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callout--general","text":"\u003Cp\u003E\u003Cstrong\u003E마케팅 AI의 정의\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003E마케팅 AI는 예측형, 생성형, 에이전틱 시스템을 활용해 마케팅 데이터를 해석하고 의사결정을 지원하며 마케팅 워크플로우 전반의 업무를 수행하는 것을 의미합니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-68f75ec35b","text":"\u003Cp\u003E최근 몇 년 동안 마케팅 조직은 AI 도입의 여러 단계를 거쳐 왔습니다. 처음에는 ‘AI를 사용해야 하는가’를 고민했고, 그다음에는 ‘어떤 업무에 활용할 수 있는가’를 검토했으며, 이후에는 ‘업무에 어떻게 적용할 것인가’에 집중했습니다. 이제 요구 수준은 한층 높아졌습니다. 리더십은 무엇을 신뢰할 수 있는지, 어떤 역량을 확장할 수 있는지, AI 투자에서 기대한 ROI를 어떻게 실현할 것인지 묻고 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E마케팅 AI의 역량 또한 크게 향상됐습니다. 이제 시스템은 사람의 개입을 최소화하면서 도구를 선택하고 워크플로우를 호출하며 의사결정에 관여하고 직접 실행할 수 있습니다. 마케팅은 지원 도구 중심에서 직접 실행하는 에이전트 중심으로 전환하고 있습니다. 이러한 변화로 수행 가능한 업무 범위가 전례 없이 확대됐지만, 동시에 워크플로우를 뒷받침하는 데이터와 정의, 통제 체계의 중요성도 더욱 커졌습니다. 입력 데이터가 불완전하거나 일관성이 없고 본래 목적과 다르게 사용되면, AI 시스템은 문제를 발견하기도 전에 오류와 개인정보 보호 위험을 다운스트림으로 확산시킬 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E따라서 과제는 단순히 더 많은 마케팅 도구에 AI를 추가하는 데 있지 않습니다. 조직은 AI 시스템을 거버넌스가 적용된 데이터, 공유된 비즈니스 컨텍스트, 명확한 운영 경계와 연결하는 동시에 목표 설정과 크리에이티브 품질, 중대한 의사결정에서는 인간의 판단을 유지해야 합니다.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/blog/mmds-ai-governance-framework-agentic-enterprise/\"\u003ESnowflake CMO Denise Persson의 글\u003C/a\u003E처럼 “에이전틱 시대를 주도할 마케팅 리더는 거버넌스와 혁신이 서로 대립하는 개념이 아님을 이해하는 사람입니다.”\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-ai-in-marketing":{"id":"title-v2-136fca204a","additionalClasses":"anchor-title anchor-title--what-is-ai-in-marketing","type":"heading2","lines":["마케팅에서 AI란?"],":type":"snowflake-site/components/title-v2"},"text_what-is-ai-in-marketing_0":{"id":"text-927d7f27c8","text":"\u003Cp\u003E마케팅 AI는 예측 AI, \u003Ca href=\"https://www.snowflake.com/ko/artificial-intelligence/generative-ai/\"\u003E생성형 AI\u003C/a\u003E, \u003Ca href=\"https://www.snowflake.com/ko/artificial-intelligence/agents/\"\u003E에이전틱 시스템\u003C/a\u003E을 활용해 마케팅 데이터를 해석하고 의사결정을 지원하며 마케팅 워크플로우 전반의 업무를 수행하는 것을 의미합니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003E예측 AI는 향후 발생 가능성을 추정합니다.\u003C/b\u003E 전환 가능성과 이탈 위험부터 수요와 고객 생애 가치에 이르기까지 고객 및 캠페인 데이터에서 다양한 패턴을 파악하도록 마케팅 담당자를 지원합니다.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E생성형 AI는\u003C/b\u003E \u003Cb\u003E컨텍스트를 새로운 콘텐츠로 전환합니다.\u003C/b\u003E 제공된 지침과 소스 정보, 잠재고객 컨텍스트를 활용해 텍스트, 이미지, 분석 결과를 생성하거나 조정합니다.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E에이전틱 AI는 여러 단계로 구성된 업무를 조율합니다.