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posthog_transaction_volume posthog_transaction_volume_mvp_v1

Transaction / volume-based SaaS

Payment and transaction platforms where value tracks with successful throughput. Positive signals end in payment completion; negative signals include refunds and invoice stall.

Value metric

Transactions processed, GMV, payments

Success event

payment_completed

entity_type: account

Scale

  • 280 accounts
  • 4 users/account (mean)
  • 7 sessions/user (mean)
  • 30 days of history

Research metrics proxied

  • — Transaction volume trend
  • — Avg transaction value growth
  • — Transaction frequency per account

Signal paths

Positive paths end in payment_completed. Negative paths do not. Every generated event_id belonging to a path is recorded in ground_truth.json.

Positive signals (2)

order_to_payment ×90
order_created → payment_completed
cohorts: high_intent, medium_intent, power_user
invoice_to_payment ×50
order_created → invoice_generated → payment_completed
cohorts: high_intent, medium_intent

Negative signals (2)

invoiced_not_paid ×55
order_created → invoice_generated
cohorts: medium_intent, low_intent, lurker
refunded_volume ×30
payment_completed → refund_issued
cohorts: low_intent, noisy_bot_like

Generate this dataset

Config file: configs/posthog_transaction_volume_mvp.yaml

Quickstart 
# Dockerized Postgres (recommended for inspection)
docker compose up -d

uv run dryfit \
  -c configs/posthog_transaction_volume_mvp.yaml \
  --dsn postgresql://dryfit_writer:dryfit_writer@127.0.0.1:54329/dryfit \
  --print-summary

# Or local Postgres
./scripts/generate-local -c configs/posthog_transaction_volume_mvp.yaml --print-summary

Full setup instructions are in the repo's README — including local Postgres, Grafana inspection, and dataset restore.

Noise parameters

DryFit injects realistic noise on top of the generated signal paths. These probabilities are per-event. Noise never touches rows referenced by ground_truth.json — your scoring logic can trust the truth file is exact.

missing event probability
4.0%
duplicate event probability
2.0%
out of order probability
2.0%
null property probability
4.0%
anonymous actor probability
1.0%
weird property probability
1.0%

Other scenarios

Combined coverage (all models)

→ upgrade_clicked

Union of event types across all business models

Contact / record-based SaaS

→ contact_created

Contacts, leads, subscribers, accounts managed

Credits / token-based

→ credits_purchased

Credits consumed, tokens used, compute units

Event-volume SaaS

→ custom_event_tracked

Events tracked, data points ingested, log lines
Browse all scenarios →

Benchmark your detector against Transaction / volume-based SaaS

Clone the repo, run the config, check your agent's output against ground_truth.json.

View on GitHub