【Python数据分析】AAARR模型实现

一、通用的漏斗图from pyecharts import options as optsfrom pyecharts.charts import Funnelfrom pyecharts.faker import Fakerc = (Funnel().add(series_name="",data_pair=[list(z) for z in zip(Faker.choose(), Faker.

♚人间海

930人浏览 · 2022-05-05 09:41:01

♚人间海 · 2022-05-05 09:41:01 发布

一、通用的漏斗图

from pyecharts import options as opts
from pyecharts.charts import Funnel
from pyecharts.faker import Faker

c = (
    Funnel()
    .add(
        series_name="",
        data_pair=[list(z) for z in zip(Faker.choose(), Faker.values())],
#         sort_="none", # 数据排序显示顺序
        label_opts=opts.LabelOpts(is_show=True, position="outside"),
    )
    .set_global_opts(title_opts=opts.TitleOpts(title="标准漏斗图"))
#     .render("标准漏斗图.html")
)
c.render_notebook()

在这里插入图片描述
二、AARRR模型主要应用于用户生命周期管理

三、从提升各个转化环节策略来看

四、AARRR模型各个环节关注指标

五、AARRR模型漏斗Python实现

from pyecharts import options as opts
from pyecharts.charts import Funnel
from pyecharts.globals import ThemeType

data = [60000, 10000, 5000, 1000, 600]
phase = ['新用户', '激活用户', '留存用户', '消费用户', '传播用户']



c = (
    Funnel(init_opts=opts.InitOpts(width="900px", height="600px",theme = ThemeType.LIGHT))
    .add(
        series_name="",
        data_pair=[list(z) for z in zip(phase,data)],
         sort_="none", # 数据排序显示顺序
        label_opts=opts.LabelOpts(is_show=True, position="inside"),
    )
    .set_global_opts(title_opts=opts.TitleOpts(title="AARRR漏斗图"))
)
c.render_notebook()