고객 응대 및 대기열 지표 에이전트 성능 지표 채팅 지표 대화 분석 지표 AI 에이전트 지표 플로우 지표 평가 지표 아웃바운드 캠페인 지표 사례 지표 봇 지표 일반적인 쿼리 패턴 에이전트 일정 준수(활동 수준)모범 사례

Connect Customer 데이터 레이크에 대한 참조 쿼리

이 주제에서는 데이터 레이크 테이블에서 일반적인 Connect Customer 지표를 계산하기 위한 Athena SQL 쿼리(Trino 엔진 v3)를 제공합니다. 모든 쿼리는 큰따옴표를 사용하고 connect_datalake 데이터베이스 이름을 가정합니다. Glue 카탈로그 구성에 맞게 데이터베이스 이름을 조정합니다.

각 쿼리<YOUR_INSTANCE_ID>에서를 Connect Customer 인스턴스 ID로 바꿉니다.

고객 응대 및 대기열 지표

중단 발생률

정의: 대기열에 있는 동안 고객이 연결 해제한 고객 응대의 비율입니다. 콜백은 제외됩니다.

소스 테이블: contact_statistic_record


SELECT
    "queue_id",
    CAST(SUM("is_abandoned") AS DOUBLE) 
        / NULLIF(SUM("is_queued"), 0) * 100.0 AS "abandonment_rate_pct"
FROM "connect_datalake"."contact_statistic_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "queue_id"
ORDER BY "abandonment_rate_pct" DESC;

중단된 연락처

정의: 대기열에서 대기하는 동안 고객이 연결 해제한 고객 응대 수입니다.

소스 테이블: contact_statistic_record


SELECT
    "queue_id",
    SUM("is_abandoned") AS "contacts_abandoned"
FROM "connect_datalake"."contact_statistic_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "queue_id";

X초 이내에 중단된 고객 응대

정의: 대기열에 추가되고 X초 이내에 중단된 고객 응대 수입니다.

소스 테이블: contact_statistic_record


SELECT
    "queue_id",
    SUM(
        CASE WHEN "is_abandoned" = 1 
             AND "queue_time_ms" <= 30000 
             THEN 1 ELSE 0 END
    ) AS "contacts_abandoned_in_30s"
FROM "connect_datalake"."contact_statistic_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "queue_id";

평균 대기열 중단 시간

정의: 고객 응대가 중단되기 전에 대기열에서 대기한 평균 시간입니다.

소스 테이블: contact_statistic_record


SELECT
    "queue_id",
    AVG("abandon_time_ms") / 1000.0 AS "avg_queue_abandon_time_sec"
FROM "connect_datalake"."contact_statistic_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "is_abandoned" = 1
  AND "abandon_time_ms" IS NOT NULL
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "queue_id";

평균 대기열 응답 시간

정의: 에이전트가 응답하기 전에 고객 응대가 대기열에서 대기한 평균 시간입니다.

소스 테이블: contact_statistic_record


SELECT
    "queue_id",
    AVG("queue_answer_time_ms") / 1000.0 AS "avg_queue_answer_time_sec"
FROM "connect_datalake"."contact_statistic_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "is_handled" = 1
  AND "queue_answer_time_ms" IS NOT NULL
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "queue_id";

서비스 수준

정의: X초 이내에 응답한 고객 응대 수 및 백분율입니다.

소스 테이블: contact_statistic_record


SELECT
    "queue_id",
    SUM(CASE WHEN "is_handled" = 1 AND "queue_answer_time_ms" <= 20000 
             THEN 1 ELSE 0 END) AS "contacts_answered_in_20s",
    SUM("is_queued") AS "contacts_queued",
    CAST(SUM(CASE WHEN "is_handled" = 1 AND "queue_answer_time_ms" <= 20000 
                  THEN 1 ELSE 0 END) AS DOUBLE)
        / NULLIF(SUM("is_queued"), 0) * 100.0 AS "service_level_20s_pct"
FROM "connect_datalake"."contact_statistic_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "queue_id";

대기 중인 연락처

정의: 대기열에 배치된 고객 응대 수입니다.

소스 테이블: contact_statistic_record


SELECT
    "queue_id",
    SUM("is_queued") AS "contacts_queued"
FROM "connect_datalake"."contact_statistic_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "queue_id";

처리된 연락처

정의: 에이전트에 연결된 고객 응대 수입니다.

