Remember when a phone was simply a phone? What began as a tool to connect voices has transformed into an ecosystem of 2.6 million apps that connect, entertain, work and essentially run our lives. Nowhere is this digital metamorphosis more profound than in the Asia Pacific, whereby in 2030, an estimated 1.8 billion people — or 61% of the region’s population — will be mobile Internet users. As these mobile-first consumers spend more time online, there is an opportunity for brands to transform everyday digital experiences into prime real estate.
Consider the battlefield of attention: consumers now spend an astounding 88% of their mobile time not browsing websites but diving deep into apps that have become extensions of our daily lives. Each swipe and each interaction represents a potential moment of connection for brands. Yet herein lies the paradox — with millions of apps competing for milliseconds of human attention, how can a single brand cut through the digital noise?
The volume of data is both a promise and a problem. Every tap, scroll and pause creates an ocean of information and transforming this deluge of data into meaningful insights has been the challenge for digital marketers.
Enter machine learning: the ultimate real estate developer, transforming mobile screen time into intelligent advertising property. Machine learning-enhanced advertising offers more than just improved marketing outcomes; it signals a fundamental change in how businesses understand and reach consumers in the digital age.
While the traditional digital advertising model relied primarily on demographic data and broad audience segments, machine learning allows for a more nuanced approach, one that identifies subtle patterns in behaviour that traditional human analysts might miss.
The hidden complexities of mobile advertising
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Imagine the digital advertising space as prime real estate in a bustling city. Every time someone opens an app or visits a website, it is like a prime storefront becomes available for a split second. Multiple real estate agents (ad exchanges) immediately start a lightning-fast auction for this space, and the highest bidder gets to display their ad to that user.
However, this can present a challenge to marketers. Most apps list their storefronts with several real estate agents at once. This means advertisers might unknowingly bid against themselves for the same space through different agents — like accidentally competing against themselves for the same property through other brokers. It drives up the costs without reaching more customers.
Besides that, these digital real estate agents also offer different ways to showcase ads. One might provide an interactive storefront window (video ad), while another only allows for basic display. Marketers must, therefore, choose the right agent (ad exchange) to serve the appropriate ad format and visual for their specific target audience so as to maximise the effectiveness of their ads.
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Additionally, the dynamic nature of the mobile app advertising landscape requires constant monitoring and adaptation to stay ahead of evolving trends and consumer behaviours. This calls for frequent optimisation of ad targeting, bidding and budgeting, which is tedious and time-consuming when dealing with multiple ad exchanges and done manually.
“As businesses in Asia Pacific become more prudent in their spending, they expect marketing dollars to be spent more responsibly. Marketers must focus on return on advertising spend (ROAS), in which the goal of an ad is to get users to perform specific valuable actions that will add to their company’s bottom line,” says Nopparat Yokubon, country lead for Moloco Ads in Southeast Asia, Australia and New Zealand.
She adds: “To achieve that and minimise marketing wastage, marketers will need to be empowered with real-time data, data transparency and data security so that they can do precise targeting and precise bidding in a high-trust environment.”
The need for machine learning
The mobile-first digital economy has created a wealth of consumer data. Every tap, scroll and transaction across mobile apps and websites, generates valuable signals — from user demographics and behaviour to in-app activities and device information. While this data is essential for marketers to gain valuable insights, the sheer volume and complexity of this data can be overwhelming.
Moloco’s Mobile App Performance Marketing Global Report 2023 reveals that companies (especially those in the e-commerce and finance industries) choose machine learning over other factors like the ability to reach a broad audience globally or creative optimisation when selecting marketing solutions.
Consider Viettel Money’s approach: In Vietnam’s crowded mobile app market, with a population of nearly 100 million, their challenges went beyond attracting new users – they had to acquire new users at scale.
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Rather than cast a wide net with generic advertising, machine learning models helped them identify patterns among their most engaged users and what distinguishes them from casual users. These insights then helped Viettel Money identify similar behavioural profiles of potential high-value customers across Vietnam’s digital ecosystem and reach out to them through precisely targeted advertising.
The results were striking: Viettel Money saw a 33% boost in new user registrations. More importantly, these new users showed significantly higher engagement rates than those acquired through traditional channels.
Yokubon advises marketers to leverage machine learning, which constantly ingests and autonomously acts on the most recent data points to make real-time predictions at scale. This can drive tangible outcomes with speed, scalability and adaptability.
With machine learning, marketers will be able to get the most out of their mobile app marketing as they can:
- Adapt their strategies dynamically based on real-time data from user behaviour to macroeconomic conditions.
- Gain granular insights on user behaviour. This allows for targeting that focuses on real-time, individual actions rather than broad audience cohorts, improving the effectiveness of marketing efforts.
- Combine quantitative marketing performance metrics with qualitative consumer insights to fine-tune their holistic strategies for maximum impact.
- Optimise their targeting efforts by efficiently finding the right users at the right time without manual intervention.
In short, machine learning helps improve a mobile app marketing campaign’s efficiency and effectiveness by automatically selecting the best channel to show an ad to the right people at the right time.
Transparency and data protection
Beyond machine learning, Yokubon also emphasises the importance of transparency in mobile app marketing solutions. She says: “To maximise their ROAS, marketers need clear visibility into performance metrics and detailed insights on their campaigns to optimise their strategies. This is why they should look for solutions that provide visibility on where their ads are shown, how those ads perform, and the flexibility to block an ad if that ad placement does not resonate with their brand.”
Marketers, she adds, must also ensure the solution they use offers robust data security and privacy features. This is because mobile app marketing deals with consumers’ data and given the rise of privacy laws such as the European Union General Data Protection Regulation. For instance, the mobile app marketing solution should always store and transfer data in encrypted form and never share data with other advertisers.
The rapid shift towards digital and mobile has opened up a plethora of advertising opportunities for businesses. This is a double-edged sword, as this abundance of choices makes it challenging for marketers to manage and ensure the effectiveness of their mobile app marketing strategies.
Machine learning can help marketers cut through the noise, automatically optimise their advertising efforts, and ensure marketing budgets are used efficiently to drive genuine business growth.
