受众分析
最近更新： 2025年5月5日
- 主题：
- 功能板
创建对象：
- 开发人员
- 用户
通过分析数据模型而获得的见解，可使您的Adobe Real-Time CDP数据更易于访问、理解并影响决策。
通过访问支持受众分析的SQL来了解这些分析，然后生成您自己的分析，以进一步探索构成受众的身份和配置文件。 通过使用现有的Real-Time CDP数据模型SQL作为灵感，根据独特的业务需求创建查询，将原始数据转换为新的可操作洞察。
有关如何直接通过PLatform UI调整分析的SQL的详细信息，请参阅查看SQL文档。
以下分析均可用作受众仪表板或自定义用户定义的仪表板的一部分。 有关如何自定义仪表板或🔗在构件库和用户定义的仪表板中创建和编辑新构件的说明，请参阅自定义概述。
以下分析均可用作受众仪表板或自定义仪表板的一部分。
受众重叠报告
通过此洞察回答的问题：
- 特定过滤受众的前50个重叠受众是什么？
- 特定过滤受众中，50个最不重叠的受众是什么？
- 对于不同的过滤受众，重叠模式会如何变化？
选择以显示生成此分析的SQL
SELECT source_segment_name,
source_segment_id,
overlap_segment_name,
overlap_segment_id,
max(source_segment_audience_count) source_segment_audience_count,
max(overlap_segment_audience_count) overlap_segment_audience_count,
max(overlap_audience_count) overlap_audience_count,
CASE
WHEN (max(source_segment_audience_count) + max(overlap_segment_audience_count) - max(overlap_audience_count)) > 0 THEN (cast(max(overlap_audience_count) AS DECIMAL(18, 2)) / cast((max(source_segment_audience_count) + max(overlap_segment_audience_count) - max(overlap_audience_count)) AS DECIMAL(18, 2))) * 100::DECIMAL(9, 2)
ELSE 100.00
END overlapping_percentage
FROM
(SELECT adwh_fact_profile_overlap_of_segments.Segment1 source_segment_id,
adwh_fact_profile_overlap_of_segments.Segment2 overlap_segment_id,
Sum(count_of_overlap) overlap_audience_count
FROM qsaccel.profile_agg.adwh_fact_profile_overlap_of_segments
WHERE qsaccel.profile_agg.adwh_fact_profile_overlap_of_segments.merge_policy_id = 2027892989
AND qsaccel.profile_agg.adwh_fact_profile_overlap_of_segments.date_key = '2024-01-10'
GROUP BY qsaccel.profile_agg.adwh_fact_profile_overlap_of_segments.Segment2 ,
qsaccel.profile_agg.adwh_fact_profile_overlap_of_segments.Segment1) a
INNER JOIN
(SELECT sum(count_of_profiles) source_segment_audience_count,
adwh_dim_segments.segment_name source_segment_name,
adwh_fact_profile_by_segment_trendlines.merge_policy_id,
adwh_fact_profile_by_segment_trendlines.segment_Id segment1
FROM qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines
JOIN qsaccel.profile_agg.adwh_dim_segments ON qsaccel.profile_agg.adwh_dim_segments.segment_id = qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines.segment_Id
WHERE qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines.merge_policy_id = 2027892989
AND qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines.date_key = '2024-01-10'
GROUP BY qsaccel.profile_agg.adwh_dim_segments.segment_name,
qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines.merge_policy_id,
qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines.segment_id) b ON a.source_segment_id = b.segment1
INNER JOIN
(SELECT sum(count_of_profiles) overlap_segment_audience_count,
adwh_dim_segments.segment_name overlap_segment_name,
adwh_fact_profile_by_segment_trendlines.merge_policy_id,
adwh_fact_profile_by_segment_trendlines.segment_Id segment2
FROM qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines
JOIN qsaccel.profile_agg.adwh_dim_segments ON adwh_dim_segments.segment_id = adwh_fact_profile_by_segment_trendlines.segment_Id
