队列分析工作流
business-operations
队列分析工作流，包含按注册日期的用户分段、留存曲线计算、LTV预测以及行为模式识别。
完整 FlowZap 代码
Analyst { # Analyst
n1: circle label:"Start"
n2: rectangle label:"Define cohort criteria"
n3: rectangle label:"Set analysis time window"
n4: rectangle label:"Review analysis results"
n5: diamond label:"Insights actionable?"
n6: rectangle label:"Create recommendations report"
n7: circle label:"End"
n1.handle(right) -> n2.handle(left)
n2.handle(right) -> n3.handle(left)
n3.handle(bottom) -> DataPipeline.n8.handle(top) [label="Extract data"]
n4.handle(right) -> n5.handle(left)
n5.handle(right) -> n6.handle(left) [label="Yes"]
n5.handle(bottom) -> n2.handle(bottom) [label="No - Refine criteria"]
n6.handle(right) -> n7.handle(left)
}
DataPipeline { # Data Pipeline
n8: rectangle label:"Query user events database"
n9: rectangle label:"Group users by signup date"
n10: rectangle label:"Calculate retention metrics"
n11: diamond label:"Data quality check?"
n12: rectangle label:"Flag data anomalies"
n8.handle(right) -> n9.handle(left)
n9.handle(right) -> n10.handle(left)
n10.handle(right) -> n11.handle(left)
n11.handle(right) -> Analytics.n13.handle(left) [label="Pass"]
n11.handle(bottom) -> n12.handle(top) [label="Fail"]
n12.handle(left) -> n8.handle(bottom) [label="Reprocess"]
}
Analytics { # Analytics Engine
n13: rectangle label:"Compute cohort curves"
n14: rectangle label:"Calculate churn rates"
n15: rectangle label:"Identify behavior patterns"
n16: rectangle label:"Generate visualizations"
n13.handle(right) -> n14.handle(left)
n14.handle(right) -> n15.handle(left)
n15.handle(right) -> n16.handle(left)
n16.handle(top) -> Analyst.n4.handle(bottom) [label="Results ready"]
}