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있습니다.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003EAnomalo의 Jonathan Karon이 Snowflake에서 자동화된 데이터 품질 관리를 구현하는 방법을 설명하는 영상을 시청해 보세요.\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_what-is-ai-in-sales_0":{"id":"embed-6a90e09f5b","youtubeVideoId":"gUaimG4dcQM","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_ai-for-prospecting-and-outreach":{"id":"title-v2-c92d76f1ca","additionalClasses":"anchor-title anchor-title--ai-for-prospecting-and-outreach","type":"heading2","lines":["잠재 고객 발굴과 아웃리치를 위한 AI"],":type":"snowflake-site/components/title-v2"},"text_ai-for-prospecting-and-outreach_0":{"id":"text-af6939a5d7","text":"\u003Cp\u003E잠재 고객 발굴은 지금 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Sales Cockpit은 영업팀이 고객 행동과 서비스 성과를 빠르게 파악하도록 지원하는 통합 AI 기반 앱입니다. Sales Cockpit은 수주 만에 구축되고 며칠 안에 배포됐습니다. 고객 인사이트 자료 작성 시간을 3~4시간에서 10분 이내로, 개인화된 아웃바운드 이메일 준비 시간을 40분에서 약 30초로 단축했습니다. 또한 영업팀에서 매월 2,000시간 이상을 절감해 약 140만 달러(USD)의 효과를 창출했습니다. \u003Ci\u003E통계는 Intercom의 내부 데이터와 추정치를 기반으로 합니다.\u003C/i\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal","title":{"id":"title","type":"heading4","lines":["Intercom"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"전체 고객 사례 읽기"},"image":{"id":"image","height":"351","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--e9795d40-050e-45c6-9574-1a683581d987/intercom.png?quality=85&preferwebp=true","alt":"intercom logo","isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card"},"title_ai-for-forecasting-and-revenue-intelligence":{"id":"title-v2-86ecc6a913","additionalClasses":"anchor-title anchor-title--ai-for-forecasting-and-revenue-intelligence","type":"heading2","lines":["예측 및 매출 인텔리전스를 위한 AI"],":type":"snowflake-site/components/title-v2"},"text_ai-for-forecasting-and-revenue-intelligence_0":{"id":"text-07302f900d","text":"\u003Cp\u003E파이프라인 보고서에는 각 영업 기회에 기록된 금액, 스테이지, 성사 확률, 예상 종료일, 예측 카테고리가 반영됩니다. 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관련 재무 데이터를 활용해 향후 수주액 또는 매출을 추정합니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003E예측 근거 설명:\u003C/strong\u003E 어떤 기회와 신호가 예측 결과나 변화에 가장 크게 기여했는지 보여줍니다.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E예측 정확도는 영업, 재무, 매출 운영 조직 전반에서 일관된 비즈니스 정의를 사용하는지에 따라 달라집니다. 