WHERE qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines.merge_policy_id = 2027892989
AND qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines.date_key = '2024-01-10'
GROUP BY qsaccel.profile_agg.adwh_dim_segments.segment_name,
qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines.merge_policy_id,
qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines.segment_id) c ON a.overlap_segment_id = c.segment2
GROUP BY source_segment_name,
source_segment_id,
overlap_segment_name,
overlap_segment_id
ORDER BY overlapping_percentage DESC
LIMIT 5;
有关此分析的外观和功能的信息，请参阅受众重叠报表构件文档。
受众重叠
通过此洞察回答的问题：
- 哪些配置文件对两个受众通用？
- 重叠对参与率或转化率有何影响？
- 如何为重叠的区段定制营销策略？
选择以显示生成此分析的SQL
SELECT Sum(overlap_col1) overlap_col1,
Sum(overlap_col2) overlap_col2,
Sum(overlap_count) Overlap_count
FROM
(SELECT 0 overlap_col1,
0 overlap_col2,
sum(count_of_overlap)Overlap_count
FROM qsaccel.profile_agg.adwh_fact_profile_overlap_of_segments
WHERE qsaccel.profile_agg.adwh_fact_profile_overlap_of_segments.merge_policy_id = 1133248113
AND qsaccel.profile_agg.adwh_fact_profile_overlap_of_segments.date_key = '2024-01-10'
AND ((qsaccel.profile_agg.adwh_fact_profile_overlap_of_segments.segment1=1870062812
AND qsaccel.profile_agg.adwh_fact_profile_overlap_of_segments.segment2=2080256533)
OR (qsaccel.profile_agg.adwh_fact_profile_overlap_of_segments.segment1=2080256533
AND qsaccel.profile_agg.adwh_fact_profile_overlap_of_segments.segment2=1870062812))
UNION ALL SELECT sum(count_of_profiles) overlap_col1,
0 overlap_col2,
0 overlap_count
FROM qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines
LEFT JOIN qsaccel.profile_agg.adwh_dim_segments ON qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines.segment_Id = qsaccel.profile_agg.adwh_dim_segments.segment_Id
WHERE qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines.merge_policy_id = 1133248113
AND qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines.date_key = '2024-01-10'
AND qsaccel.profile_agg.adwh_dim_segments.segment_Id = 1870062812
UNION ALL SELECT 0 overlap_col1,
sum(count_of_profiles) overlap_col2,
0 Overlap_count
FROM qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines
JOIN qsaccel.profile_agg.adwh_dim_segments ON qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines.segment_Id = qsaccel.profile_agg.adwh_dim_segments.segment_Id
WHERE qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines.merge_policy_id = 1133248113
AND qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines.date_key = '2024-01-10'
AND qsaccel.profile_agg.adwh_dim_segments.segment_Id = 2080256533 ) a;
有关此分析的外观和功能的信息，请参阅受众重叠构件文档。
受众规模变化趋势
通过此洞察回答的问题：
- 在过去30天、90天或12个月内，受众规模是否出现任何显着激增或骤减？
- 在特定日期内，受众规模会如何变化？
- 在过去12个月中是否检测到任何异常或重复出现的尖峰或下降模式？
选择以显示生成此分析的SQL
SELECT date_key,
Profiles_added
FROM
(SELECT rn_num,
date_key,
(count_of_profiles-lag(count_of_profiles, 1, 0) over(
ORDER BY date_key))Profiles_added
FROM
(SELECT date_key,
sum(x.count_of_profiles)count_of_profiles,
row_number() OVER (
