1409 lines
97 KiB
Plaintext
1409 lines
97 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "g1-md-intro",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 01 \u2014 Benefits Analysis\n",
|
|
"\n",
|
|
"**Study:** Forrester *Total Economic Impact\u2122 Of CX Cloud \u2014 Cost Savings\n",
|
|
"And Business Benefits Enabled By Genesys And Salesforce* (December 2025)\n",
|
|
"\n",
|
|
"Quantify the four benefit categories Forrester identified for the\n",
|
|
"composite organization (\\$2.5B revenue, 600 CX agents / 400 concurrent\n",
|
|
"licenses, 80,000 weekly interactions @ 12 min), push them into Athena,\n",
|
|
"and verify the totals match the published study\n",
|
|
"(Benefits PV \u2248 **\\$14.84M**)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "g1-md-setup",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Setup\n",
|
|
"\n",
|
|
"We add the project root to `sys.path` so the notebook can import `core` and\n",
|
|
"the study's local modules without `pip install -e .`."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "01-bootstrap",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import sys, pathlib # path shim: works on a fresh kernel\n",
|
|
"for _p in [pathlib.Path.cwd(), *pathlib.Path.cwd().parents]:\n",
|
|
" if (_p / \"pyproject.toml\").exists():\n",
|
|
" sys.path.insert(0, str(_p)); break\n",
|
|
"\n",
|
|
"from core.bootstrap import init\n",
|
|
"\n",
|
|
"pal = init(study=\"202512_GenesysCX\")\n",
|
|
"client, seed, config = pal.client, pal.seed_data, pal.config\n",
|
|
"\n",
|
|
"STUDY = pal.root / 'studies' / '202512_GenesysCX'\n",
|
|
"ROOT = pal.root\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"id": "g1-code-imports",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div style=\"padding:10px 14px;border-left:4px solid #0277bd;background:#e1f5fe;color:#1a1a1a;border-radius:4px;margin:6px 0;\">Study: <b>202512_GenesysCX</b> \u2022 discount rate 10% \u2022 3-year horizon</div>"
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.HTML object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"from core.calculations import npv, risk_adjust_benefit\n",
|
|
"from core.notebook_helpers import charts, display, tables\n",
|
|
"display.alert(\n",
|
|
" f'Study: <b>{config.STUDY_SLUG}</b> \u2022 discount rate {config.DISCOUNT_RATE:.0%} '\n",
|
|
" f'\u2022 {config.ANALYSIS_YEARS}-year horizon',\n",
|
|
" 'info',\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "g1-md-benefits-table",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Benefits \u2014 what the published study quantifies\n",
|
|
"\n",
|
|
"Forrester quantifies four benefit categories for the composite. The MTTR,\n",
|
|
"self-service deflection and revenue-lift assumptions are documented in the\n",
|
|
"`notes` field of every seed row (full PDF cell references \u2014 A1\u2013A4, B1\u2013B8,\n",
|
|
"C1\u2013C6, D1\u2013D3 \u2014 preserved for the audit trail):\n",
|
|
"\n",
|
|
"| Ref | Benefit | Y1 | Y2 | Y3 | Risk Adj |\n",
|
|
"|---|---|---|---|---|---|\n",
|
|
"| At | Retirement of legacy systems with CX Cloud adoption | \\$680K | \\$930K | \\$930K | 5% |\n",
|
|
"| Bt | Self-service savings (40 FTEs reallocated) | \\$2.33M | \\$2.33M | \\$2.33M | 15% |\n",
|
|
"| Ct | CX agent efficiency (MTTR 12\u219210 min, 60k weekly interactions) | \\$2.91M | \\$2.91M | \\$2.91M | 10% |\n",
|
|
"| Dt | Incremental sales from agent assist (1.5% lift \u00d7 8% margin \u00d7 \\$500M) | \\$0.6M | \\$0.6M | \\$0.6M | 5% |\n",
|
|
"\n",
|
|
"All four are seeded in `seed_data.BENEFITS` with full source notes."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"id": "g1-code-benefits-table",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<style type=\"text/css\">\n",
|
|
"</style>\n",
|
|
"<table id=\"T_f87c4\">\n",
|
|
" <thead>\n",
|
|
" <tr>\n",
|
|
" <th class=\"blank level0\" > </th>\n",
|
|
" <th id=\"T_f87c4_level0_col0\" class=\"col_heading level0 col0\" >field_key</th>\n",
|
|
" <th id=\"T_f87c4_level0_col1\" class=\"col_heading level0 col1\" >label</th>\n",
|
|
" <th id=\"T_f87c4_level0_col2\" class=\"col_heading level0 col2\" >category</th>\n",
|
|
" <th id=\"T_f87c4_level0_col3\" class=\"col_heading level0 col3\" >risk_adjustment</th>\n",
|
|
" <th id=\"T_f87c4_level0_col4\" class=\"col_heading level0 col4\" >Year 1</th>\n",
|
|
" <th id=\"T_f87c4_level0_col5\" class=\"col_heading level0 col5\" >Year 1 (RA)</th>\n",
