I spent $220 on Google app ads and 60% of the installs were robots
758 points
• 3 days ago
• Article
Link
当 Dayzle 的创始人 Nick Abe 决定用 Google Ads 扩大用户量时，他碰到了数字广告的一个令人沮丧的问题。最开始他把每日预算设为 CA$40，并且设定了目标单次安装成本，但一直难以达到预期。后来他取消了目标成本限制，广告活动立刻把预算超支到两倍，并报告了 21 次安装。可他在自己的管理面板里只查到 1 次真实安装，这促使他进一步调查，结果发现 Google 的报告与平台上的实际情况存在严重不符。
深入查看原始分析数据后发现，报告的 21 次安装中有 20 次来自运行已下架旧版本 App 的设备。虽然这些手机看上去是通过 Google 的渠道下载了应用，但每台设备的行为都很可疑：只打开过一次应用、在任何页面上的停留时间为零秒、此后从未返回。这些"幽灵用户"涉及多种机型、分布在多个州，却都表现出完全相同的脚本化行为，明显像是机器人农场的操作。
在为期两周的监测里，56 次被计费的安装中大多数都遵循这种可疑模式，还有一些来自广告目标以外的国家。真正由真人且有互动（进行了多局游戏）的安装只有 13 次。机器人农场很可能利用了 Google 算法会优先投放以广告主设定目标为准这一点——本案中目标只是"安装"。通过自动化手段触发转化信号，机器人群体实际上"教会"了 Google 的算法，把更多广告流量投向这些虚假设备，形成了一个自我强化的、持续浪费广告费用的循环。
为应对这种情况，Abe 把策略从以打开应用为目标改为要求完成有意义的行为，例如赢下一局解谜。提升伪造一次成功用户体验所需的技术门槛，能让应用对自动化脚本的吸引力下降。他的做法基于一个假设：让机器人农场解出一题 Sudoku 要比单纯启动应用困难且成本更高。虽然他目前正通过 Google 的无效流量举报渠道寻求退款，但这次经历已经给其他小型开发者敲响了警钟——在没有深入人工核验的情况下盲目信任平台提供的安装数据，代价可能会很高。
When Nick Abe, the creator of the puzzle app Dayzle, decided to use Google Ads to boost his user base, he encountered a frustrating reality of digital advertising. Setting a daily budget of CA$40, he initially struggled to hit his target cost per install. However, once he removed the target cost, the campaign immediately spent double his budget and reported 21 installs. A quick check of his own admin panel revealed a stark discrepancy. Only one install was accounted for, prompting an investigation that uncovered a massive disparity between Google's reporting and the actual activity on his platform.
Digging into the raw analytics revealed that 20 of the 21 reported installs were coming from devices running an outdated version of the app that was no longer available on the Play Store. Despite these phones supposedly downloading the app through official Google channels, each device showed the same suspicious behavior: they opened the app once, spent zero seconds on any screen, and never returned. These phantom users represented a wide variety of phone models across multiple states, yet they all performed the exact same scripted actions, signaling the presence of a bot farm.
Over a two-week period, the data showed that out of 56 billed installs, a majority followed this suspicious pattern, while others originated from countries outside the campaign's target area. Only 13 of the billed installs were actual, engaged humans who played multiple games. The bot farm likely exploited the fact that Google's algorithm prioritizes whatever goal the advertiser sets, which in this case was simply an app install. By triggering a conversion signal through an automated process, the bot farm essentially trained Google's algorithm to send even more ad traffic toward their fake devices, creating a self-reinforcing loop of wasted ad spend.
To combat this, Abe shifted his strategy from targeting app opens to requiring a meaningful action, such as winning a puzzle. By increasing the technical effort required to simulate a successful user experience, he aims to make his app a less attractive target for automated scripts. This approach assumes that it is significantly more difficult and costly for a bot farm to solve a Sudoku puzzle than it is to simply launch an app. While he is currently pursuing a refund through Google's invalid-traffic reporting, his experience serves as a stark warning to other small developers that reliance on platform-provided install metrics without deep manual verification can be a costly mistake.