ORDER BY date_key) rn_num
FROM qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines x
INNER JOIN
(SELECT MAX(process_date) last_process_date,
merge_policy_id
FROM qsaccel.profile_agg.adwh_lkup_process_delta_log
WHERE process_name = 'FACT_TABLES_PROCESSING'
AND process_status = 'SUCCESSFUL'
GROUP BY merge_policy_id) y ON x.merge_policy_id = y.merge_policy_id
WHERE segment_id = 1333234510
AND x.date_key >= dateadd(DAY, -30 -1, y.last_process_date)
GROUP BY x.date_key) a)b
WHERE rn_num > 1;
- 我的受众是否一直在增长、稳定或经历波动？
- 是否有任何特定身份会随着时间推移导致受众增长激增或下降？
- 随着时间的推移，我的身份增长是否存在任何异常？
选择以显示生成此分析的SQL
SELECT sum(count_of_profiles) AS identities,
date_key
FROM qsaccel.profile_agg.adwh_fact_profile_by_segment_and_namespace_trendlines x
INNER JOIN
(SELECT MAX(process_date) last_process_date,
merge_policy_id
FROM qsaccel.profile_agg.adwh_lkup_process_delta_log
WHERE process_name = 'FACT_TABLES_PROCESSING'
AND process_status = 'SUCCESSFUL'
GROUP BY merge_policy_id) y ON x.merge_policy_id = y.merge_policy_id
INNER JOIN qsaccel.profile_agg.adwh_dim_namespaces z ON x.namespace_id = z.namespace_id
AND x.merge_policy_id = z.merge_policy_id
WHERE x.date_key >= dateadd(DAY, -30, y.last_process_date)
AND x.segment_id = 1333234510
AND z.namespace_description = 'crmid'
GROUP BY date_key;
有关此分析的外观和功能的信息，请参阅按身份构件分类的受众规模趋势。
受众规模趋势
通过此洞察回答的问题：
- 受众规模随时间的变化如何，包括任何异常？
- 如何才能找到以下时段内受众规模的整体趋势：30天、90天和12个月？
- 受众的主要特征哪些会影响其规模？ 例如，由于电子邮件营销活动而出现峰值。
选择以显示生成此分析的SQL
SELECT date_key,
sum(count_of_profiles) AS audience_size
FROM qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines x
INNER JOIN
(SELECT MAX(process_date) last_process_date,
merge_policy_id
FROM qsaccel.profile_agg.adwh_lkup_process_delta_log
WHERE process_name = 'FACT_TABLES_PROCESSING'
AND process_status = 'SUCCESSFUL'
GROUP BY merge_policy_id) y ON x.merge_policy_id = y.merge_policy_id
WHERE date_key >= dateadd(DAY, -30, y.last_process_date)
AND x.segment_id = 1333234510
GROUP BY date_key,
segment_id;
- 当前的总受众规模是多少？
- 与前期或特定受众相比，当前受众规模如何？
- 最近的营销活动对受众规模有何影响？
选择以显示生成此分析的SQL
SELECT
sum(
qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines.count_of_profiles
) count_of_profiles
FROM
qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines
LEFT OUTER JOIN qsaccel.profile_agg.adwh_dim_segments ON qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines.segment_id = qsaccel.profile_agg.adwh_dim_segments.segment_id
WHERE
qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines.segment_id = -1323307941
AND qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines.merge_policy_id = 1914917902
AND qsaccel.profile_agg.adwh_fact_profile_by_segment_trendlines.date_key = '2024-01-12';
有关此分析的外观和功能的信息，请参阅受众大小小组件文档。
客户人工智能得分分布
通过此洞察回答的问题：
- 我的客户人工智能模型的每个存储桶的得分分布情况如何（按选定受众进行过滤）？
- 对于特定受众，高、中和低的得分分布情况如何？
- 按各种感兴趣的受众划分的得分分布情况如何？
选择以显示生成此分析的SQL
SELECT b.model_name,
b.model_type,
c.segment_name,
c.segment_id,
CASE
WHEN score >= 0
AND score < 25 THEN 'LOW'
WHEN score >= 25
AND score < 75 THEN 'MEDIUM'
WHEN score >= 75
