The global competition in the mobile gaming industry is intensifying, and it's not without reason.
Looking back at 2023, the average Cost Per Install (CPI) worldwide reached $2.24, with iOS platform apps at $0.86 and Android at $0.44. It's evident that the advertising monetization track is gaining increasing attention. Simultaneously, tightening data privacy policies continue to raise marketing costs.
In the current scenario of rising costs, how can one balance the speed and accuracy of ad delivery, as well as investment and returns? NetMarvel believes that machine learning is the key to solving this series of challenges.
01 Machine Learning: Speed + Accuracy = Efficiency
The advent of precision marketing demands higher frequencies of updating ad creatives and improved creative quality.
However, the multidimensional user information, growing user touchpoints, and diverse media resources make it challenging for humans to handle such complex information in a short time. Ensuring quality and making the right marketing decisions manually takes a lot of time, and after analysis and optimization, new problems may arise. It seems like a challenge to reconcile speed and quality.
Machine learning is the optimal solution to this contradiction. By conducting real-time AI model training and optimization updates through machine learning, it analyzes user/user group behavior, estimates user basic information, behavior data, etc., and accurately finds users who are the best match for the product. It generates effective buying strategies in a very short time, achieving precise engagement with users. It not only has timeliness but also ensures accuracy. "Speed + Accuracy = Efficiency" is the greatest advantage of machine learning.
02 NetMarvel Ensures Data Security and Integrity
With the successive introduction of privacy protection policies, the quality of third-party data has significantly declined, reducing the precision of ad traffic on major media platforms. At this point, third-party data is far less effective than first-hand data.
NetMarvel leverages machine learning to activate first-hand data. After advertisers complete their ad plan construction, they feed their all-channel data back to the system and initiate machine learning. The system trains models based on specific requirements, deeply analyzes audience characteristics, and customizes optimization strategies.
Advertisers only need to enable all-channel data feedback to NetMarvel, allowing them to build ads on the NetMarvel self-service platform. This is done based on different growth needs, different customer acquisition models at different user stages, all supported by NetMarvel's intelligent algorithms, enabling programmatic intelligent delivery.
The all-channel feedback data comes from the advertiser's trust. NetMarvel strictly adheres to data confidentiality principles, ensuring users are protected from risks. This allows advertisers to see the most authentic advertising results, fully understand the authenticity and reliability of traffic, and ensure data security and high transparency without deception.
Taking the ROSA model, which advertisers focus on, as an example:
ROSA Intelligent Estimation
ROAS intelligent bidding can real-time estimate the user's lifetime value and bid in real-time based on the advertiser's set ROAS target to achieve higher quality user delivery results.
ROSA Automatic Filtering
Utilizing algorithms to drive real-time automated delivery eliminates the need for manual sub-channel filtering and pricing, saving time and effort while obtaining high-quality users.
ROSA Targeting User Value
The ROAS model, compared to ordinary fixed bidding models, can globally monitor and dynamically bid based on the user's real value. This allows for a more scientific bidding strategy, discovering better-performing customer acquisition channels and higher-quality target users, achieving efficient delivery.
Machine learning has become the preferred choice for global app advertisers, and NetMarvel's machine learning platform empowers advertisers to efficiently activate first-hand data, igniting user growth.
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