Author: Dan Blechner

Not All Cheating Is the Same

Revenue loss and reduced player engagement are just two of the significant consequences of cheating in online games. While most large companies have developed home-grown solutions to deal with this significant challenge, the problem of cheating is highly fragmented and much more complicated than it appears on the surface.

Most live ops teams classify any suspicious activity under a single category of cheaters. In reality, there is a wide variety of cheating behaviors and motivations, each with different effects on the game. This diverse cheating ecosystem requires tailored responses, such as warnings, temporary suspensions, permanent bans, and other actions, depending on the severity and impact. To handle this effectively, it’s crucial to segment users based on the type and impact of their cheating behavior. But how can you determine the appropriate action for each case?

There are two main types of cheating activities: commercial cheating – mainly large operations run by cheaters motivated by money, and indie cheating – mainly hyper-engaged players who enjoy the game and want to get more out of it.

This guide reviews the various cheating typologies and motivations, and discusses how distinguishing between them gives your team crucial data that can help you determine the proper policy and response. Treating cheaters appropriately increases the engagement of your key players by removing the nuisance factor, and prevents leaks of in-game revenue to unauthorized third parties.

Why In-Game Cheating Protection Is Important

While tools like CAPTCHAs and two-factor authentication (2FA) handle cheaters at the registration and payment stages, they don’t address what happens once a cheater gets inside the game. CAPTCHAs, for instance, are commonly used in games, but often fail because bots can solve them or other advanced techniques can bypass them. For example, a CAPTCHA might prevent automated account creation, but it cannot detect cheating tactics being used during gameplay. This makes CAPTCHAs insufficient for in-game cheating protection.

In-game cheating, such as using aimbots, speed hacks, exploiting bugs, and more, can go undetected for months, causing significant damage. Cheaters can manipulate the in-game economy, frustrate genuine players, skew game analytics, and erode trust in the game’s integrity.

Adopting a holistic approach is essential: you cannot rely solely on perimeter defenses. Both perimeter defense and in-game cheat detection need to be managed to effectively combat cheating. In-game cheating detection targets the core of the player experience, ensuring long-term sustainability and player satisfaction. Without robust in-game protection, the game’s community and economic balance are at constant risk, ultimately undermining the game’s success.

Fifty-Thousand-Foot View of Cheating Tools

Before explaining the different types of cheating, it’s important to understand some of the tools that cheaters use to achieve their aims.

Bots, Scripts, and Automations

Bots, scripts, and automations are software programs that automate actions within a game, allowing players to gain unfair advantages. These tools can perform repetitive tasks, execute complex sequences, or mimic human behavior, making detection challenging for game administrators. There are various types of automated tools, including malicious bots specifically designed to exploit game mechanics and disrupt gameplay. For instance, in “World of Warcraft,” players used farming bots to collect resources like gold and rare items. By continuously performing resource-gathering tasks that players would otherwise need to do manually, farming bots can flood the in-game economy with resources, often leading to inflation and other economic imbalances.

These automated tools hurt gaming studios’ monetization strategies. Studios often design certain tasks to retain players longer, but players use bots or buy items outside the game to bypass these tasks, leading to revenue loss for the studios. These software cheating tools can be found in all game genres.

Emulators

Emulators are software programs that replicate the hardware and software of a gaming device, allowing users to play games designed for that platform on a different device. For example, they enable users to run mobile apps and games designed for specific mobile platforms on a PC or another mobile device. In “Mario Kart Tour,” players that used emulators gained an unfair advantage with more precise controls and enhanced graphics.

Emulators work by mimicking the behavior of the original hardware, including its CPU, GPU, memory, and input/output systems. Emulators also provide a fertile ground for hacks and automation by making it easier to plug additional software into the original application, automate certain interactions, make changes in the device memory, and even switch accounts/devices very quickly.

Macros and Hotkeys

Sometimes, players use hacks such as macros (enabling one button to perform several actions), giving them a superhuman capability to act faster or more accurately than other players. For example, in “Mortal Kombat,” executing a combo move like a Fatality requires a precise sequence of inputs, which can be difficult to perform quickly under pressure. A macro can automate this sequence, and would clearly give the player an edge over others.

Additionally, farming macros can automate repetitive resource-gathering tasks, allowing players to accumulate in-game currency, items, or experience points effortlessly. Often, emulators enable premade macros and hotkeys for specific games, further enhancing these automated processes.

Input Devices

In some action-packed games, some players use external hardware devices such as Cronus Zen “controller emulator” to boost their performance or make it easier to control the game. For example, in “Fortnite,” players can use Cronus Zen to enable features like rapid-fire mods, which allow for faster shooting, or aim assist enhancements, which improve aiming precision. This provides a significant and unfair advantage, compromising the competitive integrity of the game.

