# 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`](docs/202602_TEI%20Report%20Amazon%20Connect.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

1. **Set up credentials** in the project root `.env` (see `.env.example`).
2. **Create / link the TEI tool** in Athena, then put its `public_id` in
   [`config.py`](config.py).
3. **Open `notebooks/01_benefits.ipynb`** and run all — pushes the 5
   benefit rows from `seed_data.py` into Athena.
4. **`02_costs.ipynb`** — pushes the 3 cost rows.
5. **`03_business_case.ipynb`** — calls `/calculate`, renders the cash
   flow chart, runs scenario analysis. Should reproduce the PDF's
   $78.7M NPV / 342% ROI.
6. **`04_export.ipynb`** — writes `exports/export.json` for the report
   pipeline.

## Adding a new study

Copy this folder, rename to `YYYYMM_<Vendor><Solution>`, and:

1. Replace `seed_data.py` with your benefits/costs.
2. Update `config.py` with the new tool/report public IDs.
3. 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/`.
