Artificial intelligence is revolutionising industries, from customer service and finance to media and music. Digital marketing is no exception, with AI-driven technologies enabling greater personalisation, automation, and efficiency. One area undergoing particularly rapid transformation is mobile app marketing, where machine learning is unlocking new opportunities for advertisers.
Evolution of digital advertising
Since the 1990s, digital advertising has evolved significantly, progressing from simple banner ads to personalised, data-driven campaigns. Historically, much of this innovation has been driven by large platforms – often referred to as “walled gardens” – such as Google, Amazon, and Facebook. These self-contained platforms use the unique, exclusive data that they collect and own to improve ad targeting and user engagement.
Beyond these closed platforms lies the broader digital landscape, where a significant portion of app usage occurs. This includes independent websites, mobile app ecosystems, and the like. For advertisers, this presents both perceived hurdles and untapped opportunities. Smartphones account for about 70% of all digital media time in the US, with more than 80% of that time being spent on apps, according to Oberlo and Mindsea.
Many businesses have yet to fully capitalise on AI-driven marketing in mobile app advertising. However, AI advancements are changing that, enabling real-time decision-making and data-driven strategies at scale across mobile app ecosystems.
Machine learning’s transformative potential in digital advertising
In today’s highly competitive advertising and mobile marketing landscapes, app stores have become more saturated and regulatory changes have introduced new complexities. As the path to growth becomes increasingly difficult to navigate, businesses often face a pressing challenge: finding incremental growth. For marketers, it’s no longer just about scaling quickly – it’s about discovering and capitalising on new opportunities for growth, even when the playing field feels crowded.
A whopping two-thirds of app usage occurs on independent mobile app ecosystems. Consequently, expanding beyond proprietary platforms like Google and Facebook and harnessing AI across the broader digital ecosystem can be a game-changer. By utilising AI and machine learning, businesses can uncover incremental growth opportunities across a broader, less confined digital landscape.
These wider ecosystems offer expansive possibilities for targeting high-value users. Through intelligent algorithms, businesses can now identify, engage, and retain users outside of traditional boundaries, leading to more efficient and impactful marketing efforts.[ME1]
How operational machine learning transforms mobile app advertising
Unlike traditional machine learning, which relies on historical data and predefined models, operational machine learning (ML) represents a significant technological leap. By continuously adapting in real time, operational ML transforms decision-making in dynamic environments like digital advertising.
Where standard ML models require manual updates to handle new tasks, operational ML autonomously ingests and processes the latest data, automatically adjusting its approach.
This capability is especially crucial in mobile advertising, where user behaviours shift rapidly, and millisecond-level decisions can determine campaign success. Operational ML’s ability to make instantaneous, data-driven choices allows for unprecedented agility in targeting and optimising ad placements across constantly evolving digital landscapes.
Strategies to unlock the full potential of AI-driven digital advertising
Businesses are sometimes hesitant to advertise outside of walled gardens due to unfamiliarity or perceived difficulty, coupled with concerns about limited visibility into the inner workings of these platforms’ mobile advertising strategies. However, the development of AI-powered technology tools has dramatically expanded the capabilities of digital advertising beyond the closed ecosystems of giants like Amazon, Google, and Facebook. What was once considered new and intimidating is now increasingly accessible, with technological solutions that offer sophisticated targeting capabilities comparable to – and in some cases exceeding – traditional closed platforms.
But not all machine learning approaches are equally effective, so it’s crucial to prioritise:
- Real-time data integration. Real-time data provides crucial insights into customer behaviour. Factors like market conditions, trends, and events can impact consumer spending and behaviour. As customers’ preferences and actions evolve, marketers need to quickly adjust their strategies to reach and engage their audience.
- Transparency and granular insights. Digital marketers should prioritise platforms that offer clear visibility into processes and data, ensuring that insights, algorithms, and performance metrics are delivered without the hidden mechanisms or opaque practices common in less open systems.
- Enhanced user targeting. Operational ML can analyse vast amounts of data to identify the most relevant audience segments, leading to more precise targeting. By automatically finding the right users at the right time, operational ML reduces the need for manual adjustment, saving time and resources. Additionally, better targeting means higher engagement rates, improved conversion rates, and ultimately, a better ROI for marketing campaigns.
- Evaluating operational ML platforms. Platforms built on operational ML can offer scalable performance, which is crucial for businesses looking to leverage AI advancements. Marketers should evaluate the various ML capabilities across different solutions, assessing how well they meet the specific needs of their campaigns.
Adapting to the future of AI in digital marketing
As AI continues to reshape digital marketing, businesses must rethink their strategies to stay competitive. Operational ML represents the next frontier in AI-driven digital advertising, offering advertisers greater precision, automation, and scalability.
As businesses increasingly shift toward AI-powered solutions, those who embrace these innovations will be best positioned to succeed in the evolving digital landscape.
