[[["わかりやすい","easyToUnderstand","thumb-up"],["問題の解決に役立った","solvedMyProblem","thumb-up"],["その他","otherUp","thumb-up"]],[["必要な情報がない","missingTheInformationINeed","thumb-down"],["複雑すぎる / 手順が多すぎる","tooComplicatedTooManySteps","thumb-down"],["最新ではない","outOfDate","thumb-down"],["翻訳に関する問題","translationIssue","thumb-down"],["サンプル / コードに問題がある","samplesCodeIssue","thumb-down"],["その他","otherDown","thumb-down"]],["最終更新日 2025-08-27 UTC。"],[],[],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)"]]