소스 테이블: contact_statistic_record


SELECT
    "queue_id",
    SUM("is_handled") AS "contacts_handled"
FROM "connect_datalake"."contact_statistic_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "queue_id";

전송된 연락처

정의: 대기열로 전송된 고객 응대입니다.

소스 테이블: contact_statistic_record


SELECT
    "queue_id",
    SUM("is_transferred_in") AS "contacts_transferred_in"
FROM "connect_datalake"."contact_statistic_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "queue_id";

아웃바운드 전송된 연락처

정의: 대기열에서 전송된 고객 응대입니다.

소스 테이블: contact_statistic_record


SELECT
    "queue_id",
    SUM("is_transferred_out") AS "contacts_transferred_out",
    SUM("is_transferred_out_internal") AS "transferred_out_internal",
    SUM("is_transferred_out_external") AS "transferred_out_external"
FROM "connect_datalake"."contact_statistic_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "queue_id";

최대 대기 시간

정의: 고객 응대가 대기열에서 대기한 가장 긴 시간입니다.

소스 테이블: contact_record


SELECT
    "queue_id",
    MAX("queue_duration_ms") / 1000.0 AS "max_queued_time_sec"
FROM "connect_datalake"."contact_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "queue_duration_ms" IS NOT NULL
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "queue_id";

평균 고객 응대 시간

정의: 고객 응대 시작부터 연결 해제까지의 평균 시간입니다.

소스 테이블: contact_record


SELECT
    "queue_id",
    AVG(
        date_diff('millisecond', "initiation_timestamp", "disconnect_timestamp")
    ) / 1000.0 AS "avg_contact_duration_sec"
FROM "connect_datalake"."contact_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "initiation_timestamp" IS NOT NULL
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "queue_id";

에이전트 성능 지표

평균 처리 시간

정의: 고객 응대 연결부터 ACW 완료까지의 평균 시간입니다.

소스 테이블: contact_statistic_record


SELECT
    "agent_id",
    AVG("handle_time_ms") / 1000.0 AS "avg_handle_time_sec"
FROM "connect_datalake"."contact_statistic_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "is_handled" = 1
  AND "handle_time_ms" IS NOT NULL
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "agent_id";

연락처 작업 시간 후

정의: 에이전트가 ACW 상태에서 보낸 총 시간입니다.

소스 테이블: contact_statistic_record


SELECT
    "agent_id",
    SUM("after_contact_work_time_ms") / 1000.0 AS "total_acw_time_sec"
FROM "connect_datalake"."contact_statistic_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "after_contact_work_time_ms" IS NOT NULL
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "agent_id";

고객 대기 시간

정의: 고객이 에이전트에 연결한 후 대기 상태로 보낸 총 시간입니다.

소스 테이블: contact_statistic_record


SELECT
    "agent_id",
    SUM("customer_hold_time_ms") / 1000.0 AS "total_hold_time_sec"
FROM "connect_datalake"."contact_statistic_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "customer_hold_time_ms" IS NOT NULL
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "agent_id";

에이전트 유휴 시간

정의: 에이전트가 고객 응대를 처리하지 않고 사용 가능 상태로 보낸 시간입니다.

소스 테이블: agent_statistic_record


SELECT
    "user_id" AS "agent_id",
    SUM("agent_idle_time") / 1000.0 AS "total_idle_time_sec"
FROM "connect_datalake"."agent_statistic_record"
WHERE "published_date" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "published_date" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "user_id";

선점

정의: 에이전트가 고객 응대에서 활성 상태였던 시간 대 사용 가능 + 활성 상태였던 시간의 백분율입니다.

소스 테이블: agent_statistic_record


SELECT
    "user_id" AS "agent_id",
    CAST(SUM("agent_on_contact_time") AS DOUBLE)
        / NULLIF(SUM("agent_on_contact_time") + SUM("agent_idle_time"), 0) 
        * 100.0 AS "occupancy_pct"
FROM "connect_datalake"."agent_statistic_record"
WHERE "published_date" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "published_date" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "user_id";

에이전트 무응답

정의: 에이전트에게 라우팅되었지만 응답하지 않은 고객 응대 수입니다.