Hacked APKs

These are alternative software clients developed by third parties to access and play a particular online game. These clients offer additional features and modifications beyond the official game client, and often go far beyond mere “modding.” For example, in “Among Us,” a hacked APK might provide players with the ability to always be the impostor, see through walls, or have unlimited use of abilities, giving them an unfair advantage. These APKs can enhance performance, graphics, and user interface customization, or add in-game tools. For game developers, hacked APKs are particularly problematic because they block information flow, which prevents detection of cheating and often violates the game’s Terms of Service.

Multiple Accounts

While it is common for legitimate players to have several accounts, the usage of multiple accounts becomes problematic when it exceeds a certain number, a typical tactic of commercial cheaters. After a certain threshold, controlling multiple accounts can provide an unfair advantage by manipulating rankings, exploiting game mechanics, or bypassing penalties.

Additionally, some commercial cheaters exploit this practice for monetary gain, using multiple accounts to dominate in-game economies, influence markets, or sell high-level accounts. For example, commercial cheaters may control thousands of accounts, trading with each other and “feeding” certain accounts with resources. These pampered accounts can later be sold at high prices, or the accumulated goods can be sold online, disrupting the in-game economy and providing an unfair advantage.

The following diagram illustrates how commercial cheaters use multiple accounts to trade resources and feed select accounts:

Game Hacks

The use of game hacks involves employing unauthorized software or tools designed to alter game mechanics and provide unfair advantages. This includes exploiting vulnerabilities, bypassing security measures, and manipulating game data. Some highly engaged and interested players actively seek out or develop these hacks, often sharing their discoveries in online forums dedicated to game hacks. Game hacks enter the commercial cheating arena when cheaters leverage them for monetary gain, selling hacks or using them to manipulate in-game economies and rankings for profit.

Direct API Manipulation

Hackers may mimic the client’s (game app) API to directly interact with the app backend. This method is the easiest and simplest form of cheating, allowing hackers to complete game tasks more quickly, disrupting the game’s balance and fairness. For example, in “Fortnite,” hackers used API manipulation to automatically complete challenges and gain rewards without actually playing the game. This method is particularly effective for automating farming processes, where hackers gather resources at an accelerated rate, significantly impacting the gameplay experience for legitimate players.

 

Commercial vs. Indie Cheating

Commercial Cheating

Now let’s explore the business side of cheating. Commercial cheating is a business. Just like any other business, it needs to scale up to achieve worthwhile profits, since each individual transaction is relatively small. This type of fraud primarily targets games with a large number of players, so they can count on massive activity. These businesses can achieve revenues of tens of millions of dollars per year per game!

There are several attack vectors that commercial cheating operations typically use:

Selling of Unauthorized Services or Tools

Unauthorized external organizations may sell a service outside the game that helps advance players inside the game. There are several options:

• Selling a bot that players can operate on the players’ own devices, which provides unfair advantages in the game (e.g., a farming bot or an aimbot).
• Operating players’ accounts over emulators in a remote cloud, on the cheaters’ infrastructure, to nurture accounts and expedite the players’ progress in the game.
• Selling game intelligence services in real time, which provides an unfair advantage to certain players. For example, information about which opponents are the weakest and should be attacked, the capabilities of other players, or even revealing the entire world map for the player.

On some occasions, players need to hand over their credentials to the cheaters to activate the cheat. This can lead to additional fraud or scams, where cheaters take over legitimate accounts to loot them, sell their accounts to others, steal their personal data, or otherwise exploit them.

Trading and Selling In-Game Currency or Items

Cheaters may exploit the popularity of a game to offer easy gains to players. In most cases, the cheaters will gain these resources by nurturing accounts over a long period of time (through automation or manually in device farms) and then offer these resources for sale outside the game. The players pay the cheaters up to hundreds or thousands of dollars on an external platform, then receive their purchase inside the game for a token amount of game currency or trade. Once the first transaction is completed, it is very easy to purchase from the same provider again, since the buyer and seller are already connected in the game.

Sometimes, the operators behind this cheating scheme will spam other players inside the game to offer their forbidden goods, further impacting the player experience.

Another risk associated with this type of cheating is scams. An online service may promise to provide in-game goods, then take players’ money without providing anything in return, leaving the players with no one to complain to.

Win-Trading

Some cheaters manipulate the outcome of matches or competitions in multiplayer games by intentionally losing, influencing who actually wins. This behavior aims to elevate the purchaser’s ranking or help them acquire in-game rewards. In certain games, trading can only happen when winning or losing battles. Cheaters use these battles to trade in-game currency or items obtained through illegal means outside the game. So the winner, who purchased their win illegitimately, gains the win plus whatever goods the sellers transferred to them in the battle.