\u003C/b\u003E 필요한 정보를 검색하고 승인된 도구 중 적합한 도구를 선택하며 애플리케이션이 정한 범위 안에서 작업을 이어갑니다.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E이러한 역량은 하나의 워크플로우 안에서 유기적으로 연결되는 경우가 많습니다. 예를 들어 예측 모델이 이탈 가능성이 높은 고객을 식별하면, 생성형 시스템은 해당 고객 세그먼트에 맞는 메시지를 준비할 수 있습니다. 이후 에이전트가 대상 자격을 확인하고 캠페인을 승인 절차로 전달한 뒤 활성화를 조율할 수 있습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-for-marketing-content-and-creative":{"id":"title-v2-745608cad2","additionalClasses":"anchor-title anchor-title--ai-for-marketing-content-and-creative","type":"heading2","lines":["마케팅 콘텐츠와 크리에이티브를 위한 AI"],":type":"snowflake-site/components/title-v2"},"text_ai-for-marketing-content-and-creative_0":{"id":"text-2dba7e5998","text":"\u003Cp\u003E생성형 AI는 마케팅팀이 캠페인 에셋을 더 빠르게 제작하고 조정하도록 지원합니다. 특히 하나의 캠페인을 여러 잠재고객과 형식, 채널에 맞춰야 할 때 효과적입니다.\u003C/p\u003E\n\u003Cp\u003E대표적인 활용 분야는 다음과 같습니다.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003E캠페인 개발:\u003C/strong\u003E 승인된 브리프를 바탕으로 초기 광고 문안 사본, 비주얼 콘셉트, 다양한 메시지를 만듭니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003E채널별 콘텐츠 조정:\u003C/strong\u003E 원본 자료를 이메일, 유료 미디어, 소셜 미디어, 랜딩 페이지 등 다양한 형식에 맞게 재구성합니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003E콘텐츠 분석:\u003C/strong\u003E 에셋을 분류하고 고객 피드백에서 핵심 주제를 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AI는 행동, 거래, 비정형 신호를 더욱 폭넓게 해석하고 고객 컨텍스트의 변화에 따라 결과를 조정함으로써 이러한 프로세스를 고도화합니다.\u003C/p\u003E\n\u003Cp\u003E시스템은 고객을 식별하고 사용 가능한 신호를 해석한 뒤, 어떤 조치가 적절하고 허용되는지 판단해야 합니다. AI는 이 프로세스의 여러 단계를 지원할 수 있습니다.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003E세분화:\u003C/strong\u003E 수동으로 정의한 규칙만으로는 포착하기 어려운 패턴을 분석해 고객 또는 계정 그룹을 식별합니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003E성향 및 이탈 모델링:\u003C/strong\u003E 새로운 신호가 유입될 때마다 고객의 전환, 이탈 또는 특정 조치에 대한 반응 가능성을 추정합니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003E고객 생애 가치:\u003C/strong\u003E 더 폭넓은 행동 및 거래 입력을 활용해 고객 관계의 미래 가치를 예측합니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003E권장 사항\u003C/strong\u003E 고객의 현재 컨텍스트와 사용 가능한 선택지를 기준으로 제품, 오퍼, 콘텐츠 또는 다음 조치의 우선순위를 정합니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003E메시지 조정:\u003C/strong\u003E 오디언스, 채널 또는 고객 여정의 스테이지에 맞춰 언어, 형식, 크리에이티브를 조정합니다.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E이러한 활용 사례에는 서로 연관된 세 가지 요건이 필요합니다. 