{"id":78810,"date":"2025-04-09T15:20:17","date_gmt":"2025-04-09T22:20:17","guid":{"rendered":"https:\/\/blog.mozilla.org\/?p=78810"},"modified":"2025-08-12T13:06:38","modified_gmt":"2025-08-12T20:06:38","slug":"anonym-zenjob","status":"publish","type":"post","link":"https:\/\/blog.mozilla.org\/en\/advertising\/anonym-zenjob\/","title":{"rendered":"Data ethics in action: Zenjob, TikTok and Mozilla\u2019s Anonym show a better way to measure"},"content":{"rendered":"\n

Let\u2019s face it \u2014 measuring ad performance is challenging in a shifting privacy landscape. With tightening regulations and data sharing under a well-deserved microscope, advertisers are under more pressure than ever to prove their campaigns can work without<\/em> crossing privacy lines. <\/p>\n\n\n\n

That was the exact puzzle Zenjob had to solve. As a platform connecting people to flexible work, they wanted to make the most of a key hiring season in 2024 with a fresh TikTok campaign. They also wanted to do it right by keeping user privacy front and center, a goal that we at Mozilla passionately, operationally and increasingly technologically<\/em> share. <\/p>\n\n\n\n

In pursuit of a campaign that successfully derived valuable insights without exposing user-level data, Zenjob teamed up with Mozilla\u2019s Anonym, a privacy-first data analytics solution harnessed by TikTok to balance campaign performance with user privacy. Together, Zenjob, TikTok and Anonym found a way to measure what really matters \u2014 like campaign impact and attribution \u2014 without exposing any user-level data. <\/p>\n\n\n\n

Why is this story worth a read? Because it proves insights and integrity can coexist. Zenjob didn\u2019t just run a high-performing campaign \u2014 they saw a serious lift in signups and walked away with a crystal clear view of what worked, all while keeping sensitive user data secure and private. <\/p>\n\n\n\n

If you\u2019re wondering how to balance performance with privacy, this case study is a great place to start.<\/p>\n\n\n\n

\n\n\n\n

Private measurement provides Zenjob with proof of incremental performance on iOS <\/h2>\n\n\n\n

<\/p>\n\n\n\n

The objective <\/strong><\/h3>\n\n\n\n

Zenjob<\/strong>, the innovative platform connecting job seekers with flexible work opportunities, chose TikTok <\/strong>to promote its services during a key hiring season in 2024. As a platform, TikTok connects billions of users on a global scale. Zenjob\u2019s aim was to expand the reach of their job-matching marketplace while simultaneously maintaining its deep commitment to protecting user privacy. This required a solution that allowed them to measure the effectiveness of their TikTok app-focused advertising campaigns without sharing any user-level data directly with the platform. <\/p>\n\n\n\n

The solution <\/strong><\/h3>\n\n\n\n

To accomplish this objective, Zenjob, Ltd. partnered with Anonym, a privacy-first data analytics solution harnessed by TikTok to seamlessly integrate advanced privacy-preserving protections with campaign efficiency and performance measurements. Zenjob leveraged Anonym\u2019s Private Lift to measure the incrementality (or causal impact) of its four-week campaign on TikTok across Germany, and Private Attribution to determine what levers to use to optimize its campaigns (e.g. creative, geotargeting, etc.) All processing occurred in Europe and results were delivered within days of the campaign end. No integration work was required from Zenjob \u2014 they simply leveraged Anonym\u2019s drag-and-drop interface, ensuring all data was correctly formatted and encrypted. <\/p>\n\n\n\n

\"Three<\/figure>\n\n\n\n

The results <\/strong><\/h3>\n\n\n\n

After the campaign ended, Anonym matched hashed and encrypted sales data with hashed and encrypted impression data from TikTok within a confidential computing environment. The data was processed using differentially private algorithms for lift and attribution. Differential privacy is a method that adds noise to data sets, making individual data points indistinguishable to enhance user privacy \u2014 while simultaneously allowing effective, actionable analysis of ad performance. <\/p>\n\n\n\n

The results were impressive: <\/p>\n\n\n\n