
"14 Supervisor time (AI summaries/insights) 318,500 509,600 \n",
|
||
"15 Agentic Virtual Agent deflection (labour avoided) 0 2,077,459 \n",
|
||
"16 TOTAL COSTS 5,222,876 7,120,784 \n",
|
||
"17 TOTAL TAKEOUTS 650,000 1,300,000 \n",
|
||
"18 TOTAL BENEFITS 11,198,787 19,995,517 \n",
|
||
"19 NET 6,625,911 14,174,734 \n",
|
||
"20 Cumulative net 6,625,911 20,800,644 \n",
|
||
"\n",
|
||
" Y3 3-yr Total \n",
|
||
"0 2,788,232 8,364,695 \n",
|
||
"1 972,240 2,625,072 \n",
|
||
"2 2,294,640 3,900,888 \n",
|
||
"3 751,680 2,255,040 \n",
|
||
"4 1,002,240 3,006,720 \n",
|
||
"5 0 0 \n",
|
||
"6 144 420 \n",
|
||
"7 4,434,000 4,434,000 \n",
|
||
"8 1,300,000 3,250,000 \n",
|
||
"9 0 0 \n",
|
||
"10 13,264,052 31,414,859 \n",
|
||
"11 1,068,533 2,530,735 \n",
|
||
"12 2,350,772 5,567,617 \n",
|
||
"13 3,989,188 9,448,078 \n",
|
||
"14 605,150 1,433,250 \n",
|
||
"15 2,466,982 4,544,441 \n",
|
||
"16 12,243,176 24,586,835 \n",
|
||
"17 1,300,000 3,250,000 \n",
|
||
"18 23,744,677 54,938,980 \n",
|
||
"19 12,801,501 33,602,145 \n",
|
||
"20 33,602,145 61,028,701 "
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"NPV @ 8%: $28,449,896 Payback: 0.00 years 3-yr ROI: 137%\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"case = build_business_case(sites, scopes, meters, pricing, takeouts, scenario,\n",
|
||
" implementation_cost=IMPLEMENTATION_COST,\n",
|
||
" use_contracted=USE_CONTRACTED_RATES,\n",
|
||
" benefit_params=BENEFIT_PARAMS_MODE)\n",
|
||
"\n",
|
||
"pnl = pd.concat([\n",
|
||
" case[\"cost_by_year\"].drop(columns=\"confidence\"),\n",
|
||
" case[\"takeouts_by_year\"].drop(columns=\"confidence\"),\n",
|
||
" case[\"benefit_by_year\"].drop(columns=\"confidence\"),\n",
|
||
" case[\"net_by_year\"],\n",
|
||
"], ignore_index=True)\n",
|
||
"pnl[\"3-yr Total\"] = pnl[[\"Y1\", \"Y2\", \"Y3\"]].sum(axis=1)\n",
|
||
"display(pnl)\n",
|
||
"\n",
|
||
"pb = case[\"payback_period_years\"]\n",
|
||
"pb_text = f\"{pb:.2f} years\" if pb is not None else \"never\"\n",
|
||
"print(f\"NPV @ 8%: ${case['npv']:,.0f} Payback: {pb_text} \"\n",
|
||
" f\"3-yr ROI: {case['roi_3yr']:.0%}\")"
|
||
]
|
||
},
|
||
{
|
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|
||
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|
||
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|
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"fig = go.Figure()\n",
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"for name in (\"floor\", \"realistic\", \"stretch\"):\n",
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" c = build_business_case(sites, scopes, meters, pricing, takeouts, name,\n",
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" mode=\"lines+markers\", name=name.capitalize())\n",
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"source": [
|
||
"# Break-even: at what email Auto-Respond rate does the case break even?\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"rates = np.linspace(0.0, 0.5, 26)\n",
|
||
"npvs = [build_business_case(\n",
|
||
" sites, scopes, meters, pricing, takeouts,\n",
|
||
" dataclasses.replace(scenario, email_auto_respond_rate=float(r)),\n",
|
||
" benefit_params=BENEFIT_PARAMS_MODE)[\"npv\"]\n",
|
||
" for r in rates]\n",
|
||
"breakeven = next((r for r, v in zip(rates, npvs) if v >= 0), None)\n",
|
||
"print(\"Break-even auto-respond rate:\",\n",
|
||
" f\"{breakeven:.0%}\" if breakeven is not None else \"not reached ≤50%\",\n",
|
||
" f\"(NPV already ${npvs[0]:,.0f} at 0%)\" if npvs[0] >= 0 else \"\")\n",
|
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"px.line(x=rates, y=npvs, labels={\"x\": \"Email Auto-Respond rate\", \"y\": \"3-yr NPV ($)\"},\n",
|
||
" title=\"NPV vs email Auto-Respond rate\")"
|
||
]
|
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},
|
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{
|
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"cell_type": "markdown",
|
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"id": "2c4d5a54",
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"metadata": {},
|
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"source": [
|
||
"## Export"
|
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]
|
||
},
|
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{
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"cell_type": "code",
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"execution_count": 18,
|
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"id": "57611721",
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|
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|
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"iopub.status.idle": "2026-07-13T19:33:02.541639Z",
|
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"shell.execute_reply": "2026-07-13T19:33:02.541002Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Excel → /home/robert/git/palladium/studies/202607_CTM_GenesysCX/exports/ctm_token_calculator_realistic.xlsx\n",
|
||
"JSON → /home/robert/git/palladium/studies/202607_CTM_GenesysCX/exports/ctm_scenario_realistic.json\n"
|
||
]
|
||
}
|
||
],
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"source": [
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"scenario_comparison = pd.DataFrame([\n",
|
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" {\"scenario\": n,\n",
|
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" **{f\"NPV\": c[\"npv\"], \"payback_years\": c[\"payback_period_years\"],\n",
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" \"roi_3yr\": c[\"roi_3yr\"]}}\n",
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" for n, c in ((n, build_business_case(sites, scopes, meters, pricing,\n",
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" takeouts, n,\n",
|
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" benefit_params=BENEFIT_PARAMS_MODE))\n",
|
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" for n in (\"floor\", \"realistic\", \"stretch\"))\n",
|
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"])\n",
|
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"\n",
|
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"xlsx = export_excel({\n",
|
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" \"Inputs\": sites_dataframe(sites),\n",
|
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" \"Meters\": meters_dataframe(meters),\n",
|
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" \"Cost detail\": cost_3y,\n",
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" \"Benefit detail\": ben_3y,\n",
|
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" \"Business case\": pnl,\n",
|
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" \"Scenario comparison\": scenario_comparison,\n",
|
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"}, _ROOT / \"exports\" / f\"ctm_token_calculator_{SCENARIO}.xlsx\")\n",
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"json_path = _ROOT / \"exports\" / f\"ctm_scenario_{SCENARIO}.json\"\n",
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"scenario_state_to_json(sites, takeouts, scopes, json_path)\n",
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"print(f\"Excel → {xlsx}\\nJSON → {json_path}\")"
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]
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},
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"## Scratchpad\n",
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"\n",
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"```python\n",
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