Firebase BigQuery导出广告系列效果

如何解决Firebase BigQuery导出广告系列效果

我很难复制在Google Analytics（分析）控制台中访问时找到的指标。尤其是按广告系列详细信息划分时的用户和会话指标（摘自“ campaign_details”事件，并根据过去90天内的最终互动进行归因）。如果我查询数据时不考虑campaign_details，则我的值与控制台中的值相同。我想知道是否有人以前曾使用过此工具并设法像在控制台中那样获取了数据，或者是否有可能实现奇偶校验？

    with initial_prep as (
      SELECT
        (select max(value.string_value) from unnest(user_properties) where key='store') store,device.operating_system as operating_system,event_date,user_pseudo_id,event_timestamp,TIMESTAMP_MICROS(event_timestamp) AS ts,LAG(TIMESTAMP_MICROS(event_timestamp)) OVER (PARTITION BY user_pseudo_id ORDER BY event_timestamp) AS prev_evt_ts,IF(event_name = "session_start",1,0) AS is_session_start_event,IF(event_name = "first_open",0) AS is_first_visit_event,IF(event_name = "screen_view",0) AS is_screen_view,IF(event_name = "purchase",0) as is_purchase,ecommerce.purchase_revenue as value,ecommerce.shipping_value as shipping,ecommerce.total_item_quantity as quantity,FROM
  `[PROJECT DETAILS REDACTED].events_20*`
  WHERE
  parse_date('%y%m%d',_table_suffix) between DATE_sub(current_date(),interval 1 day) and  DATE_sub(current_date(),interval 1 day)   
  and 
device.operating_system = 'IOS'
 ),user_sources as (
select
        user_pseudo_id,(select max(value.string_value) from unnest(event_params) where key='source' and event_name in( 'campaign_details')) source,(select max(value.string_value) from unnest(event_params) where key='campaign' and event_name in( 'campaign_details')) campaign
from 
`[PROJECT DETAILS REDACTED].events_20*`
WHERE
   parse_date('%y%m%d',interval 90 day) and  DATE_sub(current_date(),interval 1 day)   
and 
event_name in ('campaign_details')
),session_id_created as (
    SELECT
    *,SUM(is_session_start_event) OVER (PARTITION BY user_pseudo_id ORDER BY ts) AS session_id
  FROM initial_prep
  ),session_details as (
  SELECT
  si.user_pseudo_id,store,operating_system,session_id,MAX(is_session_start_event) OVER (PARTITION BY si.user_pseudo_id,session_id) AS has_session_start_event,is_session_start_event,MAX(is_first_visit_event) OVER (PARTITION BY si.user_pseudo_id,session_id) AS has_first_visit_event,is_first_visit_event,is_screen_view,MAX(event_timestamp) OVER (PARTITION BY si.user_pseudo_id,session_id) AS max_timestamp,MIN(event_timestamp) OVER (PARTITION BY si.user_pseudo_id,session_id) AS min_timestamp,is_purchase,value,shipping,quantity,us.source,us.campaign,row_number() over (partition by si.user_pseudo_id,event_timestamp order by us.ts desc) rank,us.ts time_campaign
  from session_id_created si
  left join user_sources us on us.user_pseudo_id = si.user_pseudo_id and si.ts  >= us.ts  -->= timestamp_sub(us.ts,interval 3600000 MICROSECOND)
 ),session_fin as (
  select user_pseudo_id,source,campaign,has_session_start_event,has_first_visit_event,max_timestamp,min_timestamp,sum(is_session_start_event) sessions_alt,sum(is_screen_view) screen_views,sum(value) revenue,sum(is_purchase) transactions,sum(shipping) shipping,sum(quantity) item_quantity
  from session_details
  where rank =1
  group by 
  user_pseudo_id,min_timestamp
  )
 
  select store,event_date applicabledate,sum(sessions_alt) sessions,sum(transactions) transactions,sum(revenue) local_revenue,sum(shipping) local_shipping,sum(item_quantity) item_quantity,avg(max_timestamp/100000 - min_timestamp/100000)  avgsessionduration,count(distinct user_pseudo_id) users,count(distinct case when has_first_visit_event = 1 then user_pseudo_id end) new_users,sum(screen_views) screenviews
  from session_fin
  group by store,campaign
  order by users desc
/**/