AND score <= 100 THEN 'HIGH'
END bucket_name,
CASE
WHEN score >= 0
AND score < 5 THEN '02.50'
WHEN score >= 5
AND score < 10 THEN '07.50'
WHEN score >= 10
AND score < 15 THEN '12.50'
WHEN score >= 15
AND score < 20 THEN '17.50'
WHEN score >= 20
AND score < 25 THEN '22.50'
WHEN score >= 25
AND score < 30 THEN '27.50'
WHEN score >= 30
AND score < 35 THEN '32.50'
WHEN score >= 35
AND score < 40 THEN '37.50'
WHEN score >= 40
AND score < 45 THEN '42.50'
WHEN score >= 45
AND score < 50 THEN '47.50'
WHEN score >= 50
AND score < 55 THEN '52.50'
WHEN score >= 55
AND score < 60 THEN '57.50'
WHEN score >= 60
AND score < 65 THEN '62.50'
WHEN score >= 65
AND score < 70 THEN '67.50'
WHEN score >= 70
AND score < 75 THEN '72.50'
WHEN score >= 75
AND score < 80 THEN '77.50'
WHEN score >= 80
AND score < 85 THEN '82.50'
WHEN score >= 85
AND score < 90 THEN '87.50'
WHEN score >= 90
AND score < 95 THEN '92.50'
WHEN score >= 95
AND score <= 100 THEN '97.50'
END score_bins,
Sum(CASE
WHEN score >= 0
AND score < 25 THEN count_of_profiles
WHEN score >= 25
AND score < 75 THEN count_of_profiles
WHEN score >= 75
AND score <= 100 THEN count_of_profiles
END) count_of_profiles
FROM qsaccel.profile_agg.adwh_fact_profile_by_segment_ai_models a
JOIN qsaccel.profile_agg.adwh_dim_ai_models b ON a.merge_policy_id = b.merge_policy_id
AND a.model_id = b.model_id
JOIN qsaccel.profile_agg.adwh_dim_segments c ON a.segment_id = c.segment_id
WHERE a.merge_policy_id = 1133248113
AND a.model_id = 1829081696
AND a.segment_id = 1870062812
AND score_date =
(SELECT MAX(score_date)
FROM qsaccel.profile_agg.adwh_fact_profile_by_segment_ai_models d
WHERE d.model_id = a.model_id) GROUP BY b.model_name,
b.model_type,
c.segment_name,
c.segment_id,
CASE
WHEN score >= 0
AND score < 25 THEN 'LOW'
WHEN score >= 25
AND score < 75 THEN 'MEDIUM'
WHEN score >= 75
AND score <= 100 THEN 'HIGH'
END,
CASE
WHEN score >= 0
AND score < 5 THEN '02.50'
WHEN score >= 5
AND score < 10 THEN '07.50'
WHEN score >= 10
AND score < 15 THEN '12.50'
WHEN score >= 15
AND score < 20 THEN '17.50'
WHEN score >= 20
AND score < 25 THEN '22.50'
WHEN score >= 25
AND score < 30 THEN '27.50'
WHEN score >= 30
AND score < 35 THEN '32.50'
WHEN score >= 35
AND score < 40 THEN '37.50'
WHEN score >= 40
AND score < 45 THEN '42.50'
WHEN score >= 45
AND score < 50 THEN '47.50'
WHEN score >= 50
AND score < 55 THEN '52.50'
WHEN score >= 55
AND score < 60 THEN '57.50'
WHEN score >= 60
AND score < 65 THEN '62.50'
WHEN score >= 65
AND score < 70 THEN '67.50'
WHEN score >= 70
AND score < 75 THEN '72.50'
WHEN score >= 75
AND score < 80 THEN '77.50'
WHEN score >= 80
AND score < 85 THEN '82.50'
WHEN score >= 85
AND score < 90 THEN '87.50'
WHEN score >= 90
AND score < 95 THEN '92.50'
WHEN score >= 95
AND score <= 100 THEN '97.50'
END;
有关此洞察的外观和功能的信息，请参阅得分构件的Customer AI分发文档。
客户人工智能评分汇总
通过此洞察回答的问题：
- 对于特定受众，我的每个客户人工智能模型的得分摘要是什么？
- 我的客户人工智能倾向分数如何针对不同受众发生变化？
- 与受众概述中的其他KPI相比，我的得分摘要如何？
选择以显示生成此分析的SQL
SELECT model_name,
model_type,
segment_name,
CASE
WHEN score BETWEEN 0 AND 24 THEN 'LOW'
