{ "cells": [ { "cell_type": "markdown", "id": "g2-md-intro", "metadata": {}, "source": [ "# 02 \u2014 Costs Analysis\n", "\n", "**Study:** Forrester *TEI\u2122 Of CX Cloud \u2014 Genesys + Salesforce* (December 2025)\n", "\n", "Three published cost categories on a 3-year horizon at a 10% discount\n", "rate, **plus a fourth Palladium-added line** for Genesys AI Experience\n", "token consumption \u2014 which the published study omits despite three of its\n", "four benefits depending on AI capabilities Genesys bills via tokens.\n", "\n", "Published target Costs PV = **\\$4,057,170** (the AI-tokens line is\n", "seeded at \\$0 here; `03_business_case.ipynb` runs the sensitivity)." ] }, { "cell_type": "code", "execution_count": null, "id": "02-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": "g2-code-imports", "metadata": {}, "outputs": [], "source": [ "from core.calculations import npv, risk_adjust_cost\n", "from core.notebook_helpers import charts, display, tables" ] }, { "cell_type": "markdown", "id": "g2-md-cost-table", "metadata": {}, "source": [ "## Costs \u2014 what the deal looks like\n", "\n", "| Ref | Cost | Initial | Y1 | Y2 | Y3 | Risk Adj |\n", "|---|---|---|---|---|---|---|\n", "| Et | CX Cloud licenses (400 concurrent) | \u2014 | \\$840K | \\$840K | \\$840K | \u21915% |\n", "| Ft | Implementation & deployment (10-week project) | \\$1.19M | \u2014 | \u2014 | \u2014 | \u219110% |\n", "| Gt | Ongoing management (5 staff @ 30%) | \u2014 | \\$203K | \\$203K | \\$203K | \u219110% |\n", "| **+** | **Genesys AI Experience tokens** *(Palladium add)* | \u2014 | per quote | per quote | per quote | 0% |\n", "\n", "Note **costs are risk-adjusted *upward*** (higher risk \u2192 higher modelled\n", "cost) \u2014 Forrester convention, opposite of how benefits get adjusted.\n", "\n", "**PDF caveat:** the published Total Costs table (p.14) prints the\n", "implementation initial as \\$1,304,600, but the detail rows, the cash flow\n", "analysis, and the literal arithmetic (\\$1,190,000 \u00d7 1.10) all give\n", "\\$1,309,000. The seed uses the correct figure." ] }, { "cell_type": "code", "execution_count": 3, "id": "g2-code-table", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 field_keylabelcategoryrisk_adjustmentInitialInitial (RA)Year 1Year 1 (RA)Year 2Year 2 (RA)Year 3Year 3 (RA)TotalTotal (RA)
0cx_cloud_licensesCX Cloud solution costs (licenses)Subscription0.050000$0$0$840,000$882,000$840,000$882,000$840,000$882,000$2,520,000$2,646,000
1implementationImplementation and deployment costImplementation0.100000$1,190,000$1,309,000$0$0$0$0$0$0$1,190,000$1,309,000
2ongoing_managementOngoing management costsOperations0.100000$0$0$202,800$223,080$202,800$223,080$202,800$223,080$608,400$669,240
3genesys_ai_tokensGenesys AI Experience token consumptionSubscription0.000000$0$0$0$0$0$0$0$0$0$0
\n" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = tables.costs_table(seed.COSTS)\n", "df.style.format({c: '${:,.0f}' for c in df.columns if c not in ('field_key','label','category','risk_adjustment')})" ] }, { "cell_type": "markdown", "id": "g2-md-source-notes", "metadata": {}, "source": [ "### Where each cost comes from" ] }, { "cell_type": "code", "execution_count": 4, "id": "g2-code-source-notes", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 RefCostRisk AdjInitial3-yr Annual (Nominal)Source / formula
0EtCX Cloud solution costs (licenses)+5%$0$2,520,000PDF E1\u2013E3. Genesys Cloud CX 2 $170/user/mo + Salesforce Voice $25/user/mo + connector $25/user/mo, 400 concurrent users, 20% contractual discount \u2192 $650k + $95k + $95k. Risk adj +5%. Seat licenses ONLY \u2014 AI consumption is a separate line (genesys_ai_tokens).
1FtImplementation and deployment cost+10%$1,190,000$0PDF F1\u2013F5. 10-week implementation: 20 FTEs @ $80/hr fully burdened ($640k) + $550k professional services. Risk adj +10% \u2192 $1,309,000 (the p.14 Total Costs table's $1,304,600 is a typo in the study).
2GtOngoing management costs+10%$0$608,400PDF G1\u2013G3. 5 people @ 30% time (12 hrs/wk) @ $65/hr. Risk adj +10%.
