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AARRR分析

实现效果

在仪表板中可以如下分析某APP用户数据,基于柱形图、树形图、透视表等来进行AARRR模型的分析。

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资源下载

AARRR分析示例.zip

下载资源包,然后将其导入系统,可以看到具体的设计细节,方便您参考学习。

模型定义

AARRR分析 AARRR是一个产品生长的5个重要环节:

  1. 获取(Acquisition)

  2. 激活(Activation)

  3. 留存(Retention)

  4. 传播(Referral)

  5. 收益(Revenue)

通过这5个类别,我们可以将产品成长的阶段可视化,以便我们逐个部分进行商业分析,以及做出相应的调整。

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应用场景

  • 通过可视化各个环节的数据指标,获取产品的运营情况。

  • 分别展开分析各个环节的明细,量化分析未来的关注点。

  • 调整各个环节的关注指标,分析产品生命周期与客户维护的作用

数据处理

使用分析表达式创建度量值

激活比例

  • 激活人数/总人数

CountX(Filter('AARRR示例数据','AARRR示例数据'[是否激活]="是"), [联系电话] )/count('AARRR示例数据'[联系电话])

当日留存率

  • 登录时间差为0的用户,占总用户的人数

countx(Filter('AARRR示例数据','AARRR示例数据'[激活_登录时间差]=0),'AARRR示例数据'[联系电话])/count('AARRR示例数据'[联系电话])

k

  • 转化率 = 接受推荐人数 / 推荐次数

  • K值 = 接受推荐人数 / 用户数量

sumx('AARRR传播数据','AARRR传播数据'[接受推荐人数])/Countx('AARRR传播数据','AARRR传播数据'[联系电话])

可视化

获取:渠道对比用户是从什么渠道获得的软件?

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激活:获取到的用户是否激活? 软件对于客户是否能激发兴趣?

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留存:我们通过当日/一周/两周等多个节点来判断客户的留存情况,关注用户是否能够长期、持续的使用产品

14-04

传播:用户能否通过你的产品,推荐引导更多的用户来加入使用?

14-06

收益:用户能否为你的产品提供收益?

14-05