고객의 현재 상황을 이해할 수 있는 충분한 컨텍스트, 허용되는 대응을 결정하는 규칙, 그리고 활동을 올바른 고객, 계정 또는 구매 그룹과 연결하는 신뢰할 수 있는 방법입니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_ai-for-personalization-and-audience-intelligence_0":{"id":"card-v2-77930ea182","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","layoutStyle":"horizontal","text":{"id":"text","text":"\u003Cp\u003EHelloFresh는 Snowflake AI 데이터 클라우드를 중앙 집중식 데이터 플랫폼으로 활용하여 분산된 데이터 소스를 통합하고, 인사이트 도출 시간을 단축했으며, 메뉴 개발, 가격 책정, 프로모션, 창고 운영, 공급망 관리 전반에서 실시간 의사 결정을 지원합니다. 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AI는 오디언스, 크리에이티브, 채널, 성과 신호를 더욱 폭넓게 분석하고 실적 변화에 따라 추천을 조정함으로써 이러한 방법론의 연계 방식을 혁신합니다.\u003C/p\u003E\n\u003Cp\u003E대표적인 활용 분야는 다음과 같습니다.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003E오디언스 계획:\u003C/strong\u003E 성향, 이탈, 고객 가치 추정치를 결합해 특정 캠페인 목표에 적합한 고객 또는 계정의 우선순위를 정합니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003E예측:\u003C/strong\u003E 현재 캠페인, 고객, 시장 신호를 활용해 수요, 전환, 파이프라인 또는 매출을 추정합니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003E리드 스코어링:\u003C/strong\u003E 새로운 행동 데이터, 퍼모그래픽 정보, 다운스트림 성과가 확보될 때마다 리드 또는 계정의 순위를 업데이트합니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003E예산 배분:\u003C/strong\u003E 캠페인과 채널별 성과를 비교한 뒤, 추가 지출이 목표 성과 개선에 가장 효과적인 영역을 추천합니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003E프로그래매틱 광고:\u003C/strong\u003E 성과와 광고 인벤토리 상황의 변화에 따라 입찰가, 게재 위치, 오디언스 노출을 조정합니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003E캠페인 최적화:\u003C/strong\u003E 잠재고객, 크리에이티브, 오퍼, 채널, 지출 가운데 어떤 요소를 변경해야 성과를 가장 효과적으로 개선할 수 있는지 파악합니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003E마케팅에서 영업으로 인계:\u003C/strong\u003E 점수와 근거 신호, 예측 결과를 영업팀에 전달해 우선순위가 다음 조치에 반영되도록 합니다.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EAI는 캠페인의 더 많은 요소를 분석하고 변경 사항을 더욱 빈번하게 제안할 수 있습니다. 그러나 조직은 여전히 목표를 명확히 정의하고 기반 지표를 표준화해야 합니다. 또한 실험이나 적절한 기여도 분석 방법을 활용해 관찰된 결과가 실제로 캠페인에서 비롯됐는지 판단해야 합니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-automation-and-marketing-agents":{"id":"title-v2-d17f8090e0","additionalClasses":"anchor-title anchor-title--ai-automation-and-marketing-agents","type":"heading2","lines":["AI 자동화와 마케팅 에이전트"],":type":"snowflake-site/components/title-v2"},"text_ai-automation-and-marketing-agents_0":{"id":"text-35a89edc27","text":"\u003Cp\u003E기존 마케팅 자동화는 팀이 트리거와 대응 방식을 사전에 정의할 수 있을 때 효과적입니다. 반면 AI 에이전트는 다음 조치를 결정하기 전에 조사가 필요한 워크플로우를 지원합니다. 예를 들어 캠페인 성과가 하락했을 때는 원인에 따라 대응이 달라집니다. 낮은 게재율, 잠재고객 피로도, 효과가 낮은 크리에이티브, 불완전한 전환 데이터에는 각각 다른 조치가 필요합니다.\u003C/p\u003E\n\u003Cp\u003E마케팅 에이전트가 지원할 수 있는 업무는 다음과 같습니다.