在此頁面上：探索Adobe Journey Optimizer中的同事功能，以建立忠誠度挑戰並分析忠誠度計畫績效。
Adobe Journey Optimizer中的忠誠度功能可協助您建立和管理忠誠度挑戰，並透過自然語言提示分析忠誠度計畫的績效。 使用忠誠度挑戰管理來定義挑戰對象、動作、時機和獎勵。 使用忠誠度分析來探索忠誠度積分、成員層級、贖回、收入和折扣趨勢。
了解更多：
- Journey Optimizer的同事技能 — Journey Optimizer中跨歷程、忠誠度、內容管理及決策的同事技能概觀。
- 同事檔案 — 同事的行銷活動、聊天和專案功能概觀。
- 同事聊天UI指南 — 如何存取和瀏覽同事聊天。
熟客技能可讓熟客管理員和分析人員使用自然語言建立、管理和分析熟客方案。 透過這些AI支援的技能，您可以快速設計引人入勝的忠誠度挑戰、追蹤績效量度，並做出資料導向式決策，以將成員參與度和計畫獲利能力最佳化。 無論您是要提出新的挑戰，或是要分析熟客方案趨勢，熟客技能都能簡化整個熟客方案管理工作流程。
忠誠度挑戰管理 loyalty-challenge-management
忠誠度挑戰管理可讓Journey Optimizer使用者使用自然語言提示，在同事中建立和管理忠誠度挑戰。 如需建立、設定和管理忠誠度挑戰的完整檔案，包括詳細的設定指示，請參閱忠誠度挑戰指南。
主要使用案例
提示最佳實務
- 將其命名為：在引號中加上清楚、難忘的標題。
- 指定對象：符合資格者（例如，所有成員、階層、區段、新註冊者、持卡人、訂閱者）。
- 定義動作和數量：成員必須做什麼，以及計為完成的頻率、臨界值或順序。
- 設定時間範圍：開始日期（若為固定期間，則為結束日期）加上時區。
- 說明獎勵：積分、里程、宣告積分、狀態延期、憑單，或完成時授予的額外津貼。
- 參考資格確認事件：指向挑戰追蹤的特定SKU、產品、帳戶動作或參與事件。
超出範圍技能
目前不支援以下功能：
- 挑戰刪除
- 針對所有情況下的挑戰訊息提供完整的內容製作自動化
熟客方案分析技能 loyalty-data-insight
熟客方案深入分析技能可讓Journey Optimizer使用者使用自然語言來分析和查詢熟客方案績效資料。 此技能可提供忠誠度點數、成員層級、贖回和收入量度的深入分析，讓忠誠度管理員和分析師可針對其忠誠度計畫進行資料導向式決策。
主要使用案例：
2026年8月期間，會員在每個忠誠度等級中獲得的忠誠點數是多少？
顯示2026年8月期間依會員忠誠度狀態（而非忠誠度等級）兌換的忠誠度點數總計。
顯示2026年8月依忠誠度等級劃分的忠誠度點數餘額總計。
2026年8月期間，每個忠誠度等級套用了多少忠誠度折扣？
顯示2026年8月依忠誠度方案劃分的總忠誠度折扣。
每個忠誠度方案在2026年8月產生的總訂單收入是多少？
顯示2026年8月期間依產品類別劃分的熟客方案總折扣。
顯示2026年第三季的熟客方案績效報表。
挑戰推薦技能 loyalty-challenge-recommendations
同事建議功能可讓行銷人員透過CX Coworker中的自然語言交談，請求以基礎為基礎的特定挑戰建議。 Recommendations會使用忠誠度計畫趨勢和績效資料，並專注於增加成員支出、贏回非作用中的成員、改善表現不佳的挑戰、專注於產品或類別，或解決點數過期的問題。
行銷人員可將建議縮小至他們關心的目標，並將建議傳遞給「忠誠度挑戰管理」技能，以建立或編輯即時挑戰，而不需離開交談。
主要使用案例
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以自然語言要求建議
在同事交談中詢問挑戰建議，並根據忠誠度計畫趨勢和績效資料獲得以基礎為基礎的特定想法。
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將建議聚焦於目標
將建議範圍縮小至特定目標，例如增加成員支出、贏回非作用中成員、改善表現缺佳的挑戰、聚焦於產品或類別，或解決點數過期的問題。
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檢閱機會及其理由
開啟 機會 標籤，並選取 檢視詳細資料 以檢閱建議、建議的挑戰及理由。
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將建議實作即時挑戰
選取「使用AI建立」，將建議傳遞給「忠誠度挑戰管理」技能。 繼續交談以提供遺漏的詳細資料，建立或編輯挑戰，而不離開同事聊天。
建立或編輯挑戰後，請先檢閱挑戰詳細資訊，並在發佈前完成挑戰撰寫歷程。
This section contains structured knowledge intended to support interpretation, retrieval, and question answering related to this topic.
