我在 Google 应用广告上花费了 220 美元,其中 60% 的安装量来自机器人。
I spent $220 on Google app ads and 60% of the installs were robots

原始链接: https://dayzlegame.com/blog/google-ads-bot-farm/

益智应用《Dayzle》的开发者近期发现,其旨在提升安装量的谷歌广告(Google Ads)活动遭到了刷量工作室的攻击。在取消了每次安装成本(CPI)上限后,该活动的每日支出翻了一倍,但数据分析显示,绝大多数“安装”均为虚假流量。 这些机器人通过侧载(sideloading)过时的应用版本,在完全绕过 Play 商店的情况下触发了谷歌的转化跟踪。这些设备表现出高度统一且不自然的规律——仅短暂打开应用后便不再使用。这形成了一个反馈循环,导致谷歌的算法误将机器人工作室判定为高性能的广告来源。 在计费的 56 次安装中,仅有 13 次是真实玩家。为此,开发者将广告活动的转化目标从“打开应用”调整为“完成拼图”,从而提高了机器人模拟人类行为的技术门槛。这是一个警示:虽然谷歌的安装指标在技术上是准确的,但极易受到欺诈行为的影响。开发者不应仅关注基础的转化数量,还需审计流量来源,因为刷量工作室会专门瞄准小型广告预算,以利用自动化优化算法进行牟利。

最近一则 Hacker News 的讨论引发了对广告欺诈日益严重问题的关注,起因是一位开发者发现其 Google 应用广告的安装量中有 60% 来自机器人。 评论者指出,广告欺诈手段正变得越来越高明,从简单的点击演变为虚假安装和模仿用户行为,且通常处于检测阈值之下。用户指出,由于谷歌能从这些流量中获利,因此它缺乏积极打击此类行为的动力。虽然有人建议采取技术性缓解措施,例如排除数据中心 IP 段或使用 knock-knock.net 等数据库,但也有人认为,平台应当为提供“经验证的销售”而非仅仅是“安装量”负责。 资深开发者的共识是,在线广告对新手而言充满陷阱。许多人建议初创企业不要在早期投放广告,并指出只有拥有专门的监控团队和对安装后事件进行复杂跟踪,才有可能取得成功。归根结底,这篇讨论将数字营销描绘成一个高风险领域,其中“欺诈”往往伪装成合法流量,使得个人开发者难以实现正向的投资回报。
相关文章

原文

I run a small puzzle app called Dayzle. I’m a numbers guy, so a marketing optimization problem is right up my alley. Two weeks ago I turned on a Google Ads campaign for Android at CA$40 a day, with the goal set to installs.

For the first few days it barely spent anything. I had a target cost per install of $1.50, and Google couldn’t find installs at that price. So, as a test, I removed the target. It immediately spent double my daily budget, CA$80, and reported 21 installs. I was excited. Then I checked my admin panel, which said 1. Turns out I didn’t need to be a numbers guy to see something didn’t add up.

The panel had missed them because old versions of the app don’t report an install date. When I went into the raw analytics there were 21 new Android devices that day, and 20 of them were running an old version of the app that the Play Store had stopped serving days earlier. You can’t get an old version from Play, so these phones got the app from somewhere else, even though every one of them said Google Play was the installer. Each opened the app once, spent zero seconds on any screen, and never came back. Twenty-eight phone models across nineteen states, which is a lot of variety for twenty phones that all did exactly the same thing.

Over the whole two weeks: 56 installs billed, 33 with that pattern, 7 more from countries the campaign wasn’t targeting, and 13 people. The 13 people finished 92 games between them, which is a nice signal that real people enjoyed what we’ve built.

The 33 weren’t behaving like people, so I suspected a bot farm, and the analytics export bears it out. Google optimizes for whatever goal you give it, and my goal was installs. This farm would watch the shortest video in my ad group, not click it, and then install our app from a saved copy of the file instead of from the store, because that’s faster and Play might notice. Google counts a view followed by an install as a conversion, so the irony is that the more the farm “installed” our app, the better it looked to Google’s algorithm, which sent more of my ads to the farm, which installed it more. A loop that guaranteed my ad spend was wasted.

Where I am now: waiting on Google’s answer to the invalid-traffic form, and the campaign’s goal is now “won a puzzle” instead of “opened the app”. It’s low effort to make a script open an app and click around; it’s higher effort to make one solve a Sudoku. The idea is just to make us more expensive to farm than the next app. That’s probably decent protection for an app my size. Larger apps are worth the extra effort, and I’d guess they see a lot more of this than they know. I’ll report back on the refund.

So I guess this is my PSA: if you’re relying on Google’s install count for your ads, it’s a real number, but it’s definitely worth digging into. If a bot farm can find my tiny ad budget, it can definitely find yours.

联系我们 contact @ memedata.com