标签：taf 自制 lin render show date类 价值 方法 sid
下载数据后，利用kettle将文本文件导入mysql数据库。数据time从 2014-11-18 00到2014-12-18 23。包含如下字段：
item_id（产品ID），behavior_type（包含点击、收藏、加购物车、购买四种行为，分别用数字1、2、3、4表示），user_geohash（地理位置），item_category（品类ID），time（发生行为的时间）。
1）缺失值
user_geohash列含大量NULL值，后续不对该字段处理
2）数据一致化处理
time字段含年月日和小时，将小时单独分解为1列。time列之前新建1列time的副本date 存年月日，time列存时间。方法用到replace和substring_index(按特点分隔符，分隔后索要截取字符串)
alter table exc1 add date varchar(20) not null after item_category; update exc1 set date = time update exc1 set date = replace(date,date,substring_index(date,‘ ‘,1)) update exc1 set time = replace(time, time, substring_index(time, ‘ ‘, -1 ))
3）behavior_type列4个值（点击1、收藏2、加购物车3、购买4）转换为‘pv’,‘fav‘,‘cart‘,‘buy‘
update exc1 set behavior_type = replace(behavior_type ,1,‘pv‘)； update exc1 set behavior_type = replace(behavior_type ,2,‘fav‘) update exc1 set behavior_type = replace(behavior_type ,3,‘cart‘) update exc1 set behavior_type = replace(behavior_type ,4,‘buy‘)
4) 查看表结构，发现date类型不是date型，将其转换为date型
alter table exc1 modify date date;
1）整体用户购物情况
select count(behavior_type) as ‘总访问量‘ from exc1 group by behavior_type having behavior_type = ‘pv‘;
select date, count(behavior_type) as ‘日均访问量‘ from exc1 where behavior_type = ‘pv‘ group by date order by date ;
select count(distinct user_id) ‘用户总数‘ from exc1;
有购买行为的用户数量
select count(distinct user_id) ‘购买用户数量‘ from exc1 where behavior_type = ‘buy‘;
用户的购物情况
create view user_behavior as select user_id, count(behavior_type), sum(case when behavior_type = ‘pv‘ then 1 else 0 end) as ‘点击次数‘, sum(case when behavior_type = ‘fav‘ then 1 else 0 end) as ‘收藏次数‘, sum(case when behavior_type = ‘cart‘ then 1 else 0 end) as ‘加购数‘, sum(case when behavior_type = ‘buy‘ then 1 else 0 end) as ‘购买次数‘ from exc1 group by user_id order by count(behavior_type) desc;
复购率
select concat(round(sum(case when 购买次数>=2 then 1 else 0 end)/sum(case when 购买次数>0 then 1 else 0 end)*100), ‘%‘) as ‘复购率‘ from user_behavior
2）用户购买行为漏斗
用户购物行为总计
select sum(点击次数) ‘总点击数‘, sum(收藏次数) ‘收藏总数‘,sum(加购数) ‘加购物车总数‘, sum(购买次数) ‘购买总数‘ from user_behavior;
用户购买行为转换率
select CONCAT( round(sum(点击次数)*100/sum(点击次数),2),‘%‘) as ‘pv‘, CONCAT( round((sum(收藏次数)+sum(加购数))*100/sum(点击次数),2),‘%‘) as ‘pv_to_favcart‘, CONCAT( round(sum(购买次数)*100/sum(点击次数),2),‘%‘) as ‘pv_to_buy‘ from user_behavior;
制作动态漏斗图：
import pandas as pd
import pyecharts as pec
dict = {‘pv‘:100, ‘pv_to_favcart‘:5.07,‘pv_to_buy‘:1.04}
user = pd.DataFrame(data = dict,index= range(1))
attr = [‘点击‘,‘收藏或加购物车‘,‘购买‘]
value = [user.ix[:,‘pv‘],user.ix[:,‘pv_to_favcart‘],user.ix[:,‘pv_to_buy‘]]
funnel = pec.Funnel(‘用户行为漏斗图‘,width=800,height = 600,title_pos = ‘left‘)
funnel.add(name = ‘用户行为情况‘ ,
attr = attr,
value = value,
is_label_show = True,
label_formatter =‘{b}{c}%‘,
label_pos = ‘outside‘,
is_legend_show = True
)
funnel.render()
可以看出用户点击后收藏和加购物车的概率在5.04%左右，真正购买的只有1.04%，购买转化率比较低，说明后期还有很大的空间。
3）、购买率高和购买率为0的人群特征分析
购买率高的人群特征（购买率降序排序）：
select user_id, 点击次数,收藏次数,加购数,购买次数, round(购买次数/点击次数*100,2) as 购买率 from user_behavior group by user_id order by 购买率 desc ;
按购买次数进行排序（购买次数降序排序）
select user_id, 点击次数,收藏次数,加购数,购买次数, concat(round(购买次数/点击次数*100,2),‘%‘) as 购买率 from user_behavior group by user_id order by 购买次数 desc;