파이프라인 금액, 스테이지, 성사 예정일, 예측 카테고리는 CRM에서 표준화된 것처럼 보일 수 있지만, 실제로는 지역이나 제품, 관리자에 따라 다르게 적용되는 경우가 많습니다.\u003C/p\u003E\n\u003Cp\u003E따라서 예측에 사용하는 필드와 결과에 대해 거버넌스가 적용된 정의를 마련하고, 해당 정의가 변경된 시점을 기록해 두어야 합니다. 훈련 데이터와 모델 출력, 예측 보고서는 모두 동일한 시맨틱 컨텍스트를 사용해야 합니다. 그래야 예측이 애초에 추정하도록 설계된 비즈니스 성과를 기준으로 평가될 수 있습니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_sales-automation-and-ai-sales-agents":{"id":"title-v2-831ab5229e","additionalClasses":"anchor-title anchor-title--sales-automation-and-ai-sales-agents","type":"heading2","lines":["영업 자동화와 AI 영업 에이전트"],":type":"snowflake-site/components/title-v2"},"text_sales-automation-and-ai-sales-agents_0":{"id":"text-a31f617f7a","text":"\u003Cp\u003E전통적인 영업 자동화는 사전에 정의된 조건에 따라 특정 작업을 실행합니다. 예를 들어 워크플로우가 담당 지역에 따라 리드를 배정하거나, 기회에 최근 활동이 없을 때 작업을 생성할 수 있습니다. 이러한 시스템은 반복 가능한 프로세스에는 효과적이지만, 적절한 조치가 여러 다른 요인에 따라 달라지는 상황에는 유연하게 대응하기 어렵습니다.\u003C/p\u003E\n\u003Cp\u003EAI 에이전트는 여러 시스템과 단계에 걸쳐 영업 업무를 수행하면서, 새로운 계정·기회·구매자 정보가 확보되는 대로 수행 방식을 조정할 수 있습니다. 예를 들어 기회가 정체되었을 때 적절한 대응은 그동안 어떤 일이 있었는지에 따라 달라집니다. 구매자가 보안 관련 답변을 기다리고 있을 수도 있고, 의사 결정권자가 논의에서 이탈했을 수도 있으며, 제안서의 유효 기간이 만료되었거나 단순히 CRM 레코드가 최신 상태가 아닐 수도 있습니다. 에이전트는 이러한 컨텍스트를 활용해 누락된 정보를 조회할지, 후속 커뮤니케이션을 준비할지, 기회를 업데이트할지, 아니면 담당자에게 이슈를 전달할지 판단할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E영업 에이전트가 지원할 수 있는 업무는 다음과 같습니다.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003E잠재 고객 검증:\u003C/strong\u003E 계정을 조사하고 승인된 기준에 따라 평가한 뒤, 검토에 필요한 근거 자료를 준비합니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003E회의 준비:\u003C/strong\u003E 계정 이력, 최근 활동, 기회 현황, 미해결 질문을 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업데이트하며, 작업을 생성하거나 승인을 위해 업무를 전달하는 등 워크플로우의 더 많은 부분에서 직접 조치를 수행할 수 있습니다. 시스템이 더 많은 업무를 담당하게 되면, 검토 없이 수행할 수 있는 작업과 사람의 승인이 필요한 지점을 명확히 정의해야 합니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_sales-automation-and-ai-sales-agents_0":{"id":"text-9625a35716","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003E자주 하는 실수\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003E각각의 작업이 위험도가 낮아 보인다는 이유로 에이전트에 광범위한 액세스 권한을 부여하는 경우가 많습니다. 