Account Selling

In this case, cheaters create game accounts and build bots to play the game. The bots play consistently so that the account achieves increasingly higher levels, prizes, unique products, and so on. These accounts run on emulators that mimic the behavior of iOS or Android.

In many cases, the emulators run on cloud servers, which makes cheating very scalable. Millions of fake users are created daily in various games worldwide. The cheaters grow these accounts, and once they have reached maturity (i.e. a certain level in the game), they sell them for tens or even hundreds of dollars online. This is gamer revenue leaking directly out of the gaming company’s sales funnel.

Account Phishing/Takeover

In this case, cheaters aim to take over existing accounts to sell them in a marketplace or try to blackmail the owners. These attack vectors are mostly active in the game login or support areas. Sometimes cheaters use third-party services, fake or real, as bait for players to hand over their credentials and make their accounts an easy takeover target.

Money Laundering

Some cheaters use stolen credit card information to purchase in-game currency or items, which they then resell for a fraction of the original cost to other players. The transactions are made with the intent to launder money from stolen credit card details. Accepting in-app purchases with stolen credit cards can lead to chargebacks and, again, lost revenue.

Indie Cheating

Indie cheating is less about the business, and more about the players’ love for the game. Typically, the cheater simply wants to progress further or faster, or they may be curious about bots and the technology behind the game. They are typically looking for an unfair advantage, like extra in-game resources, to make the game more fun.

For example, players may cheat by purchasing bots from commercial cheating companies. These bots can systematically grow their accounts while the players are not actively playing, such as while they are sleeping. Then they continue playing genuinely with the same account during the day. Others purchase third-party services which activate cheats on their accounts in a remote environment. Usually, these types of commercial cheating services are also a source of additional trouble for the players – from account takeover to password spoofing and a wide range of other harmful behaviors.

These highly engaged indie cheaters may also invest their own time and attention in creating automations or hacks. For example, they can purchase or deploy (or even build themselves, due to the rapid development of generative AI) a bot to win certain battles instead of the player.

Unfortunately, indie cheating has a negative community element to it; that is, when players notice a massive amount of cheating around them, they either get turned off completely, or they look for ways to cheat themselves, to be at a similar level to the players around them.

 

How to Mitigate Cheating

Now that we have outlined the various types of cheaters and cheating techniques, let’s talk about next steps. The goal of each gaming company or studio should be to mitigate the risks of cheaters and preserve engaged players, while maintaining an environment of fair play. We recommend aiming for real-time detection and policy enforcement to limit the impact of cheating on your legitimate players, and to get the message across to commercial cheaters that their nefarious ways are unwelcome.

Mitigation is a combination of acting fast, acting smart, and acting often.

Let’s start with fast. Both the cheaters and the gaming companies have access to advanced coding tools and GenAI. This means the pace of change is quicker than ever before. The only solution is to make an effort to always be one step (or more) ahead of the cheaters. The static prevention tools used by many gaming companies, such as emulation detection or anti-modding tools, typically delay cheaters but do not block them. We have seen over and over again how the fraud perpetrators eventually bypass these defenses.

Acting fast is not enough, you must also act smart. Consider your short- and long-term goals, and build a strategy that can accomplish them. For example, naively banning every type of cheating may simply encourage the cheaters to evolve more quickly.

And lastly, cheating is an ongoing arms race between the cheaters – whether indie or commercial – and the game developers. Developers need to continuously deal with this threat, or it will take over the game and push the engaged players out. So you must act often.

Managing Cheating in Practice

Managing cheating successfully is a complicated process. Unfortunately, most companies lack the tools to classify and differentiate between different types of cheating. Without highly granular visibility into player behavior inside the game, it is nearly impossible to make smart decisions that will benefit the game and its user experience.

Gaming companies need a way to identify the most damaging cheaters that are costing them money and player engagement, in a highly complex environment that is full of mixed signals. Recognizing that there are diverse groups of cheaters, each with different characteristics, is a crucial start. Each group should be treated with a relevant policy – banning is not the only solution. Methods like shadowbanning, temporary suspensions, or restrictions on certain game features can be effective alternatives. There are also other options available.

Accuracy is of the utmost importance. Misclassifying a highly engaged player as a cheater could lead to massive dissatisfaction and disengagement. Accurately identifying your cheaters’ typology allows appropriate policy enforcement that increases overall efficiency and preserves players’ trust.

