> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mavera.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Pipedrive
> Integrate Mavera with Pipedrive — pipeline voice analysis, activity-based persona scoring, lost deal focus groups, and org-level account research
## Overview
These jobs show how to **pull data from Pipedrive** (Deals, Notes, Activities, Organizations) → **analyze with Mavera** (Chat, Focus Group, Mave Agent) → **get persona-validated insights** for sales coaching, pipeline optimization, and account intelligence.
**Pipedrive API v2** is the primary API used below. Some endpoints (Notes) still use v1. Authenticate with an **OAuth 2.0 token** or an **API token** query parameter.
| Detail | Value |
| ------------------- | ------------------------------------------------------- |
| **Base URL** | `https://{companydomain}.pipedrive.com/api/v2/` |
| **Auth** | OAuth 2.0 Bearer token *or* `?api_token=` query param |
| **Rate limits** | 30,000 base tokens/day (each request costs 1–10 tokens) |
| **Mavera surfaces** | Chat, Focus Group, Mave Agent |
***
## Prerequisites
Generate an API token from **Settings → Personal preferences → API**, or set up an OAuth 2.0 app.
Grab your key from [app.mavera.io/settings/api](https://app.mavera.io/settings/api).
```bash theme={"dark"}
export PIPEDRIVE_API_TOKEN="pd_tok_..."
export PIPEDRIVE_DOMAIN="yourcompany"
export MAVERA_API_KEY="mvr_..."
```
```bash Python theme={"dark"}
pip install requests openai
```
```bash JavaScript theme={"dark"}
npm install openai
```
***
## Jobs
Analyze deal notes by pipeline stage to find messaging patterns that correlate with wins
Map activity patterns (calls, emails, meetings) to buyer personas and discover touchpoint thresholds
Create persona archetypes from lost-deal patterns and run Focus Groups to surface qualitative reasoning
Use Mave Agent to research organizations and write intelligence briefs back to Pipedrive
***
## Production Tips
Pipedrive v2 endpoints use cursor pagination. Read `additional_data.next_cursor` from each response:
```python theme={"dark"}
cursor = None
all_deals = []
while True:
params = {"limit": 100}
if cursor:
params["cursor"] = cursor
resp = pd_get("/api/v2/deals", params)
all_deals.extend(resp.get("data", []) or [])
cursor = resp.get("additional_data", {}).get("next_cursor")
if not cursor:
break
```
Use Pipedrive webhooks (**Settings → Webhooks**) to trigger jobs when deals change stage, are lost, or organizations update. This reduces daily token consumption.
Freetext reasons create sparse clusters. Map synonyms before Focus Group:
```python theme={"dark"}
REASON_MAP = {
"too expensive": "Pricing", "price": "Pricing", "budget": "Pricing",
"competitor": "Competitor", "went with": "Competitor",
"timing": "Timing", "not now": "Timing", "next quarter": "Timing",
}
def normalize(raw):
for kw, cat in REASON_MAP.items():
if kw in raw.lower().strip():
return cat
return "Other"
```
***
## Credits
| Mavera surface | Credits/call | Typical usage |
| -------------- | ------------ | ------------------------------------ |
| Chat | \~1–5 | Per-stage voice analysis |
| Focus Group | \~10–30 | Activity scoring, lost deal analysis |
| Mave Agent | \~5–15 | Org research |
See [Credits](/guides/credits) for details.
***
Email A/B testing, call analysis, lead-to-persona automation
8 jobs — lead scoring, deal-stage focus groups, account research
8 jobs — lifecycle personas, meeting analysis, engagement refinement
All integrations