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003E캠페인 모니터링:\u003C/strong\u003E 현재 성과를 예산, 집행 속도, 결과 목표와 비교합니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003E성과 원인 분석:\u003C/strong\u003E 잠재고객 노출 빈도, 채널 게재 현황, 크리에이티브 반응, 측정 데이터의 최신성을 점검합니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003E워크플로우 조정:\u003C/strong\u003E 마케팅 시스템 간에 권고 사항과 승인 요청, 완료된 조치를 전달합니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003E리드 및 잠재고객 운영:\u003C/strong\u003E 최신 퍼스트 파티 데이터와 정의된 비즈니스 규칙을 활용해 레코드의 적격성을 평가하고 정보를 보강하거나 적절한 대상으로 전달합니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003E차선 조치 워크플로우:\u003C/strong\u003E 고객 컨텍스트를 바탕으로 승인된 대응을 권고하거나 실행합니다.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E각 워크플로우에서 시스템이 담당하는 책임의 범위는 서로 다릅니다. 코파일럿은 인터랙티브 작업을 지원합니다. 마케터가 분석을 요청하고 코파일럿이 제공한 결과를 검토한 뒤 다음 조치를 결정하는 방식입니다. 에이전트는 승인된 도구를 사용해 문제를 조사하고 권고안을 마련하며 업무를 전달하거나 허용된 조치를 수행하는 등 워크플로우의 더 많은 단계를 이어서 처리할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E시스템이 더 많은 중간 단계 업무를 맡을수록 명확한 운영 경계가 중요해집니다. 서비스 아이덴티티, 도구 권한, 승인 임계값, 예산 한도, 중단 조건을 통해 이러한 경계를 설정하면 에이전트가 허용되지 않은 조치를 수행하는 것을 방지할 수 있습니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-to-make-ai-marketing-workflows-more-reliable":{"id":"title-v2-2bc2d7e7a7","additionalClasses":"anchor-title anchor-title--how-to-make-ai-marketing-workflows-more-reliable","type":"heading2","lines":["AI 마케팅 워크플로우의 신뢰성을 높이는 방법"],":type":"snowflake-site/components/title-v2"},"text_how-to-make-ai-marketing-workflows-more-reliable_0":{"id":"text-f50e4c8093","text":"\u003Cp\u003E워크플로우의 각 스테이지에서 적절한 컨텍스트와 통제 장치를 일관되게 유지해야 신뢰성을 확보할 수 있습니다. 이를 위해 고객 데이터와 캠페인 데이터를 연결하고 비즈니스 정의를 표준화하며 개인정보 보호 및 액세스 요건을 적용해야 합니다. 또한 에이전트의 권한을 제한하고 결과물이 의도한 비즈니스 성과를 뒷받침하는지 평가해야 합니다.\u003C/p\u003E\n\u003Cp\u003E모델 성능을 높이는 것만으로는 신뢰하기 어려운 정의, 파편화된 고객 컨텍스트, 부적절한 액세스 문제를 해결할 수 없습니다. \u003Ca href=\"https://www.snowflake.com/en/blog/ai-agents-for-marketing-governed-context/\"\u003ESnowflake의 Patrick Crosby가 지적했듯이\u003C/a\u003E, “더 나은 모델만으로는 해결할 수 없습니다. 유연한 데이터 및 AI 기반을 갖춰야 해결할 수 있습니다.” 워크플로우의 각 스테이지에서 적절한 컨텍스트와 통제 장치를 일관되게 유지해야 신뢰성을 확보할 수 있습니다.\u003C/p\u003E\n\u003Ch3\u003E고객 데이터와 캠페인 데이터 연결\u003C/h3\u003E\n\u003Cp\u003E마케팅 의사결정에는 고객 관계 관리 레코드, 거래, 제품 활동, 웹 행동, 캠페인 노출, 동의 상태, 다운스트림 결과 등 다양한 정보가 활용됩니다. 하지만 각 시스템이 포착하는 정보는 고객 관계의 일부에 불과합니다. 이러한 레코드가 분리된 상태로 남아 있으면 세분화, 활성화, 측정에서 서로 다른 고객 아이덴티티를 사용할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E서로 연결된 \u003Ca href=\"https://www.snowflake.com/en/solutions/departments/marketing/customer-360/\"\u003E고객 360\u003C/a\u003E은 모델과 애플리케이션이 일관된 아이덴티티와 상호작용 이력에 액세스할 수 있도록 합니다. 아이덴티티 확인은 여러 시스템에 분산된 레코드를 연결합니다. 동시에 데이터 최신성은 워크플로우의 요구 수준에 맞아야 합니다. 