Giveaway Automation at Scale: What 2,080 Sweepstakes Entries Revealed About Entry Paths & Winner Slots

Описание и применение

Автоматизация участия в 2 080 розыгрышах выявила ключевые паттерны входных путей, распределения победных слотов и эффективности ключевых слов, позволяя повысить шансы победы за счёт структурной видимости.

Задача

Automating sweepstakes entries at scale to uncover structural patterns in entry paths, winner slots, and keyword effectiveness.

Результат

The study quantified entry‑rate lift per keyword, mapped winner slot structures, and identified the invisible entry barrier, demonstrating that structural visibility drives win rate improvements.

Рабочий процесс

• Collect sweepstakes data via automated scraping
• Execute automated entries across multiple accounts
• Log raw entry data
• Analyze logs to identify entry paths, winner slot distribution, and keyword lift
• Publish statistical JSON outputs
• Publish detailed tables on Qiita
• Provide scraping infrastructure on Apify Store

Доказательства

I automated entries for 2,080 domestic sweepstakes across 9 X (Twitter) accounts, executing 2,304 total entries.

Источник

Identified winner slots: 130,249 slots / estimated prize value ¥1,732,843

Источник

Entry paths: X 84.9%, undetermined 12.8%, needs-confirmation 0.8%. Cases that complete entirely within X are automatable; LINE, external forms, and member IDs each require manual work or custom implementation — this is the real entry barrier.

Источник

Top keywords by volume: Amazon (176), #gift-campaign (138), gift (105), gift card (73), autograph (73), QUO (72), #recruiting-reviewers (63), anniversary (61).

Источник

Statistical JSON outputs to reports/journalism/2026W45.json. Deterministic — no random or time-dependent aggregation.

Источник