{"id":3458,"date":"2024-09-11T10:10:18","date_gmt":"2024-09-11T10:10:18","guid":{"rendered":"https:\/\/persona.ly\/blog\/?p=3458"},"modified":"2024-09-29T07:00:21","modified_gmt":"2024-09-29T07:00:21","slug":"persona-ly-tripled-roas-d14-for-playios-play-to-earn-app","status":"publish","type":"post","link":"https:\/\/persona.ly\/blog\/2024\/09\/persona-ly-tripled-roas-d14-for-playios-play-to-earn-app\/","title":{"rendered":"How Persona.ly Tripled ROAS D14 for Playio’s Play-to-earn App"},"content":{"rendered":"\n

Download the pdf version of the case study<\/em><\/a><\/p>\n\n\n\n

\n
\n
\"\"<\/a><\/figure>\n<\/div>\n\n\n\n
\n

We worked with Playio <\/strong>to acquire new users<\/a> for their play-to-earn Android app in Korea. As a top-grossing platform in the category, the main goal of the campaign was to acquire active users who would play the games on the Playio’s app to earn rewards<\/strong>.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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

Results at a Glance<\/h2>\n\n\n\n
\n
\n

2.5x<\/strong><\/p>\n\n\n\n

Campaign scaling within 3 months<\/p>\n<\/div>\n\n\n\n

\n

73%<\/strong><\/p>\n\n\n\n

CPI reduction within 3 months<\/p>\n<\/div>\n\n\n\n

\n

209%<\/strong><\/p>\n\n\n\n

ROAS D14 growth by week 3<\/p>\n<\/div>\n<\/div>\n\n\n\n

\n

\"Ha-Young<\/p>\n\n\n\n

“From day one, Persona.ly\u2019s team demonstrated a deep understanding of our play-to-earn model and the importance of acquiring not just users, but engaged players who would generate revenue. Their sophisticated ML-driven platform allowed us to target the right audience with remarkable precision. <\/p>\n\n\n\n

What impressed us most was Persona.ly’s commitment to continuous optimization. They didn’t just set up the campaign and let it run \u2013 they constantly refined their targeting, resulting in significant improvements in the performance.”<\/p>\n\n\n\n

<\/p>\nHa-Young Park<\/strong><\/a>  – Marketing Lead at Playio<\/strong><\/cite><\/blockquote>\n\n\n\n

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

Programmatic UA Process<\/h2>\n\n\n\n

At the beginning of the campaign, using unattributed first-party data, we promptly generated a lookalike audience<\/strong> to target. Our programmatic DSP (demand-side platform) is integrated with all tier-1 ad exchanges. It processes over 4 million auctions per second and analyzes over 60 data-points before serving each impression. This allowed us to quickly gather initial data to narrow the targeting. After the initial phase targeting wider segments, our bidder was able to determine which segments were more likely to install the app.<\/p>\n\n\n\n

During the next phase, our ML-driven engine generated a basic targeting model<\/strong> focusing on app installs.<\/p>\n\n\n\n

As Playio’s main goal was to acquire active users who would install the app, create an account, and start performing in-app actions (play games, reach milestones, complete streaks), thus generating revenue for Playio, our ML-based targeting engine developed a unique classification model to target audiences that would more likely to retain and generate high ROAS.<\/strong><\/p>\n\n\n\n

Once we were able to identify these audience segments in the bid stream, we could safely scale the campaign while maintaining the CPI and improving ROAS.<\/strong><\/p>\n\n\n\n

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

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

Campaign Results<\/h2>\n\n\n\n

Campaign Scaling and CPI<\/h3>\n\n\n\n

When we started running the campaign for Playio, within the first few days, we were able to determine which audience segments would install the app. This allowed us to reduce the CPI by almost 60% by week 2. As we gathered enough positive signals to target audiences who would be more active in the app, by week 12 we scaled the campaign by 2.5 times while efficiently reducing the CPI by 73% compared to week 1.<\/strong><\/p>\n\n\n\n

\"Campaign-Scaling-and-CPI-for-Playio-UA-campaign-persona.ly-case-study\"<\/a><\/figure>\n\n\n\n
<\/div>\n\n\n\n
<\/div>\n\n\n\n

ROAS Growth<\/h3>\n\n\n\n

Due to the nature of the Playio app (where users are rewarded for in-app actions like playing games), it was crucial to acquire a highly engaged audience who would play games, thus generating revenue for Playio.Shortly after the exploration campaign, our ML algorithm was able to determine the most prominent placements. We used a wider contextual approach<\/strong><\/a> where the audience is targeted based on multiple contextual signals such as:<\/p>\n\n\n\n