소스 테이블: agent_queue_statistic_record


SELECT
    "user_id" AS "agent_id",
    "queue_id",
    SUM("agent_non_response") AS "agent_non_response_count"
FROM "connect_datalake"."agent_queue_statistic_record"
WHERE "published_date" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "published_date" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "user_id", "queue_id";

에이전트 응답률

정의: 에이전트가 응답한 라우팅된 고객 응대의 비율입니다.

소스 테이블: agent_queue_statistic_record


SELECT
    "user_id" AS "agent_id",
    CAST(SUM("contacts_handled") AS DOUBLE) 
        / NULLIF(SUM("contacts_offered"), 0) * 100.0 AS "agent_answer_rate_pct"
FROM "connect_datalake"."agent_queue_statistic_record"
WHERE "published_date" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "published_date" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "user_id";

온라인 시간

정의: 에이전트 CCP가 오프라인 이외의 상태로 설정된 총 시간입니다.

소스 테이블: agent_statistic_record


SELECT
    "user_id" AS "agent_id",
    SUM("online_time") / 1000.0 AS "total_online_time_sec"
FROM "connect_datalake"."agent_statistic_record"
WHERE "published_date" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "published_date" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "user_id";

채팅 지표

평균 에이전트 최초 응답 시간

정의: 채팅 고객 응대를 받은 후 에이전트가 첫 번째 메시지를 보내는 평균 시간입니다.

소스 테이블: contact_record


SELECT
    "queue_id",
    AVG("chat_contact_metrics_agent_first_response_time_ms") / 1000.0 
        AS "avg_agent_first_response_sec"
FROM "connect_datalake"."contact_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "channel" = 'CHAT'
  AND "chat_contact_metrics_agent_first_response_time_ms" IS NOT NULL
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "queue_id";

평균 에이전트 응답 시간

정의: 에이전트가 고객 메시지에 응답하는 데 걸리는 평균 시간입니다.

소스 테이블: contact_record


SELECT
    "queue_id",
    CAST(SUM("chat_agent_metrics_total_response_time_ms") AS DOUBLE)
        / NULLIF(SUM("chat_agent_metrics_num_responses"), 0) / 1000.0
        AS "avg_agent_response_time_sec"
FROM "connect_datalake"."contact_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "channel" = 'CHAT'
  AND "chat_agent_metrics_total_response_time_ms" IS NOT NULL
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "queue_id";

평균 총 메시지

정의: 채팅 고객 응대당 평균 총 메시지 수입니다.

소스 테이블: contact_record


SELECT
    "queue_id",
    AVG(CAST("chat_contact_metrics_total_messages" AS DOUBLE)) AS "avg_total_messages"
FROM "connect_datalake"."contact_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "channel" = 'CHAT'
  AND "chat_contact_metrics_total_messages" IS NOT NULL
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "queue_id";

중단된 대화

정의: 에이전트 또는 고객이 채팅을 중단한 고객 응대입니다.

소스 테이블: contact_record


SELECT
    "queue_id",
    COUNT(*) AS "conversations_abandoned"
FROM "connect_datalake"."contact_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "channel" = 'CHAT'
  AND ("chat_agent_metrics_conversation_abandon" = true 
       OR "chat_customer_metrics_conversation_abandon" = true)
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "queue_id";

대화 분석 지표

평균 발언 시간

정의: 음성 고객 응대당 평균 결합된 에이전트 및 고객 통화 시간입니다.

소스 테이블: contact_lens_conversational_analytics


SELECT
    AVG("talk_time_total_ms") / 1000.0 AS "avg_talk_time_sec"
FROM "connect_datalake"."contact_lens_conversational_analytics"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "channel" = 'VOICE'
  AND "instance_id" = '<YOUR_INSTANCE_ID>';

평균 침묵 시간

정의: 음성 고객 응대당 평균 대기 시간 + 무음 시간.

소스 테이블: contact_lens_conversational_analytics


SELECT
    AVG("non_talk_time_total_ms") / 1000.0 AS "avg_non_talk_time_sec"
FROM "connect_datalake"."contact_lens_conversational_analytics"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "channel" = 'VOICE'
  AND "instance_id" = '<YOUR_INSTANCE_ID>';

감정 점수

정의: 에이전트 및 고객의 전체 감정 점수입니다.