429 comments • Comments Link
• 广告主经常成为广告欺诈的受害者，机器人网络制造虚假流量、点击或安装。当开发者通过 Google Ads 推广集成了 AdMob 的应用时，这个问题会加剧，导致账户因"无效流量"（invalid traffic）被封禁，而为这些非法行为付费的却是广告主。
• 机器人网络通常采用复杂策略，包括利用被感染的设备（如智能电视和路由器）以及数据中心收集的住宅代理（residential proxies）。这些手段能够绕过简单的 IP 过滤，并模拟合法用户行为。
• 在最大化广告收入的动机与消除欺诈的技术难度之间存在明显矛盾。由机器人驱动的流量会推高广告支出，平台（如 Google）如果积极清除机器人会在短期内减少收益，并影响那些负责提升广告消费的内部团队的绩效指标。
• "劣币驱逐良币"的动态对数字广告生态构成持续威胁。如果平台被认为充斥着机器人流量，合法广告主会为补偿较低的转化质量而降低出价，最终导致价格下跌，迫使诚实的发布者离开该网络。
• 大规模广告欺诈通常是结构性的，而非偶发的。许多营销活动默认预算的一部分不可避免会浪费在"灰色"渠道上，管理者往往更注重预算花出去的数量，而不是实际的投资回报或归因准确性。
• 对独立开发者而言，普遍共识是在线广告平台往往是需要深厚专业知识才能驾驭的陷阱。常见建议包括禁用所有"智能"自动化功能，将广告限制为仅限搜索流量，并设置严格的手动控制，以防系统自动把全部预算花在低质量的第三方投放位上。
• 内容营销、自然社区建设和联盟模式通常被认为比付费广告更加可持续，尽管进展较慢。一些人认为数字营销应当把回报严格与经核实的销售挂钩，而不是与展示或点击挂钩——把它视为一个"已解决的问题"。
• 机器人流量工厂的盛行由广告技术生态的经济性驱动。流量拥有者创建低质量的站点，放入广告，并用自动化系统模拟交互。这既为站点所有者创造了收入，也形成了反馈循环：广告平台因这些站点在互动数据上的良好表现而继续投放广告。
• 消费者行为正越来越倾向于拦截广告或避开广告密集的平台。烦人、重复或冗长的广告促使用户使用广告拦截器，并远离像 YouTube 这样的服务，进一步削弱了广告支持模式的长期可行性。
• 衡量广告的真实投资回报率仍然非常困难，这滋生了一种忽悠式文化。投资者和公司往往优先关注虚荣指标（例如安装量或用户获取数），这些指标易被机器人操纵，用以支撑估值，尽管它们无法转化为实际收入。
数字广告生态正面临由机器人流量激增以及平台与用户之间根本性利益冲突驱动的系统性危机。虽然广告网络具备在技术上缓解大部分此类欺诈的能力，但其商业模式严重依赖高额广告支出，因此缺乏解决会缩减自身收入问题的紧迫感。这种环境把数字广告变成小企业为保持可见度不得不支付的"税"，且常伴随收益递减，精明的投机者则利用系统固有的低效率。最终，该行业陷入一种失调：虚假的互动被用来衡量成功的同一套指标所激励，结果是广告主要么承担欺诈成本，要么完全退出付费平台。 • Advertisers frequently fall victim to ad fraud where bot networks generate fake traffic, clicks, or installs. This problem is exacerbated when developers use Google Ads to promote apps that incorporate AdMob, leading to account bans for "invalid traffic" despite the advertiser being the one paying for the illicit activity.
• Bot networks often employ sophisticated tactics, including the use of residential proxies harvested from compromised devices (such as smart TVs and routers) and data centers. These methods allow bad actors to bypass simple IP filtering and simulate legitimate user behavior.
• A significant tension exists between the incentive to maximize ad revenue and the technical difficulty of eliminating fraud. Because bot-driven traffic inflates ad spend, platforms like Google are conflicted; aggressively purging bots would reduce short-term revenue and harm the metrics of internal teams tasked with increasing ad consumption.
• The "Market for Lemons" dynamic is a persistent threat to the digital advertising ecosystem. If platforms are perceived as being overrun by bots, legitimate advertisers reduce their bids to compensate for low conversion quality, eventually driving down prices and forcing honest publishers away from the network.
• Large-scale ad fraud is often structural rather than accidental. Many marketing campaigns operate on the assumption that a percentage of the budget will inevitably be wasted on "grey" channels, with managers prioritizing budget expenditure over actual return on investment or attribution accuracy.
• For independent developers, the consensus is that online advertising platforms are often traps that require deep expertise to navigate. Common advice includes disabling all "smart" automations, restricting ads to search-only traffic, and setting strict manual controls to prevent the system from auto-spending the entire budget on low-quality third-party placements.
• Content marketing, organic community building, and affiliate models are frequently cited as more sustainable, albeit slower, alternatives to paid advertising. Some argue that digital marketing is a "solved problem" where compensation should be tied strictly to verified sales rather than impressions or clicks.
• The prevalence of bot farms is fueled by the economics of the ad-tech cycle. Bot owners create low-quality properties, populate them with ads, and use automated systems to simulate interactions. This generates revenue for the site owner while creating a feedback loop where ad platforms continue to serve ads to these properties because they appear to be performing well in terms of engagement.
• Consumer behavior is increasingly shifting toward blocking or avoiding ad-heavy platforms. Annoying, repetitive, or excessively long ads are driving users toward ad-blockers and away from platforms like YouTube, further damaging the long-term viability of the ad-supported model.
• Measuring true ROI in advertising remains notoriously difficult, allowing a culture of "bullshit" to persist. Investors and companies often prioritize vanity metrics—such as install counts or user acquisition numbers—which can be easily gamed by bots to justify valuations, despite these metrics failing to translate into actual revenue.
The digital advertising ecosystem faces a systemic crisis driven by the proliferation of bot traffic and a fundamental conflict of interest between platforms and their users. While ad networks possess the technical capability to mitigate much of this fraud, their business models are heavily reliant on high volumes of ad spend, creating a lack of urgency to address issues that would deflate their own revenue. This environment has turned digital advertising into a "tax" that small businesses must pay to remain visible, often with diminishing returns, while savvy actors exploit the inherent inefficiencies of the system. Ultimately, the industry struggles with a misalignment where fake engagement is incentivized by the same metrics used to measure success, leaving advertisers to either bear the cost of fraud or retreat from paid platforms entirely.