|
|
" <th id=\"T_f87c4_level0_col6\" class=\"col_heading level0 col6\" >Year 2</th>\n",
|
|
" <th id=\"T_f87c4_level0_col7\" class=\"col_heading level0 col7\" >Year 2 (RA)</th>\n",
|
|
" <th id=\"T_f87c4_level0_col8\" class=\"col_heading level0 col8\" >Year 3</th>\n",
|
|
" <th id=\"T_f87c4_level0_col9\" class=\"col_heading level0 col9\" >Year 3 (RA)</th>\n",
|
|
" <th id=\"T_f87c4_level0_col10\" class=\"col_heading level0 col10\" >Total</th>\n",
|
|
" <th id=\"T_f87c4_level0_col11\" class=\"col_heading level0 col11\" >Total (RA)</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_f87c4_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
|
|
" <td id=\"T_f87c4_row0_col0\" class=\"data row0 col0\" >legacy_retirement</td>\n",
|
|
" <td id=\"T_f87c4_row0_col1\" class=\"data row0 col1\" >Retirement of legacy systems with CX Cloud adoption</td>\n",
|
|
" <td id=\"T_f87c4_row0_col2\" class=\"data row0 col2\" >Cost Savings</td>\n",
|
|
" <td id=\"T_f87c4_row0_col3\" class=\"data row0 col3\" >0.050000</td>\n",
|
|
" <td id=\"T_f87c4_row0_col4\" class=\"data row0 col4\" >$680,000</td>\n",
|
|
" <td id=\"T_f87c4_row0_col5\" class=\"data row0 col5\" >$646,000</td>\n",
|
|
" <td id=\"T_f87c4_row0_col6\" class=\"data row0 col6\" >$930,000</td>\n",
|
|
" <td id=\"T_f87c4_row0_col7\" class=\"data row0 col7\" >$883,500</td>\n",
|
|
" <td id=\"T_f87c4_row0_col8\" class=\"data row0 col8\" >$930,000</td>\n",
|
|
" <td id=\"T_f87c4_row0_col9\" class=\"data row0 col9\" >$883,500</td>\n",
|
|
" <td id=\"T_f87c4_row0_col10\" class=\"data row0 col10\" >$2,540,000</td>\n",
|
|
" <td id=\"T_f87c4_row0_col11\" class=\"data row0 col11\" >$2,413,000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_f87c4_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
|
|
" <td id=\"T_f87c4_row1_col0\" class=\"data row1 col0\" >self_service_savings</td>\n",
|
|
" <td id=\"T_f87c4_row1_col1\" class=\"data row1 col1\" >Cost savings from reallocated workers and avoided seasonal hires with increased customer self-service</td>\n",
|
|
" <td id=\"T_f87c4_row1_col2\" class=\"data row1 col2\" >Productivity</td>\n",
|
|
" <td id=\"T_f87c4_row1_col3\" class=\"data row1 col3\" >0.150000</td>\n",
|
|
" <td id=\"T_f87c4_row1_col4\" class=\"data row1 col4\" >$2,329,600</td>\n",
|
|
" <td id=\"T_f87c4_row1_col5\" class=\"data row1 col5\" >$1,980,160</td>\n",
|
|
" <td id=\"T_f87c4_row1_col6\" class=\"data row1 col6\" >$2,329,600</td>\n",
|
|
" <td id=\"T_f87c4_row1_col7\" class=\"data row1 col7\" >$1,980,160</td>\n",
|
|
" <td id=\"T_f87c4_row1_col8\" class=\"data row1 col8\" >$2,329,600</td>\n",
|
|
" <td id=\"T_f87c4_row1_col9\" class=\"data row1 col9\" >$1,980,160</td>\n",
|
|
" <td id=\"T_f87c4_row1_col10\" class=\"data row1 col10\" >$6,988,800</td>\n",
|
|
" <td id=\"T_f87c4_row1_col11\" class=\"data row1 col11\" >$5,940,480</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_f87c4_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
|
|
" <td id=\"T_f87c4_row2_col0\" class=\"data row2 col0\" >agent_efficiency</td>\n",
|
|
" <td id=\"T_f87c4_row2_col1\" class=\"data row2 col1\" >CX agent efficiency gains</td>\n",
|
|
" <td id=\"T_f87c4_row2_col2\" class=\"data row2 col2\" >Productivity</td>\n",
|
|
" <td id=\"T_f87c4_row2_col3\" class=\"data row2 col3\" >0.100000</td>\n",
|
|
" <td id=\"T_f87c4_row2_col4\" class=\"data row2 col4\" >$2,912,000</td>\n",
|
|
" <td id=\"T_f87c4_row2_col5\" class=\"data row2 col5\" >$2,620,800</td>\n",
|
|
" <td id=\"T_f87c4_row2_col6\" class=\"data row2 col6\" >$2,912,000</td>\n",
|
|
" <td id=\"T_f87c4_row2_col7\" class=\"data row2 col7\" >$2,620,800</td>\n",
|
|
" <td id=\"T_f87c4_row2_col8\" class=\"data row2 col8\" >$2,912,000</td>\n",
|
|
" <td id=\"T_f87c4_row2_col9\" class=\"data row2 col9\" >$2,620,800</td>\n",
|
|
" <td id=\"T_f87c4_row2_col10\" class=\"data row2 col10\" >$8,736,000</td>\n",
|
|
" <td id=\"T_f87c4_row2_col11\" class=\"data row2 col11\" >$7,862,400</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_f87c4_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
|
|
" <td id=\"T_f87c4_row3_col0\" class=\"data row3 col0\" >agent_assist_sales</td>\n",
|
|
" <td id=\"T_f87c4_row3_col1\" class=\"data row3 col1\" >Incremental sales from agent assist capabilities</td>\n",
|
|
" <td id=\"T_f87c4_row3_col2\" class=\"data row3 col2\" >Revenue</td>\n",
|
|