Highly engaged players – even those who pay for external services to promote themselves – do not typically pose the same danger as commercial cheating companies. In fact, if a gaming company blocks these players, it can hurt the game in the long run by upsetting true fans. The goal should be to limit the inventory and effectiveness of their cheating tools while also making an effort to preserve them as engaged players. In fact, analyzing their behavior can help improve the game, since in their love for the game, they are trying to find ways to make it even more enjoyable. Turning some of their cheats into legitimate features can be a win-win, showing them that you are listening and learning from them and that you care as much about improving the game as they do. In other words, it can be OK to allow some cheating as long as it is carefully tracked. You should, however, make a concerted effort to eliminate cheating in highly visible locations such as tournaments and leaderboards, to maintain the authenticity of the game.

Commercial cheaters are usually well-funded companies that are driven by profits. This means that limiting their revenue and changing the mechanics of their underlying ROI is the best way to handle them. After taking action against the commercial cheating accounts, they will reorganize, make changes to their activity to avoid detection, and then come back in measured steps. Since it is a cat-and-mouse game, it is important to limit their ability to learn your detection mechanisms. For instance, banning all of their accounts on an hourly basis will provide them with a very quick feedback loop to learn the detection system. It is important to spread out any bans to increase confusion among the cheaters, preventing them from learning how to evade your cheating detection patterns.

Gain Crucial Visibility Into Cheating

By maintaining engaged players and identifying cheaters’ behaviors, companies can limit the impact of cheating and its associated revenue loss and reduction in player engagement.

Contact Quago to learn how to gain crucial visibility into cheating.

We are always looking for the best way to help our game developer customers provide a more authentic and fair gaming experience. It was natural for us to partner with Databricks for data research, managing our machine learning pipelines, and lifting the complexity of Apache Spark™.

Our partnership with Databricks helps us enhance our cheat detection capabilities, benefiting our customers in several key ways:

• Reducing the training time for our supervised and unsupervised models.
• Enabling the detection of new patterns as player behavior evolves.
• Enables unprecedented precision with practically 0.000000016 false positive rate.

Our use of Databricks allows us to analyze player behavior in real-time, swiftly identifying and mitigating cheating. This ensures game integrity, boosts player trust, and enhances loyalty. Our advanced, scalable solutions support gaming companies in optimizing their live ops and retention strategies.

Discover how Databricks helps us deliver these benefits in the full case study.

Learn about how cheating impacts the gaming industry in our latest blog: The Impact of Cheating in Online Gaming: Insights for 2025.

 

Multiple recent research have found that 60% of online gamers feel that their multiplayer gaming experience has been negatively impacted due to other cheating players.

In the world of online gaming, cheating is like that annoying kid at the playground who keeps bending the rules to win. It’s not cool, it’s not fair, and it ruins the fun for everyone else. But in this article, we discuss how cheating leads to far-reaching consequences in the gaming industry as a whole.

The costs of cheating span from industry efforts to detect and limit it, to losses in monetization to brand reputation issues and to victims’ monetary losses due to scams. 

 Cheating is prevalent in most multiplayer online games, though measuring its full extent remains challenging. Various methods of cheating in online games can take the form of software (scripts, automation, moding) assistance or various forms of unsporting play taking advantage of exploits within the game. The Internet and darknets offer many tools and methodologies built to cheat in online games, such as this content piece.

A closer look into the online gaming cheating phenomena

The evolution of cheating in online gaming:

From classic aimbots and wall hacks to sophisticated cheat detection systems, the cat-and-mouse game between developers and cheaters has reached new heights. 

In the early days of online gaming, cheating was primarily limited to exploiting glitches and using rudimentary aimbots. These methods were relatively easy to detect and counter, and game developers quickly implemented measures to address these vulnerabilities.

As multiplayer and esports games gain prominence, the methods of cheating have become more intricate, enforcing constant innovation in anti-cheat measures. 

Cheaters began to utilize software that could analyze game data and make automated decisions, giving them superhuman reflexes and unbeatable aim. These advancements made cheating more difficult to detect and counter, as the lines between legitimate player behavior and automated cheating became increasingly blurred.

Maybe the most important aspect of this phenomenon is the fact that cheaters are constantly adapting their tactics to overcome anti-cheat technologies. 

 

Let’s draw a timeline of the evolution of cheating: 

1. Early Days:

• Methods: Glitch exploitation, basic aimbots.
• Detection and Countermeasures: Developers quickly respond to glitch exploitation, implementing measures to fix bugs and vulnerabilities.
  

2. Growth of Multiplayer games (~2010):

• Methods: Introduction of more advanced aimbots, and wallhacks.
• Detection and Countermeasures: Developers enhance anti-cheat measures as multiplayer gaming gains popularity. Early stages of signature-based detection.

3. Esports Emergence (~2010+):

• Methods: Automated decision-making software for superhuman reflexes.
• Detection and Countermeasures: Anti-cheat measures evolve with a focus on heuristic analysis. Increased sophistication in cheat tactics requires constant innovation in detection systems.