소스 테이블: contact_lens_conversational_analytics


SELECT
    AVG("sentiment_overall_score_agent") AS "avg_agent_sentiment",
    AVG("sentiment_overall_score_customer") AS "avg_customer_sentiment",
    AVG("sentiment_end_score_agent") AS "avg_agent_end_sentiment",
    AVG("sentiment_end_score_customer") AS "avg_customer_end_sentiment"
FROM "connect_datalake"."contact_lens_conversational_analytics"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "instance_id" = '<YOUR_INSTANCE_ID>';

평균 에이전트 중단

정의: 고객 응대당 평균 에이전트 중단 수입니다.

소스 테이블: contact_lens_conversational_analytics


SELECT
    AVG(CAST("interruptions_agent_count" AS DOUBLE)) AS "avg_agent_interruptions"
FROM "connect_datalake"."contact_lens_conversational_analytics"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "channel" = 'VOICE'
  AND "instance_id" = '<YOUR_INSTANCE_ID>';

AI 에이전트 지표

AI 에이전트 호출 성공률

정의: AI 에이전트 호출 성공률입니다.

소스 테이블: ai_agent


SELECT
    "ai_agent_name",
    SUM(CASE WHEN "invocation_success" = true THEN 1 ELSE 0 END) AS "success_count",
    COUNT(*) AS "total_invocations",
    CAST(SUM(CASE WHEN "invocation_success" = true THEN 1 ELSE 0 END) AS DOUBLE)
        / NULLIF(COUNT(*), 0) * 100.0 AS "success_rate_pct"
FROM "connect_datalake"."ai_agent"
WHERE "creation_timestamp" >= CAST('2026-06-09' AS TIMESTAMP) * 1000
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
  AND "ai_agent_id" IS NOT NULL
GROUP BY "ai_agent_name";

AI 핸드오프 비율

정의: 인간 에이전트에게 에스컬레이션된 AI 세션의 비율입니다.

소스 테이블: ai_session


SELECT
    SUM(CASE WHEN "is_handed_off" = true THEN 1 ELSE 0 END) AS "ai_handoffs",
    COUNT(*) AS "ai_involved_contacts",
    CAST(SUM(CASE WHEN "is_handed_off" = true THEN 1 ELSE 0 END) AS DOUBLE)
        / NULLIF(COUNT(*), 0) * 100.0 AS "handoff_rate_pct"
FROM "connect_datalake"."ai_session"
WHERE "creation_timestamp" >= CAST('2026-06-09' AS TIMESTAMP) * 1000
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
  AND "ai_session_id" IS NOT NULL;

AI 품질 점수

정의: 평균 목표 성공, 충실도 및 완전성 점수.

소스 테이블: ai_session


SELECT
    AVG("goal_success_rate") AS "avg_goal_success_rate",
    AVG("faithfulness_score") AS "avg_faithfulness_score",
    AVG("completeness_score") AS "avg_completeness_score"
FROM "connect_datalake"."ai_session"
WHERE "creation_timestamp" >= CAST('2026-06-09' AS TIMESTAMP) * 1000
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
  AND "goal_success_rate" IS NOT NULL;

AI 도구 정확도

정의: AI 도구 파라미터 사용, 선택 및 사용률의 정확도 점수입니다.

소스 테이블: ai_tool


SELECT
    "ai_tool_name",
    AVG("ai_tool_parameter_accuracy") AS "avg_parameter_accuracy",
    AVG("ai_tool_selection_accuracy") AS "avg_selection_accuracy",
    AVG("ai_tool_utilization_accuracy") AS "avg_use_accuracy"
FROM "connect_datalake"."ai_tool"
WHERE "creation_timestamp" >= CAST('2026-06-09' AS TIMESTAMP) * 1000
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
  AND "ai_tool_id" IS NOT NULL
GROUP BY "ai_tool_name";

플로우 지표

흐름 시작됨

정의: 실행을 시작한 흐름 수입니다.

소스 테이블: contact_flow_events


SELECT
    "flow_resource_id",
    "flow_type",
    COUNT(*) AS "flows_started"
FROM "connect_datalake"."contact_flow_events"
WHERE "start_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "start_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "flow_resource_id", "flow_type";

흐름 결과 백분율

정의: 각 흐름 결과 유형의 백분율입니다.