" <td id=\"T_f87c4_row3_col3\" class=\"data row3 col3\" >0.050000</td>\n",
|
|
" <td id=\"T_f87c4_row3_col4\" class=\"data row3 col4\" >$600,000</td>\n",
|
|
" <td id=\"T_f87c4_row3_col5\" class=\"data row3 col5\" >$570,000</td>\n",
|
|
" <td id=\"T_f87c4_row3_col6\" class=\"data row3 col6\" >$600,000</td>\n",
|
|
" <td id=\"T_f87c4_row3_col7\" class=\"data row3 col7\" >$570,000</td>\n",
|
|
" <td id=\"T_f87c4_row3_col8\" class=\"data row3 col8\" >$600,000</td>\n",
|
|
" <td id=\"T_f87c4_row3_col9\" class=\"data row3 col9\" >$570,000</td>\n",
|
|
" <td id=\"T_f87c4_row3_col10\" class=\"data row3 col10\" >$1,800,000</td>\n",
|
|
" <td id=\"T_f87c4_row3_col11\" class=\"data row3 col11\" >$1,710,000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n"
|
|
],
|
|
"text/plain": [
|
|
"<pandas.io.formats.style.Styler at 0x10382ae10>"
|
|
]
|
|
},
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df = tables.benefits_table(seed.BENEFITS)\n",
|
|
"df.style.format({col: '${:,.0f}' for col in df.columns if col not in ('field_key','label','category','risk_adjustment')})"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "g1-md-source-notes",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Where each benefit comes from\n",
|
|
"\n",
|
|
"The seed-data notes capture the per-line Forrester math so the model\n",
|
|
"stays auditable. Surface them next to the headline numbers:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"id": "g1-code-source-notes",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<style type=\"text/css\">\n",
|
|
"#T_eab42_row0_col0, #T_eab42_row0_col1, #T_eab42_row0_col2, #T_eab42_row0_col3, #T_eab42_row0_col4, #T_eab42_row1_col0, #T_eab42_row1_col1, #T_eab42_row1_col2, #T_eab42_row1_col3, #T_eab42_row1_col4, #T_eab42_row2_col0, #T_eab42_row2_col1, #T_eab42_row2_col2, #T_eab42_row2_col3, #T_eab42_row2_col4, #T_eab42_row3_col0, #T_eab42_row3_col1, #T_eab42_row3_col2, #T_eab42_row3_col3, #T_eab42_row3_col4 {\n",
|
|
" text-align: left;\n",
|
|
"}\n",
|
|
"</style>\n",
|
|
"<table id=\"T_eab42\">\n",
|
|
" <thead>\n",
|
|
" <tr>\n",
|
|
" <th class=\"blank level0\" > </th>\n",
|
|
" <th id=\"T_eab42_level0_col0\" class=\"col_heading level0 col0\" >Ref</th>\n",
|
|
" <th id=\"T_eab42_level0_col1\" class=\"col_heading level0 col1\" >Benefit</th>\n",
|
|
" <th id=\"T_eab42_level0_col2\" class=\"col_heading level0 col2\" >Risk Adj</th>\n",
|
|
" <th id=\"T_eab42_level0_col3\" class=\"col_heading level0 col3\" >3-yr Nominal</th>\n",
|
|
" <th id=\"T_eab42_level0_col4\" class=\"col_heading level0 col4\" >Source / formula</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_eab42_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
|
|
" <td id=\"T_eab42_row0_col0\" class=\"data row0 col0\" >At</td>\n",
|
|
" <td id=\"T_eab42_row0_col1\" class=\"data row0 col1\" >Retirement of legacy systems with CX Cloud adoption</td>\n",
|
|
" <td id=\"T_eab42_row0_col2\" class=\"data row0 col2\" >5%</td>\n",
|
|
" <td id=\"T_eab42_row0_col3\" class=\"data row0 col3\" >$2,540,000</td>\n",
|
|
" <td id=\"T_eab42_row0_col4\" class=\"data row0 col4\" >PDF A1\u2013A4. Telephony $250k Y1 ramping to $500k (legacy sunset completes mid-Y1) + WFM/recording/transcription apps $100k + reduced dev effort $230k (2,400 hrs @ $94) + reduced platform mgmt $100k (1,500 hrs @ $65). Risk adj 5%.</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_eab42_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
|
|
" <td id=\"T_eab42_row1_col0\" class=\"data row1 col0\" >Bt</td>\n",
|
|
" <td id=\"T_eab42_row1_col1\" class=\"data row1 col1\" >Cost savings from reallocated workers and avoided seasonal hires with increased customer self-service</td>\n",
|
|
" <td id=\"T_eab42_row1_col2\" class=\"data row1 col2\" >15%</td>\n",
|
|
" <td id=\"T_eab42_row1_col3\" class=\"data row1 col3\" >$6,988,800</td>\n",
|
|
" <td id=\"T_eab42_row1_col4\" class=\"data row1 col4\" >PDF B1\u2013B8. Self-service completion 15%\u219225% on 80k weekly interactions \u2192 8,000 deflected/week \u2192 40 FTEs @ $58,240 fully burdened. Risk adj 15%. (PDF B7 formula cites B2 where the 12-min interaction length is meant; 40 FTEs is correct.)</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_eab42_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
|
|
" <td id=\"T_eab42_row2_col0\" class=\"data row2 col0\" >Ct</td>\n",
|
|
