In recent years, the myths of overseas marketing have all been granted by machine learning:

Machine learning significantly reduces CPI, machine learning greatly increases download volume, machine learning leverages large traffic at low cost...

Previously, each step of user growth heavily relied on the operational experience of marketing personnel; now, machine learning is completely overturning the acquisition model to expand marketing patterns, continuously infiltrating global business operations.

This is not an exaggeration! Whether it's developers or advertisers, in order not to fall behind, utilizing machine learning to create comprehensive and efficient user growth strategies is definitely the next possible increment.

It's not that it's not your chance, it's just not recognized as a chance.

01 Machine Learning, How to Accelerate App Growth?

There is a noticeable phenomenon: in the past, data advertisements were just basic ad placements where users clicked if interested. Nowadays, advertisers focus on being "more understanding than yourself", leveraging their own traffic advantages to create colorful personalized ads that bring endless revenue opportunities for countless developers. This approach, when reflected in the current overseas app growth monetization, holds high reference value.

The key to these personalized ads lies in machine learning models, which are becoming an essential part of the overseas marketing journey. To understand it, you need to consider it from four points:

1. Real-Time Data Integration

What is the biggest challenge in internet marketing? It's the lack of rich data to rely on. 

In the user acquisition field, on one hand, due to the lack of user data, it's challenging to accurately attribute advertising effects, and ad placements cannot be precisely targeted to the desired users. On the other hand, during the ad placement process, a large amount of precise data is needed for timely optimization and adjustment. Relying solely on manual operations results in lower efficiency and higher demands on ad placement personnel.

Real-time machine learning, relying on algorithms, can quickly achieve targeted advertising and product recommendations, enhancing ad relevance and engagement, and improving ad monetization capabilities. Compared to manual operations, machine learning technology can significantly reduce costs and enhance efficiency.

2. Fine Insight into Consumer Behavior

Predicting customer behavior can fill market gaps, enabling the early identification of needed products which can generate greater revenue. 

This applies to the user acquisition field as well.

Machine learning models train real-time AI models using massive first-hand data, enabling timely updates, adjustments, and optimizations. By combining algorithmic analysis of user behavior, estimating user preferences, and accurately identifying potential target users for apps based on data, user acquisition strategies can be quickly formulated and promptly implemented, effectively reaching users in a short period of time.

3. Multiple Effect Metrics to Meet Diverse Growth Needs

There are currently many machine learning models available in the market, common models include ROAS, CPE, CPI, etc. Similar to Return on Investment (ROI), the ROAS model helps advertisers target a large number of high-paying users. When advertisers need to drive high volume, the CPI model can be considered, and when advertisers need to promote a specific payment behavior, the CPE model can be useful.

Machine learning replaces empiricism with data, making advertising more efficient, scientific, and accurate, thereby assisting advertisers in international marketing efforts.

4. Collaborating with Leading Machine Learning Platforms

Looking at the marketing environment in overseas markets, privacy regulations, pricing, ad fraud, and real-time adaptability are all pain points for growth. Collaborating with leading machine learning platforms is key to breaking through in international user acquisition.

NetMarvel's machine learning solutions have gained significant market attention and are the preferred marketing choice for many app advertisers and developers expanding internationally.

02 Advantages of NetMarvel's Machine Learning Models

1. In-house Traffic Matrix

NetMarvel has its own first-hand data, based on its IN-App traffic matrix, ensuring 100% real user traffic covering regions including Europe, America, Southeast Asia, Japan, South Africa, and South America, reaching over 1 billion daily active users. By analyzing parameters of nearly 1 billion users and algorithmically breaking down user behavior, precise ad targeting is achieved with ease.

2. Precise Bidding on Programmatic Advertising Platforms

NetMarvel processes over 5 billion bid requests daily, making data-driven real-time decisions to connect demand and supply. The DSP demand-side platform aggregates the world's highest-quality traffic, while the SSP supply-side platform conducts comprehensive analysis and management to maximize ad revenue. The ADX advertising exchange platform enables one-stop buying, facilitating multi-channel programmatic management with precise and intelligent allocation strategies to help developers quickly achieve monetization revenue.

3. Real and Transparent Data

NetMarvel's all-in-one advertising platform integrates channel traffic and user behavioral data to conduct anti-fraud identification on all incoming traffic, ensuring authenticity and transparency. Suspicious ad activities are intercepted in real-time, and through the comparison of multiple dimensions such as timestamps, sources, and device information of clicks, displays, and installations, fraudulent ad behavior is accurately identified, guaranteeing that every penny spent by advertisers reaches genuine users.

For developers, NetMarvel offers a stable and efficient SDK, secure data management, and can help solve issues related to fill rate, losses, technology, and more during the advertising monetization process, maximizing revenue generation.