GeeMee’s AI-driven ad solutions in mobile advertising

This article first appeared on GeeMee‘s own blog.

Machine learning (ML) and artificial intelligence (AI) technologies have rapidly become the core driving forces behind in-app mobile advertising campaigns, primarily due to their ability to analyse massive datasets and extract valuable insights, significantly enhancing user growth.

As a transformative force in the ad-tech space, GeeMee leverages advanced AI-driven ad solutions to redefine user engagement and revenue generation patterns. GeeMee’s AI technology has fundamentally transformed every aspect of mobile marketing, including ad creative generation, precise targeting, bid optimisation, and performance analysis.

In an environment of evolving privacy regulations, GeeMee’s ML technology can transcend the limitations of walled garden media by utilising first-party data to provide advanced targeting options. Through these first-party data sources, GeeMee’s AI-empowered platform can distribute highly relevant advertisements to a broader audience.

This article will explore how GeeMee is revolutionising the mobile user acquisition space through AI and machine learning technologies, as well as the future direction of these technologies.

Real-time ad optimisation

GeeMee’s machine learning excels at processing large volumes of historical and real-time datasets to optimise ad delivery.

The system makes decisions to maximise desired outcomes such as user conversions or app installations, ensuring advertising spend flows to the most effective channels. The precision of GeeMee’s machine learning models in real-time ad optimisation is crucial to the success of mobile advertising campaigns.

To achieve this goal, GeeMee has deployed several key strategies:

• Data feeds and adaptive learning: GeeMee’s real-time systems are designed to continuously collect and analyse massive amounts of data. This includes user engagement, ad performance metrics, and current market trends, ensuring models use the most up-to-date information for decision-making. This data is processed by adaptive learning algorithms that are specifically designed to optimise their predictions based on new data.
• Anomaly detection: An important feature of GeeMee’s system is its ability to detect and alert on anomalous data patterns or performance changes. This functionality triggers human review or automatic adjustments, safeguarding the accuracy of the models.
• A/B testing: GeeMee incorporates regular A/B testing as a standard practice, allowing for the comparison of different strategies. By selecting the most effective approaches, the system can methodically optimise the accuracy of its algorithms.

Large-scale personalisation

Privacy regulations and significant changes in the ad tech industry, including IDFA limitations, the trend toward a cookieless future, and Google’s Privacy Sandbox, have prompted app developers to reassess their user acquisition methods. This reassessment has challenged marketers, requiring them to find innovative ways to reach and engage users in an environment where traditional data sources are increasingly restricted.

GeeMee’s ML technology enhances user segmentation capabilities, enabling customised ads to be delivered based on individual user profiles and interests, significantly increasing personalisation levels. This advantage is particularly evident in the delivery of ad creatives, where GeeMee’s machine learning models play a crucial role in understanding the unique needs of each audience segment.

By analysing user feedback data, GeeMee helps marketers identify specific interests and preferences of their target audiences. This allows ad creatives to be adjusted in real-time, ensuring marketing efforts align closely with user expectations and preferences.

Content creation enhancement

For ad creatives, GeeMee’s datasets provide rich insights into audience engagement preferences, such as preferred colours, characters, and call-to-action (CTA) buttons. This capability enables marketers to examine and evaluate creative elements in detail.

When large datasets contain audience engagement preferences (such as colours, characters, and CTAs), GeeMee can analyse creative data at scale and iterate on each new creative. This process, known as Dynamic Creative Optimisation (DCO), achieves a more customised and iterative approach to engaging with target audiences by continuously adjusting ad elements to match user preferences.

For example, if DCO determines that a particular creative element increases game engagement for a specific user group, the system will optimise for that element. GeeMee’s platform makes DCO more accessible, providing advertisers with tools to create and optimise creative formats such as videos and playable ads without coding. This is achieved through intuitive drag-and-drop editors and a collection of high-converting creative templates, simplifying the ad creation and optimisation process.

Emerging technologies such as Computer Vision (CV) and Natural Language Processing (NLP) are also particularly exciting in improving ad creatives. These tools can deeply analyse visual elements in creativity, identifying the most engaging advertisements. NLP facilitates the analysis of text and sentiment.

NLP also plays a key role in localising ad content for different languages, ensuring messages resonate with regional audiences. It also ensures brand consistency by making sure the text and visuals in ads align with brand identity.

ROAS and bid optimisation strategies

GeeMee’s Return on Ad Spend (ROAS) bidding approach automates the process of identifying and targeting users most likely to generate revenue, significantly expanding the reach of user acquisition.

ROAS bidding dynamically adjusts bids to meet advertisers’ specific ROAS targets. Through automated optimisation, ML significantly reduces the need for manual intervention, streamlining the bid optimisation process, targeting users ready to convert, and enabling more profitable app growth.

Multiple global developers have successfully utilised GeeMee’s ML-driven target ROAS bidding and shared insights on how this technology has helped them achieve their user acquisition and growth objectives. For example, a leading Southeast Asian e-commerce platform saw its ROAS increase to over 1.4x, thanks to ML’s ability to optimise bids and effectively target high-value users, significantly boosting installs and overall performance.

Integrating machine learning and AI into mobile advertising not only streamlines processes but also significantly enhances the impact and productivity of advertising campaigns. By continuing to adopt these cutting-edge technologies from GeeMee, advertisers can achieve new levels of personalisation and optimisation in their marketing strategies, delivering exceptional ROAS performance that establishes GeeMee as an industry leader with results far above industry averages.

Conclusion

GeeMee, through its advanced AI and machine learning technologies, is redefining the future of mobile advertising.

From real-time optimisation to large-scale personalisation, from creative enhancement to ROAS optimisation, GeeMee’s technology stack provides advertisers with unprecedented capabilities, enabling them to thrive in an increasingly complex digital advertising landscape. 

As technology continues to evolve, GeeMee will remain at the forefront of innovation, driving the mobile advertising industry forward and creating greater value for advertisers and developers alike.