Robert Helewka
a2420ed692
Reorganize Palladium codebase into a modular architecture with `core/` shared logic and `app/` Streamlit UI, separating per-study assets into `studies/YYYYMM_<Vendor>/` folders containing notebooks, seed data, and configuration. Update README to reflect new structure, add `.gitignore` entries for `.env` and study exports, and refresh component documentation.
72 lines
2.5 KiB
Markdown
72 lines
2.5 KiB
Markdown
# 202602 — Amazon Connect TEI
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Self-contained TEI study folder. All data, notebooks, and exports for the
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Forrester *Total Economic Impact™ Of Amazon Connect* (February 2026,
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commissioned by AWS) live here.
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## Source
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The full Forrester study is at [`docs/202602_TEI Report Amazon Connect.pdf`](docs/202602_TEI%20Report%20Amazon%20Connect.pdf).
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Key composite numbers reproduced in `seed_data.py`:
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| Metric | Value |
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|---|---|
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| ROI | **342%** |
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| NPV | **$78.7M** |
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| Benefits PV | $101.7M |
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| Costs PV | $23.0M |
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| Payback | <6 months |
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| Discount rate | 10% |
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| Analysis period | 3 years |
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## Composite organization
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* Global B2C, ~$10B revenue (Y1), 30% YoY growth
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* 2,000 contact-center agents, 200 supervisors
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* 20M annual contacts (75% calls, 25% chat)
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* 10-min average handle time
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## Layout
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```
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202602_AmazonConnect/
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├── README.md ← this file
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├── config.py ← TOOL_PUBLIC_ID, REPORT_PUBLIC_ID, study slug
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├── seed_data.py ← BENEFITS, COSTS, ASSUMPTIONS as Python dicts
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├── notebooks/
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│ ├── 01_benefits.ipynb ← quantify the 5 benefits, push to Athena
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│ ├── 02_costs.ipynb ← quantify the 3 costs
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│ ├── 03_business_case.ipynb ← /calculate, charts, scenarios
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│ └── 04_export.ipynb ← /export → exports/export.json
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├── exports/ ← generated; .gitignored
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└── docs/
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└── 202602_TEI Report Amazon Connect.pdf
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```
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## Workflow
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1. **Set up credentials** in the project root `.env` (see `.env.example`).
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2. **Create / link the TEI tool** in Athena, then put its `public_id` in
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[`config.py`](config.py).
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3. **Open `notebooks/01_benefits.ipynb`** and run all — pushes the 5
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benefit rows from `seed_data.py` into Athena.
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4. **`02_costs.ipynb`** — pushes the 3 cost rows.
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5. **`03_business_case.ipynb`** — calls `/calculate`, renders the cash
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flow chart, runs scenario analysis. Should reproduce the PDF's
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$78.7M NPV / 342% ROI.
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6. **`04_export.ipynb`** — writes `exports/export.json` for the report
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pipeline.
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## Adding a new study
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Copy this folder, rename to `YYYYMM_<Vendor><Solution>`, and:
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1. Replace `seed_data.py` with your benefits/costs.
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2. Update `config.py` with the new tool/report public IDs.
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3. Tweak the notebooks' narrative; the helper imports are the same.
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The only thing that changes between studies is the **data** and the
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**narrative prose** in the notebooks. All math, charts, and API calls
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come from `core/`.
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