" <td id=\"T_eab42_row2_col1\" class=\"data row2 col1\" >CX agent efficiency gains</td>\n",
|
|
" <td id=\"T_eab42_row2_col2\" class=\"data row2 col2\" >10%</td>\n",
|
|
" <td id=\"T_eab42_row2_col3\" class=\"data row2 col3\" >$8,736,000</td>\n",
|
|
" <td id=\"T_eab42_row2_col4\" class=\"data row2 col4\" >PDF C1\u2013C6. MTTR 12\u219210 min on 60k agent-handled interactions per week \u2192 104,000 hrs/yr @ $28 fully burdened. Risk adj 10%.</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_eab42_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
|
|
" <td id=\"T_eab42_row3_col0\" class=\"data row3 col0\" >Dt</td>\n",
|
|
" <td id=\"T_eab42_row3_col1\" class=\"data row3 col1\" >Incremental sales from agent assist capabilities</td>\n",
|
|
" <td id=\"T_eab42_row3_col2\" class=\"data row3 col2\" >5%</td>\n",
|
|
" <td id=\"T_eab42_row3_col3\" class=\"data row3 col3\" >$1,800,000</td>\n",
|
|
" <td id=\"T_eab42_row3_col4\" class=\"data row3 col4\" >PDF D1\u2013D3. $500M revenue impacted (20% of $2.5B) \u00d7 1.5% lift \u00d7 8% gross margin. Risk adj 5%.</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n"
|
|
],
|
|
"text/plain": [
|
|
"<pandas.io.formats.style.Styler at 0x10609c290>"
|
|
]
|
|
},
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"import pandas as pd\n",
|
|
"\n",
|
|
"notes_df = pd.DataFrame([\n",
|
|
" {\n",
|
|
" 'Ref': ref,\n",
|
|
" 'Benefit': b['label'],\n",
|
|
" 'Risk Adj': f\"{b['risk_adjustment']:.0%}\",\n",
|
|
" '3-yr Nominal': sum(b['year_values'].values()),\n",
|
|
" 'Source / formula': b['notes'],\n",
|
|
" }\n",
|
|
" for ref, b in zip(['At', 'Bt', 'Ct', 'Dt'], seed.BENEFITS)\n",
|
|
"])\n",
|
|
"notes_df.style.format({'3-yr Nominal': '${:,.0f}'}).set_properties(**{'text-align': 'left'})"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "g1-md-validation",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Local validation against the PDF\n",
|
|
"\n",
|
|
"Re-derive the per-benefit risk-adjusted PV (`value \u00d7 (1 \u2212 risk_adj)` per\n",
|
|
"year, NPV at 10%) and confirm we land on Forrester's **\\$14,840,638**\n",
|
|
"total within rounding."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "g1-code-validation",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<style type=\"text/css\">\n",
|
|
"</style>\n",
|
|
"<table id=\"T_44a00\">\n",
|
|
" <thead>\n",
|
|
" <tr>\n",
|
|
" <th class=\"blank level0\" > </th>\n",
|
|
" <th id=\"T_44a00_level0_col0\" class=\"col_heading level0 col0\" >Benefit</th>\n",
|
|
" <th id=\"T_44a00_level0_col1\" class=\"col_heading level0 col1\" >Y1 (RA)</th>\n",
|
|
" <th id=\"T_44a00_level0_col2\" class=\"col_heading level0 col2\" >Y2 (RA)</th>\n",
|
|
" <th id=\"T_44a00_level0_col3\" class=\"col_heading level0 col3\" >Y3 (RA)</th>\n",
|
|
" <th id=\"T_44a00_level0_col4\" class=\"col_heading level0 col4\" >PV</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_44a00_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
|
|
" <td id=\"T_44a00_row0_col0\" class=\"data row0 col0\" >Retirement of legacy systems with CX Cloud adoption</td>\n",
|
|
" <td id=\"T_44a00_row0_col1\" class=\"data row0 col1\" >$646,000</td>\n",
|
|
" <td id=\"T_44a00_row0_col2\" class=\"data row0 col2\" >$883,500</td>\n",
|
|
" <td id=\"T_44a00_row0_col3\" class=\"data row0 col3\" >$883,500</td>\n",
|
|
" <td id=\"T_44a00_row0_col4\" class=\"data row0 col4\" >$1,981,225</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_44a00_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
|
|
" <td id=\"T_44a00_row1_col0\" class=\"data row1 col0\" >Cost savings from reallocated workers and avoided seasonal hires with increased customer self-service</td>\n",
|
|
" <td id=\"T_44a00_row1_col1\" class=\"data row1 col1\" >$1,980,160</td>\n",
|
|
" <td id=\"T_44a00_row1_col2\" class=\"data row1 col2\" >$1,980,160</td>\n",
|
|
" <td id=\"T_44a00_row1_col3\" class=\"data row1 col3\" >$1,980,160</td>\n",
|
|
" <td id=\"T_44a00_row1_col4\" class=\"data row1 col4\" >$4,924,365</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_44a00_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
|
|
" <td id=\"T_44a00_row2_col0\" class=\"data row2 col0\" >CX agent efficiency gains</td>\n",
|
|
" <td id=\"T_44a00_row2_col1\" class=\"data row2 col1\" >$2,620,800</td>\n",
|
|
" <td id=\"T_44a00_row2_col2\" class=\"data row2 col2\" >$2,620,800</td>\n",
|
|