4. Technological Advancements (2015 and onwards):

• Methods: Machine learning-powered automation, cloud-based cheating services.
• Detection and Countermeasures: Developers integrate machine learning algorithms into anti-cheat systems. Cloud-based cheating services pose new challenges.

5. Present Day (2023):

• Methods: Device sensor-based analytics, adaptive cheats using machine learning.
• Detection and Countermeasures: Introduction of real-time sensor-based analytics for immediate identification of suspicious behavior. Ongoing arms race with machine learning-powered cheats.

6. Future Challenges (Beyond 2023):

• Anticipated Methods: Continued use of machine learning-powered cheats, challenges in cloud gaming and cross-platform play.
• Evolution in Detection: Enhanced sensor-based ML analytics to counter emerging threats. Focus on minimizing false positives through robust evidence collection.

 

 

Common types of cheating: 

Software-based cheats:

• Aimbots: These programs automatically aim at opponents, giving players an unfair advantage in shooting games.

• Wallhacks: These enable players to see opponents through walls.

• Speedhacks: These programs increase the speed of a player’s character, making them difficult to catch.

• Lag switches: These programs artificially induce lag, causing the player’s character to appear to teleport around the map, making them difficult to hit.

Exploitation of game mechanics:

• Glitching: This involves taking advantage of unintended bugs or glitches in the game to gain an unfair advantage.

• Boosting: This involves collaborating with other players to artificially inflate one player’s rank or statistics.

• Farming: This involves using automated scripts or bots to perform repetitive tasks in the game, such as gathering resources or killing enemies.

Hardware-based cheats:

• Game console mods: These mods allow players to install custom software on their game consoles, which can be used to cheat.

• Input modifiers: These devices allow players to input commands into the game that are not normally possible, such as rapid fire or macros.

• Memory editors: These devices allow players to modify the game’s memory in real time, which can be used to cheat.
  

Other, more sophisticated methods include: 

• Data Manipulation: Cheaters can manipulate game data packets to gain an unfair advantage. This involves intercepting and modifying data exchanged between the game server and the player’s device. By altering data related to player positions, resources, or inventory, cheaters can gain control over the game world and manipulate outcomes.
• Reverse Engineering and Code Injection: Cheaters with programming expertise may resort to reverse engineering game code to identify vulnerabilities and inject malicious code. This injected code can alter game mechanics, bypass anti-cheat measures, or grant cheaters unauthorized privileges.
• Machine Learning-Powered automation: Advanced cheaters employ machine learning algorithms to create sophisticated software that can learn and adapt to game environments. These cheats can mimic human behavior, making them more difficult to detect by traditional anti-cheat software.
• Cloud-Based Cheating Services: Cheaters can utilize cloud-based services to access remote cheating tools and resources. These services may provide access to aimbots, wallhacks, or other cheating software, making it easier for cheaters to evade detection.
• DDoS (Distributed Denial-of-Service) Attacks: Cheaters may launch DDoS attacks to disrupt game servers and gain an unfair advantage. These attacks overwhelm servers with traffic, causing them to crash or become unresponsive, giving cheaters a temporary advantage in the game.
• Social Engineering and Collusion: Cheaters may employ social engineering tactics to trick other players into revealing sensitive information or gain access to their accounts. This information can then be used to exploit vulnerabilities or manipulate game mechanics.
• Hardware Tampering: Cheaters may tamper with hardware components, such as game controllers or input devices, to gain an unfair advantage. This may involve modifying hardware to achieve rapid-fire capabilities or altering input signals to manipulate game mechanics.

 

To summarize this part, even the top industry gaming professionals, such as game managers, product managers, and alike are not aware of all these methods described above or the extent to which they are used. 

 

Real time examples for major cheating incidents covered by the media, include: 

Call of Duty

source

The publisher has taken action against a massive wave of cheating, banning over 50,000 accounts from the game. This helped to restore some semblance of fairness to the game, but nonetheless, it is estimated that the cheating plague caused major losses to the brand.  

Lately it had to ban 14K accounts again> 

🛡 #MWII #Warzone

In the last 24 hours, new detections directly targeting cheat developers at the source has led to over 14,000 account bans for cheating and hacking in Modern Warfare II and Warzone.

— Call of Duty Updates (@CODUpdates) July 28, 2023

Moreover, in its effort to satisfy honest players and support fair play, Call of Duty now shows you when a cheater has been booted right in the kill feed. source  

Destiny 2

Another example includes this recent news of ‘Destiny 2’ developer Bungie winning $6.7M in lawsuit against video game cheat-maker. source

The article states that:  “It’s an endemic problem in online gaming, particularly on the PC platform” 

Overwatch 2

An additional example is Overwatch 2 which has banned 250,000+ cheaters since launch.