하지만 허용된 작업의 시퀀스가 결합되면 고위험 결과를 초래할 수 있으므로, 전체 워크플로우를 종합적으로 평가하고 거버넌스를 적용해야 합니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_what-keeps-ai-sales-workflows-accurate-and-accountable":{"id":"title-v2-f78dd56839","additionalClasses":"anchor-title anchor-title--what-keeps-ai-sales-workflows-accurate-and-accountable","type":"heading2","lines":["AI 영업 워크플로우의 정확성과 책임성을 확보하는 기반"],":type":"snowflake-site/components/title-v2"},"text_what-keeps-ai-sales-workflows-accurate-and-accountable_0":{"id":"text-ccfca8618e","text":"\u003Cp\u003E영업 애플리케이션에서 AI가 사용하는 레코드는 워크플로우 전반에서 서로 연결된 상태를 유지하고 일관된 비즈니스 의미를 보존해야 합니다. 신뢰할 수 있고 책임 있는 AI 시스템을 구축하려면 데이터 액세스, 고객 대상 설명, 에이전트가 수행할 수 있는 작업의 범위를 명확히 제한해야 합니다.\u003C/p\u003E\n\u003Cp\u003ESnowflake의 제품 부문 VP인 Baris Gultekin은 다음과 같이 강조합니다. “AI 에이전트는 기존 자동화나 챗봇에서 크게 도약한 기술입니다. 하지만 이를 대규모로 배포하려면 AI에 최적화된 정보 생태계가 필요합니다.”\u003C/p\u003E\n\u003Ch3\u003E계정, 활동 및 매출 데이터 연결\u003C/h3\u003E\n\u003Cp\u003E영업 의사 결정에는 CRM 레코드, 마케팅 참여 데이터, 영업 활동, 회의 녹취록, 제품 사용 현황, 고객 지원 상호작용, 견적서, 계약서, 재무 시스템 등 다양한 정보가 활용됩니다. 각 소스에는 상업적 관계의 일부가 담겨 있으며, 서로 다른 식별자와 갱신 주기를 사용하는 경우가 많습니다.\u003C/p\u003E\n\u003Cp\u003E연결된 계정 이력은 모델과 애플리케이션이 활동을 올바른 고객, 담당자, 영업 기회 또는 구매 그룹과 일관되게 연결할 수 있도록 합니다. 이러한 연결이 없으면 애플리케이션이 진행 중인 고객 지원 문제를 고려하지 않은 채 계정을 평가하거나, 최신 제안서에 대한 가시성 없이 영업 기회를 요약할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E여러 담당자가 구매에 참여하는 경우에는 아이덴티티 확인이 특히 중요합니다. 시스템은 각 개인을 구분하는 동시에 동일한 계정 및 영업 기회와 이들이 맺고 있는 관계를 이해해야 합니다. 또한 담당자의 이직이나 하나의 영업 기회가 여러 구매 건으로 분리되는 상황처럼 시간에 따른 변화도 보존해야 합니다.\u003C/p\u003E\n\u003Cp\u003E데이터 최신성은 의사 결정의 특성에 부합해야 합니다. 예를 들어 연간 영업 구역 계획에는 주기적으로 갱신되는 계정 정보를 활용할 수 있지만, 실시간 영업 기회 지원에는 최근 상호작용과 현재 가격, 최신 거래 상태가 필요합니다. 누락된 레코드, 지연된 업데이트, 소스 범위의 변화를 지속적으로 모니터링해야 합니다. 애플리케이션에 명확한 오류가 없더라도 이러한 요인으로 결과가 달라질 수 있기 때문입니다.\u003C/p\u003E\n\u003Ch3\u003E영업 및 매출 정의 표준화\u003C/h3\u003E\n\u003Cp\u003E용어는 애플리케이션과 팀 전반에서 명확하고 일관되게 정의해야 합니다. 예를 들어 거래 가치는 연간 계약 가치, 총계약 가치 또는 당기에 예상되는 매출을 의미할 수 있습니다. 영업, 재무 및 매출 운영팀은 각 용어의 의미에 합의해야 합니다. 그래야 모델과 보고서, 에이전트가 작업에 적합한 정의를 사용할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E시맨틱 컨텍스트는 거버넌스가 적용된 지표, 뷰, 메타데이터와 계정, 담당자, 영업 기회, 제품, 매출 이벤트 간 관계를 통해 이러한 차이를 명확히 구분합니다. 또한 스테이지 진입 기준, 예측 카테고리 규칙, 영업 구역 할당, 검증 기준, 수주와 인식 매출의 관계를 보존할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E중요한 정의마다 담당자를 명확히 지정해야 합니다. 담당자는 변경을 승인하고 영업, 재무, 마케팅 및 매출 운영 간의 정의 차이를 조정합니다. 검증 기준이나 예측 프로세스가 변경되면 과거 레코드가 여전히 동일한 결과를 나타내는지도 확인해야 합니다. 