Posthog Onboarding Conversion

Which onboarding steps turn trials into paying customers?

Question: Which onboarding actions best predict that a trial becomes a paying customer? For: Growth · Difficulty: Beginner · Shape: a funnel + a join Data sources: PostHog funnels (onboarding steps) + Stripe (first successful payment)

What this produces

A saved PostHog insight comparing onboarding actions completed by users who reached a first Stripe payment against those who never paid, ranked by how strongly each step predicts conversion — so the user knows which steps to push everyone toward.

Workflow

First read references/posthog-workflow.md for the shared setup: confirm the PostHog MCP is connected, ensure the Stripe source exists (set it up via the secure connect-link flow if not), and learn this project's real schema. Then do the question-specific part below.

1. Identify the pieces in this project

• Onboarding steps. Use event-definitions-list to find signup → activation events. Confirm the intended step order with the user; onboarding funnels are product-specific.
• First payment. Identify each user's first successful payment from Stripe — typically the earliest paid stripe_invoice or successful stripe_charge per customer. Join to PostHog people on lower(email) (see the join gotchas in the shared reference).
• Conversion label. For each user, a boolean: did they ever reach a first Stripe payment?

2. Build and validate the query

Two good approaches — use whichever the user prefers:

• Funnel insight split by converted vs not (most native): build the onboarding funnel and break it down by a "paid" cohort. This shows step-by-step drop-off for payers vs non-payers directly.
• Comparison query (ranks predictive steps): compute, per onboarding action, the completion rate among payers vs non-payers and the lift. Start from this shape and adapt names, then validate with query-run:

-- Completion rate of each onboarding action among payers vs non-payers, with lift.
-- Adapt: onboarding event names, the payment definition, and the email join key.
WITH payers AS (
    SELECT DISTINCT lower(email) AS email
    FROM stripe_invoice
    WHERE status = 'paid'
),
user_actions AS (
    SELECT
        e.person.id AS person_id,
        lower(e.person.properties.email) AS email,
        e.event AS action,
        min(e.timestamp) AS first_did
    FROM events AS e
    WHERE e.event IN ('signed_up', 'completed_profile', 'created_project', 'invited_teammate', 'activated')
    GROUP BY e.person.id, lower(e.person.properties.email), e.event
)
SELECT
    ua.action,
    countIf(p.email != '') AS payers_who_did,
    countIf(p.email = '') AS nonpayers_who_did,
    round(countIf(p.email != '') / nullif(count(), 0), 3) AS share_of_doers_who_paid
FROM user_actions AS ua
LEFT JOIN payers AS p ON ua.email = p.email
GROUP BY ua.action
ORDER BY share_of_doers_who_paid DESC

Note the caveat honestly when you report: this is correlation, not proof of causation — steps that correlate with paying aren't guaranteed to cause it.

3. Save the insight

Save as a funnel insight (if you went the funnel route) or a SQL/HogQL table insight named "Onboarding steps that predict conversion", described with the payment definition and window. Return the URL and tell the user which steps most separate payers from non-payers.

Self-driving development (offer this)

With the steps that predict conversion, the user can spot trials stalling before them. Offer to help set up in-product prompts, a lifecycle email trigger, or an experiment that moves stalled users forward — turning more trials into customers.