顧客分群

使用 pandas 分析行銷活動

Jill Rosok

Data Scientist

常見的受眾分群方式

分群圖

年齡
性別
位置
與品牌的過去互動
接觸過的行銷管道

用 pandas 進行分群

# Subset to include only House Ads
house_ads = marketing\
         [marketing['subscribing_channel'] == 'House Ads']

retained = house_ads[house_ads['is_retained'] == True]\
                    ['user_id'].nunique()
subscribers = house_ads[house_ads['converted'] == True]\
                     ['user_id'].nunique()

retention_rate = retained/subscribers
print(round(retention_rate*100,2), '%')

58.05 %

一定有更簡單的分群方式！

用 pandas 分群 - groupby()

# Group by subscribing_channel and calculate retention
retained = marketing[marketing['is_retained'] == True]\
          .groupby(['subscribing_channel'])\
          ['user_id'].nunique()
print(retained)

subscribing_channel
Email        109
Facebook     152
House Ads    173
Instagram    158
Push          54
Name: user_id, dtype: int64

用 pandas 分群 - groupby()

# Group by subscribing_channel and calculate subscribers
subscribers = marketing[marketing['converted'] == True]\
            .groupby(['subscribing_channel'])\
            ['user_id'].nunique()
print(subscribers)

subscribing_channel
Email        125
Facebook     221
House Ads    298
Instagram    232
Push          77
Name: user_id, dtype: int64

分群結果

# Calculate the retention rate across the DataFrame
channel_retention_rate = (retained/subscribers)*100
print(channel_retention_rate)

subscribing_channel
Email        87.200000
Facebook     68.778281
House Ads    58.053691
Instagram    68.103448
Push         70.129870
Name: user_id, dtype: float64

一起來練習吧！

使用 pandas 分析行銷活動