Over 250,000 Cheaters have been penalized in #Overwatch2 🚨

The team took action against thousands more for grouping up with cheaters, and they are actively addressing inappropriately named custom games and BattleTags.

📰 Read More: https://t.co/mNwc2ltyEI pic.twitter.com/TZB6cNyKr5

— Overwatch Cavalry (@OWCavalry) September 8, 2023

Valve 

Valve banned 40,000 Dota 2 cheaters; The company said it was able to spot players that read ‘secret’ data not available during normal gameplay, as part of an ongoing campaign to reduce cheating in the game (Source) 

There are many more examples, and one can say that the industry as been busy fighting this pandemic for quite some time now, with only partial success. 

 

The cost of cheating: A multifaceted burden on the gaming industry

Cheating in online gaming creates more than just frustration—it messes with fair play, saps player excitement, increases costs, and hurts brand names. Recognizing these issues is key for game makers and industry players to tackle this widespread problem head-on. 

We can split this discussion into discrete areas: 

 

The impact of cheating on IAP and player engagement

Cheating directly impacts in-app purchases (IAP) by deterring legitimate players from spending money on a game if they perceive that the playing field is uneven. A study revealed that 60% of gamers across the globe have had their multiplayer gaming experience negatively impacted by other players cheating. And 77% are likely to walk away from a multiplayer online game if they feel that other players are gaining an unfair advantage through cheating. 

This translates into lost revenue for game developers, who rely on IAP to sustain their businesses – another study shares that 48% of online gamers said that they would buy less in-game content as a result of cheating

Moreover, cheating erodes player engagement and retention, as players become discouraged by unfair competition and seek out more enjoyable gaming experiences. A survey by Newzoo revealed that 57% of gamers have quit a game due to cheating, highlighting the detrimental impact on player loyalty.

 

The cost of cloud 

The increased traffic generated by cheating tools can overload game servers, leading to performance issues and additional costs associated with server maintenance and upgrades.

 

Industry estimates of the financial costs of cheating

Industry experts estimate that cheating costs the gaming industry billions of dollars annually in lost revenue, increased operational expenses, and reputation issues. In 2019 alone, the industry reportedly lost an estimated $29 billion due to video game cheating.

 

The indirect impact of cheating on different areas

Beyond the direct financial costs, cheating also has indirect consequences that affect various aspects of the gaming experience.

Competitive Gameplay: Cheating damages the gamer experience which leads to a decline in viewership and sponsorship opportunities.

Overall Gaming Experience: Cheating creates frustration among legitimate players. This leads to a decline in player engagement and increases churn. 

Game Reputation: Cheating can lead to negative publicity around the game brand, damaging brand image and future revenue potential. 

 

Combating cheating: The industry’s arsenal

In the battle against cheating, the gaming industry has deployed a multifaceted arsenal of countermeasures, ranging from sophisticated anti-cheat software to comprehensive cheat mitigation policies.

 

Anti-cheat software and technologies: The first line of defense

At the forefront of the anti-cheating fight lies a diverse array of software and technologies designed to identify and neutralize cheating attempts. These tools employ various techniques, including:

• Signature-based detection: This method involves identifying and blocking known cheating patterns or signatures within game code.

• Heuristic analysis: This approach analyzes player behavior and game data to detect anomalies indicative of cheating, such as superhuman reflexes or impossible movements.

• Machine learning-based tools: Advanced algorithms are trained on vast amounts of data to identify patterns and anomalies that could signal cheating activity.

• Kernel-level protection: Deeply embedded anti-cheat software can monitor and intercept system calls, gaining access to a lower level of the game’s operation to detect and prevent cheating attempts.

Cheat mitigation policies: Beyond detection; addressing the root causes

While anti-cheat software and technologies play a crucial role in detecting and preventing cheating, cheat mitigation policies address the underlying causes of cheating. These policies outline clear guidelines on acceptable behavior, penalties for violations, and appeal processes.

It is important to mention here that identifying cheaters doesn’t necessarily mean that companies will prevent them from playing – it depends on the impact on the game community and the game’s monetization. In the past, companies chose to either turn a blind-eye to cheating as long as their user base was growing or simply ban players for a lifetime to keep the game clean. Today, gaming companies evolved their policies but still, they need advanced anti-cheat detection tools in order to have full visibility over what’s going on in their games. For instance, if players use simple cheats to improve their own experience without impacting others, companies may decide not to ban them.  Understanding cheaters’ motivation also contributes to optimizing the game, so players will get what they want without using cheats. 

When cheaters lead to monetization loss, game publishers insis on a rapid action that stops this impact. 