WHEN score BETWEEN 25 AND 74 THEN 'MEDIUM'
WHEN score BETWEEN 75 AND 100 THEN 'HIGH'
END score_buckets,
sum(count_of_profiles) count_of_profiles
FROM QSAccel.profile_agg.adwh_fact_profile_by_segment_ai_models a
JOIN QSAccel.profile_agg.adwh_dim_ai_models b ON a.merge_policy_id=b.merge_policy_id
AND a.model_id=b.model_id
JOIN QSAccel.profile_agg.adwh_dim_segments c ON a.segment_id=c.segment_id
WHERE a.merge_policy_id=1133248113
AND a.model_id =1829081696
AND a.segment_id=1870062812
AND score_date=
(SELECT max(score_date)
FROM QSAccel.profile_agg.adwh_fact_profile_by_segment_ai_models d
WHERE d.model_id=a.model_id)
GROUP BY model_name,
model_type,
segment_name,
CASE
WHEN score BETWEEN 0 AND 24 THEN 'LOW'
WHEN score BETWEEN 25 AND 74 THEN 'MEDIUM'
WHEN score BETWEEN 75 AND 100 THEN 'HIGH'
END;
- 对于过滤的受众，标识类型A和标识类型B之间的公共交叉点是什么？
- 如何根据特定身份类型的重叠情况优化客户受众，以增强有针对性的营销策略？
- 评估交叉区域内的营销活动绩效可以获得哪些见解？
- 根据这些见解，如何优化未来的营销工作？
选择以显示生成此分析的SQL
SELECT Sum(overlap_col1) overlap_col1,
Sum(overlap_col2) overlap_col2,
Sum(overlap_count) Overlap_count
FROM
(SELECT 0 overlap_col1,
0 overlap_col2,
Sum(count_of_profiles) Overlap_count
FROM qsaccel.profile_agg.adwh_fact_profile_overlap_of_namespace_by_segment
WHERE qsaccel.profile_agg.adwh_fact_profile_overlap_of_namespace_by_segment.segment_id = 1333234510
AND qsaccel.profile_agg.adwh_fact_profile_overlap_of_namespace_by_segment.merge_policy_id = 1709997014
AND qsaccel.profile_agg.adwh_fact_profile_overlap_of_namespace_by_segment.date_key = '2024-01-10'
AND qsaccel.profile_agg.adwh_fact_profile_overlap_of_namespace_by_segment.overlap_id IN
(SELECT a.overlap_id
FROM
(SELECT qsaccel.profile_agg.adwh_dim_overlap_namespaces.overlap_id overlap_id,
count(*) cnt_num
FROM qsaccel.profile_agg.adwh_dim_overlap_namespaces
WHERE qsaccel.profile_agg.adwh_dim_overlap_namespaces.merge_policy_id = 1709997014
AND qsaccel.profile_agg.adwh_dim_overlap_namespaces.overlap_namespaces in ('crmid',
'email')
GROUP BY qsaccel.profile_agg.adwh_dim_overlap_namespaces.overlap_id)a
WHERE a.cnt_num>1 )
UNION ALL SELECT count_of_profiles overlap_col1,
0 overlap_col2,
0 Overlap_count
FROM qsaccel.profile_agg.adwh_fact_profile_by_segment_and_namespace_trendlines
LEFT OUTER JOIN qsaccel.profile_agg.adwh_dim_namespaces ON qsaccel.profile_agg.adwh_fact_profile_by_segment_and_namespace_trendlines.namespace_id = qsaccel.profile_agg.adwh_dim_namespaces.namespace_id
AND qsaccel.profile_agg.adwh_fact_profile_by_segment_and_namespace_trendlines.merge_policy_id = qsaccel.profile_agg.adwh_dim_namespaces.merge_policy_id
WHERE qsaccel.profile_agg.adwh_dim_namespaces.namespace_description = 'crmid'
AND qsaccel.profile_agg.adwh_fact_profile_by_segment_and_namespace_trendlines.segment_id = 1333234510
AND qsaccel.profile_agg.adwh_fact_profile_by_segment_and_namespace_trendlines.merge_policy_id = 1709997014
AND qsaccel.profile_agg.adwh_fact_profile_by_segment_and_namespace_trendlines.date_key = '2024-01-10'
UNION ALL SELECT 0 overlap_col1,
count_of_profiles overlap_col2,
