I sold a list of cafés with no website. Then I checked it, and half the list was wrong.

Title: I sold a list of cafés with no website. Then I checked it, and half the list was wrong.

Earlier this year I built what looked like a clean little data product: coffee shops in New York and Chicago that have no website. The pitch writes itself. A café with no site is a web designer’s best prospect, and the data came from public map listings, so pulling it felt like an afternoon of work.

今年早些时候，我制作了一个看起来很简洁的小型数据产品：纽约和芝加哥没有网站的咖啡店。这个卖点不言自明。对于网页设计师来说，没有网站的咖啡馆是最好的潜在客户，而且这些数据来自公共地图列表，所以提取这些数据感觉就像一下午的工作量。

Then I spot checked it. 51 rows sampled across the two cities, each one searched by hand. 28 of the 51 businesses that were supposedly siteless had an official website. One had a phone number with two digits transposed. A few were just closed. The list wasn’t a scam, it was worse than that, it was plausible. It looked right and wasn’t.

然后我进行了抽查。在两个城市中抽取了 51 行样本，每一行都经过人工搜索。在 51 家本应没有网站的企业中，有 28 家拥有官方网站。其中一家电话号码的两位数字颠倒了。还有几家已经倒闭了。这份名单并非骗局，但情况更糟，它看起来很可信。它看起来是对的，但实际上并非如此。

So the whole thing got rebuilt, and the rebuild is the actual product now. The candidates come from public map data (OpenStreetMap, ODbL, attribution in every file). Across 17 cities that data flagged 612 cafés as having no website. Every one of the 612 got rechecked against live sources: the business’s own domain if one exists, current directories, review pages. A Facebook page doesn’t count as a website. A DoorDash page doesn’t count. A chain’s location page on the chain’s own domain does.

于是整个产品被重新构建，现在的重构版本才是真正的产品。候选名单来自公共地图数据（OpenStreetMap，ODbL，每个文件中都有署名）。在 17 个城市中，该数据标记了 612 家没有网站的咖啡馆。这 612 家咖啡馆中的每一家都根据实时来源进行了复核：包括企业自己的域名（如果存在）、当前目录和评论页面。Facebook 页面不算作网站。DoorDash 页面也不算。连锁店在自己域名下的分店页面才算。

Here is what the 612 turned into: 288 had an official website after all 59 were permanently closed, including one café inside a grocery store that shut its doors 33 couldn’t be confirmed either way, and unconfirmed rows don’t ship 84 phone numbers got corrected against current listings on the way through 231 cafés survived.

这 612 家咖啡馆最终的结果如下：288 家最终被发现拥有官方网站；59 家永久关闭，其中包括一家位于杂货店内的咖啡馆；33 家无法确认情况，而未经确认的行不会被交付；84 个电话号码在核对过程中根据当前列表进行了更正；最终有 231 家咖啡馆被保留。

New York has 97 of them out of a full list of 1,446 cafés. Brooklyn alone has 42. San Francisco, of all places, has 31 out of 377 checked. The full lists ride along in every file, 5,308 cafés total, with a has_website column so buyers can cut the data their own way.

在 1,446 家咖啡馆的总名单中，纽约有 97 家。仅布鲁克林就有 42 家。旧金山在 377 家被核查的咖啡馆中，有 31 家符合条件。完整名单包含在每个文件中，总计 5,308 家咖啡馆，并附带一个 has_website 列，以便买家可以按照自己的方式筛选数据。

Pricing was the easy part. The tools that rent this kind of data charge $35 to $49 a month, and the pay-as-you-go scrapers charge per record for data nobody verified. A freelancer who wants cafés in one metro doesn’t need a subscription. They need a file that’s right. So it’s $19 per city, one time, or $49 for all 17 cities. The sample is free because after the first version of this list, “trust me” isn’t a pitch I get to make.

定价反而是最简单的部分。出租此类数据的工具每月收费 35 到 49 美元，而按需付费的抓取工具则对未经核实的数据按记录收费。想要获取某个城市咖啡馆信息的自由职业者不需要订阅。他们需要的是一份准确的文件。因此，定价为每个城市 19 美元（一次性），或 17 个城市共 49 美元。样本是免费的，因为在经历了第一版名单之后，“相信我”已经不再是一个有效的推销方式了。

Store: https://tidyrows.gumroad.com Free NYC sample, the full 97-row format in miniature: https://tidyrows.gumroad.com/l/jjrflz

商店：https://tidyrows.gumroad.com 免费的纽约样本，97 行完整格式的缩影：https://tidyrows.gumroad.com/l/jjrflz

What I’d actually like feedback on: if you prospect local businesses, what kills a list for you? Stale rows, missing emails (this data is phone-first, emails are rare in the source and I say so on the listing), wrong category? The verification pass can check almost anything a second time. It just can’t check everything.

我真正想征求反馈的是：如果你在开发本地企业客户，什么样的情况会让你觉得这份名单作废？是陈旧的行、缺失的电子邮件（此数据以电话为主，来源中很少有电子邮件，我在列表上也说明了这一点），还是错误的分类？核查流程几乎可以对任何内容进行二次检查。只是它无法检查所有内容。