소스 테이블: contact_flow_events


WITH flow_counts AS (
    SELECT
        "flow_resource_id",
        "flow_outcome",
        COUNT(*) AS "outcome_count",
        SUM(COUNT(*)) OVER (PARTITION BY "flow_resource_id") AS "total_completed"
    FROM "connect_datalake"."contact_flow_events"
    WHERE "start_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
      AND "start_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
      AND "end_timestamp" IS NOT NULL
      AND "instance_id" = '<YOUR_INSTANCE_ID>'
    GROUP BY "flow_resource_id", "flow_outcome"
)
SELECT
    "flow_resource_id",
    "flow_outcome",
    "outcome_count",
    CAST("outcome_count" AS DOUBLE) / "total_completed" * 100.0 AS "outcome_pct"
FROM flow_counts
ORDER BY "flow_resource_id", "outcome_pct" DESC;

평균 흐름 시간

정의: 흐름 실행의 평균 기간입니다.

소스 테이블: contact_flow_events


SELECT
    "flow_resource_id",
    AVG(
        date_diff('millisecond', "start_timestamp", "end_timestamp")
    ) / 1000.0 AS "avg_flow_time_sec"
FROM "connect_datalake"."contact_flow_events"
WHERE "start_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "start_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "end_timestamp" IS NOT NULL
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "flow_resource_id";

평가 지표

수행된 평가

정의: 제출된 평가 수입니다.

소스 테이블: contact_evaluation_record


SELECT
    COUNT(DISTINCT "evaluation_id") AS "evaluations_performed"
FROM "connect_datalake"."contact_evaluation_record"
WHERE "evaluation_submitted_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "evaluation_submitted_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "item_type" = 'Form'
  AND "to_delete" = false
  AND ("evaluation_type" IS NULL OR "evaluation_type" != 'calibration')
  AND "instance_id" = '<YOUR_INSTANCE_ID>';

평균 평가 점수

정의: 제출된 평가의 평균 평가 점수입니다.

소스 테이블: contact_evaluation_record


SELECT
    AVG("score") AS "avg_evaluation_score_pct"
FROM "connect_datalake"."contact_evaluation_record"
WHERE "evaluation_submitted_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "evaluation_submitted_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "item_type" = 'Form'
  AND "to_delete" = false
  AND ("evaluation_type" IS NULL OR "evaluation_type" != 'calibration')
  AND "instance_id" = '<YOUR_INSTANCE_ID>';

자동 실패율

정의: 자동 실패를 트리거한 평가의 비율입니다.

소스 테이블: contact_evaluation_record


SELECT
    CAST(
        COUNT(DISTINCT CASE WHEN "automatic_fail" = true THEN "evaluation_id" END) AS DOUBLE
    ) / NULLIF(COUNT(DISTINCT "evaluation_id"), 0) * 100.0 
        AS "automatic_fail_pct"
FROM "connect_datalake"."contact_evaluation_record"
WHERE "evaluation_submitted_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "evaluation_submitted_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "item_type" = 'Form'
  AND "to_delete" = false
  AND ("evaluation_type" IS NULL OR "evaluation_type" != 'calibration')
  AND "instance_id" = '<YOUR_INSTANCE_ID>';

아웃바운드 캠페인 지표

캠페인 고객 응대

정의: 아웃바운드 캠페인 고객 응대 수입니다.

소스 테이블: contact_record


SELECT
    "campaign_id",
    COUNT(*) AS "campaign_contacts"
FROM "connect_datalake"."contact_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "campaign_id" IS NOT NULL
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "campaign_id";

사람이 답변

정의: 라이브 고객과 연결된 아웃바운드 캠페인 통화입니다.

소스 테이블: contact_record


SELECT
    "campaign_id",
    COUNT(*) AS "human_answered"
FROM "connect_datalake"."contact_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "campaign_id" IS NOT NULL
  AND "answering_machine_detection_status" = 'HUMAN_ANSWERED'
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "campaign_id";

사례 지표

생성된 사례

정의: 특정 기간에 생성된 총 사례 수입니다.

소스 테이블: case_events


SELECT
    COUNT(DISTINCT "case_id") AS "cases_created"
FROM "connect_datalake"."case_events"
WHERE "event_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "event_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "event_type" = 'CASE.CREATED'
  AND "instance_id" = '<YOUR_INSTANCE_ID>';

평균 사례 해결 시간

정의: 사례 생성부터 종료까지의 평균 시간입니다.