0 Overlap_count
FROM qsaccel.profile_agg.adwh_fact_profile_by_segment_and_namespace_trendlines
LEFT OUTER JOIN qsaccel.profile_agg.adwh_dim_namespaces ON qsaccel.profile_agg.adwh_fact_profile_by_segment_and_namespace_trendlines.namespace_id = qsaccel.profile_agg.adwh_dim_namespaces.namespace_id
AND qsaccel.profile_agg.adwh_fact_profile_by_segment_and_namespace_trendlines.merge_policy_id = qsaccel.profile_agg.adwh_dim_namespaces.merge_policy_id
WHERE qsaccel.profile_agg.adwh_dim_namespaces.namespace_description = 'email'
AND qsaccel.profile_agg.adwh_fact_profile_by_segment_and_namespace_trendlines.segment_id = 1333234510
AND qsaccel.profile_agg.adwh_fact_profile_by_segment_and_namespace_trendlines.merge_policy_id = 1709997014
AND qsaccel.profile_agg.adwh_fact_profile_by_segment_and_namespace_trendlines.date_key = '2024-01-10' ) a;
有关此分析的外观和功能的信息，请参阅身份重叠构件文档。
按标识列出的轮廓
通过此洞察回答的问题：
- 在选定受众的配置文件总数中，哪种身份类型所占比例最高？
- 对于特定受众，不同身份类型之间是否存在显着差异？
- 按受众划分的身份类型总体分布情况如何？
- 不同受众的身份计数是否存在任何显着差异或异常？
选择以显示生成此分析的SQL
SELECT qsaccel.profile_agg.adwh_dim_namespaces.namespace_description,
sum(qsaccel.profile_agg.adwh_fact_profile_by_segment_and_namespace_trendlines.count_of_profiles) count_of_profiles
FROM qsaccel.profile_agg.adwh_fact_profile_by_segment_and_namespace_trendlines
LEFT OUTER JOIN qsaccel.profile_agg.adwh_dim_namespaces ON qsaccel.profile_agg.adwh_fact_profile_by_segment_and_namespace_trendlines.namespace_id = qsaccel.profile_agg.adwh_dim_namespaces.namespace_id
AND qsaccel.profile_agg.adwh_fact_profile_by_segment_and_namespace_trendlines.merge_policy_id = qsaccel.profile_agg.adwh_dim_namespaces.merge_policy_id
WHERE qsaccel.profile_agg.adwh_fact_profile_by_segment_and_namespace_trendlines.segment_id = 1333234510
AND qsaccel.profile_agg.adwh_fact_profile_by_segment_and_namespace_trendlines.merge_policy_id = 1709997014
AND qsaccel.profile_agg.adwh_fact_profile_by_segment_and_namespace_trendlines.date_key = '2024-01-10'
GROUP BY qsaccel.profile_agg.adwh_dim_namespaces.namespace_description
ORDER BY count_of_profiles DESC;
- 特定平台上特定受众表现最佳的激活的开始日期和结束日期是什么？
- 哪些平台最常用于特定受众的计划激活？
- 平台使用中是否有任何模式可指导做出针对特定受众的激活策略优先级或多元化决策？
选择以显示生成此分析的SQL
SELECT p.destination_platform ,
p.destination_platform_name AS platform ,
d.destination_name ,
d.destination ,
br.start_date ,
CASE
WHEN br.end_date = '9999-12-31' THEN 'Ongoing'
ELSE br.end_date
END AS end_date
FROM qsaccel.profile_agg.adwh_dim_br_segment_destinations br
JOIN qsaccel.profile_agg.adwh_dim_destination d ON br.destination_id = d.destination_id
JOIN qsaccel.profile_agg.adwh_dim_destination_platform p ON d.destination_platform_id = p.destination_platform_id
JOIN
(SELECT MAX(process_date) AS last_process_date
FROM qsaccel.profile_agg.adwh_lkup_process_delta_log
WHERE process_name = 'FACT_TABLES_PROCESSING'
AND process_status = 'SUCCESSFUL' ) lpd ON lpd.last_process_date BETWEEN br.start_date AND br.end_date
AND br.segment_id = 1333234510;
有关此分析的外观和功能的信息，请参阅计划激活构件文档。
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