{"id":81321,"date":"2025-07-28T06:00:00","date_gmt":"2025-07-28T13:00:00","guid":{"rendered":"https:\/\/blog.mozilla.org\/?p=81321"},"modified":"2025-07-27T20:57:56","modified_gmt":"2025-07-28T03:57:56","slug":"taimi","status":"publish","type":"post","link":"https:\/\/blog.mozilla.org\/en\/advertising\/anonym\/taimi\/","title":{"rendered":"Lightning performance, protecting PII: Taimi\u2019s privacy-first TikTok campaign"},"content":{"rendered":"\n
\"Taimi<\/figure>\n\n\n\n

For marketers, understanding how a campaign performed shouldn\u2019t come at the cost of ceding proprietary data, or time-consuming integration acrobatics, with the platforms they run on.<\/strong> But that\u2019s often the tradeoff they encounter, particularly when it comes to measuring impact on closed platforms or in privacy-sensitive environments like iOS. <\/p>\n\n\n\n

Taimi<\/strong><\/a>, an LGBTQ+ dating app that champions safety and inclusivity, faced exactly this challenge. As the company expanded its reach into the U.S., it needed to understand what was driving installs and sign-ups on TikTok \u2014 without compromising its core values or giving up control of its potentially sensitive user data.<\/p>\n\n\n\n

Enter Anonym, Mozilla\u2019s privacy-first measurement solution built to help marketers unlock lightning-fast performance insights without exposing valuable data to platforms \u2014 and with minimal integration effort from Taimi. With little more than a drag-and-drop interface, Anonym enabled Taimi to run a fast, clean, statistically significant incrementality test with TikTok, revealing a 67% lift in app installs and a 40% lift in sign-ups.

\u201cAnonym helped us grow faster than ever \u2014 with full confidence we were gaining full visibility into all our conversions,\u201d<\/em> said Oleksandra Kryshtal, Taimi\u2019s User Acquisition Team Lead. \u201cFor an app-based company like ours, that\u2019s non-negotiable.<\/em>\u201d<\/p>\n\n\n\n

The results weren\u2019t just a win for Taimi. They point to a fast and effective new model for marketers more broadly: one where actionable advertising performance metrics are swift and insightful, and come without sacrificing sensitive and potentially competitive user-level data to the platforms they depend on.

\u201cThanks to our partnership with TikTok and Anonym,\u201d<\/em> Kryshtal added, \u201cwe implemented a privacy-enhancing technology that was a game-changer.\u201d<\/em><\/p>\n\n\n\n

For more insights into this Taimi-Anonym-TikTok test, read on!<\/p>\n\n\n\n

\n\n\n\n

Private measurement provides Taimi with first look at performance<\/h2>\n\n\n
\n
\"<\/figure><\/div>\n\n\n

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

Taimi<\/strong>, an LGBTQ+ dating app that embraces diversity and inclusivity, chose TikTok<\/strong> to promote its installs & sign-ups. Taimi cares deeply about privacy and needed a solution that allowed it to measure the impact of its advertising on TikTok for iOS users without sending any user-level data to TikTok directly.<\/p>\n\n\n\n

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

To accomplish this objective, Taimi partnered with Anonym<\/strong>, a TikTok partner that uses advanced, privacy preserving techniques to measure and improve advertising performance. Taimi leveraged Anonym Private Lift to measure the incrementality (or causal impact) of its four week campaign on TikTok across the United States. Taimi also utilized Anonym Private Attribution to obtain more granular and actionable insights into its media strategies and tactics, including which ad creatives were driving performance. All processing occurred in the U.S. and results were delivered within days of the campaign end. No integration work was required from Taimi. They simply shared a data file using a drag-and-drop interface, which ensured the data was correctly formatted and encrypted.<\/p>\n\n\n\n

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

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

After the campaign ended, Taimi provided hashed and encrypted conversion data to Anonym. Anonym matched this conversion data with hashed and encrypted impression data from TikTok within a confidential computing environment. The data was processed using a differentially private conversion lift algorithm. Differential privacy is a method that adds noise to data sets to make it nearly impossible to re-identify users.<\/p>\n\n\n\n

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