그렇지 않으면 모델이 서로 다른 두 운영 정의를 하나의 일관된 레이블로 취급할 수 있습니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_what-keeps-ai-sales-workflows-accurate-and-accountable_0":{"id":"text-9881296c7e","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003E빠른 팁\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003E모델이나 에이전트가 사용하는 모든 필드에 간단한 정의 카드를 만드세요. 비즈니스 의미, 담당자, 계산 방식, 시행일과 지역 또는 제품군별 차이를 포함합니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_what-keeps-ai-sales-workflows-accurate-and-accountable_1":{"id":"text-ac040289c6","text":"\u003Ch3\u003E승인된 영업 정보를 기반으로 생성 결과의 신뢰성 확보\u003C/h3\u003E\n\u003Cp\u003E영업 애플리케이션은 계정별 컨텍스트와 조직의 공식 소스 자료를 함께 사용합니다. 계정 컨텍스트는 구매자의 상황과 영업 기회의 이력을 설명합니다. 조직이 승인한 소스 자료는 제품, 가격, 보안 관행, 구현 요건 및 계약 조건과 관련해 전달할 수 있는 정보의 범위를 규정합니다.\u003C/p\u003E\n\u003Cp\u003E승인된 정보는 고객 대상 콘텐츠에서 특히 중요합니다. 사용 가능한 소스만으로 구매자의 질문에 답할 수 없다면 그럴듯한 답변을 생성해서는 안 됩니다. 대신 영업 담당자나 해당 분야 전문가가 확인할 수 있도록 미해결 상태로 남겨야 합니다.\u003C/p\u003E\n\u003Cp\u003E소스 정보에는 필요한 경우 담당자, 버전 및 유효 기간을 지정해야 합니다. 정보 검색 시 최신 자료를 우선하되, 거래 과정에서 이미 이루어진 약속을 설명할 수 있도록 충분한 이력을 보존해야 합니다.\u003C/p\u003E\n\u003Ch3\u003E액세스 및 허용된 사용 범위의 엄격한 적용\u003C/h3\u003E\n\u003Cp\u003E영업 워크플로우에는 개인정보, 계정 전략, 통화 녹음, 가격, 제품 사용 현황 및 계약 세부 정보가 포함됩니다. AI 애플리케이션이 이러한 레코드를 검색하거나 결합해 새로운 결과를 생성할 때도 액세스 및 허용된 사용 요건은 그대로 적용됩니다.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/data-security/rbac/\"\u003E역할 기반 액세스 제어\u003C/a\u003E(RBAC)는 사용자와 서비스 아이덴티티가 열람할 수 있는 고객 수준 정보 또는 상업적 민감 정보의 범위를 제한합니다. 분류와 마스킹은 민감 필드를 보호하며, 영업 구역, 계정팀 및 딜룸 권한을 통해 특정 영업 기회에 액세스할 수 있는 사용자를 추가로 제한할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E애플리케이션은 데이터가 수집된 목적과 조건도 준수해야 합니다. 고객 지원이나 제품 분석을 위한 정보 사용 승인을 받았다고 해서 조직이 해당 정보를 영업 타기팅이나 다른 목적으로 사용할 수 있는 것은 아닙니다. 계약, 지역별 개인정보 보호 요건 및 내부 정책에 따라 활동 데이터와 통화 녹음을 분석할 수 있는 범위가 제한될 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E감사 기록에는 애플리케이션이 액세스한 데이터, 적용된 정책, 결과가 영업 워크플로우에 반영된 방식이 명시되어야 합니다. 이는 추천 결과가 계정 우선순위에 영향을 미치거나, 고객이 생성된 메시지를 받거나, 에이전트가 기록 시스템을 수정하는 경우 특히 중요합니다.\u003C/p\u003E\n\u003Ch3\u003E에이전트 권한과 고객 대상 작업의 제한\u003C/h3\u003E\n\u003Cp\u003E에이전틱 영업 워크플로우에는 데이터 액세스 제어뿐 아니라 작업 수준의 권한도 필요합니다.\u003C/p\u003E\n\u003Cp\u003E워크플로우에서는 다음 항목을 정의해야 합니다.