By combining sophisticated anti-cheat technologies with comprehensive and dynamic cheat mitigation policies, the gaming industry can create a fairer and more enjoyable experience for all players. 

 

Problems and limitations: 

However, things aren’t perfect…

Most anti-cheat technologies often fall short of addressing the ever-evolving tactics of cheaters, leaving loopholes that can compromise fair gameplay. Moreover, some tools have limited scope and can’t identify new or modified cheating methods. Some tools require high maintenance. False positives are also a major concern, as they result in intolerable inaccuracies that unjustly subject innocent players to policing. Several of these tools introduce privacy-related challenges and there are other limitations not mentioned here.  

 

The role of machine learning: adaptive defense against evolving threats

Machine learning (ML) has emerged as a powerful tool in the anti-cheating arsenal. ML algorithms can continuously analyze vast amounts of game data, identifying patterns and anomalies that indicate cheating activity. This adaptive approach allows anti-cheat systems to stay ahead of evolving cheating methods, as ML can learn and adapt to new threats.  

But, there’s a problem, again… 

The role of machine learning in the battle against cheating is a double-edged sword. While it empowers anti-cheat systems to detect sophisticated techniques, it also provides cheaters with tools, like ChatGPT or Google Bard, to create advanced cheating software that can deal with the newest anti-cheat tools.
This leads to an ongoing arms race, requiring constant refinement of anti-cheat algorithms. 

So, what’s the deal? 

 

The missing element- Device Sensor-Based In-Game Analytics

As mentioned, traditional anti-cheat methods, while effective to some extent, often fall short in addressing the sophistication and adaptability of modern cheating techniques. 

Gaming devices are equipped with a plethora of sensors that capture a wealth of data about user actions and interactions with the device in real-time. These sensor signals, ranging from accelerometer and gyroscope data to touch and pressure inputs, provide the most valuable insights into player behavior and identify anomalies in a way that no other technology can. 

For instance, a sudden spike in movement speed or an unnatural precision in aiming can be indicative of the use of aimbots or wallhacks. Similarly, discrepancies between in-game actions and sensor data can reveal attempts to manipulate the game mechanics.

Sensors help us understand human behavior in its most refined/raw form, and are very hard to trick. 

 

The Benefits of Using Mobile Device Sensor Data for Anti-Cheat Purposes

Incorporating device sensor data into anti-cheat strategies offers several key advantages:

• Real-time detection enabling immediate identification of suspicious behavior and cheating attempts.
• Granular data analysis that allows a much more precise and nuanced analysis of potential cheating activities compared to all other tools. 
• Dealing with evolving threats by identifying patterns and anomalies in real time.
• Privacy is fully kept as the technology doesn’t include a private-data sensor such as camera, microphone and does not collect PII (personal identifying information).
• Clear visual proof of the cheating behavior can be generated.

Addressing the everlasting ML-based arms race with sensor-based analytics:  

By focusing on real-time data from in-game sensors rather than solely relying on algorithmic detection, this approach introduces a more immediate and context-aware defense against cheating tactics. This shift minimizes the reactive nature of traditional anti-cheat methods, providing game developers with a dynamic and effective means to stay ahead in the ongoing battle against gaming misconduct.

 

Overcoming traditional game analytics gaps

Sensor-based analytics can identify subtle and dynamic patterns in player behavior and device interactions that event-based analytics cannot detect, enabling the recognition of sophisticated cheating methods that would otherwise go unnoticed.

For instance, event-based analytics may be able to identify a player who suddenly teleports across the map, indicating the use of a wallhack. However, it would struggle to detect a more sophisticated wallhack that dynamically adjusts its behavior to avoid detection. Sensor-based analytics, on the other hand, can continuously monitor the player’s movement and device interactions, identifying subtle changes in aiming patterns, movement speeds, and input timing that may indicate the use of such a cheat.

 

How does it work? A couple of examples: 

Speedhack Detection

Speedhack cheating involves using software that increases the speed of a player’s character, making them difficult to catch. Sensor-based AI solutions can detect speedhacks by analyzing data from the device’s accelerometer and gyroscope. These sensors track the movement of the device, which can reveal unnatural movement speeds that are indicative of speedhack usage. For instance, a speedhacker might exhibit an unrealistic acceleration or maintain a constant speed that exceeds the limitations of the game’s physics. Sensor-based AI algorithms can identify these patterns and flag the player for further investigation.

 

Recoil Control Cheating

Recoil control cheating involves using software that automatically compensates for the recoil of a weapon, making it easier to maintain accurate fire. Sensor based analysis can reveal it. For instance, a recoil control cheater might exhibit a consistent downward movement of the device, compensating for the upward recoil of the weapon. 