3+ AIGenesys AI Experience token consumption+0%$0$0NOT in the published study \u2014 Forrester modeled $0 AI consumption even though benefits B (self-service uplift), C (AI coaching/assist), and D (agent assist upsell) all depend on AI capabilities that Genesys bills via AI Experience tokens. Seeded at $0 to reproduce the published totals. For client cases, enter the negotiated annual token cost from the Genesys quote and document the quote details (token volume, unit price, tier) in these notes.
\n" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "notes_df = pd.DataFrame([\n", " {\n", " 'Ref': ref,\n", " 'Cost': c['label'],\n", " 'Risk Adj': f\"+{c['risk_adjustment']:.0%}\",\n", " 'Initial': c.get('initial', 0),\n", " '3-yr Annual (Nominal)': sum(c['year_values'].values()),\n", " 'Source / formula': c['notes'],\n", " }\n", " for ref, c in zip(['Et', 'Ft', 'Gt', '+ AI'], seed.COSTS)\n", "])\n", "notes_df.style.format({'Initial': '${:,.0f}', '3-yr Annual (Nominal)': '${:,.0f}'}).set_properties(**{'text-align': 'left'})" ] }, { "cell_type": "markdown", "id": "g2-md-validation", "metadata": {}, "source": [ "## Local validation against the PDF\n", "\n", "Reproduce the **\\$4,057,170** Costs PV from the PDF Cash Flow Analysis.\n", "Forrester applies risk *upward* to costs (`value \u00d7 (1 + risk_adj)`),\n", "leaves Year-0 *Initial* amounts undiscounted, and discounts Years 1\u20133\n", "at 10%. The `genesys_ai_tokens` line is seeded at \\$0, so it doesn't\n", "move the published total." ] }, { "cell_type": "code", "execution_count": 5, "id": "g2-code-validation", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 CostInitial (RA)Y1 (RA)Y2 (RA)Y3 (RA)PV
0CX Cloud solution costs (licenses)$0$882,000$882,000$882,000$2,193,403
1Implementation and deployment cost$1,309,000$0$0$0$1,309,000
2Ongoing management costs$0$223,080$223,080$223,080$554,767
3Genesys AI Experience token consumption$0$0$0$0$0
4TOTAL$1,309,000$1,105,080$1,105,080$1,105,080$4,057,170
\n" ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rows = []\n", "for c in seed.COSTS:\n", " rf = c['risk_adjustment']\n", " init_ra = risk_adjust_cost(c.get('initial') or 0, rf)\n", " yr = [c['year_values'][str(y)] for y in (1, 2, 3)]\n", " yr_ra = [risk_adjust_cost(v, rf) for v in yr]\n", " pv = npv(yr_ra, config.DISCOUNT_RATE, initial=init_ra)\n", " rows.append({\n", " 'Cost': c['label'],\n", " 'Initial (RA)': init_ra,\n", " 'Y1 (RA)': yr_ra[0],\n", " 'Y2 (RA)': yr_ra[1],\n", " 'Y3 (RA)': yr_ra[2],\n", " 'PV': pv,\n", " })\n", "df_check = pd.DataFrame(rows)\n", "totals = df_check.drop(columns='Cost').sum()\n", "df_check.loc[len(df_check)] = ['TOTAL'] + totals.tolist()\n", "df_check.style.format({c: '${:,.0f}' for c in df_check.columns if c != 'Cost'})" ] }, { "cell_type": "code", "execution_count": 6, "id": "g2-code-pv-check", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Computed Costs PV: $4,057,170
Forrester target: $4,057,170
\u0394 = $0
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "expected_pv = seed.PUBLISHED['total_costs_pv'] # 4,057,170\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 Costs PV: ${computed_pv:,.0f}
'\n", " f'Forrester target: ${expected_pv:,.0f}
'\n", " f'\u0394 = ${delta:,.0f}',\n", " kind,\n", ")" ] }, { "cell_type": "markdown", "id": "g2-md-athena-delta", "metadata": {}, "source": [ "### Athena vs Forrester \u2014 the Year-0 discounting delta\n", "\n", "Forrester leaves the \\$1.309M implementation initial undiscounted in\n", "Year 0. **Athena treats non-annual values as Year-1 cashflows**, so it\n", "discounts the same amount (\\$1,309,000 / 1.10 = \\$1,190,000), making\n", "Athena's Costs PV ~\\$119k *lower* than Forrester's. That's methodology,\n", "not data error \u2014 `seed_data.ATHENA_EXPECTED` records the expected\n", "server-side total, and `00_provision.ipynb` verifies the pipeline\n", "matches it tightly before reconciling against the published number." ] }, { "cell_type": "code", "execution_count": 7, "id": "g2-code-athena-delta", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 metricForrester (published)Athena (expected)
0total_benefits_pv14,840,63814,840,640
1total_costs_pv4,057,1703,938,170
2net_present_value10,783,46810,902,470
3roi_percentage266277