소스 테이블: case_events


SELECT
    AVG(
        date_diff('hour', "created_timestamp", "last_closed_timestamp")
    ) AS "avg_resolution_time_hours"
FROM "connect_datalake"."case_events"
WHERE "last_closed_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "last_closed_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "created_timestamp" IS NOT NULL
  AND "instance_id" = '<YOUR_INSTANCE_ID>';

봇 지표

봇 대화 결과

정의: 봇 대화 결과의 백분율 분석입니다.

소스 테이블: bot_conversations


WITH bot_outcomes AS (
    SELECT
        "bot_id",
        "bot_conversation_outcome",
        COUNT(*) AS "cnt",
        SUM(COUNT(*)) OVER (PARTITION BY "bot_id") AS "total"
    FROM "connect_datalake"."bot_conversations"
    WHERE "bot_conversation_start_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
      AND "bot_conversation_start_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
      AND "instance_id" = '<YOUR_INSTANCE_ID>'
    GROUP BY "bot_id", "bot_conversation_outcome"
)
SELECT
    "bot_id",
    "bot_conversation_outcome",
    "cnt",
    CAST("cnt" AS DOUBLE) / "total" * 100.0 AS "outcome_pct"
FROM bot_outcomes;

일반적인 쿼리 패턴

다음 패턴은 포괄적인 대시보드 및 보고를 위해 여러 데이터 레이크 테이블을 결합하는 방법을 보여줍니다.

일별 요약 대시보드

정의: 서비스 수준을 포함한 포괄적인 일일 대기열 지표입니다.

소스 테이블: contact_statistic_record


SELECT
    "queue_id",
    SUM("is_queued") AS "contacts_queued",
    SUM("is_handled") AS "contacts_handled",
    SUM("is_abandoned") AS "contacts_abandoned",
    AVG(CASE WHEN "is_handled" = 1 THEN "queue_answer_time_ms" END) / 1000.0 
        AS "avg_answer_time_sec",
    AVG(CASE WHEN "is_handled" = 1 THEN "handle_time_ms" END) / 1000.0 
        AS "avg_handle_time_sec",
    CAST(SUM(CASE WHEN "is_handled" = 1 AND "queue_answer_time_ms" <= 20000 
                  THEN 1 ELSE 0 END) AS DOUBLE)
        / NULLIF(SUM("is_queued"), 0) * 100.0 AS "sl_20s_pct"
FROM "connect_datalake"."contact_statistic_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY "queue_id"
ORDER BY "contacts_queued" DESC;

시간당 추세 분석

정의: 시간당 고객 응대 볼륨 및 서비스 수준 추세입니다.

소스 테이블: contact_statistic_record


SELECT
    date_trunc('hour', "disconnect_timestamp") AS "hour",
    "queue_id",
    SUM("is_queued") AS "contacts_queued",
    SUM("is_handled") AS "contacts_handled",
    SUM("is_abandoned") AS "contacts_abandoned",
    CAST(SUM("is_abandoned") AS DOUBLE) 
        / NULLIF(SUM("is_queued"), 0) * 100.0 AS "abandon_rate_pct",
    AVG(CASE WHEN "is_handled" = 1 THEN "handle_time_ms" END) / 1000.0 AS "aht_sec"
FROM "connect_datalake"."contact_statistic_record"
WHERE "disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND "disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND "instance_id" = '<YOUR_INSTANCE_ID>'
GROUP BY date_trunc('hour', "disconnect_timestamp"), "queue_id"
ORDER BY "hour";

Contact Lens 강화 고객 응대

정의: Contact Lens 분석을 사용하여 고객 응대 레코드를 강화합니다.

