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移动归因的区别与配合。",{"id":318,"alt":319,"updatedAt":320,"createdAt":320,"url":321,"thumbnailURL":322,"filename":323,"mimeType":324,"filesize":325,"width":326,"height":327},"published","foursquare-attribution-guide-zh",{"id":59,"name":335,"avatar":336,"updatedAt":342,"createdAt":343},"DeepClick",{"id":337,"alt":335,"updatedAt":338,"createdAt":338,"url":339,"thumbnailURL":322,"filename":340,"mimeType":324,"filesize":341,"width":327,"height":327},25,"2026-04-22T08:09:22.606Z","https://cms-r2.deepclick.com/头像-白.png","头像-白.png",26626,"2026-04-22T08:09:35.299Z","2026-04-22T06:42:49.116Z",{"id":345,"titleZh":346,"titleEn":347,"slug":348,"order":349,"updatedAt":350,"createdAt":351},7,"技术导航","Tech Guides","tech-guides",5,"2026-04-27T08:37:10.576Z","2026-04-23T02:59:13.436Z","2026-06-22T06:49:19.879Z","2026-06-22T06:49:20.127Z","\u003Cdiv class=\"payload-richtext\">\u003Ch2>什么是 Foursquare 归因？\u003C/h2>\u003Cp>Foursquare 归因是一种测量产品，把"数字广告曝光"和"真实世界的到店访问"连起来。它不止步于点击或线上转化，而是回答一个更难的问题：看过你广告的人，事后真的走进了线下门店吗？\u003C/p>\u003Cp>它属于\u003Cstrong>到店归因（基于位置的归因 / 线下归因）\u003C/strong>这一类，和大多数效果广告主日常用的点击、安装追踪不是一回事。\u003C/p>\u003Ch2>Foursquare 归因怎么运作\u003C/h2>\u003Cp>方法建立在三块之上：\u003C/p>\u003Col class=\"list-number\">\u003Cli\n          class=\"\"\n          style=\"\"\n          value=\"1\"\n        >\u003Cp>\u003Cstrong>位置面板（panel）。\u003C/strong> Foursquare 维护着一个庞大的用户面板，这些用户通过其自有 App，以及一批接入其 SDK 的合作 App，主动授权共享位置。这个面板是到店行为的"地面真值"。\u003C/p>\u003C/li>\u003Cli\n          class=\"\"\n          style=\"\"\n          value=\"2\"\n        >\u003Cp>\u003Cstrong>广告曝光日志。\u003C/strong> 来自你广告系列的曝光与点击数据，用来识别谁被广告触达。\u003C/p>\u003C/li>\u003Cli\n          class=\"\"\n          style=\"\"\n          value=\"3\"\n        >\u003Cp>\u003Cstrong>增量（lift）测量。\u003C/strong> Foursquare 比较\u003Cstrong>曝光组\u003C/strong>与一个匹配的\u003Cstrong>对照组\u003C/strong>（没看过广告）的到店率，两者之差就是可以归功于广告的增量到店。\u003C/p>\u003C/li>\u003C/ol>\u003Cp>因为用了对照组，Foursquare 报告的是增量——广告真正带来的到店——而不仅仅是相关性。\u003C/p>\u003Ch2>你能测到什么\u003C/h2>\u003Cul class=\"list-bullet\">\u003Cli\n          class=\"\"\n          style=\"\"\n          value=\"1\"\n        >\u003Cp>广告系列带来的增量到店\u003C/p>\u003C/li>\u003Cli\n          class=\"\"\n          style=\"\"\n          value=\"2\"\n        >\u003Cp>到店率与单次增量到店成本\u003C/p>\u003C/li>\u003Cli\n          class=\"\"\n          style=\"\"\n          value=\"3\"\n        >\u003Cp>按创意、受众、媒体或时段拆分的表现\u003C/p>\u003C/li>\u003Cli\n          class=\"\"\n          style=\"\"\n          value=\"4\"\n        >\u003Cp>全渠道广告系列的线上到线下行为\u003C/p>\u003C/li>\u003C/ul>\u003Ch2>优势与局限\u003C/h2>\u003Cp>\u003Cstrong>优势\u003C/strong>\u003C/p>\u003Cul class=\"list-bullet\">\u003Cli\n          class=\"\"\n          style=\"\"\n          value=\"1\"\n        >\u003Cp>为有线下门店的品牌打通"线上到线下"闭环\u003C/p>\u003C/li>\u003Cli\n          class=\"\"\n          style=\"\"\n          value=\"2\"\n        >\u003Cp>对照组设计测的是真增量，而非虚荣的相关性\u003C/p>\u003C/li>\u003Cli\n          class=\"\"\n          