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Photo","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--de4dfe49-467b-4cac-8e3e-01628927f2aa/erin-foxworthy-photo-headshot.jpg?preferwebp=true&quality=85","height":"1652","width":"1314","lazyEnabled":true,":type":"snowflake-site/components/image"},"authorCta":{"id":"button-0053ed073c","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/erin-foxworthy/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Erin Foxworthy"}}],"tag":{"tagText":"業界別ソリューション","tagColor":"#C54985"},"title":{"lines":["生成AIがブランド広告主と広告代理店の消費者エンゲージメントに役立つ4つの方法"],"type":"heading2",":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/blog/blog-hero"}},":itemsOrder":["blog_hero"],":type":"snowflake-site/components/container"},"responsivegrid_content":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"blog_text":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,"appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text":{"id":"blog-text-24135f3d44","text":"\u003Cp\u003E不確実性は、今日のブランド広告主や広告代理店にとっての新しい規範です。GoogleはサードパーティCookieに\u003Ca href=\"https://digiday.com/marketing/after-years-of-uncertainty-google-says-it-wont-be-deprecating-third-party-cookies-in-chrome/\" target=\"_blank\"\u003E対する姿勢を\u003C/a\u003E再び変え、少なくとも当面はサードパーティCookieを保持します。Cookieの将来が依然として不透明なため、多くの広告主はすでにファーストパーティデータやコンテキストターゲティングなどのオルタナティブターゲティング戦略の採用に向けて準備を進めています。 \u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.researchandmarkets.com/reports/5781270/digital-publishing-content-streaming-global\" target=\"_blank\"\u003Eデジタル出版およびコンテンツストリーミング市場が拡大\u003C/a\u003Eし、新しいストリーミングチャネルやオリジナルコンテンツが続々と登場する中、オーディエンスの分断と消費者行動の変化が進んでいます。コンテンツの過剰は、\u003Ca href=\"https://www.adweek.com/media/every-penny-counts-how-your-marketing-efforts-can-avoid-ad-fatigue/\" target=\"_blank\"\u003E広告の疲れ\u003C/a\u003E、\u003Ca href=\"https://columncontent.com/content-fatigue/\" target=\"_blank\"\u003Eコンテンツの疲れ\u003C/a\u003E、\u003Ca href=\"https://www.theeducationmagazine.com/generation-z-and-their-attention-time-span/\" target=\"_blank\"\u003E集中力の低下\u003C/a\u003Eなど、多大な影響を与えています 。これらはすべて、消費者の広告に対する反応を変えています。 \u003C/p\u003E\n\u003Cp\u003E同時に、今年の米国の広告支出は10.4%急増し、5,700億ドルに達すると予測されています（\u003Ca href=\"https://winterberrygroup.com/whats-new/winterberry-group-announces-annual-outlook-for-estimated-u.s.-ad-marketing-spend-in-2024-fueled-by-digital-ad-growth-connected-tv-data-infrastructure-demand\" target=\"_blank\"\u003EWinterberry Group\u003C/a\u003E調べ）。デジタルチャネルが\u003Ca href=\"https://winterberrygroup.com/whats-new/winterberry-group-announces-annual-outlook-for-estimated-u.s.-ad-marketing-spend-in-2024-fueled-by-digital-ad-growth-connected-tv-data-infrastructure-demand\" target=\"_blank\"\u003E広告支出の64%\u003C/a\u003Eを占めるようになり、コネクテッドテレビ、ビデオ、ソーシャルメディアチャネルが成長を加速させています。広告主には、このトレンドを利用してリーチを最大化し、キャンペーン効果を高め、最終的に成長と利益を促進する絶好の機会があります。\u003C/p\u003E\n\u003Cp\u003E\u003Cspan style=\"font-style: inherit;\"\u003E生成AIは、ブランド広告主や広告代理店が課題に対処し、その時々の可能性を引き出すのに役立ちます。大量のデータに基づいてトレーニングされた生成AIモデルは、テキスト、画像、動画、音声、音楽の形式で新しいデータやコンテンツを生成できます。生成AIを使用する広告主は、広告コンテンツの強化とパーソナライゼーション、広告キャンペーンのパフォーマンスの最適化、より効果的な消費者ターゲティングにより、ブランドイメージと市場シェアを高めることができます。\u003C/span\u003E\u003Cbr\u003E\n\u003C/p\u003E\n\u003Ch3\u003E広告主向け生成AIユースケース\u003C/h3\u003E\n\u003Cp\u003Eここでは、ブランド広告主と広告代理店が生成AIを活用して消費者にエンゲージする4つの方法をご紹介します。\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003E1.広告キャンペーンのパフォーマンス最適化\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003E広告キャンペーンのパフォーマンスを最適化することで、クリックスルー率、コンバージョン率、広告費用対効果（ROAS）を高めることができます。しかし、広告コンテンツのパーソナライゼーションと同様に、キャンペーンのパフォーマンスを最適化するには時間と費用が必要です。これは、ブランド広告主と広告代理店にとって不足している2つのリソースです。AIアルゴリズムは、パフォーマンス指標と消費者フィードバックを分析することで、改善すべき領域を特定し、最適なチャネルやエンゲージメント時間などの戦略的調整を推奨します。生成AIは、予測モデルを使用して広告パフォーマンスを評価することもできます。