Robert Helewka
64fb83257d
- Add 202512_GenesysCX TEI study (config, seed data, notebooks, README) with NPV $10.8M / ROI 266% including AI-token cost line - Add explicit `key` parameter to all chart wrappers in app/components to prevent StreamlitDuplicateElementId errors when the same figure type renders across Summary/Benefits/Costs tabs - Render benefits bar and cost pie charts on their respective tabs - Add benefits_vs_costs_by_year chart wrapper
202602 — Amazon Connect TEI
Self-contained TEI study folder. All data, notebooks, and exports for the Forrester Total Economic Impact™ Of Amazon Connect (February 2026, commissioned by AWS) live here.
Source
The full Forrester study is at docs/202602_TEI Report Amazon Connect.pdf.
Key composite numbers reproduced in seed_data.py:
| Metric | Value |
|---|---|
| ROI | 342% |
| NPV | $78.7M |
| Benefits PV | $101.7M |
| Costs PV | $23.0M |
| Payback | <6 months |
| Discount rate | 10% |
| Analysis period | 3 years |
Composite organization
- Global B2C, ~$10B revenue (Y1), 30% YoY growth
- 2,000 contact-center agents, 200 supervisors
- 20M annual contacts (75% calls, 25% chat)
- 10-min average handle time
Layout
202602_AmazonConnect/
├── README.md ← this file
├── config.py ← TOOL_PUBLIC_ID, REPORT_PUBLIC_ID, study slug
├── seed_data.py ← BENEFITS, COSTS, ASSUMPTIONS as Python dicts
├── notebooks/
│ ├── 01_benefits.ipynb ← quantify the 5 benefits, push to Athena
│ ├── 02_costs.ipynb ← quantify the 3 costs
│ ├── 03_business_case.ipynb ← /calculate, charts, scenarios
│ └── 04_export.ipynb ← /export → exports/export.json
├── exports/ ← generated; .gitignored
└── docs/
└── 202602_TEI Report Amazon Connect.pdf
Workflow
- Set up credentials in the project root
.env(see.env.example). - Create / link the TEI tool in Athena, then put its
public_idinconfig.py. - Open
notebooks/01_benefits.ipynband run all — pushes the 5 benefit rows fromseed_data.pyinto Athena. 02_costs.ipynb— pushes the 3 cost rows.03_business_case.ipynb— calls/calculate, renders the cash flow chart, runs scenario analysis. Should reproduce the PDF's $78.7M NPV / 342% ROI.04_export.ipynb— writesexports/export.jsonfor the report pipeline.
Adding a new study
Copy this folder, rename to YYYYMM_<Vendor><Solution>, and:
- Replace
seed_data.pywith your benefits/costs. - Update
config.pywith the new tool/report public IDs. - Tweak the notebooks' narrative; the helper imports are the same.
The only thing that changes between studies is the data and the
narrative prose in the notebooks. All math, charts, and API calls
come from core/.