" <td id=\"T_44a00_row2_col3\" class=\"data row2 col3\" >$2,620,800</td>\n",
|
|
" <td id=\"T_44a00_row2_col4\" class=\"data row2 col4\" >$6,517,542</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_44a00_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
|
|
" <td id=\"T_44a00_row3_col0\" class=\"data row3 col0\" >Incremental sales from agent assist capabilities</td>\n",
|
|
" <td id=\"T_44a00_row3_col1\" class=\"data row3 col1\" >$570,000</td>\n",
|
|
" <td id=\"T_44a00_row3_col2\" class=\"data row3 col2\" >$570,000</td>\n",
|
|
" <td id=\"T_44a00_row3_col3\" class=\"data row3 col3\" >$570,000</td>\n",
|
|
" <td id=\"T_44a00_row3_col4\" class=\"data row3 col4\" >$1,417,506</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_44a00_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
|
|
" <td id=\"T_44a00_row4_col0\" class=\"data row4 col0\" >TOTAL</td>\n",
|
|
" <td id=\"T_44a00_row4_col1\" class=\"data row4 col1\" >$5,816,960</td>\n",
|
|
" <td id=\"T_44a00_row4_col2\" class=\"data row4 col2\" >$6,054,460</td>\n",
|
|
" <td id=\"T_44a00_row4_col3\" class=\"data row4 col3\" >$6,054,460</td>\n",
|
|
" <td id=\"T_44a00_row4_col4\" class=\"data row4 col4\" >$14,840,637</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n"
|
|
],
|
|
"text/plain": [
|
|
"<pandas.io.formats.style.Styler at 0x107c35610>"
|
|
]
|
|
},
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"rows = []\n",
|
|
"for b in seed.BENEFITS:\n",
|
|
" rf = b['risk_adjustment']\n",
|
|
" yr = [b['year_values'][str(y)] for y in (1, 2, 3)]\n",
|
|
" yr_ra = [risk_adjust_benefit(v, rf) for v in yr]\n",
|
|
" pv = npv(yr_ra, config.DISCOUNT_RATE)\n",
|
|
" rows.append({\n",
|
|
" 'Benefit': b['label'],\n",
|
|
" 'Y1 (RA)': yr_ra[0],\n",
|
|
" 'Y2 (RA)': yr_ra[1],\n",
|
|
" 'Y3 (RA)': yr_ra[2],\n",
|
|
" 'PV': pv,\n",
|
|
" })\n",
|
|
"df_check = pd.DataFrame(rows)\n",
|
|
"df_check.loc[len(df_check)] = ['TOTAL', df_check['Y1 (RA)'].sum(), df_check['Y2 (RA)'].sum(), df_check['Y3 (RA)'].sum(), df_check['PV'].sum()]\n",
|
|
"df_check.style.format({c: '${:,.0f}' for c in df_check.columns if c != 'Benefit'})"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "g1-code-pv-check",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div style=\"padding:10px 14px;border-left:4px solid #2e7d32;background:#e8f5e9;color:#1a1a1a;border-radius:4px;margin:6px 0;\">Computed Benefits PV: <b>$14,840,637</b><br>Forrester target: <b>$14,840,638</b><br>\u0394 = $-1 (rounding)</div>"
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.HTML object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"expected_pv = seed.PUBLISHED['total_benefits_pv'] # 14,840,638\n",
|
|
"computed_pv = df_check.iloc[-1]['PV']\n",
|
|
"delta = computed_pv - expected_pv\n",
|
|
"kind = 'success' if abs(delta) < 100 else 'warning'\n",
|
|
"display.alert(\n",
|
|
" f'Computed Benefits PV: <b>${computed_pv:,.0f}</b><br>'\n",
|
|
" f'Forrester target: <b>${expected_pv:,.0f}</b><br>'\n",
|
|
" f'\u0394 = ${delta:,.0f} (rounding)',\n",
|
|
" kind,\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "g1-md-visualize",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Visualize\n",
|
|
"\n",
|
|
"Horizontal bar chart of risk-adjusted three-year totals \u2014 mirrors the\n",
|
|
"*Benefits (Three-Year)* exhibit on PDF p.6 of the published study."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "g1-code-bar",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.plotly.v1+json": {
|
|
"config": {
|
|
"plotlyServerURL": "https://plot.ly"
|
|
},
|
|
"data": [
|
|
{
|
|
"marker": {
|
|
"color": "#2E7D32"
|
|
},
|
|
"orientation": "h",
|
|
"text": [
|
|
"$2.4M",
|
|
"$5.9M",
|
|
"$7.9M",
|
|
"$1.7M"
|
|
],
|
|
"textposition": "auto",
|
|
"type": "bar",
|
|
"x": [
|
|
2413000,
|
|
5940480,
|
|
7862400,
|
|
1710000
|
|
],
|
|
"y": [
|
|
"Retirement of legacy systems with CX Cloud adoption",
|
|
"Cost savings from reallocated workers and avoided seasonal hires with increased customer self-service",
|
|
"CX agent efficiency gains",
|
|
"Incremental sales from agent assist capabilities"
|
|
]
|
|
}
|
|
],
|
|
"layout": {
|
|
"font": {
|
|
"color": "#1F2937",
|
|
"family": "Helvetica, Arial, sans-serif"
|
|
},
|
|
"legend": {
|
|
"font": {
|
|
"color": "#1F2937",
|
|
"family": "Helvetica, Arial, sans-serif"
|
|
}
|
|
},
|
|
"margin": {
|
|
"b": 40,
|
|
"l": 40,
|
|
"r": 20,
|
|
"t": 60
|
|
},
|
|
"template": {