style=\"\"\n          value=\"3\"\n        >\u003Cp>适合零售、快餐、快消、汽车等靠到店驱动的品类\u003C/p>\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>局限\u003C/strong>\u003C/p>\u003Cul class=\"list-bullet\">\u003Cli\n          class=\"\"\n          style=\"\"\n          value=\"1\"\n        >\u003Cp>它是\u003Cstrong>基于面板\u003C/strong>的——是抽样而非全量——所以需要一定投放规模才可靠\u003C/p>\u003C/li>\u003Cli\n          class=\"\"\n          style=\"\"\n          value=\"2\"\n        >\u003Cp>位置精度受 GPS 与设备设置影响\u003C/p>\u003C/li>\u003Cli\n          class=\"\"\n          style=\"\"\n          value=\"3\"\n        >\u003Cp>隐私规则收紧、移动位置数据萎缩，长期对面板规模形成压力\u003C/p>\u003C/li>\u003Cli\n          class=\"\"\n          style=\"\"\n          value=\"4\"\n        >\u003Cp>它不测量 App 安装、应用内事件或纯电商成交\u003C/p>\u003C/li>\u003C/ul>\u003Ch2>Foursquare 归因 vs 数字归因\u003C/h2>\u003Cp>到店归因和数字归因解决的是不同问题，大多数广告主两者都需要。\u003C/p>\u003Cul class=\"list-bullet\">\u003Cli\n          class=\"\"\n          style=\"\"\n          value=\"1\"\n        >\u003Cp>\u003Cstrong>Foursquare（到店归因）：\u003C/strong> 广告有没有带来一次线下到店？适合靠客流和全渠道的品牌。\u003C/p>\u003C/li>\u003Cli\n          class=\"\"\n          style=\"\"\n          value=\"2\"\n        >\u003Cp>\u003Cstrong>移动归因平台（MMP）：\u003C/strong> 广告有没有带来一次安装或应用内事件？可参考我们的\u003Ca href=\"/zh-CN/resources/blog/mobile-advertising-attribution-guide-zh/\">移动归因体系指南\u003C/a>和\u003Ca href=\"/zh-CN/resources/blog/best-attribution-software-zh/\">归因软件横评\u003C/a>，它们处理点击、安装与转化。\u003C/p>\u003C/li>\u003Cli\n          class=\"\"\n          style=\"\"\n          value=\"3\"\n        >\u003Cp>\u003Cstrong>刚接触这个领域？\u003C/strong> 先了解\u003Ca href=\"/zh-CN/resources/blog/mobile-measurement-partner-zh/\">移动归因平台（MMP）\u003C/a>到底做什么。\u003C/p>\u003C/li>\u003C/ul>\u003Cp>做数字与 App 投放，你主要依赖 MMP；要证明线下到店影响，则用 Foursquare 这类到店归因来补位。\u003C/p>\u003Ch2>什么时候用到店归因\u003C/h2>\u003Cp>出现以下情况就该考虑 Foursquare 式归因：\u003C/p>\u003Cul class=\"list-bullet\">\u003Cli\n          class=\"\"\n          style=\"\"\n          value=\"1\"\n        >\u003Cp>你的生意有线下门店，"到店"才是真正目标\u003C/p>\u003C/li>\u003Cli\n          class=\"\"\n          style=\"\"\n          value=\"2\"\n        >\u003Cp>你做全渠道投放，需要为上漏斗媒体定价值\u003C/p>\u003C/li>\u003Cli\n          class=\"\"\n          style=\"\"\n          value=\"3\"\n        >\u003Cp>你要的是增量，而不是最后一次点击的归功\u003C/p>\u003C/li>\u003C/ul>\u003Cp>把它和数字归因搭配，你就能看到完整旅程——从 MMP 记录的点击，到 Foursquare 测量的到店。\u003C/p>\u003Ch2>常见问题 FAQ\u003C/h2>\u003Ch3>Foursquare 归因测量的是什么？\u003C/h3>\u003Cp>它测量数字广告系列带来的增量线下到店：把看过广告的曝光受众和没看过广告的对照组对比，差值即增量到店。\u003C/p>\u003Ch3>Foursquare 归因和 MMP 是一回事吗？\u003C/h3>\u003Cp>不是。MMP（如 AppsFlyer、Adjust、Branch、Kochava）归因的是 App 安装和应用内事件，Foursquare 归因的是线下到店。两者回答不同问题，经常配合使用。\u003C/p>\u003Ch3>到店归因准不准？\u003C/h3>\u003Cp>它基于面板，所以是方向性的而非精确值。投放规模越大越准，同时受 GPS 精度和共享位置的用户数量影响。\u003C/p>\u003Ch3>Foursquare 归因有什么替代方案？\u003C/h3>\u003Cp>市面上还有其他到店测量厂商；如果你要的是线上结果，则应使用 MMP 加点击后归因。具体选哪种，取决于你测的是线下客流还是线上转化。\u003C/p>\u003C/div>","https://deepclick.com/zh-CN/resources/blog/foursquare-attribution-guide-zh",{"zh-CN":333},1782197830560]