\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003E2.コンテンツとコンセプトの作成\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003E広告主は、ROIを生み出す広告コンテンツの開発と制作に多大なコストを負担することになります。\u003Ca href=\"https://www.mediaculture.com/how-much-does-it-cost-to-make-a-commercial\" target=\"_blank\"\u003Eある代理店\u003C/a\u003Eによると、1つの広告の制作コストは100万ドルにも上ると言われています。生成AIは、これらのコストを大幅に削減できます。生成AIは、人間のクリエイターの創造性やコンテキスト知識を再現することはできませんが、広告主がアイデアをブレインストーミングしてコラボレーションしたり、コンテンツ制作の側面を自動化したり、既存のアセットからコンテンツを転用して広告コピー、画像、動画、音声、さらにはマルチモーダル広告を作成したりするのに役立ちます。これにより、コンテンツマーケターはよりクリエイティブで戦略的な業務に注力し、広告制作プロセスを加速させることができます。 \u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003E3.広告のパーソナライゼーション\u003C/b\u003E\u003Cbr\u003E\n\u003C/p\u003E\n\u003Cp\u003Eパーソナライズされた広告コンテンツがポジティブな結果をもたらす：\u003Ca href=\"https://www.zippia.com/advice/personalization-statistics/#:~:text=89%25%20of%20marketers%20see%20positive%20ROI%20from%20personalization.,earning%20up%20to%20%248%20for%20every%20%241%20spent.\" target=\"_blank\"\u003E調査\u003C/a\u003Eによると、マーケターの89%は、独自の特性、嗜好、行動に基づいて広告を個人またはグループに合わせて調整することでROIがプラスになると回答しています。しかし、パーソナライゼーションを成功させるには、データプライバシー規制を順守しながら、消費者データの収集と分析、セグメンテーション、テスト、コンテンツの作成と配信などの時間とコストのかかるプロセスを行う必要があります。生成AIは、大量のデータを処理、分析し、ハイパーパーソナライズされた広告を作成することで、時間とコストを削減し、きめ細かいパーソナライゼーションを実現します。ブランドは生成AIを使用して、たとえば顧客のフィードバックを解釈し、より良い製品提案を行うことができます。\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003E4.コンテキストターゲティング\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003Eコンテキストターゲティングにより、ブランドは消費者が最もエンゲージする可能性の高い場所に広告を配置できます。また、訪問者に関するデータではなく、ウェブサイトのコンテンツに基づいて広告をターゲティングすることで、プライバシー規制や障害を回避します。適切に配置された広告は、それを見た消費者に関連性の高いものとなる可能性が高く、クリックスルー率とコンバージョン率の向上につながります。生成AIは、ウェブサイトとURLを素早くスキャンして分析し、コンテンツとプレースメントを分類できます。また、センチメントや包括性などの特性に基づいて大量のコンテンツのコンテキストトリガーを作成し、より効果的なプレースメントを可能にします。\u003C/p\u003E\n\u003Cp\u003Eブランドや広告代理店が生成AIからどのようなメリットを得られるかについては、eBook\u003Ca href=\"https://www.snowflake.com/resource/gen-ai-in-advertising-media-and-entertainment-4-things-you-need-to-know/\" target=\"_blank\"\u003E広告・メディア・エンターテイメントの生成AI：知っておくべき4つのこと\u003C/a\u003Eをダウンロードしてください。\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{},"columnCount":12,"appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{},":itemsOrder":[],":type":"wcm/foundation/components/responsivegrid"},"container_author_chip":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"author_chip":"aem-GridColumn 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CodeがFP&Aのレポーティングからインサイトへの移行をどのように支援しているか"},"image":{"id":"image-e3845c50fc","alt":"A man working on a laptop and a tablet, looking at financial data","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--0a12cb36-7c51-4015-afbd-c1e94310f818/03-finserv.jpg?preferwebp=true&quality=85","height":"3331","width":"5922","lazyEnabled":true,":type":"snowflake-site/components/image"},"title":{"lines":["Cortex 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Hurd","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--033c2dfe-61af-4c66-9a61-c8954f343e69/sarahhurd-headshot-final.png?preferwebp=true&quality=85","height":"1200","width":"896","lazyEnabled":true,":type":"snowflake-site/components/image"},"authorCta":{"id":"button-59c7204cf6","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/sarah-hurd/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Sarah Hurd"}}],":type":"snowflake-site/components/blog/card","publicationDate":"JUN 12, 2026","button":{"id":"button-f99982c7cf","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/ja/blog/financial-services-ai-roi-agentic/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"金融サービスはAIの次のフェーズへ：測定可能でガバナンスの効いたエージェント型へ"},"image":{"id":"image-c457007302","alt":"Three people having a meeting in a conference 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