|
|
"data": {
|
|
"bar": [
|
|
{
|
|
"error_x": {
|
|
"color": "#2a3f5f"
|
|
},
|
|
"error_y": {
|
|
"color": "#2a3f5f"
|
|
},
|
|
"marker": {
|
|
"line": {
|
|
"color": "#E5ECF6",
|
|
"width": 0.5
|
|
},
|
|
"pattern": {
|
|
"fillmode": "overlay",
|
|
"size": 10,
|
|
"solidity": 0.2
|
|
}
|
|
},
|
|
"type": "bar"
|
|
}
|
|
],
|
|
"barpolar": [
|
|
{
|
|
"marker": {
|
|
"line": {
|
|
"color": "#E5ECF6",
|
|
"width": 0.5
|
|
},
|
|
"pattern": {
|
|
"fillmode": "overlay",
|
|
"size": 10,
|
|
"solidity": 0.2
|
|
}
|
|
},
|
|
"type": "barpolar"
|
|
}
|
|
],
|
|
"carpet": [
|
|
{
|
|
"aaxis": {
|
|
"endlinecolor": "#2a3f5f",
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"minorgridcolor": "white",
|
|
"startlinecolor": "#2a3f5f"
|
|
},
|
|
"baxis": {
|
|
"endlinecolor": "#2a3f5f",
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"minorgridcolor": "white",
|
|
"startlinecolor": "#2a3f5f"
|
|
},
|
|
"type": "carpet"
|
|
}
|
|
],
|
|
"choropleth": [
|
|
{
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
},
|
|
"type": "choropleth"
|
|
}
|
|
],
|
|
"contour": [
|
|
{
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
},
|
|
"colorscale": [
|
|
[
|
|
0,
|
|
"#0d0887"
|
|
],
|
|
[
|
|
0.1111111111111111,
|
|
"#46039f"
|
|
],
|
|
[
|
|
0.2222222222222222,
|
|
"#7201a8"
|
|
],
|
|
[
|
|
0.3333333333333333,
|
|
"#9c179e"
|
|
],
|
|
[
|
|
0.4444444444444444,
|
|
"#bd3786"
|
|
],
|
|
[
|
|
0.5555555555555556,
|
|
"#d8576b"
|
|
],
|
|
[
|
|
0.6666666666666666,
|
|
"#ed7953"
|
|
],
|
|
[
|
|
0.7777777777777778,
|
|
"#fb9f3a"
|
|
],
|
|
[
|
|
0.8888888888888888,
|
|
"#fdca26"
|
|
],
|
|
[
|
|
1,
|
|
"#f0f921"
|
|
]
|
|
],
|
|
"type": "contour"
|
|
}
|
|
],
|
|
"contourcarpet": [
|
|
{
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
},
|
|
"type": "contourcarpet"
|
|
}
|
|
],
|
|
"heatmap": [
|
|
{
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
},
|
|
"colorscale": [
|
|
[
|
|
0,
|
|
"#0d0887"
|
|
],
|
|
[
|
|
0.1111111111111111,
|
|
"#46039f"
|
|
],
|
|
[
|
|
0.2222222222222222,
|
|
"#7201a8"
|
|
],
|
|
[
|
|
0.3333333333333333,
|
|
"#9c179e"
|
|
],
|
|
[
|
|
0.4444444444444444,
|
|
"#bd3786"
|
|
],
|
|
[
|
|
0.5555555555555556,
|
|
"#d8576b"
|
|
],
|
|
[
|
|
0.6666666666666666,
|
|
"#ed7953"
|
|
],
|
|
[
|
|
0.7777777777777778,
|
|
"#fb9f3a"
|
|
],
|
|
[
|
|
0.8888888888888888,
|
|
"#fdca26"
|
|
],
|
|
[
|
|
1,
|
|
"#f0f921"
|
|
]
|
|
],
|
|
"type": "heatmap"
|
|
}
|
|
],
|
|
"histogram": [
|
|
{
|
|
"marker": {
|
|
"pattern": {
|
|
"fillmode": "overlay",
|
|
"size": 10,
|
|
"solidity": 0.2
|
|
}
|
|
},
|
|
"type": "histogram"
|
|
}
|
|
],
|
|
"histogram2d": [
|
|
{
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
},
|
|
"colorscale": [
|
|
[
|
|
0,
|
|
"#0d0887"
|
|
],
|
|
[
|
|
0.1111111111111111,
|
|
"#46039f"
|
|
],
|
|
[
|
|
0.2222222222222222,
|
|
"#7201a8"
|
|
],
|
|
[
|
|
0.3333333333333333,
|
|
"#9c179e"
|
|
],
|
|
[
|
|
0.4444444444444444,
|
|
"#bd3786"
|
|
],
|
|
[
|
|
0.5555555555555556,
|
|
"#d8576b"
|
|
],
|
|
[
|
|
0.6666666666666666,
|
|
"#ed7953"
|
|
],
|
|
[
|
|
0.7777777777777778,
|
|
"#fb9f3a"
|
|
],
|
|
[
|
|
0.8888888888888888,
|
|
"#fdca26"
|
|
],
|
|
[
|
|
1,
|
|
"#f0f921"
|
|
]
|
|
],
|
|
"type": "histogram2d"
|
|
}
|
|
],
|
|
"histogram2dcontour": [
|
|
{
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
},
|
|
"colorscale": [
|
|
[
|
|
0,
|
|
"#0d0887"
|
|
],
|
|
[
|
|
0.1111111111111111,
|
|
"#46039f"
|
|
],
|
|
[
|
|
0.2222222222222222,
|
|
"#7201a8"
|
|
],
|
|
[
|
|
0.3333333333333333,
|
|
"#9c179e"
|
|
],
|
|
[
|
|
0.4444444444444444,
|
|
"#bd3786"
|
|
],
|
|
[
|
|
0.5555555555555556,
|
|
"#d8576b"
|
|
],
|
|
[
|
|
0.6666666666666666,
|
|
"#ed7953"
|
|
],
|
|
[
|
|
0.7777777777777778,
|
|
"#fb9f3a"
|
|
],
|
|
[
|
|
0.8888888888888888,
|
|
"#fdca26"
|
|
],
|
|
[
|
|
1,
|
|
"#f0f921"
|
|
]
|
|
],
|
|
"type": "histogram2dcontour"
|
|
}
|
|
],
|
|
"mesh3d": [
|
|
{
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
},
|
|
"type": "mesh3d"
|
|
}
|
|
],
|
|
"parcoords": [
|
|
{
|
|
"line": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"type": "parcoords"
|
|
}
|
|
],
|
|
"pie": [
|
|
{
|
|
"automargin": true,
|
|
"type": "pie"
|
|
}
|
|
],
|
|
"scatter": [
|
|
{
|
|
"fillpattern": {
|
|
"fillmode": "overlay",
|
|
"size": 10,
|
|
"solidity": 0.2
|
|
},
|
|
"type": "scatter"
|
|
}
|
|
],
|
|
"scatter3d": [
|
|
{
|
|
