Background

Bitmango is a mobile puzzle game company founded in 2011, famous for its hit titles like Word Cookies and Bubble Pop Origin. Bitmango achieved 100 billion won(approximately $80 million USD) in revenue last year alone and has been growing steadily, ranking sixth in global puzzle game publishing as of the second quarter of 2020.

Bitmango credits agile data-based decision-making among its strengths. When promoting its gaming titles, Bitmango took a programmatic approach that enabled marketing efficiency by targeting the most applicable audience based on data.

Bitmango first started its collaboration with MOLOCO in 2018 while searching for new ways to secure the volume of its game’s users. In 2018, MOLOCO ranked highly in the AppsFlyer Performance Index, which prompted Bitmango to try out a user acquisition (UA) test campaign with MOLOCO. Since then, MOLOCO and Bitmango have embarked on a long-lasting relationship that continues to this day.

 

Challenge

After Apple announced the iOS 14 update and the removal of IDFA tracking, Bitmango was concerned that it would be difficult to target and measure the performance of iOS users using Apple devices. As its marketing partner, MOLOCO stepped forward with new, industry-forward solutions. MOLOCO and Bitmango decided to run test campaigns for Limit Ad Tracking (LAT) traffic using Bitmango titles. The goal here was to gather insights from these tests and prepare for the fast-approaching future.

 

Strategy

The two partners created separate campaigns to measure performance differences between LAT and non-LAT traffic. Bitmango selected four of its titles from current campaigns with MOLOCO that had significant volume. In addition to the existing campaigns, they created new LAT and non-LAT campaigns and compared the results between the two.

MOLOCO’s machine-learning model studies user signals in combination with contextual signals such as publisher apps, IP addresses, location, and mobile devices in order to optimize for better targeting. However, given LAT campaigns cannot track user signals (IDFA), the model only studies other contextual signals. MOLOCO was able to leverage its model for LAT traffic from multiple test campaigns that had achieved ROAS similar to non-LAT traffic with Bitmango's test campaigns.

The role of MOLOCO’s account manager also played a key role throughout the entire collaboration. MOLOCO supported the entire process: setting up the LAT test campaign, managing operations, and organizing reports after the completion of the test period. In particular, the post-report included a collection of results comparing the efficiency between existing and new LAT campaigns. Analysis of these test results also led to recommendations on  next steps that enabled Bitmango to establish a new UA strategy based on the results.

"We were able to carry out the test campaign without any difficulties thanks to MOLOCO checking and sharing the data every day," said So Hyun Choi, Business Development Manager at Bitmango. "After the test, we were grateful for various proposals suggested by MOLOCO, such as expanding the title for the LAT campaign and switching the pricing model to dCPM."

 

Results

Throughout the LAT test campaign with MOLOCO, Bitmango was able to experience and prepare for the changes to come before the post-IDFA era. Bitmango found that LAT traffic was relatively less competitive and could bring back user traffic that had not been previously reached with lower eCPM. Although the conversion rate was lower than that of the previous campaign, there was an opportunity to acquire users of similar quality at lower unit prices thanks to the lower CPM.

In the case of Bitmango's Brickout - Shoot the Ball title, the installation volume coming through MOLOCO immediately after the LAT campaign had increased by about 6%. Recent data shows that this portion has grown to 70%, proving MOLOCO’s LAT traffic-targeting model is playing a significant role in the app’s increased performance. Above all, this was really significant because it maintained the campaign ROAS figures while achieving high volume.

"It was difficult to actively expand LAT campaigns because we weren't sure about the quality of its users, but after the test campaigns with MOLOCO, we were able to confirm that LAT users also had similar retention and a high value Paying User Rate (PUR). Thanks to MOLOCO’s machine learning engine, we were able to target the most suitable users and the performance got better as more data was collected" 

So Hyun Choi, BD Manager, Bitmango.

 

To learn more about MOLOCO's LAT test campaigns and how you can prepare for the fast-approaching change, contact the machine learning and ad performance experts at MOLOCO today

 

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