BCI and Big Data in gaming: A new data-driven approach to monetisation

The global gaming industry is one of the world's fastest-growing markets, valued at over $360 billion as of 2023, with the US share alone estimated at around $100 billion.

Even though the sector's expansion incites new developers to join in, the space is heavily dominated by a handful of leading studios. In mobile gaming, 1% of the top publishers account for 79% of all downloads, while we see the same situation in the console and PC gaming sectors.

Surprisingly enough, the traditional gaming space preserves the technological status quo, so — largely because of this alignment — we haven't seen much innovation in gaming monetisation models. According to the 'The State of Mobile Game Monetisation 2022' report by SensorTower, the lion's market revenue share is generated from three distinct models: retail, advertising, and microtransactions.

In my opinion, this lack of innovation doesn't only limit the video game market potential but also impacts player experience. The good news is the current capabilities of AI and Big Data Analytics are promising enhancements to the traditional monetisation models.

Let's discuss in-depth.

Legacy monetisation and its drawbacks

Today, retail-based monetisation models expand beyond purchasing a game from your nearest outlet, as physical retail is being replaced by digital distribution and subscription models. Most players buy titles directly from their device's app store or subscribe to game libraries pulled together by aggregators or gaming service providers.

Retail remains one of the most popular monetisation models in the gaming industry. Still, its major drawback is that it heavily impacts the gamer's experience when it comes to perceived value. The reason is that video games often follow a variable pricing strategy: after the first release, the player demand is usually high but can decline over time. As a result, a game is likely to drop in price by half within 6 to 12 months, so not everyone will receive the same value for their money.

The same problem occurs in the subscription model. The service providers don't always consistently update their libraries. There might be months when no new or exciting game titles are available in the library, but you're still paying the same monthly fees.

Then, there's the microtransaction model, otherwise known as in-game purchases. In this model, players are offered paid in-game content to enhance their gaming experience. One of the biggest problems with the microtransaction model is that it can be addictive and cause players to overspend, especially young gamers who don't have a sound understanding of financial management yet.

Moreover, it can give players an unfair competitive advantage by creating a pay-to-win scenario. This means players willing to spend more money will have a significant advantage over those who don't.

Lastly, there is the ad placement model, which is more frequently used in mobile gaming. Its drawbacks are obvious: nobody likes their gaming experience interrupted every now and then by ad popups.

Innovating gaming monetisation with AI and Big Data

In the past few years, immersive technologies and AI development have made revolutionary advances, mostly due to the ongoing development of the metaverse and Web3 gaming and investments concentrated in the space. In particular, the space has seen a considerable breakthrough in the field of advanced brain-computer interfaces (BCIs).

The potential of BCI tech for various sectors, namely gaming, has already been recognised. The global BCI market was valued at $1,488.00 million in 2020 and is projected to grow to $5,463 million by 2030, with giants like Valve exploring the niche to make video games more interactive and exciting.

This new segment of video games eliminates the need for traditional controllers. In gaming, non-invasive BCIs — smart wearables such as VR headsets and sensor-integrated mouses, controllers, gloves, or headphones — are aimed at creating individual experiences and boosting immersion.

By combining these devices with AI-based immersive gaming, developers can obtain real-time data regarding a gamer's physical, psychological, and behavioural data to develop more engaging and immersive gaming content. For instance, Neurogaming by Emotiv, Inc builds video games that use non-invasive BCI techniques to analyse the user's mood and adjust visual graphics and music accordingly.

Additionally, BCI is integrated into mobile and virtual gaming through virtual reality (VR) headsets, allowing users to control the game environment and objects with their minds, thus optimising their gaming experience. Advanced AI-based modules that can be integrated into game engines (e.g., Unreal Engine) to track player emotions.

The question is, is there a way to monetise the immense amounts of data the industry generates while ensuring it's handled ethically?

Big Data for a scientific breakthrough?

In fact, such data is immensely valuable for scientists working in health tech, who need a large volume of physiological and psychological data for analysing and tackling physical and mental health issues. At the same time, data acquisition is a costly process, which is why scientific trials on psychological and behavioural experiments have declined by 90% in the past two decades.

The data collected by the BCIs — with the player's consent, of course — can be used by scientific researchers and medical trials for more effective health tech and treatment development. The revenue from such transactions can be redirected to the players as in-game rewards, enhancing their experience.

As for privacy concerns, developers can integrate this data-sharing model with blockchain technology. This offers gamers full transparency in tracking where and how their data is shared and what value they get in return.

Overall, it's important to understand that this new Big Data and AI-based monetisation model is not an alternative to the existing models; but a supporting framework that can reduce the previous model's drawbacks. The industry still needs extensive research and simulations before this model can be suggested as a stand-alone solution to monetisation.

However, experimenting with this model will allow developers to widen their revenue stream and rely less on retail, microtransactions, and ad-based practices.

By Dr Dimitry Mihaylov

Dimitry Mihaylov is an experienced research scientist, professor, lecturer, entrepreneur, and business innovator. He fuses Big Data, AI, and engineering to create new products that enhance sustainability and efficiency in the fields of tech, biopharma, heavy machinery, and environmental protection. 

Quite frequently, he gives talks at industry events on blockchain and associated risks. He was also invited to present research on cryptocurrency circulation for Egmont Group – an international organization that facilitates cooperation between national financial intelligence units to investigate and prevent money laundering and terrorist financing.

Dimitry has authored 12 books in 3 languages, over 100 scientific publications, and 27 international patents. (Check out the list of key publications and patents here.)