The Trade Desk 助您充分運用第一方數據

在 The Trade Desk 平台上，我們希望能夠確保您能掌握與消費者的關係，精確地鎖定目標受眾，並有效地衡量廣告活動。在消費者行為變化萬千和傳統身分識別數據日益稀缺的情況下，如何達成行銷目標變得更有挑戰性。

But remember that third-party cookies are not the only asset at your disposal. Mobile ad IDs (MAIDs), device IDs, and alternative identifiers like Unified ID 2.0 (UID2) are reliable signals to support known audience targeting at scale. What’s more, users’ attentions are shifting toward new devices — connected TV, gaming, streaming on mobile apps, even audio and podcasting — all of which are cookie-free environments that can be included in your holistic targeting strategy.

我們將在文章中分享，如何在我們的平台上充分運用第一方數據，極大化其價值並提升媒體支出效益的三項策略。

設定您的受眾鎖定策略

1. 整理您的受眾數據。將數據匯入我們的平台之前，請務必根據您的廣告活動目標和受眾統計數據，對其進行整理和分類。不論您是想使用特定收集點或品牌的第一方數據，或者設定目標對象，還是想爭取競爭對手的客戶，第一個步驟都是使用正確數據來設定您的廣告活動。運用所有可用的 ID，建立可觸及最大覆蓋範圍的種子受眾，從傳統的 ID 如第三方 Cookie、MAID，到新興的 UID2 以及 RampID。
2. Implement cross-device. Cross-device identity resolution can help you pinpoint where consumers are spending time with quality content and can best engage with your brand. If your campaign requires specific device or channel targeting, you can still customize these details while leveraging the power of Identity Alliance, The Trade Desk’s graph solution.
3. Leverage the data marketplace. The Trade Desk’s data marketplace is a way to layer, enrich, and expand your audience targeting based on demo, behavior, and context. Composed of some of the most sought-after data providers (including retail), data marketplace has segments containing many ID types — including UID2, to help diversify your ID mix and reduce dependence on cookies. For a list of relevant segments leveraging UID2, reach out to your account team.

建立受眾模型

1. 考慮相似受眾模型 (lookalike model)ing on our platform. There are many ways you can build out net-new audiences with first-party data. Within our platform, you can create lookalike models based on first-party data with Audience Predictor, which considers your achievable scale within a given time frame against a specific ID type. Newest to the party, customer relationship management (CRM) data can now create cross-device lookalike models for additional reach against new users, inclusive of many ID types, along with UID2. Addressability on Connected TV can be fundamentally improved with alternative IDs like UID2.
2. 探索數據合作夥伴的相似受眾功能。如果您與特定的數據合作夥伴或平台合作，可以了解他們是否具有這種相似受眾功能。許多客戶數據平台（customer data platform, CDP）及資料無塵室（clean rooms）服務也採用集成方法拓展及建立模型，因此您可以將相似受眾數據匯入我們的平台，並在平台內比較這些受眾模型及成效表現，利用第一方數據逐步完善您的廣告策略。
3. 以不同受眾群測試相似受眾模型。將所有數據導入平台以建立模型，並測試不同受眾及 ID 類型的可擴充性與效果，是一個很好的想法。利用 CRM 數據，您可以透過 UID2 建立跨裝置和跨 ID 的相似受眾模型。

套用排除功能

1. 識別您不想觸及的受眾。第一方數據的最大用處或許是能夠不要針對現有客戶進行廣告投放行動，例如向新訂閱者促銷，抑或是爭取競爭對手客戶的時候。對於主要目標是觸及新客戶並進行轉換的廣告活動，排除第一方受眾有助於確保不會將廣告曝光浪費在既有客戶上。如果您的受眾數據在上傳至我們的平台前已經整理完畢，您也可以排除特定的行銷漏斗下層的受眾，像是過往廣告活動中的購買者或是品牌忠誠使用者。
2. 應用 Identity Alliance排除跨裝置。Identity Alliance 可以在您的排除名單中，協助您排除某個人擁有的所有 ID 及裝置。Identity Alliance 甚至有排除整個家庭層級的功能，這個功能對於車用和民生消費用品品牌來說特別實用。
3. 使用 the Exclusion Savings Report. We can provide an exclusion savings report with every campaign, so you can monitor the benefits of exclusions and see the overall impact against your various audiences.