예를 들어 분기별 계획 모델은 갱신 주기가 다소 느려도 무방하지만, 차선 조치 워크플로우는 대응 방식을 선택하기 전에 최신 구매 내역, 제품 활동, 캠페인 노출, 동의 상태를 확인해야 합니다.\u003C/p\u003E\n\u003Cp\u003E팀은 워크플로우에 제공되는 데이터도 지속적으로 모니터링해야 합니다. 필드 누락, 업데이트 지연, 소스 범위 변경은 그럴듯해 보이지만 부정확한 결과를 초래할 수 있습니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_how-to-make-ai-marketing-workflows-more-reliable_0":{"id":"text-0d23705527","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003E빠른 팁\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003E전체 워크플로우에서 아이덴티티가 정확히 전달되는지 테스트합니다. 결과를 신뢰하기 전에 세분화, 활성화, 측정 전반에서 동일한 고객 또는 계정 정의를 사용하는지 확인해야 합니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_how-to-make-ai-marketing-workflows-more-reliable_1":{"id":"text-e5bdcf8607","text":"\u003Ch3\u003E지표와 비즈니스 정의 표준화\u003C/h3\u003E\r\n\u003Cp\u003E컬럼 이름만으로는 조직이 해당 항목을 어떻게 정의하는지 모델이 알 수 없습니다. 예를 들어 전환은 단일 플랫폼에서는 양식 제출을, 다른 플랫폼에서는 구매 완료를, 또 다른 플랫폼에서는 영업팀이 수락한 영업 기회를 의미할 수 있습니다. 매출 역시 수주액, 인식 매출 또는 예상 고객 가치를 의미할 수 있습니다. 각 정의는 모두 타당할 수 있지만 서로 다른 마케팅 의사결정을 뒷받침합니다.\u003C/p\u003E\r\n\u003Cp\u003E시맨틱 컨텍스트는 거버넌스가 적용된 지표, 뷰, 메타데이터, 비즈니스 엔터티 간 관계를 통해 이러한 차이를 명확히 구분합니다. 또한 기여도 산정 기간, 잠재고객 제외 조건, 오퍼 적격성, 마케팅에서 영업으로 인계하는 기준을 일관되게 유지할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E핵심 정의에는 변경을 승인하고 마케팅, 영업, 재무, 분석 부문 간의 차이를 조정할 소유자 또는 스튜어드를 명확히 지정해야 합니다. 이러한 소유권이 명확하지 않으면 조직이 이미 해결됐다고 판단한 뒤에도 모델과 보고서가 동일한 지표의 서로 다른 정의를 계속 사용할 수 있습니다.\u003C/p\u003E\r\n\u003Ch3\u003E개인정보 보호, 동의 및 액세스 제어 강화\u003C/h3\u003E\r\n\u003Cp\u003E마케팅 워크플로우에서는 개인 정보, 행동 데이터, 상업적 민감 데이터가 자주 활용됩니다. 모델이 해당 기록을 검색하거나 에이전트가 이를 바탕으로 작업할 때에도 액세스, 동의 및 허용된 사용 범위에 관한 요건이 적용됩니다.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/ko/data-governance/data-security/rbac/\"\u003E역할 기반 액세스 제어\u003C/a\u003E(RBAC)는 고객 수준 데이터를 조회할 수 있는 사용자와 서비스 ID를 제한합니다. 분류와 마스킹은 민감한 필드를 보호하며, 동의 및 선호도 기록은 워크플로우에서 사용할 수 있는 오디언스, 오퍼, 채널을 결정합니다.\u003C/p\u003E\r\n\u003Cp\u003E데이터를 수집한 목적도 고려해야 합니다. 서비스 제공을 위해 수집한 정보는 타기팅이나 모델링에 사용하지 못할 수 있습니다. 지역별 개인정보 보호 요건과 계약, 보존 정책에 따라 허용되는 사용 범위가 더 좁아질 수도 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E파트너 협업에서는 \u003Ca href=\"https://www.snowflake.com/en/fundamentals/what-is-a-data-clean-room/\"\u003E데이터 클린룸\u003C/a\u003E을 통해 다른 참여자에게 고객 원본 기록을 노출하지 않고 승인된 오디언스 및 측정 분석을 수행할 수 있습니다. 데이터 결합 및 분석 방식을 통제하는 데 도움이 되지만, 동의 절차나 계약 검토, 관련 법적 요건을 대체하지는 않습니다.\u003C/p\u003E\r\n\u003Cp\u003E감사 기록에는 애플리케이션이 액세스한 데이터, 적용된 정책, 결과물의 활용 방식이 명시되어야 합니다.\u003C/p\u003E\r\n\u003Ch3\u003E에이전트 권한과 작업 범위 제한\u003C/h3\u003E\r\n\u003Cp\u003E시스템에 더 많은 권한을 부여할수록 운영 리스크도 커집니다. 