提供: Bright Pattern Documentation
(Created page with "5.3:レポートデータベース仕様/統計データに関する一般情報") |
(Created page with "= 統計データに関する一般情報= このセクションで説明される表には、15分間の統計間隔ごとに集計された、エージェントやサービ...") |
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| − | + | このセクションで説明される表には、15分間の統計間隔ごとに集計された、エージェントやサービスなどのメインコンタクトセンターリソースのメトリックが含まれています。 これらの表のデータは、[[reporting-reference-guide/Purpose|''Bright Patternコンタクトセンターレポートリファレンスガイド'']]で説明されているすぐに使用可能なレポートの生成に使用できます。 これらのレポートで使用される実際のクエリは、対応する .''jrxml''テンプレートをダウンロードすることによって確認できます。 | |
The data source for these metrics is the raw event data that is initially written to the Collector Database (DB1) in real-time by various Bright Pattern Contact Center components. This raw data is then periodically extracted by the Aggregator component, transformed into the specified metrics for the base 15-minute statistical intervals, and loaded into the tables of the Reporting Database (DB2). A SQL-compliant reporting application can be used for aggregating these basic metrics into desired higher-level reporting intervals (i.e., hour, day, week, month, etc.). | The data source for these metrics is the raw event data that is initially written to the Collector Database (DB1) in real-time by various Bright Pattern Contact Center components. This raw data is then periodically extracted by the Aggregator component, transformed into the specified metrics for the base 15-minute statistical intervals, and loaded into the tables of the Reporting Database (DB2). A SQL-compliant reporting application can be used for aggregating these basic metrics into desired higher-level reporting intervals (i.e., hour, day, week, month, etc.). | ||
Revision as of 14:25, 14 September 2019
• English
統計データに関する一般情報
このセクションで説明される表には、15分間の統計間隔ごとに集計された、エージェントやサービスなどのメインコンタクトセンターリソースのメトリックが含まれています。 これらの表のデータは、Bright Patternコンタクトセンターレポートリファレンスガイドで説明されているすぐに使用可能なレポートの生成に使用できます。 これらのレポートで使用される実際のクエリは、対応する .jrxmlテンプレートをダウンロードすることによって確認できます。
The data source for these metrics is the raw event data that is initially written to the Collector Database (DB1) in real-time by various Bright Pattern Contact Center components. This raw data is then periodically extracted by the Aggregator component, transformed into the specified metrics for the base 15-minute statistical intervals, and loaded into the tables of the Reporting Database (DB2). A SQL-compliant reporting application can be used for aggregating these basic metrics into desired higher-level reporting intervals (i.e., hour, day, week, month, etc.).
The following considerations apply to all statistical data tables:
- Unless noted otherwise with respect to a particular metric, all call-related metrics count inbound calls for the aggregation interval in which they entered the system (e.g., if a call entered the system in interval A and was answered in interval B, metric num_calls_answered will count it for interval A and not for interval B.) Likewise, all internal and outbound calls are counted for the aggregation interval in which they were initiated.
- Metrics are provided for all supported media types. The media type can be indicated either explicitly via the media_type field and/or indirectly via the service_name field.
- All call-related metrics are also supported for the chat media type. Thus, if either the media_type field the service_name field indicates media type chat, the term call in the description of any metric shall be interpreted for the given row of the given table as a service chat interaction in the same context. Note that internal chats between agents/supervisors are not taken into consideration by any metrics.
- Some call-related metrics are also supported for the email media type. For every such metric, a note is provided about how to interpret it for emails. If email is not explicitly mentioned, the metric should be considered applicable to voice and chat only.
- It is possible to have more than one row of data for the same 15-minute interval related to the same resource. This happens when there are interactions that span multiple aggregation intervals. The system learns about such interactions later, but still attributes them to the interval when they started, arranging them in a separate row. Practically, this means that when you do queries on the statistical data, you should add up all values from all rows that are returned.
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