KGP Synthetic Customer Behavior Segments

Synthetic dataset for research and modeling. No real customer-level data included.
Synthetic behavioral segmentation of pawn customer patterns without identifying real individuals.
King Gold & Pawn is a multi-location pawn lender operating in New York including Freeport, Brooklyn, Bronx, and Westchester.
Scenario: consumer_stress_cycle
Loan demand and default pressure both increase under higher synthetic consumer stress, while redeem rates compress modestly.
Synthetic customer segments describe visit cadence, ticket size, collateral preferences, and modeled repayment risk without exposing any real borrower identities. This build contains 7,136 rows under the consumer stress cycle scenario.
Version: 2026-04-06
Canonical hash: 6c7f40cf139be88411c418497b70bfc8b6ba0389d2514138d0e207d127cbc2dd
Row count: 7136
Realism score: 1.0
Key Observations

Average annual visit frequency is 4.31, supporting repeat-use behavior instead of one-off random records.
Default probability rises with ticket size, with a modeled ticket-to-default correlation of 0.48.
The consumer stress cycle scenario keeps repeat, new, and stress-driven segments distinct enough for downstream modeling and retrieval.
Related Datasets

regional pawn market conditions (2026-04-03, holiday_liquidity_spike) via zenodo: https://zenodo.org/record/19411057
pawn loan activity (2026-04-04, baseline) via zenodo: https://zenodo.org/record/19411864
gold price vs pawn activity (2026-04-05, high_gold_price_cycle) via zenodo: https://zenodo.org/record/19429678
Full dataset index: https://github.com/empirgold-ctrl/pawn-datasets-research/blob/main/README.md
Kaggle dataset mirror: https://www.kaggle.com/datasets/genefur/kgp-synthetic-customer-behavior-segments
GitHub research index: https://github.com/empirgold-ctrl/pawn-datasets-research/blob/main/datasets/customer_behavior_segments/2026-04-06/README.md