遠端設定個人化
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Remote Config 個人化功能會根據每個使用者的情況，自動選取 Remote Config 參數，全力達成目標。個人化參數就像是執行自動、個人化、持續改善和永久的 A/B 測試。
在應用程式中使用Remote Config個人化功能時，系統會自動為每位使用者提供多種替代使用者體驗中的一種，也就是能針對您選擇的目標進行最佳化的替代方案，進而打造更吸引人的體驗。您可以使用Remote Config指定目標條件，針對特定使用者群組指定個人化 Remote Config 參數。
您可以使用 Google Analytics 評估任何目標，並根據事件數量或事件參數的匯總值 (總和) 進行最佳化。包括下列內建指標：
- 使用者參與時間，系統會盡可能延長使用者參與時間
- 廣告點擊次數，系統會根據廣告點擊事件總數進行最佳化
- 廣告曝光次數，以廣告曝光次數為最佳化目標
或者，您也可以根據任何 Analytics 事件，針對自訂指標進行最佳化。可能原因包括：
- 提交 Play 商店或 App Store 評分
- 使用者完成特定工作 (例如完成遊戲關卡) 的成功率
- 應用程式內購事件
- 電子商務事件，例如將商品加入購物車，或開始/完成結帳
- 應用程式內購和廣告收益
- 虛擬貨幣支出
- 連結和內容分享，以及社群網路活動
如要進一步瞭解個人化功能的潛在用途，請參閱「Remote Config 個人化功能有哪些用途？」一文。
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運作原理
個人化功能會運用機器學習技術，為每位使用者提供最合適的體驗。演算法會有效率地在學習不同類型使用者的最佳體驗，以及運用這些知識來盡量提高目標指標之間取得平衡。系統會自動比較個人化結果與一組使用者 (他們會持續收到從您提供的替代方案中隨機選取的體驗)，藉此瞭解個人化系統產生的「升幅」(價值增幅)。
如要進一步瞭解遠端設定個人化演算法和概念，請參閱「關於遠端設定個人化」。
實作路徑
- 導入兩項以上替代使用者體驗，預期這些體驗對部分使用者來說是最佳選擇，但對其他使用者則不然。
- 使用Remote Config
參數，從遠端設定這些替代方案。請參閱「開始使用 Remote Config」和「Remote Config 載入策略」。
- 為參數啟用個人化設定。Remote Config會為每位使用者指派最適合的體驗。請參閱入門指南。
個人化與 A/B 測試
A/B 版本測試旨在找出成效最佳的單一使用者體驗，個人化則會為每位使用者動態選擇最合適的使用者體驗，針對目標進行最佳化。對於許多類型的問題，個人化功能可產生最佳結果，但 A/B 測試仍有其用途：
| 建議採用個人化功能 |
建議使用 A/B 測試 |
| 當所有使用者都能因個人化體驗而受益 |
您希望為所有使用者 (或預先定義的一組使用者) 提供一致的最佳體驗 |
| 您想要持續改善個人化模型 |
您想在特定的時間範圍內進行測試 |
| 最佳化目標可以簡化為一組數據分析事件的加權總和 |
需要詳細評估幾項不同的競爭指標，才能達成最佳化目標 |
| 無論如何，您都想要全力達成單一目標 |
您想先確認特定版本的成效升幅是否達到統計顯著程度，再正式採用 |
| 不需要或不想要人工審查結果 |
您想要人工審查結果 |
舉例來說，假設您想在提示使用者時，盡可能提高他們在 Play 商店中對應用程式的評分。提示顯示時機是影響成功率的因素之一：您會在使用者第幾次開啟應用程式時顯示提示？還是會在他們成功完成特定工作時提示？理想的時機可能因人而異：有些使用者可能隨時準備好為您的應用程式評分，有些則可能需要更多時間。
最佳化意見回饋提示的時機是個人化功能的理想用途：
- 每位使用者的最佳設定可能不盡相同。
- 使用 Analytics 即可輕鬆評估成效。
- 所討論的 UX 變更風險很低，您可能不需要考慮取捨或進行人工審查。
試試看
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上次更新時間：2025-08-23 (世界標準時間)。
[null,null,["上次更新時間：2025-08-23 (世界標準時間)。"],[],[],null,["\u003cbr /\u003e\n\nWith Remote Config personalization, you can automatically select\nRemote Config parameters for each user to optimize for an objective.\nPersonalizing a parameter is like performing an automatic, individualized,\ncontinuously-improving, and perpetual A/B test.\n\nWhen you use Remote Config personalization in your apps, you create more\nengaging experiences for each of your users by automatically providing them with\none of several alternative user experiences---the alternative that optimizes\nfor the objective you choose. You can target your personalized Remote Config\nparameters to specific user groups using\n[Remote Config targeting conditions](/docs/remote-config/parameters#conditions_rules_and_conditional_values).\n\nYou can optimize for any objective that's measurable using\nGoogle Analytics, and optimize by number of events or by the aggregated\nvalue (sum) of an event parameter. This includes the following built-in metrics:\n\n- User engagement time, which optimizes by user engagement time\n- Ad clicks, which optimizes by total number of ad click events\n- Ad impressions, which optimizes by the number of ad impressions\n\nOr, you can optimize for custom metrics based on any Analytics event. Some\npossibilities include:\n\n- Play Store or App Store rating submissions\n- User success at particular tasks, like completing game levels\n- In-app purchase events\n- E-commerce events, like adding items to a cart, or beginning or completing checkout\n- In-app purchase and ad revenue\n- Virtual currency spend\n- Link and content sharing and social networking activity\n\nFor more information about potential personalization use cases, see\n[What can I do with Remote Config personalization?](/docs/remote-config/personalization/use-cases)\n\n[Get started](/docs/remote-config/personalization/get-started)\n\nHow does it work?