시스템이 작업을 추천하는 데 그치는지, 직접 실행하는지에 따라 동일한 오류라도 전혀 다른 결과를 초래할 수 있기 때문입니다. 따라서 에이전틱 워크플로우에는 데이터 액세스 제어뿐 아니라 작업 수준의 권한도 필요합니다.\u003C/p\u003E\r\n\u003Cp\u003E에이전트에 부여할 수 있는 권한의 예는 다음과 같습니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E캠페인 성과는 조회할 수 있지만 지출액은 변경하지 못하게 하거나, 일정 범위 내에서만 조정하도록 제한\u003C/li\u003E\r\n\u003Cli\u003E오디언스 초안은 작성할 수 있지만 활성화는 제한\u003C/li\u003E\r\n\u003Cli\u003E정해진 조건에서 캠페인을 일시 중지할 수 있지만 재개하려면 승인 필요\u003C/li\u003E\r\n\u003Cli\u003E콘텐츠를 생성하고 전달할 수 있지만 게시 권한은 제한\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E서비스 ID, 도구 권한, 승인 임계값, 예산 한도, 중단 조건이 에이전트의 운영 경계를 결정합니다. 애플리케이션에는 할당된 작업에 필요한 도구와 권한만 부여해야 합니다.\u003C/p\u003E\r\n\u003Cp\u003E또한 에이전트가 사용한 데이터와 호출한 도구, 최종 결과에 도달한 과정을 보여 주는 추적 기록이 필요합니다. 이러한 기록은 워크플로우가 예상치 못한 결과를 내거나 승인 임계값을 넘었을 때 감사와 검토를 지원합니다.\u003C/p\u003E\r\n\u003Ch3\u003E비즈니스 성과를 기준으로 결과 평가\u003C/h3\u003E\r\n\u003Cp\u003E평가에서는 두 가지 질문에 각각 답해야 합니다. AI 시스템이 할당된 작업을 정확히 수행했는가? 그리고 해당 작업이 조직에서 개선하려는 마케팅 성과에 기여했는가?\u003C/p\u003E\r\n\u003Cp\u003E작업 수준의 평가 기준은 적용된 AI 유형에 따라 달라집니다. 예측 모델은 실제 관측 결과와 부합하는 추정치를 산출하고, 이를 활용하는 의사결정의 품질을 높여야 합니다. 생성형 시스템은 정확한 사실과 승인된 용어를 사용하고 필수 고지 사항을 포함해야 합니다. 에이전트는 적절한 도구를 선택하고 부여된 권한 범위 내에서 작동해야 하며, 데이터 소스나 다운스트림 서비스에 장애가 발생하면 상위 단계로 에스컬레이션하거나 복구해야 합니다.\u003C/p\u003E\r\n\u003Cp\u003E시스템이 올바른 결과를 최적화했는지 평가하려면 특정 지표를 기준으로 최적화를 시작하기 전에 목표, 측정 기간, 비교 방법을 정의해야 합니다. 그렇지 않으면 워크플로우가 적격 파이프라인을 희생하면서 클릭률만 높이거나, 추가 매출 없이 기여 전환만 늘릴 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E측정 설계에 따라 조직이 뒷받침할 수 있는 결론의 범위가 달라집니다. 캠페인 대상으로 선정된 고객은 제외된 고객보다 이미 구매 의향이 높을 수 있습니다. 따라서 캠페인의 실질적 효과와 캠페인 없이도 발생했을 결과를 구분하려면 실험, 대조군, 적절한 기여도 산정 방법이 필요합니다.\u003C/p\u003E\r\n\u003Ch3\u003E콘텐츠 품질과 편향 모니터링\u003C/h3\u003E\r\n\u003Cp\u003EAI 시스템의 결과는 시스템이 사용하는 정보의 품질과 분포를 반영합니다. 소스 데이터가 오래되거나 불완전하거나 일관되지 않으면 오류가 발생할 수 있습니다. 또한 특정 고객, 행동 또는 결과가 다른 항목보다 더 충실하게 반영될 경우 \u003Ca href=\"https://www.snowflake.com/en/data-governance/policy/data-ethics/data-bias/\"\u003E편향\u003C/a\u003E이 개입할 수 있습니다. 예를 들어 과거 마케팅 데이터에는 이전의 타기팅, 자격 기준, 측정 결정이 미친 영향이 남아 있습니다. 따라서 데이터의 패턴은 동일한 조건에서 고객이 보였을 반응이 아니라 조직이 과거에 운영한 방식을 반영할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E콘텐츠 품질과 편향을 개선하려면 거버넌스가 적용된 최신 소스 데이터와 문서화된 정의를 기반으로 삼고, 적극적인 테스트도 병행해야 합니다. 검토 기준, 대표성 있는 평가 데이터, 그룹별 성과 분석, 사람의 감독을 통해 근거 없는 콘텐츠, 집단별로 불균등한 오류율, 오디언스에 따라 체계적으로 달라지는 결과를 식별할 수 있습니다. 