Over the last two years, COVID lockdowns accelerated already existing mobile gaming trends and pushed increasing numbers of people to the most popular form of gaming. A survey by IDC and LoopMe found 63 per cent of participants increased their time playing mobile games once the pandemic began and that 75 per cent of the mobile gaming boost will persist post-pandemic.

There was also a strong adoption of mobile games by people who were previously non-gamers, with six per cent of those surveyed reporting that they didn't play any games before COVID-19. In total, app usage grew by 36 per cent from April 2020 to April 2022.

Increased time spent in mobile games, combined with the biggest and most diverse mobile gaming audience at any point in its history, has demonstrated gaming’s lasting popularity. But what works on desktop or mobile web doesn’t seamlessly transfer to in-app gaming. Mobile gaming is a distinct vertical with specific formats and technological nuances which puts a thin line between success and failure. To optimise chances of success, advertisers should lean into the experience, data, and technology of a mobile-first demand side platform (DSP).

Do not set and forget

Having the right data and algorithms in place is fundamental to a campaign, but programmatic media buying is not 'set it and forget it'. Success still requires the knowledge and experience of a customer success manager. A good customer success manager already knows which types of marketing and optimisation strategies have seen the most success, saving an advertiser time and resources on testing new hypotheses that will drive performance.

By focusing and dedicating themselves exclusively to one ecosystem, mobile DSP customer success managers offer invaluable industry-specific knowledge that a peer working across a variety of channels has yet to accumulate.

Data is king

Data is king in advertising, and if the data you’re predicting on is too different from the data that you trained on, then ROAS will suffer. DSPs need to ingest and process multiple disparate data types unique to mobile that an omnichannel partner may not be set up to process. Many omnichannel DSPs were part of the first wave of programmatic media buying and were designed to help advertisers drive brand awareness across different marketing channels such as desktop, video, and audio.

But a performance marketer’s primary goal is not just to drive brand awareness, but to drive revenues. And in performance marketing, driving revenue often relies on acquiring as many high-value, high-retention users as possible without compromising user experience. Not to mention, a user's behaviour can be more important than the install itself.

What does engagement look like? Will they make an in-app purchase? In order for a DSP to effectively predict the probability of a desired outcome, it needs to find patterns in the right type of data. And ever since the release of iOS 14, ATT has forced advertisers to pivot from relying on behavioural signals and refocus their targeting using non-personalised contextual signals such as app, geo, device and app-device signals.

Today, there are many new, previously unshared privacy-first contextual signals available to mobile DSPs and it's crucial that their machine learning algorithms can use these in-app events to predict the optimal bid price depending on the likelihood that a user is going to click, install or engage with their app.

Mobile DSPs that have been using non-personalised signals to find users since the release of iOS 10 are at a competitive advantage as they have more relevant training data for their ML models. And more training data is the fuel that improves a model’s predictive accuracy.

The value of the right tools

Our mobile devices generate more data than ever before but how a DSP uses that data to intelligently hone in on an advertiser’s most valuable audience is what ultimately determines a DSPs real effectiveness. Having a data science team dedicated to building mobile-first models means they can test new models faster and find better models more quickly, which eventually leads to higher ROAS.

Bidding, targeting, and optimisation tools must be designed for mobile-specific data and a particular set of marketing needs. From privacy regulations to the implementation of creating user-friendly mobile ad formats, mobile DSPs use 100 per cent of their engineering resources on solving problems specific to an in-app environment.

Tools such as how to operate and drive performance using SKAN data, leveraging first-party and contextual signals, and video ad serving, all have technical complexities unique to mobile and mobile DSPs have teams dedicated to resolving any issues that may arise.

In-app gaming is neither a phase nor a fad

The aim of working with any DSP should be to drive performance, and to increase the amount of data available to a marketer. In practical terms, this means any advertiser ought to work with a range of DSPs - both omnichannel and specialised - to ensure they have access to the right toolset to find their target audience in a sea of countless data points.

Indeed, while an omnichannel DSP can operate in the mobile gaming space, campaign performance may suffer from death by a thousand cuts, as small hits to performance may at first go unnoticed, but over time can compound - turning a campaign unprofitable.

Ultimately, by working with a mobile DSP, marketers can drive incremental ROAS. When all is said and done, the small gains in performance - and the small gaps that an omnichannel DSP is unable to fill - begin to add up and can be the difference between a successful campaign and one which is considered a failure. In-app gaming is not a phase nor a fad. To that end, partnering with a DSP which is an expert in the mobile space is the surest way to reap the benefits of the post-pandemic screen time boom.