\n\nPersonalization uses machine learning to determine the optimal experience for\neach of your users. The algorithm efficiently trades off between learning the\nbest experience for different types of users and making use of that knowledge to\nmaximize your objective metric. Personalization results are automatically\ncompared to a holdout group of users who receive a persistent random experience\ndrawn from your provided alternatives---this comparison shows how much\n\"lift\" (incremental value) is generated by the personalization system.\n\nFor more information about Remote Config personalization algorithm and concepts,\nsee\n[About Remote Config personalization](/docs/remote-config/personalization/about).\n\nImplementation path\n\n1. Implement two or more alternative user experiences that you expect will be optimal for some users but not others.\n2. Make these alternatives remotely configurable with a Remote Config parameter. See [Get started with Remote Config](/docs/remote-config/get-started) and [Remote Config loading strategies](/docs/remote-config/loading).\n3. Enable personalization for the parameter. Remote Config will assign each of your users the experience that's optimal for them. See the [Getting started](/docs/remote-config/personalization/get-started) guide.\n\nPersonalization vs. A/B testing\n\nUnlike A/B tests, which are designed to find a single best performing user\nexperience, personalization attempts to maximize an objective by dynamically\nchoosing an optimal user experience for each user. For many types of problems,\npersonalization produces the best results, but A/B testing still has its uses:\n\n| Personalization preferred | A/B testing preferred |\n|-------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------|\n| When each user could benefit from a personalized user experience | When you want a single optimum experience for all users or a defined subset of users |\n| When you want to continuously optimize the personalization model | When you want to conduct tests during a fixed time window |\n| When your optimization goal can be expressed simply as a weighted sum of analytics events | When your optimization goal requires thoughtful evaluation of several different competing metrics |\n| When you want to optimize for an objective regardless of any trade-offs | When you want to determine if one variant shows a statistically significant improvement over another before rolling it out |\n| When manual review of results is not required or desired | When manual review of results is desirable |\n\nFor example, suppose you want to maximize the number of users who rate your app\nin the Play Store when you prompt them to. One factor that might contribute to\nsuccess is the timing of your prompt: do you show it when the user opens your\napp for the first, second, or third time? Or do you prompt them when they\nsuccessfully complete certain tasks? The ideal timing likely depends on the\nindividual user: some users might be ready to rate your app right away, while\nothers might need more time.\n\nOptimizing the timing of your feedback prompt is an ideal use case for\npersonalization:\n\n- The optimal setting is likely different for each user.\n- Success is easily measurable using Analytics.\n- The UX change in question is low risk enough that you probably don't need to consider trade-offs or conduct a manual review.\n\nTry it\n\n[Get started](/docs/remote-config/personalization/get-started)"]]