이러한 패턴이 발견되면 다운스트림 검토에만 의존하지 말고 원인이 된 데이터, 레이블, 지침 또는 의사결정 규칙을 바로잡아야 합니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_how-to-make-ai-marketing-workflows-more-reliable_1":{"id":"text-7b5b228bdf","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003E자주 하는 실수\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003E작업 성능 향상을 비즈니스 효과의 증거로 간주하는 것은 흔히 발생하는 오류입니다. AI 워크플로우는 예측 정확도, 콘텐츠 품질, 클릭률을 높이면서도 적격 파이프라인이나 추가 매출에는 기여하지 못할 수 있습니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_how-snowflake-supports-ai-in-marketing":{"id":"title-v2-40f9c28e5d","additionalClasses":"anchor-title anchor-title--how-snowflake-supports-ai-in-marketing","type":"heading2","lines":["Snowflake가 마케팅 AI를 지원하는 방식"],":type":"snowflake-site/components/title-v2"},"text_how-snowflake-supports-ai-in-marketing_0":{"id":"text-7cc678718e","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/ko/solutions/departments/marketing/\"\u003ESnowflake의 마케팅 AI 데이터 클라우드\u003C/a\u003E는 마케팅팀이 고객, 제품, 캠페인 데이터를 함께 활용하고, 사용 사례마다 별도 아키텍처를 구축하지 않고도 해당 정보에 AI를 적용할 수 있는 공유 환경을 제공합니다.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/ko/product/snowflake-cowork/\"\u003ESnowflake CoWork\u003C/a\u003E는 마케터와 기타 비즈니스 사용자가 거버넌스가 적용된 엔터프라이즈 데이터를 탐색하고 승인된 업무를 이어갈 수 있도록 대화형 인터페이스를 제공합니다. 마케터는 CoWork를 활용해 캠페인 성과 변화를 분석하고 미디어 성과를 파이프라인 또는 트랜잭션 데이터와 비교하며, 조직의 최신 데이터와 시맨틱 컨텍스트를 근거로 권고안을 마련할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003ESnowflake AI Functions는 이러한 경험을 뒷받침하는 데이터 워크플로우에서 반복 가능한 AI 처리를 지원합니다. 마케팅, 분석, 데이터 팀은 기초 데이터를 별도의 AI 처리 환경으로 이동하지 않고도 콘텐츠 분류, 정보 추출, 캠페인 결과 요약, 고객 피드백 분석은 물론 승인된 제품 또는 캠페인 컨텍스트를 기반으로 한 자료 생성까지 수행할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E여러 단계나 도구에 걸쳐 실행되는 워크플로우의 기반 오케스트레이션은 \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-cortex-agents/\"\u003ECortex Agents\u003C/a\u003E가 담당합니다. 에이전트는 Cortex Analyst로 정형 데이터를 쿼리하고, Cortex Search로 비정형 소스에서 정보를 검색하며, 사용자 지정 도구를 통해 도출된 인사이트를 승인된 비즈니스 프로세스와 연결할 수 있습니다. 에이전트에 할당된 도구와 권한에 따라 워크플로우는 분석 단계에서 종료되거나, 권고안을 작성하거나, 승인을 위해 작업을 전달하거나, 다른 시스템에서 후속 작업을 이어갑니다.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/ko/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E는 이러한 데이터 및 AI 워크로드 전반에 거버넌스를 적용합니다. 분류, 마스킹, 역할 기반 액세스, 계보가 기초 데이터와 연결된 상태로 유지되므로, 어떤 사용자와 서비스 ID가 고객 정보에 액세스할 수 있는지, 해당 정보가 어떻게 사용되는지를 제어할 수 있습니다. Cortex AI Guardrails는 지원되는 에이전틱 경험에서 프롬프트 인젝션과 탈옥 시도를 차단하는 런타임 보호 기능을 제공합니다.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/data-clean-rooms/\"\u003ESnowflake Data Clean Room\u003C/a\u003E은 광고주, 퍼블리셔 및 기타 파트너 간 협업에도 동일한 거버넌스 접근 방식을 확장합니다. 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