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E에이전트가 액세스할 수 있는 계정, 영업 기회 및 데이터 소스\u003C/li\u003E\n\u003Cli\u003E에이전트가 변경을 제안하거나 업데이트할 수 있는 CRM 필드\u003C/li\u003E\n\u003Cli\u003E에이전트가 사용할 수 있는 커뮤니케이션 채널\u003C/li\u003E\n\u003Cli\u003E아웃바운드 메시지에 영업 담당자의 승인이 필요한지 여부\u003C/li\u003E\n\u003Cli\u003E에이전트가 포함할 수 있는 제품, 가격 또는 계약 관련 설명\u003C/li\u003E\n\u003Cli\u003E관리자, 재무팀 또는 법무팀의 승인이 필요한 상업적 변경 사항\u003C/li\u003E\n\u003Cli\u003E에이전트가 작업을 중단하거나 추가 설명을 요청하거나 상위 담당자에게 에스컬레이션해야 하는 시점\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E서비스 ID, 도구 권한, 승인 임계값 및 중단 조건이 에이전트의 운영 범위를 결정합니다. 애플리케이션에는 영업 스택 전반에 대한 광범위한 액세스 권한이 아니라, 할당된 작업에 필요한 데이터와 도구만 제공해야 합니다. 또한 에이전트가 검색한 정보, 호출한 도구, 완료한 작업을 보여 주는 추적 기록이 필요합니다.\u003C/p\u003E\n\u003Cp\u003E이러한 제어를 통해 에이전트가 관련 조사, 조율, 기록 관리 업무를 더 많이 수행하도록 지원하는 동시에, 고객 관계와 중대한 상업적 의사 결정에 대한 사람의 책임성을 유지할 수 있습니다.\u003C/p\u003E\n\u003Ch3\u003E영업 성과를 기준으로 한 결과 평가\u003C/h3\u003E\n\u003Cp\u003E신뢰할 수 있는 평가는 일반적인 사례와 까다로운 사례는 물론, 중단하거나 에스컬레이션하는 것이 올바른 대응인 상황까지 실제 영업 업무를 반영한 데이터 세트에서 시작됩니다. 테스트에 앞서 예상 결과, 허용된 작업 및 수용 기준을 정의해야 합니다. 그래야 검토자의 주관적 인상이 아닌 일관된 기준에 따라 시스템을 평가할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E구체적인 평가 방법은 기능에 따라 달라집니다. 예측 모델은 캘리브레이션, 순위 품질, 관련 세그먼트별 성능 등의 지표로 평가해야 합니다. 생성형 시스템은 사실 정확성, 승인된 소스에 대한 근거성, 완전성, 필수 표현 준수 여부를 테스트해야 합니다. 에이전트는 결과뿐 아니라 수행 과정도 평가해야 합니다. 적절한 도구를 선택하고 올바른 데이터를 사용했는지, 권한 범위를 벗어나지 않았는지, 오류에서 복구했는지, 작업의 중단 조건을 충족했는지 확인해야 합니다.\u003C/p\u003E\n\u003Cp\u003E그러나 여러 모델과 검색 단계, 도구가 하나의 워크플로우에 관여한다면 구성 요소 수준의 결과만으로는 충분하지 않습니다. 단계 간에 컨텍스트가 유지되는지, 초기 오류가 이후의 의사 결정이나 작업에 영향을 미치는지를 파악할 수 있도록 엔드투엔드 평가도 실행해야 합니다.\u003C/p\u003E\n\u003Cp\u003E배포 후에는 프로덕션 추적 기록, 수정률, 에스컬레이션 및 결과 데이터를 통해 영업 데이터, 제품, 운영 규칙이 변화함에 따라 성능도 달라지는지 파악할 수 있습니다. 이러한 관찰 결과를 평가 세트에 다시 반영해 테스트가 시스템의 실제 업무를 지속적으로 대표하도록 해야 합니다.\u003C/p\u003E\n\u003Ch3\u003E데이터 품질 및 편향 모니터링\u003C/h3\u003E\n\u003Cp\u003E영업 데이터에는 구매자의 행동뿐 아니라 조직이 과거에 내린 의사 결정도 반영됩니다. 과거의 성과는 어떤 계정이 CRM에 등록되었는지, 영업 구역이 어떻게 배정되었는지, 어떤 리드에 후속 조치가 이루어졌는지, 영업 담당자가 활동을 얼마나 일관되게 기록했는지에 따라 달라집니다. 이러한 기록으로 학습한 모델은 고객 수요 패턴뿐 아니라 조직이 과거에 관심과 자원을 배분한 방식까지 학습할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E불완전한 활동 기록은 또 다른 왜곡을 초래합니다. 주요 마일스톤만 기록하는 우수한 영업 담당자는 모든 통화와 메시지를 기록하는 담당자보다 적은 상호작용으로 거래를 진전시키는 것처럼 보일 수 있습니다. 비공식 채널을 통해 운영되는 관리형 계정은 활동이 자동으로 수집되는 계정보다 참여도가 낮아 보일 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E이러한 문제를 해결하려면 최신 거버넌스가 적용된 소스 데이터와 문서화된 결과 정의가 기본적으로 필요하며, 적극적인 테스트도 병행해야 합니다. 