可以发现购买率高点击次数并不高，一部分人点击2次就购买了，这部分人没有收藏也没有加入购物车，属于有目的的购物群体，缺啥买啥，一般属于理智型购物群体。
购买率为0的人群：
select user_id, 点击次数,收藏次数,加购数,购买次数, round(购买次数/点击次数*100,2) as 购买率 from user_behavior group by user_id order by 购买率 asc
点击次数多，加购物车或收藏较多考虑有可能是为商家活动做准备。
点击次数多，购买率低或为0的客户为克制型客户，此类客户爱比较，思虑多，自制性比较强。或者说不会支付。
4）基于时间维度了解用户行为习惯
一天中用户活跃时段分布
select time, count(behavior_type), sum(case when behavior_type = ‘pv‘ then 1 else 0 end) as ‘点击次数‘, sum(case when behavior_type = ‘fav‘ then 1 else 0 end) as ‘收藏次数‘, sum(case when behavior_type = ‘cart‘ then 1 else 0 end) as ‘加购数‘, sum(case when behavior_type = ‘buy‘ then 1 else 0 end) as ‘购买次数‘ from exc1 group by time order by time
mysql数据导出为csv表：
select time, count(behavior_type), sum(case when behavior_type = ‘pv‘ then 1 else 0 end) as ‘点击次数‘, sum(case when behavior_type = ‘fav‘ then 1 else 0 end) as ‘收藏次数‘, sum(case when behavior_type = ‘cart‘ then 1 else 0 end) as ‘加购数‘, sum(case when behavior_type = ‘buy‘ then 1 else 0 end) as ‘购买次数‘ from exc1 group by time order by time into outfile ‘E:/Pro/users.csv‘ fields terminated by ‘,‘enclosed by ‘"‘lines terminated by ‘\r\n‘;
一周中用户活跃分布
select date_format(date,‘%W‘) as weeks, count(behavior_type), sum(case when behavior_type = ‘pv‘ then 1 else 0 end) as ‘点击次数‘, sum(case when behavior_type = ‘fav‘ then 1 else 0 end) as ‘收藏次数‘, sum(case when behavior_type = ‘cart‘ then 1 else 0 end) as ‘加购数‘, sum(case when behavior_type = ‘buy‘ then 1 else 0 end) as ‘购买次数‘ from exc1 group by weeks order by weeks
5) 基于RFM模型找出有价值的客户
R-Recency: 最近一次购买时间
F-Frequency: 消费频率
M-Money：消费金额
数据集没有消费金额，对最近一次购买时间和消费频率进行评分
select a.* , (@r:=@r+1) as recent_rank from ( select user_id, datediff(‘2014-12-19‘, max(date)) as recent from exc1
where behavior_type = ‘buy‘ group by user_id order by recent )a,(select @r:=0)b ;
select a.* , (@r2:=@r2+1) as freq_rank from ( select user_id, count(behavior_type) as frequency from exc1 where behavior_type = ‘buy‘ group by user_id order by frequency desc )a,(select @r2:=0)b
select m.user_id , n.frequency , recent_rank, freq_rank, concat( case when recent_rank <= (4330)/4 then ‘4‘ when recent_rank > (4330)/4 and recent_rank <= (4330)/2 then ‘3‘ when recent_rank > (4330)/2 and recent_rank <= (4330)/4*3 then ‘2‘ else ‘1‘ end , case when freq_rank <= (4330)/4 then ‘4‘ when freq_rank > (4330)/4 and freq_rank <= (4330)/2 then ‘3‘ when freq_rank > (4330)/2 and freq_rank <= (4330)/4*3 then ‘2‘ else ‘1‘ end ) as user_value from ( select a.*,(@r1:=@r1+1) as recent_rank from ( select user_id, datediff(‘2014-12-19‘,max(date)) as recent from exc1 where behavior_type = ‘buy‘ group by user_id order by recent ) a, (select @r1:=0 )as b) m , ( select a.* , (@r2:=@r2+1) as freq_rank from ( select user_id, count(behavior_type) as frequency from exc1 where behavior_type = ‘buy‘ group by user_id order by frequency desc )a ,(select @r2:=0) as b) as n where m.user_id = n.user_id;
通过得分可以看出user_value 为‘41’的用户为关注频次比较高，购买能力不足的用户，可以选择适当促销打折或其他捆绑销售来促进客户下单
而user_value 为‘14’的用户这类关注度忠诚度不高，购买力强的客户需要关注其购物习性做精准化营销。
标签：taf 自制 lin render show date类 价值 方法 sid
原文地址：https://www.cnblogs.com/hqczsh/p/11878859.html
