> AI agents: this is one page from PostHog's docs. Full index of Markdown docs for LLMs: https://posthog.com/llms.txt

# Which leads deserve the sales team's time? – Data Stack pocket guide

[](/pocket-guides.md)

AA

Contents

-   [Introduction](/pocket-guides/data-stack.md)
-   Ask a quick question

    [Features & revenue01](/pocket-guides/data-stack/features-drive-revenue.md)
-   [Onboarding conversion02](/pocket-guides/data-stack/onboarding-conversion.md)
-   [Upsell-ready accounts03](/pocket-guides/data-stack/upsell-ready-accounts.md)
-   [Pre-cancellation behavior04](/pocket-guides/data-stack/pre-cancellation-behavior.md)
-   [Tickets & churn05](/pocket-guides/data-stack/support-tickets-churn.md)
-   [ARR vs engagement06](/pocket-guides/data-stack/value-vs-engagement.md)
-   [Channels & retention07](/pocket-guides/data-stack/acquisition-channels-retention.md)
-   [Lead scoring08](/pocket-guides/data-stack/lead-scoring.md)
-   [Feature revenue impact09](/pocket-guides/data-stack/feature-revenue-impact.md)
-   Data modeling

    [Foundations10](/pocket-guides/data-stack/warehouse-foundations.md)
-   [Revenue11](/pocket-guides/data-stack/revenue-metrics.md)
-   [Conversion12](/pocket-guides/data-stack/conversion-metrics.md)
-   [Activation13](/pocket-guides/data-stack/activation-metrics.md)
-   [Usage14](/pocket-guides/data-stack/product-usage-metrics.md)
-   [Dimensions15](/pocket-guides/data-stack/dimension-tables.md)

[](/pocket-guides.md)Aa

[](/pocket-guides/data-stack/acquisition-channels-retention.md)[](/pocket-guides/data-stack/feature-revenue-impact.md)

# Which leads deserve the sales team's time? – Data Stack pocket guide

A CRM full of leads doesn't tell sales who to call first; product usage does. This Skill scores CRM leads by recent product usage and surfaces the warmest ones sales hasn't yet reached: a product-qualified-lead list.

## Example data and how to read it

The Skill looks for patterns like Fig. 1: Priya was seen 6 hours ago and is still listed as a trial, not yet a marketing-qualified lead: exactly the kind of engaged, uncontacted account this list exists to surface before someone else notices.

| Lead | Score | Last seen | Stage |
| --- | --- | --- | --- |
| [jane@acme.com](mailto:jane@acme.com) | 34 | 2 days ago | MQL |
| [sam@northwind.io](mailto:sam@northwind.io) | 29 | 1 day ago | MQL |
| [priya@globex.com](mailto:priya@globex.com) | 26 | 6 hours ago | Trial |

Fig. 1 – Example data: leads scored by product usage, sorted by who's warmest right now.

## Get an agent to do the work

Copy the Skill below and add it to your agent. The agent defines, with you, the handful of events that signal real intent. Then it scores each lead by recency and frequency of those events, weighted however you like. It joins the score to CRM lead records on email and filters to leads not yet marked contacted. The scoring stays transparent on purpose so that everyone using it can see where it comes from.

This ranks individual leads to action right now. For analyzing which onboarding *steps* drive trial-to-paid conversion in aggregate instead, that's [Onboarding conversion](/pocket-guides/data-stack/onboarding-conversion.md).