소스 테이블:와 contact_record 조인됨 contact_lens_conversational_analytics


SELECT
    cr."contact_id",
    cr."queue_id",
    cr."agent_id",
    cr."agent_interaction_duration_ms" / 1000.0 AS "interaction_sec",
    cl."talk_time_agent_ms" / 1000.0 AS "agent_talk_sec",
    cl."talk_time_customer_ms" / 1000.0 AS "customer_talk_sec",
    cl."sentiment_overall_score_agent",
    cl."sentiment_overall_score_customer"
FROM "connect_datalake"."contact_record" cr
JOIN "connect_datalake"."contact_lens_conversational_analytics" cl
    ON cr."contact_id" = cl."contact_id"
    AND cr."instance_id" = cl."instance_id"
WHERE cr."disconnect_timestamp" >= TIMESTAMP '2026-06-09 00:00:00'
  AND cr."disconnect_timestamp" <  TIMESTAMP '2026-06-10 00:00:00'
  AND cr."instance_id" = '<YOUR_INSTANCE_ID>'
  AND cr."channel" = 'VOICE';

에이전트 일정 준수(활동 수준)

정의: 에이전트의 실제 활동 상태(에서agent_statistic_record)를 하루의 각 시간 간격에 대해 예약된 교대 근무 활동(예약 테이블에서)과 비교합니다. IN(에이전트가 예약된 작업을 수행 중) 또는 OUT(그렇지 않음)의 간격당 준수 결정을 생성합니다.

출력 열: 에이전트, 날짜, 시작, 종료, 예약된 활동, 실제 활동, 준수 상태, 기간

소스 테이블:

staff_shifts - 해당 날짜의 에이전트 교대 근무(삭제되지 않은 최신 버전)
staff_shift_activities - 각 교대 근무 내에서 예약된 활동 블록
shift_activities - 활동 이름 조회(ARN을 사람이 읽을 수 있는 이름에 매핑)
agent_statistic_record - 간격당 실제 에이전트 상태
users - 에이전트 이름 및 ARN 확인

규정 준수 로직(단순):

예약된 "Open" - 상태가 Available, On Contact 또는 ACW인 경우 에이전트가 IN입니다.
예약된 "휴식" - 상태가 휴식 또는 점심 식사인 경우 에이전트가 IN입니다.
예약된 "회의" - 상태가 훈련 또는 회의인 경우 에이전트가 IN입니다.
그렇지 않으면 - OUT


WITH latest_shift_versions AS (
    -- Get the latest (non-deleted) shift version per shift_id
    SELECT
        shift_id,
        MAX(shift_version) AS max_version
    FROM "connect_datalake"."staff_shifts"
    WHERE is_deleted = false
      AND CAST(shift_start_timestamp AS DATE) = DATE '2026-06-10'  -- SET REPORT DATE
    GROUP BY shift_id
),

latest_shifts AS (
    SELECT
        ss.shift_id,
        ss.agent_arn,
        ss.shift_start_timestamp,
        ss.shift_end_timestamp
    FROM "connect_datalake"."staff_shifts" ss
    INNER JOIN latest_shift_versions lsv
        ON ss.shift_id = lsv.shift_id
        AND ss.shift_version = lsv.max_version
    WHERE ss.is_deleted = false
),

-- Get scheduled activity blocks with human-readable activity names
scheduled_blocks AS (
    SELECT
        ls.agent_arn,
        ssa.activity_start_timestamp,
        ssa.activity_end_timestamp,
        sa.shift_activity_name,
        CASE
            WHEN sa.shift_activity_name IN ('Work', 'Overtime') THEN 'Open'
            WHEN sa.shift_activity_name IN ('Break', 'Lunch') THEN 'Break'
            WHEN sa.shift_activity_name = 'Training' THEN 'Meeting'
            WHEN sa.shift_activity_name = 'PTO' THEN 'PTO'
            ELSE sa.shift_activity_name
        END AS scheduled_activity_label
    FROM "connect_datalake"."staff_shift_activities" ssa
    INNER JOIN latest_shifts ls
        ON ssa.shift_id = ls.shift_id
    INNER JOIN latest_shift_versions lsv
        ON ssa.shift_id = lsv.shift_id
        AND ssa.shift_version = lsv.max_version
    INNER JOIN "connect_datalake"."shift_activities" sa
        ON ssa.shift_activity_arn = sa.shift_activity_arn
    WHERE ssa.is_deleted = false
),

