One time Account Scoring

Advantages

• Pain-based account scoring: Scores accounts from 0-100 based on pain-point relevance, urgency, and buying signals so teams can quickly prioritize where to focus.
• Evidence-backed rankings: Uses real OpenFunnel data like hiring activity, signal timelines, and account context so the score reflects actual buying momentum instead of guesswork.
• Timing-aware prioritization: Weighs recency and signal buildup heavily so users can focus on accounts that are active now, not just accounts that look like a fit on paper.Installation

Installation

npx skills add openfunnel/openfunnel --skill account-scoring

Copy skill

---
name: account-scoring
description: Score accounts 0-100 on pain-point relevance with evidence and reasoning
---

# Static Account Scoring

Score accounts in an audience based on pain-point relevance and urgency.

## Conversation Flow

The agent MUST follow this sequence, do NOT skip steps.

### Step 1: List audiences for user to pick

- List audiences (see Runtime below for which tool to call)
- Present the list
- Ask: "Which audience would you like to score?"

### Step 2: User selects an audience

- Load the audience to get account IDs
- Confirm: "Got it, [audience name] with X accounts."

### Step 3: Present scoring models, MANDATORY, DO NOT SKIP

> Do NOT auto-score. Do NOT skip to results.

Present:

> How would you like to score these accounts?
>
> 1. **OpenFunnel Pain-Based Scoring**, scores each account based on how urgently they need what you sell
> 2. **Custom**, describe your own scoring criteria in plain language

- User picks 1 → proceed to Step 3a
- User picks 2 → user describes custom logic, agent interprets and applies

### Step 3a: Get pain description (Pain-Based Scoring only)

- Ask: "What pain-points does your product/service solve?
  Example: 'We help companies migrate off legacy ERPs to cloud-native'"
- User provides pain description
- This is the lens through which ALL signals will be interpreted

### Step 4: Score each account

For each account_id in the audience:

1. Gather evidence (summary + timeline + full details, see Runtime)
2. Read the evidence against the user's pain description
3. Apply the pain-based scoring rubric below
4. Assign score (0-100) + reasoning (1-2 sentences explaining why)

Sort by score descending. Present as a ranked list.

---

## Pain-Based Scoring, How to Reason

**TIME IS THE MOST IMPORTANT FACTOR.** Pain has a shelf life. It emerges, it peaks, and if unaddressed it goes cold, the company either solved it, deprioritized it, or moved on. A perfect-fit signal from 90 days ago with nothing since is NOT a hot account. It's stale. Score accordingly.

Read the timeline as a narrative, not a list. A single signal is a hint. A sequence of signals over weeks is a story. Timestamps matter as much as content.

Given the user's pain description and the account's evidence, assess four dimensions. These are NOT mechanical point buckets, use judgment. The evidence may be rich or sparse; score what you see.

### 1. Pain Relevance

Does this account show signs of having this pain?

- Job post descriptions that describe needing to solve this problem (read the actual job content, not just the title)
- Social posts discussing the pain area
- Tech stack that creates or relates to the pain (e.g. legacy tool that your product replaces)
- Industry/vertical alignment with the pain

### 2. Temporal Signal

When did the pain surface, and is it still alive? Pain goes cold. **This dimension can override everything else.**

- A highly relevant signal from 90 days ago with no follow-up = cold. The pain may be solved, deprioritized, or abandoned. **Score LOW.**
- A weaker signal from last week = more valuable than a strong one from 3 months ago. Recency wins.
- **Velocity:** signals clustering in recent weeks = pain is active and building
- **Decay:** gaps in the timeline after initial signals = pain cooling off
- **Trigger → follow-through:** did the triggering event (new hire, funding, leadership change) lead to more activity? If yes = escalating. If no = stalled.

### 3. Buying Window

Can they act on it now?

- New leadership in relevant function (someone with mandate to change)
- Recent funding (budget to spend)
- Active hiring in the area (committed to solving, building team)
- Signal stacking: multiple signal types pointing at same pain (hiring + posting about the problem + using legacy tech = high conviction)

### 4. Story Coherence

Do the signals tell a connected narrative?

- Isolated signal = maybe noise, score conservatively
- Connected signals across time = real pattern, score higher
- Example: Jan, hired VP Eng → Feb, posted about migration challenges → Mar, started hiring cloud engineers = clear escalating story
- Counter-example: one social post 80 days ago, nothing since = low confidence

**The score should reflect: "If I were selling a solution to [pain], how excited would I be about this account RIGHT NOW, today?"**

Output per account: score (0-100) + reasoning (1-2 sentences).

---

## Runtime

| Step | Endpoint |
|------|----------|
| Step 1 | `POST https://api.openfunnel.dev/api/v1/audience/get-audience-list { limit, offset }` |
| Step 2 | `POST https://api.openfunnel.dev/api/v1/audience/ { audience_id }` |
| Step 4 | `POST https://api.openfunnel.dev/api/v2/account/batch { account_ids: [id] }` → full details + inline signal content (preferred) |

**Prefer V2 batch for scoring.** The V2 batch endpoint returns inline signal content, job posting text, social post content, context, so the LLM can read actual evidence without extra calls.

> **NOTE:** Large audiences (100+) will be slow.

All endpoints require headers: `X-API-Key`, `X-User-ID`, `Content-Type: application/json`.