\n" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "delta_df = pd.DataFrame([\n", " {'metric': k,\n", " 'Forrester (published)': seed.PUBLISHED[k],\n", " 'Athena (expected)': seed.ATHENA_EXPECTED[k]}\n", " for k in ('total_benefits_pv', 'total_costs_pv', 'net_present_value', 'roi_percentage')\n", "])\n", "delta_df.style.format({\n", " 'Forrester (published)': '{:,.0f}',\n", " 'Athena (expected)': '{:,.0f}',\n", "})" ] }, { "cell_type": "markdown", "id": "g2-md-mix", "metadata": {}, "source": [ "## Cost mix\n", "\n", "At seeded values, **CX Cloud licenses dominate** (~62% of 3-year cost),\n", "with implementation a one-time investment. The AI-tokens slice is zero\n", "until you enter the quote in `03_business_case.ipynb` \u2014 and that's\n", "exactly the point: in real deals it can rival the license line." ] }, { "cell_type": "code", "execution_count": 8, "id": "g2-code-pie", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hole": 0.35, "labels": [ "CX Cloud solution costs (licenses)", "Implementation and deployment cost", "Ongoing management costs", "Genesys AI Experience token consumption" ], "marker": { "colors": [ "#1565C0", "#2E7D32", "#C62828", "#F9A825", "#6A1B9A", "#00838F", "#EF6C00", "#5D4037", "#37474F", "#AD1457" ] }, "type": "pie", "values": [ 2646000, 1309000, 669240.0000000001, 0 ] } ], "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 }, 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"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": "Cost Breakdown (Three-Year, Risk-Adjusted)" } } }, "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "charts.cost_breakdown_pie(seed.COSTS).show()" ] }, { "cell_type": "markdown", "id": "g2-md-ai-callout", "metadata": {}, "source": [ "## Why the AI-tokens line matters\n", "\n", "Genesys bills AI consumption in *AI Experience tokens* \u2014 pricing is\n", "tiered, capability-dependent, and deal-specific. The Forrester study\n", "modelled **\\$0** of AI consumption even though benefits **Bt**, **Ct**\n", "and **Dt** all rely on AI features that draw tokens:\n", "\n", "- **Bt** Self-service uplift (15\u219225%) \u2014 voice/digital virtual agents\n", "- **Ct** Agent efficiency (MTTR -2 min) \u2014 real-time AI coaching/assist\n", "- **Dt** Agent-assist incremental sales \u2014 next-best-action suggestions\n", "\n", "For sizing context, the study's own drivers imply ~1.04M self-service\n", "interactions/yr (B5 \u00d7 52) and ~3.12M agent-assisted interactions/yr\n", "(C1 \u00d7 52) would draw tokens. Athena stores a single annual cost value\n", "per cost line, and so does the seed \u2014 enter the negotiated annual\n", "figure from the Genesys quote in `03_business_case.ipynb` and document\n", "the quote details (volume, unit price, tier) in the field notes." ] }, { "cell_type": "markdown", "id": "g2-md-push", "metadata": {}, "source": [ "## Push to Athena (optional)" ] }, { "cell_type": "code", "execution_count": 9, "id": "g2-code-push", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
No PALLADIUM_GENESYSCX_TOOL_PUBLIC_ID set \u2014 skipped Athena push.
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "if config.TOOL_PUBLIC_ID:\n", " from core.tei_client import TEIClient\n", "\n", " client = TEIClient()\n", " # Push pre-multiplied by (1 + risk_adj) per Palladium convention\n", " payload = []\n", " for c in seed.COSTS:\n", " f = 1 + c['risk_adjustment']\n", " payload.append({\n", " 'field_key': c['field_key'],\n", " 'year_values': {y: round(v * f, 2) for y, v in c['year_values'].items()},\n", " 'initial': round((c.get('initial') or 0) * f, 2),\n", " 'notes': c['notes'],\n", " })\n", " client.update_values(config.TOOL_PUBLIC_ID, payload)\n", " display.alert(f'Pushed {len(payload)} cost rows to '\n", " f'tool {config.TOOL_PUBLIC_ID}.', 'success')\n", "else:\n", " display.alert('No PALLADIUM_GENESYSCX_TOOL_PUBLIC_ID set \u2014 skipped Athena push.', 'info')" ] }, { "cell_type": "markdown", "id": "g2-md-next", "metadata": {}, "source": [ "Continue with [`03_business_case.ipynb`](03_business_case.ipynb) \u2192" ] }, { "cell_type": "code", "execution_count": null, "id": "e1507f5a-3a42-4c9e-bbce-a86ff1a1dbb7", "metadata": {}, "outputs": [], "source": [] } ], "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 }