"line": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"marker": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"type": "scatter3d"
|
|
}
|
|
],
|
|
"scattercarpet": [
|
|
{
|
|
"marker": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"type": "scattercarpet"
|
|
}
|
|
],
|
|
"scattergeo": [
|
|
{
|
|
"marker": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"type": "scattergeo"
|
|
}
|
|
],
|
|
"scattergl": [
|
|
{
|
|
"marker": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"type": "scattergl"
|
|
}
|
|
],
|
|
"scattermap": [
|
|
{
|
|
"marker": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"type": "scattermap"
|
|
}
|
|
],
|
|
"scattermapbox": [
|
|
{
|
|
"marker": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"type": "scattermapbox"
|
|
}
|
|
],
|
|
"scatterpolar": [
|
|
{
|
|
"marker": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"type": "scatterpolar"
|
|
}
|
|
],
|
|
"scatterpolargl": [
|
|
{
|
|
"marker": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"type": "scatterpolargl"
|
|
}
|
|
],
|
|
"scatterternary": [
|
|
{
|
|
"marker": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"type": "scatterternary"
|
|
}
|
|
],
|
|
"surface": [
|
|
{
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
},
|
|
"colorscale": [
|
|
[
|
|
0,
|
|
"#0d0887"
|
|
],
|
|
[
|
|
0.1111111111111111,
|
|
"#46039f"
|
|
],
|
|
[
|
|
0.2222222222222222,
|
|
"#7201a8"
|
|
],
|
|
[
|
|
0.3333333333333333,
|
|
"#9c179e"
|
|
],
|
|
[
|
|
0.4444444444444444,
|
|
"#bd3786"
|
|
],
|
|
[
|
|
0.5555555555555556,
|
|
"#d8576b"
|
|
],
|
|
[
|
|
0.6666666666666666,
|
|
"#ed7953"
|
|
],
|
|
[
|
|
0.7777777777777778,
|
|
"#fb9f3a"
|
|
],
|
|
[
|
|
0.8888888888888888,
|
|
"#fdca26"
|
|
],
|
|
[
|
|
1,
|
|
"#f0f921"
|
|
]
|
|
],
|
|
"type": "surface"
|
|
}
|
|
],
|
|
"table": [
|
|
{
|
|
"cells": {
|
|
"fill": {
|
|
"color": "#EBF0F8"
|
|
},
|
|
"line": {
|
|
"color": "white"
|
|
}
|
|
},
|
|
"header": {
|
|
"fill": {
|
|
"color": "#C8D4E3"
|
|
},
|
|
"line": {
|
|
"color": "white"
|
|
}
|
|
},
|
|
"type": "table"
|
|
}
|
|
]
|
|
},
|
|
"layout": {
|
|
"annotationdefaults": {
|
|
"arrowcolor": "#2a3f5f",
|
|
"arrowhead": 0,
|
|
"arrowwidth": 1
|
|
},
|
|
"autotypenumbers": "strict",
|
|
"coloraxis": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"colorscale": {
|
|
"diverging": [
|
|
[
|
|
0,
|
|
"#8e0152"
|
|
],
|
|
[
|
|
0.1,
|
|
"#c51b7d"
|
|
],
|
|
[
|
|
0.2,
|
|
"#de77ae"
|
|
],
|
|
[
|
|
0.3,
|
|
"#f1b6da"
|
|
],
|
|
[
|
|
0.4,
|
|
"#fde0ef"
|
|
],
|
|
[
|
|
0.5,
|
|
"#f7f7f7"
|
|
],
|
|
[
|
|
0.6,
|
|
"#e6f5d0"
|
|
],
|
|
[
|
|
0.7,
|
|
"#b8e186"
|
|
],
|
|
[
|
|
0.8,
|
|
"#7fbc41"
|
|
],
|
|
[
|
|
0.9,
|
|
"#4d9221"
|
|
],
|
|
[
|
|
1,
|
|
"#276419"
|
|
]
|
|
],
|
|
"sequential": [
|
|
[
|
|
0,
|
|
"#0d0887"
|
|
],
|
|
[
|
|
0.1111111111111111,
|
|
"#46039f"
|
|
],
|
|
[
|
|
0.2222222222222222,
|
|
"#7201a8"
|
|
],
|
|
[
|
|
0.3333333333333333,
|
|
"#9c179e"
|
|
],
|
|
[
|
|
0.4444444444444444,
|
|
"#bd3786"
|
|
],
|
|
[
|
|
0.5555555555555556,
|
|
"#d8576b"
|
|
],
|
|
[
|
|
0.6666666666666666,
|
|
"#ed7953"
|
|
],
|
|
[
|
|
0.7777777777777778,
|
|
"#fb9f3a"
|
|
],
|
|
[
|
|
0.8888888888888888,
|
|
"#fdca26"
|
|
],
|
|
[
|
|
1,
|
|
"#f0f921"
|
|
]
|
|
],
|
|
"sequentialminus": [
|
|
[
|
|
0,
|
|
"#0d0887"
|
|
],
|
|
[
|
|
0.1111111111111111,
|
|
"#46039f"
|
|
],
|
|
[
|
|
0.2222222222222222,
|
|
"#7201a8"
|
|
],
|
|
[
|
|
0.3333333333333333,
|
|
"#9c179e"
|
|
],
|
|
[
|
|
0.4444444444444444,
|
|
"#bd3786"
|
|
],
|
|
[
|
|
0.5555555555555556,
|
|
"#d8576b"
|
|
],
|
|
[
|
|
0.6666666666666666,
|
|
"#ed7953"
|
|
],
|
|
[
|
|
0.7777777777777778,
|
|
"#fb9f3a"
|
|
],
|
|
[
|
|
0.8888888888888888,
|
|
"#fdca26"
|
|
],
|
|
[
|
|
1,
|
|
"#f0f921"
|
|
]
|
|
]
|
|
},
|
|
"colorway": [
|
|
"#636efa",
|
|
"#EF553B",
|
|
"#00cc96",
|
|
"#ab63fa",
|
|
"#FFA15A",
|
|
"#19d3f3",
|
|
"#FF6692",
|
|
"#B6E880",
|
|
"#FF97FF",
|
|
"#FECB52"
|
|
],
|
|
"font": {
|
|
"color": "#2a3f5f"
|
|
},
|
|
"geo": {
|
|
"bgcolor": "white",
|
|
"lakecolor": "white",
|
|
"landcolor": "#E5ECF6",
|
|
"showlakes": true,
|
|
"showland": true,
|
|
"subunitcolor": "white"
|
|
},
|
|
"hoverlabel": {
|
|
"align": "left"
|
|
},
|
|
"hovermode": "closest",
|
|
"mapbox": {
|
|
"style": "light"
|
|
},
|
|
"paper_bgcolor": "white",
|
|
"plot_bgcolor": "#E5ECF6",
|
|
"polar": {
|
|
"angularaxis": {
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"ticks": ""