posthog-lead-scoring/SKILL.md

```markdown
---
name: posthog-lead-scoring
description: >
  Score CRM leads (HubSpot/Salesforce) by recent product usage and surface the warmest leads sales hasn't contacted
  yet, a product-qualified-lead list saved as a reusable PostHog insight. Use this whenever someone wants
  product-qualified leads (PQLs), a lead score based on product usage, the warmest leads to prioritize, engaged
  signups sales should call, or a usage-based ranking of leads joined to the CRM. Triggers on phrasings like 'which
  leads should sales call', 'product qualified leads', 'score my leads by usage', 'hottest leads we have not
  contacted', 'PQL list', or 'which signups are sales-ready'. Boundary: this ranks individual leads to action now.
  For analyzing which onboarding STEPS drive trial-to-paid conversion in aggregate, use
  posthog-onboarding-conversion. It sets up the HubSpot/Salesforce source if needed and builds the insight
  end-to-end.
---

# Which leads deserve the sales team's time?

**Question:** Which leads are most product-engaged — especially ones sales hasn't contacted yet?
**For:** Sales & Growth · **Difficulty:** Intermediate · **Shape:** a scored query
**Data sources:** PostHog events (key product actions) + HubSpot / Salesforce (lead records)

## What this produces

A saved PostHog insight scoring CRM leads by recent product usage and highlighting the most engaged ones that
sales hasn't yet reached — a product-qualified-lead list.

## Workflow

First read `references/posthog-workflow.md` for the shared setup: confirm the PostHog MCP is connected, ensure the
CRM source (HubSpot or Salesforce) exists (secure connect-link flow if not), and learn the real schema. Then:

### 1. Identify the pieces in this project

- **Key product actions.** With the user, define the handful of events that signal buying intent / real usage
  (`event-definitions-list`). Assign each a weight, or keep it simple (count of key events, recency + frequency).
- **Lead records.** From the CRM: lead/contact rows with email, owner, and a "contacted"/lifecycle field so you can
  tell who sales has already worked. Confirm the field that means "not yet contacted".
- **Join key.** CRM lead ↔ PostHog person by `lower(email)` (see join gotchas in the shared reference).

### 2. Build and validate the query

Adapt names and validate with `query-run`. Produce one scored row per lead, filtered/sorted to surface warm,
uncontacted leads.

```sql
-- Score leads by recent product usage; surface the warmest uncontacted ones.
-- Adapt: key events + weights, the CRM table/fields, and the "contacted" flag.
WITH usage_score AS (
    SELECT
        lower(person.properties.email) AS email,
        sum(multiIf(event = 'created_project', 5,
                    event = 'invited_teammate', 4,
                    event = 'ran_query', 2, 1)) AS score,
        max(timestamp) AS last_seen
    FROM events
    WHERE timestamp >= now() - INTERVAL 30 DAY
      AND event IN ('created_project', 'invited_teammate', 'ran_query', 'viewed_pricing')
    GROUP BY lower(person.properties.email)
)
SELECT
    l.email,
    l.lead_owner,
    us.score,
    us.last_seen,
    l.lifecycle_stage
FROM hubspot_contacts AS l
INNER JOIN usage_score AS us ON lower(l.email) = us.email
WHERE l.lifecycle_stage NOT IN ('customer')          -- still a lead
  AND (l.last_contacted IS NULL OR l.last_contacted < now() - INTERVAL 30 DAY)  -- "not contacted yet"
ORDER BY us.score DESC
LIMIT 100
```

Keep the scoring transparent and let the user tune weights — a lead score sales doesn't understand won't get used.
Note that this ranks by product signal only; a real PQL model may blend in firmographics.

### 3. Save the insight

Save as a SQL/HogQL table insight named "Product-qualified leads (uncontacted)", documenting the scoring weights
and window. Return the URL and name the top few leads to call today.

## Self-driving development (offer this)

With leads scored by real product usage, the user can catch the warmest ones as they heat up. Offer to help set up
an alert or a cohort that flags a lead when its score crosses a threshold — filling the pipeline while they sleep.
(Pushing the score back into the CRM is a separate reverse-ETL step; offer to explain it.)
```

Show full example

Fig. 2 – The Skill itself, copy and paste this into your agent to answer the question.

## Doing this by hand

1.  **Connect [HubSpot](/docs/cdp/sources/hubspot.md) or [Salesforce](/docs/cdp/sources/salesforce.md)** via [Data pipeline > Sources](/docs/data-warehouse/sources.md).
2.  **Define your key [events](/docs/data/events.md)** under Data management > Events: define what signals real intent for you.
3.  **For a ranked list**, check each lead's activity in the [Persons](/docs/data/persons.md) explorer, or use the [SQL editor](/docs/data-warehouse/sql.md), or ask PostHog AI to score every lead in one query.

See also: [Onboarding conversion](/pocket-guides/data-stack/onboarding-conversion.md) · [Features & revenue](/pocket-guides/data-stack/features-drive-revenue.md)

[‹ Channels & retention](/pocket-guides/data-stack/acquisition-channels-retention.md)[All guides](/pocket-guides.md)p. 8 of 15[Feature revenue impact ›](/pocket-guides/data-stack/feature-revenue-impact.md)