For complete understanding, this information should be combined with the documentation on this page. Neither source is intended to stand alone; the page describes the feature, while this section provides additional context that helps disambiguate terminology, intent, applicability, and constraints.
- TL;DR: This page describes the Coworker capabilities in Adobe Journey Optimizer for creating loyalty challenges and analyzing loyalty program performance: Loyalty Challenge Management, Loyalty Insights Skill, and the Challenge Recommendations Skill, including their use cases and what is out of scope.
Intents
- Create and manage loyalty challenges using natural language prompts with Loyalty Challenge Management.
- Query and analyze loyalty program performance data — points, tiers, redemptions, and revenue — using natural language with Loyalty Insights Skill.
- Use Challenge Recommendations to review an opportunity and its rationale and pass the recommendation to the Loyalty Challenge Management skill with Create with AI.
- Review the challenge details and journey in challenge authoring before publishing.
- Learn which functionalities are currently not supported, such as challenge deletion and full content authoring automation for challenge messaging in all cases.
- Learn how to structure a prompt to create a loyalty challenge (audience, action, timing, reward).
Glossary
- Loyalty Challenge Management (product-specific): Coworker skill that creates and manages loyalty challenges through natural language prompts.
- Loyalty Insights Skill (product-specific): Coworker skill that analyzes and queries loyalty program performance data — points, tiers, redemptions, and revenue metrics — using natural language.
- Challenge Recommendations (product-specific): Coworker feature that enables marketers to request grounded, specific challenge recommendations through natural language conversations, narrow them to their goals, and implement them as live challenges.
Guardrails
- Loyalty skills are available in Coworker for eligible organizations. Customers with a Loyalty license can access these loyalty skills, even if they do not have an additional Coworker license.
- Challenge deletion and full content authoring automation for challenge messaging in all cases are currently not supported (listed under out of scope skills in the Loyalty Challenge Management section).
- Challenge Recommendations uses loyalty program trends and performance data. It can pass a recommendation to Loyalty Challenge Management, which creates or edits the live challenge.
Terminology
- Do not confuse: “Loyalty Challenge Management” (creates and edits challenges), “Loyalty Insights Skill” (analyzes and queries loyalty program performance data), and “Challenge Recommendations” (provides challenge recommendations based on loyalty program trends and performance data) are described in separate sections of this page.
FAQ
- Do I need a separate Coworker license to use Loyalty skills? No, customers with a Loyalty license can access Loyalty skills even without an additional Coworker license.
- Can Loyalty Challenge Management delete a challenge? No, challenge deletion is currently not supported.
- What kind of data can Loyalty Insights Skill query? Loyalty points granted, earned, and redeemed; order revenue and loyalty discount trends; and program performance metrics by tier, program, product category, or time period.
- What does Challenge Recommendations provide? It provides grounded, specific challenge recommendations based on loyalty program trends and performance data, including recommendations for increasing member spend, winning back inactive members, improving underperforming challenges, focusing on products or categories, and addressing points expiry.
- What happens after I select Create with AI? The recommendation is passed to the Loyalty Challenge Management skill. Continue the conversation to provide missing details and create or edit the challenge without leaving Coworker Chat. Then review the challenge details and journey in challenge authoring before publishing.
- What should I include when asking Loyalty Challenge Management to create a challenge? A clear title, the qualifying audience, the required action and how much/how often, the time window and timezone, the reward, and the qualifying event.