|
|
},
|
|
"bgcolor": "#E5ECF6",
|
|
"radialaxis": {
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"scene": {
|
|
"xaxis": {
|
|
"backgroundcolor": "#E5ECF6",
|
|
"gridcolor": "white",
|
|
"gridwidth": 2,
|
|
"linecolor": "white",
|
|
"showbackground": true,
|
|
"ticks": "",
|
|
"zerolinecolor": "white"
|
|
},
|
|
"yaxis": {
|
|
"backgroundcolor": "#E5ECF6",
|
|
"gridcolor": "white",
|
|
"gridwidth": 2,
|
|
"linecolor": "white",
|
|
"showbackground": true,
|
|
"ticks": "",
|
|
"zerolinecolor": "white"
|
|
},
|
|
"zaxis": {
|
|
"backgroundcolor": "#E5ECF6",
|
|
"gridcolor": "white",
|
|
"gridwidth": 2,
|
|
"linecolor": "white",
|
|
"showbackground": true,
|
|
"ticks": "",
|
|
"zerolinecolor": "white"
|
|
}
|
|
},
|
|
"shapedefaults": {
|
|
"line": {
|
|
"color": "#2a3f5f"
|
|
}
|
|
},
|
|
"ternary": {
|
|
"aaxis": {
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"ticks": ""
|
|
},
|
|
"baxis": {
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"ticks": ""
|
|
},
|
|
"bgcolor": "#E5ECF6",
|
|
"caxis": {
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"title": {
|
|
"x": 0.05
|
|
},
|
|
"xaxis": {
|
|
"automargin": true,
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"ticks": "",
|
|
"title": {
|
|
"standoff": 15
|
|
},
|
|
"zerolinecolor": "white",
|
|
"zerolinewidth": 2
|
|
},
|
|
"yaxis": {
|
|
"automargin": true,
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"ticks": "",
|
|
"title": {
|
|
"standoff": 15
|
|
},
|
|
"zerolinecolor": "white",
|
|
"zerolinewidth": 2
|
|
}
|
|
}
|
|
},
|
|
"title": {
|
|
"font": {
|
|
"color": "#1F2937",
|
|
"family": "Helvetica Neue, Arial, sans-serif"
|
|
},
|
|
"text": "Benefits (Three-Year)"
|
|
},
|
|
"xaxis": {
|
|
"tickformat": "$,.0f"
|
|
},
|
|
"yaxis": {
|
|
"autorange": "reversed"
|
|
}
|
|
}
|
|
},
|
|
"image/png": "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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"charts.benefits_bar(seed.BENEFITS).show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "g1-md-where-tokens",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Heads-up: three of these benefits depend on AI consumption\n",
|
|
"\n",
|
|
"Benefits **Bt** (self-service uplift), **Ct** (AI coaching / agent\n",
|
|
"efficiency) and **Dt** (agent-assist sales) are all delivered through\n",
|
|
"Genesys AI capabilities that are **billed via AI Experience tokens**.\n",
|
|
"The published study modelled \\$0 of token consumption \u2014 costs notebook\n",
|
|
"(`02_costs.ipynb`) adds the line back, and `03_business_case.ipynb`\n",
|
|
"exposes a sensitivity sweep so you can see what the AI cost does to NPV."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "g1-md-push",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Push to Athena (optional)\n",
|
|
"\n",
|
|
"When `config.TOOL_PUBLIC_ID` is set (filled in by `00_provision.ipynb`),\n",
|
|
"persist the seed values to the live TEI tool. Otherwise this cell is a\n",
|
|
"no-op so the notebook still runs offline."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "g1-code-push",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div style=\"padding:10px 14px;border-left:4px solid #0277bd;background:#e1f5fe;color:#1a1a1a;border-radius:4px;margin:6px 0;\">No PALLADIUM_GENESYSCX_TOOL_PUBLIC_ID set \u2014 skipped Athena push. Run <code>00_provision.ipynb</code> to provision the tool.</div>"
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.HTML object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"if config.TOOL_PUBLIC_ID:\n",
|
|
" from core.tei_client import TEIClient\n",
|
|
"\n",
|
|
" client = TEIClient()\n",
|
|
" client.update_values(config.TOOL_PUBLIC_ID, seed.BENEFITS)\n",
|
|
" display.alert(f'Pushed {len(seed.BENEFITS)} benefit rows to '\n",
|
|
" f'tool <code>{config.TOOL_PUBLIC_ID}</code>.', 'success')\n",
|
|
"else:\n",
|
|
" display.alert(\n",
|
|
" 'No PALLADIUM_GENESYSCX_TOOL_PUBLIC_ID set \u2014 skipped Athena push. '\n",
|
|
" 'Run <code>00_provision.ipynb</code> to provision the tool.',\n",
|
|
" 'info',\n",
|
|
" )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "g1-md-next",
|
|
"metadata": {},
|
|
"source": [
|
|
"---\n",
|
|
"\n",
|
|
"Continue with [`02_costs.ipynb`](02_costs.ipynb) \u2192"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.12.7"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|