# Kpi Engine

> Pull and grade your cold-calling and lead-generation KPIs straight from your DataSift (REISift) account - dials, answer/conversation/contact rates, correct numbers, dispositions, leads (including new_lead statuses most reports miss), talk time, and full funnel pacing toward appointments and contracts - over any date range, per caller and account-wide, exported as markdown, CSV, Excel, or a Slack digest. Use when asked for KPI reports, weekly caller numbers, lead pacing, "how many dials/correct numbers/leads this week", or to set up recurring KPI tracking.

- Skill: `datasift-ty-personal/kpi-engine` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add datasift-ty-personal/kpi-engine`
- Raw SKILL.md: https://api.skillmd.com/api/skills/datasift-ty-personal/kpi-engine/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: DataSift-Ty-Personal (https://skillmd.com/u/datasift-ty-personal)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/datasift-ty-personal/kpi-engine

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# KPI Engine - universal DataSift KPI reporting

Pulls your real calling activity from DataSift's per-record activity log (every call, text, and disposition event, caller-attributed and timestamped) and turns it into a graded KPI report: per caller, per day, and account-wide, with funnel pacing toward deals.

Why the activity log: DataSift's dashboard widgets are not exposed to scripts and cannot cleanly isolate "what happened on the phones this week." The per-record log is the one date-accurate, caller-attributed source.

## Quick start

1. Log into app.reisift.io. Open DevTools (F12) -> Network tab -> click any request to `apiv2.reisift.io` -> copy the `authorization: Bearer <token>` value (the long JWT, without the word Bearer).
2. Save it: set env var `REISIFT_TOKEN`, or paste it into a file named `reisift_token.txt` next to the script. Tokens last about 48 hours; repeat when it expires.
3. Run:

```bash
python scripts/pull_kpis.py --days 7                 # trailing week
python scripts/pull_kpis.py --from 2026-07-06 --to 2026-07-16
python scripts/pull_kpis.py --days 1                 # today
python scripts/pull_kpis.py --days 7 --xlsx          # also build an Excel workbook
python scripts/pull_kpis.py --days 7 --detail        # + record-level CSV (one row per record worked)
python scripts/pull_kpis.py --days 7 --slack <webhook-url>   # post digest to Slack
```

No dependencies for markdown/CSV output (pure standard library). Excel output needs `pip install openpyxl`. Expect roughly 2-5 minutes per week of data (it reads each worked record's log).

## What you get

- **Volume:** dials, answered, no-answer, records touched, talk time, first/last call window, dials per hour.
- **Three rates, never collapsed into one "connect rate":**
  - answer rate = answered / dials (loosest - includes voicemail pickups)
  - conversation rate = answered calls of 60s+ / dials (meaningful = 120s+)
  - contact rate = correct numbers / dials (right party confirmed)
- **Dispositions:** correct / wrong / dead / DNC numbers; not interested, follow-ups, dead leads.
- **Leads that actually count:** most reports only count Cold/Warm/Hot Lead statuses and miss that first-touch leads land in `new_lead` and `No Contact New Lead` - so leads read 0 while your callers produce 2-3 a day. This skill counts the full lead-status set (configurable in `benchmarks.json`).
- **Funnel pacing:** dials per correct number vs the ~9 (phone-scored) / ~32 (blind) benchmarks, correct numbers toward the 100-correct ~= 1-deal ratio, projected appointments from your appointment-take rate, projected contracts from your leads-per-contract ratio.
- **Per-caller scorecards:** dial floor (150/day) and conversation floor (5/day) MET or BELOW per caller, scaled by days active, plus lead targets.

## Benchmarks are baselines, not rules

The numbers in `references/kpi-catalog.md` and the script's defaults (150-dial floor, 2-3 leads/caller/day, 25% appointment take, 1 contract per 15-20 leads, 32:1 -> 9:1 dials per correct) are working baselines from a live operation and the DataSift 5-Day Deal Flow Challenge. Treat them as min-max starting points and tune them to your market in a `benchmarks.json` next to the script (the script prints its full config with `--show-benchmarks`).

Tips for clean numbers:
- Add your admin/owner logins to `excluded_callers` in `benchmarks.json` so testing and admin activity does not pollute caller tables.
- Appointments count from completed tasks with appointment/appt/meeting in the title - make logging those part of your lead-handoff routine or the appointment column stays empty.
- Statuses are matched by exact name. If your account uses custom status names, add them to `lead_statuses`.

## Recurring use

- Windows: Task Scheduler -> run `python pull_kpis.py --days 1 --slack <webhook>` daily at 9pm; weekly on Friday with `--days 7 --xlsx`.
- Mac/Linux: same via cron.
- The report is deterministic from your account data: re-running a range is safe and just refreshes the outputs.

## Reference

`references/kpi-catalog.md` holds the full KPI catalog: every metric with its formula, the benchmark ladder (dials -> correct numbers -> leads -> appointments -> contracts), role-level KPIs for prospectors / lead managers / closers, and where each number comes from.