-- Get actual agent state intervals for the day
actual_states AS (
    SELECT
        u.user_arn AS agent_arn,
        u.first_name,
        u.last_name,
        asr.interval_start_time,
        asr.interval_end_time,
        asr.agent_status_name,
        asr.online_time,
        asr.agent_idle_time,
        asr.agent_on_contact_time,
        asr.non_productive_time,
        CASE
            WHEN asr.agent_on_contact_time IS NOT NULL AND asr.agent_on_contact_time > 0
                THEN 'On Inbound Call'
            WHEN asr.agent_idle_time IS NOT NULL AND asr.agent_idle_time > 0
                THEN 'Available'
            WHEN asr.non_productive_time IS NOT NULL AND asr.non_productive_time > 0
                THEN COALESCE(asr.agent_status_name, 'Non-Productive')
            WHEN asr.online_time IS NOT NULL AND asr.online_time > 0
                THEN 'Available'
            ELSE COALESCE(asr.agent_status_name, 'Offline')
        END AS actual_activity_label
    FROM "connect_datalake"."agent_statistic_record" asr
    INNER JOIN "connect_datalake"."users" u
        ON asr.user_id = u.user_id
    WHERE asr.interval_start_time >= TIMESTAMP '2026-06-10 00:00:00' -- SET REPORT DATE (UTC)
      AND asr.interval_start_time < TIMESTAMP '2026-06-11 00:00:00'
),

-- Join actual states with scheduled blocks
activity_timeline AS (
    SELECT
        act.first_name || ' ' || act.last_name AS agent_name,
        act.interval_start_time,
        act.interval_end_time,
        act.actual_activity_label,
        act.agent_status_name,
        COALESCE(sb.scheduled_activity_label, 'Open') AS scheduled_activity
    FROM actual_states act
    LEFT JOIN scheduled_blocks sb
        ON act.agent_arn = sb.agent_arn
        AND act.interval_start_time < sb.activity_end_timestamp
        AND act.interval_end_time > sb.activity_start_timestamp
)

SELECT
    agent_name AS "AGENT",
    CAST(interval_start_time AS DATE) AS "DATE",
    DATE_FORMAT(interval_start_time, '%H:%i:%s') AS "BEGIN",
    DATE_FORMAT(interval_end_time, '%H:%i:%s') AS "END",
    scheduled_activity AS "SCHEDULED ACTIVITY",
    actual_activity_label AS "ACTUAL ACTIVITY",
    CASE
        WHEN scheduled_activity = 'Open'
            AND actual_activity_label IN ('Available', 'On Inbound Call', 'On Outbound Call',
                                          'Call Ringing', 'Aftercall (ACW)')
            THEN 'IN'
        WHEN scheduled_activity = 'Break'
            AND agent_status_name IN ('Break', 'Lunch')
            THEN 'IN'
        WHEN scheduled_activity = 'Meeting'
            AND agent_status_name IN ('Training', 'Meeting')
            THEN 'IN'
        ELSE 'OUT'
    END AS "ADHERENCE STATE",
    CAST(DATE_DIFF('second', interval_start_time, interval_end_time) / 3600 AS VARCHAR)
        || ':' ||
        LPAD(CAST((DATE_DIFF('second', interval_start_time, interval_end_time) % 3600) / 60 AS VARCHAR), 2, '0')
        || ':' ||
        LPAD(CAST(DATE_DIFF('second', interval_start_time, interval_end_time) % 60 AS VARCHAR), 2, '0')
    AS "DURATION"
FROM activity_timeline
ORDER BY interval_start_time ASC;

모범 사례

파티션 정리 disconnect_timestamp- 스캔 비용을 최소화하기 위해 항상 파티션 필터(published_date, 또는 creation_timestamp)를 포함합니다.
중복 제거 - Connect Customer는 레코드를 한 번 이상 전송합니다. 정확한 개수가 필요한 경우 기본 키DISTINCT에를 사용합니다.
시간대 - 모든 타임스탬프는 UTC입니다. 로컬 보고를 AT TIME ZONE 신청합니다.
밀리초 - 대부분의 기간 필드는 밀리초 단위로 저장됩니다. 초당 1000.0으로 나눕니다.
인스턴스 ID 필터 - 다중 인스턴스 환경에서 항상 기준으로 필터링instance_id합니다.
실시간 지표 - 실제 실시간 지표의 경우 GetCurrentMetricData API를 사용합니다. 데이터 레이크는 기록 데이터만 제공합니다.

javascript가 브라우저에서 비활성화되거나 사용이 불가합니다.

AWS 설명서를 사용하려면 Javascript가 활성화되어야 합니다. 지침을 보려면 브라우저의 도움말 페이지를 참조하십시오.

문서 규칙

데이터 예약

데이터 보존