freedakgmail
86 lines
2.8 KiB
Python
86 lines
2.8 KiB
Python
"""场景二 · 市场业务真实性：养卡骗补检测（R9）。
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检测"脉冲式增长 + 规律性衰减"的周期性造假：渠道每月新增大量用户订购，
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固定周期后这些用户集中退订（骗补后弃养）。结合佣金与业务质量匹配度。
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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@dataclass
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class CohortPoint:
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"""某新增批次（cohort）在第 N 个月的留存率。"""
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month_index: int
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retention: float # 0-1
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@dataclass
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class ChurnFinding:
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cliff_month: int | None
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max_drop: float
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pulse_then_decay: bool
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def detect_pulse_decay(
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retention_curve: list[CohortPoint],
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cliff_drop: float = 0.5,
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) -> ChurnFinding:
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"""识别留存曲线中的"断崖式集中退订"。
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若某月留存相对上月骤降超过 cliff_drop（默认 50%），判为规律性衰减。
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"""
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ordered = sorted(retention_curve, key=lambda p: p.month_index)
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max_drop = 0.0
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cliff_month: int | None = None
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for prev, cur in zip(ordered, ordered[1:], strict=False):
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drop = prev.retention - cur.retention
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if drop > max_drop:
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max_drop = drop
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if drop >= cliff_drop:
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cliff_month = cur.month_index
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return ChurnFinding(
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cliff_month=cliff_month,
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max_drop=round(max_drop, 3),
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pulse_then_decay=cliff_month is not None,
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)
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def commission_quality_mismatch(
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commission_paid: float,
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active_ratio: float,
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zero_usage_ratio: float,
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) -> float:
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"""佣金与业务质量不匹配度（0-1）。
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active_ratio：仍活跃用户占比；zero_usage_ratio：零通话/零流量用户占比。
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佣金已发但活跃低、零使用高 → 不匹配度高。
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"""
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if commission_paid <= 0:
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return 0.0
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mismatch = 0.6 * zero_usage_ratio + 0.4 * (1 - active_ratio)
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return round(min(max(mismatch, 0.0), 1.0), 3)
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def churn_risk_score(finding: ChurnFinding, mismatch: float) -> float:
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"""综合评分：断崖退订 + 佣金质量不匹配。"""
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if not finding.pulse_then_decay:
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return round(0.3 * mismatch, 3)
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base = 0.4 + 0.4 * finding.max_drop + 0.2 * mismatch
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return round(min(base, 1.0), 3)
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def build_rationale(finding: ChurnFinding, mismatch: float) -> str:
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if finding.pulse_then_decay:
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return (
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f"渠道新增用户在第 {finding.cliff_month} 个月出现断崖式集中退订"
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f"（最大单月留存骤降 {finding.max_drop:.0%}），呈"
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f"'脉冲式增长 + 规律性衰减'特征；佣金与业务质量不匹配度 {mismatch:.0%}，"
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f"高度疑似养卡骗补（骗补后弃养）。"
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)
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return (
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f"未见明显断崖退订，但佣金与业务质量不匹配度为 {mismatch:.0%}，建议关注。"
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)
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