영업 구역, 계정 그룹, 고객 확보 경로 및 기타 관련 세그먼트별 성능을 비교하고, 결측치가 예측을 바꾸는지 점검해야 합니다. 또한 모델의 추천이 향후 학습에 사용될 데이터에 어떤 영향을 미치는지도 검토해야 합니다.\u003C/p\u003E\n\u003Cp\u003E시스템이 불균등한 결과를 내놓는다면 해당 결과를 초래한 기록, 레이블, 의사 결정 규칙 및 운영 프로세스를 조사해야 합니다. 워크플로우 마지막 단계에서 사람이 검토하더라도, 처음부터 어떤 계정에 관심을 기울일지를 반복적으로 결정하는 스코어링 또는 우선순위 지정 패턴까지 바로잡을 수는 없습니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_why-ai-for-sales-runs-on-snowflake":{"id":"title-v2-f4b310ff75","additionalClasses":"anchor-title anchor-title--why-ai-for-sales-runs-on-snowflake","type":"heading2","lines":["영업 AI가 Snowflake에서 실행되어야 하는 이유"],":type":"snowflake-site/components/title-v2"},"text_why-ai-for-sales-runs-on-snowflake_0":{"id":"text-5be508a347","text":"\u003Cp\u003ESnowflake는 영업, 매출 운영, 데이터 및 재무팀이 CRM, 활동, 제품, 지원 및 매출 데이터를 함께 활용할 수 있는 공유 환경을 제공합니다. 각 영업 워크플로우마다 별도의 데이터 아키텍처를 구축하지 않고도 예측형, 생성형 및 에이전틱 AI를 적용할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/ko/product/features/end-to-end-ml-workflows/\"\u003ESnowflake ML\u003C/a\u003E은 Snowflake 내에서 거버넌스가 적용된 데이터를 기반으로 구축하고 실행하는 예측 모델을 지원합니다. Classification은 정의된 과거 결과와 연관된 패턴을 학습해 리드, 계정 또는 영업 기회 스코어링과 같은 워크로드를 지원할 수 있습니다. Forecasting 함수는 시계열 데이터에서 미래의 수치를 예측하며, 보다 폭넓은 기능을 통해 워크플로우에 필요한 사용자 지정 모델을 개발하고 운영할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E모델이 기초 데이터가 저장된 위치에서 바로 실행되므로, 별도의 스코어링 사본을 유지하지 않고도 연결된 CRM, 활동 및 제품 데이터를 기반으로 점수와 예측을 업데이트할 수 있습니다. 다만 목표 결과와 학습 데이터, 평가 기준, 예측을 영업 프로세스에 반영하는 방식은 여전히 조직이 결정합니다.\u003C/p\u003E\r\n\u003Cp\u003ECortex AI Functions는 Snowflake에 저장된 정보에 언어 모델과 멀티 모달 모델을 적용합니다. 영업팀과 데이터팀은 이러한 함수를 사용해 SQL 기반 데이터 워크플로우에서 회의 녹취록을 요약하고, 고객의 이의를 분류하고, 요구 사항이나 실행 항목을 추출하며, 노트나 문서 모음을 분석할 수 있습니다. 이러한 결과를 정형 계정 및 영업 기회 데이터와 결합해 보고, 코칭 또는 다운스트림 애플리케이션에 활용할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E여러 단계에 걸쳐 정보를 조사하고 작업을 이어 가야 하는 워크플로우에서는 \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-cortex-agents/\"\u003ECortex Agents\u003C/a\u003E가 정형 데이터용 Cortex Analyst, 비정형 정보용 Cortex Search, 승인된 비즈니스 프로세스에 연결된 사용자 지정 도구를 결합할 수 있습니다. 영업 애플리케이션은 이러한 기능으로 영업 기회를 분석하고, 관련 상호작용과 문서를 검색하고, 추천을 작성한 뒤, 그 결과로 생성된 작업을 다른 시스템으로 전달할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003ECortex Agents에 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