# Dryfit Scenario: Credits / token-based

> Prepaid credit or token systems where burn and top-up patterns matter more than traditional funnels. Low-balance warnings into purchases are the key positive signal.

*Source: [https://www.getbeton.ai/oss-tools/dryfit/scenarios/posthog-credits-token/](https://www.getbeton.ai/oss-tools/dryfit/scenarios/posthog-credits-token/)*

**Scenario kind:** posthog_credits_token
**Use-case kind:** credit
**Value metric:** Credits consumed, tokens used, compute units
**Config file:** `configs/posthog_credits_token_mvp.yaml`
**Dataset id:** `posthog_credits_token_mvp_v1`

## Scale

- Accounts: 3000
- Users per account (mean): 8
- Sessions per user (mean): 16
- Duration: 300 days

**Success event:** `credits_purchased` (account)

## Research metrics

- Burn rate
- Days-to-zero
- Top-up frequency
- Auto-refill adoption

## Positive signals (ground truth)

- **warning_to_purchase** (count 850): `low_balance_warning → credits_purchased`
- **auto_refill_conversion** (count 450): `low_balance_warning → auto_refill_triggered → credits_purchased`

## Negative signals (ground truth)

- **burn_to_warning_only** (count 650): `credits_used → low_balance_warning`
- **repeated_warning_decline** (count 350): `low_balance_warning → low_balance_warning`