 

Rapid-Fire Cheating

Rapid-fire cheating involves using an external controller that can send multiple inputs to the game in a very short period, resulting in an abnormally high rate of fire. Sensor-based AI solutions can detect rapid-fire cheating by analyzing data from the external controller’s input events. These events include button presses, joystick movements, and other actions that are registered by the controller.

 

Unrealistic Aiming Assistance

Unrealistic aiming assistance cheating involves using an external controller that provides aimbot-like assistance to the player. For instance, a cheater using an unrealistic aiming assistance device might exhibit an unnatural smoothness in their aim or a constant lock-on to opponents. Sensor-based AI algorithms can analyze the timing, precision, and consistency of input events along with the movement patterns of the device to identify these anomalies and flag the player for further investigation.

 

Adaptive Wallhack and Aimbot

Traditional anti-cheat tools often rely on signature-based detection and heuristic analysis, which can be ineffective against sophisticated cheating methods that adapt to countermeasures. Sensor-based AI solutions, on the other hand, can continuously learn and adapt to identify new cheating techniques, including adaptive wallhack and aimbot.

Adaptive wallhack and aimbot utilize machine learning algorithms to analyze in-game data and sensor inputs, constantly adjusting their behavior to circumvent traditional detection methods. These cheats may employ techniques like dynamic aiming patterns and real-time adjustments based on player actions, making them difficult to identify using conventional approaches.

Sensor-based AI solutions can overcome these limitations by continuously monitoring player behavior, device interactions, and sensor data. AI algorithms can analyze patterns and anomalies in real time, identifying subtle changes in aiming patterns, movement speeds, and input timing that may indicate adaptive cheating.

Event-based analytics, which relies on analyzing discrete events in the game, falls short in detecting adaptive cheating methods due to its inability to capture the continuous and dynamic nature of these cheats..

 

Pretends to be human, fulls “regular” in-game analytics: 

For instance, an adaptive aimbot might exhibit slight variations in its aiming trajectory, appearing less predictable and more human-like to traditional anti-cheat tools based on event-based analysis. However, sensor-based AI algorithms can easily detect these subtle variations.

 

The Future of Online Gaming Cheating

The world of online gaming is constantly evolving, with new game mechanics, complex environments, and emerging technologies creating new opportunities for cheaters to exploit. Traditional cheating methods, such as aimbots and wallhacks, continue to pose a threat, but new techniques are emerging that are more difficult to detect and counter.

 

New and Emerging Cheating Methods

Machine learning-powered cheats are becoming increasingly prevalent, utilizing algorithms to analyze in-game data and adapt their behavior to avoid detection. These cheats can dynamically adjust aiming patterns, movement speeds, and input timing, making them difficult to distinguish from legitimate player actions. 

Moreover, the rise of cloud gaming and cross-platform play introduces new challenges for anti-cheat measures.

 

How Technology Will Evolve as Well

As a consequence, anti-cheat technologies are (and will continue to) adapt as well.  Sensor-based ML analytics, which leverages data from device sensors like accelerometers and gyroscopes, offer an example of such adaptation.

 

False Positives: A Critical Concern

One of the biggest challenges with cheating is the need for accuracy. False positives, where legitimate players are mistakenly identified as cheaters, can have severe consequences, causing frustration, alienating players, and damaging the game’s reputation. Emerging anti-cheat solutions will employ robust evidence-collection and analysis techniques. 

Sensor-based data and AI insights should be combined with traditional anti-cheat methods to create a comprehensive and reliable detection system.

 

Evidence-Based Decisions: The Key to Effective Anti-Cheat

Evidence plays a crucial role in ensuring the accuracy and fairness of anti-cheat measures. By collecting and analyzing a wide range of data, game developers can gain a deeper understanding of cheating patterns and make informed decisions about how to combat them.

This evidence-based approach allows for more targeted and effective anti-cheat measures, reducing the risk of false positives and ensuring that legitimate players are not unfairly penalized.

In conclusion, the future of online gaming cheating presents both challenges and opportunities. As cheaters become more sophisticated, anti-cheat technology must adapt and evolve, leveraging advancements in sensor-based analytics, AI, and evidence-based decision-making to maintain a fair gaming experience for all players.

 

Conclusion 

Cheating in online gaming is like that annoying kid at the playground, but in the gaming world, it’s a financial buzzkill. From aimbots to cloud-based mischief, and in terms of the cost of cheating, the industry’s bleeding billions. 

Enter anti-cheat technology, AI, and sensors—the ultimate defense against cheat wizards. With this tech arsenal, fair play is more likely to prevail in the gaming universe. 

 

 

If you’re part of the gaming industry, you’re likely well aware of the pervasive problem of in-game cheating, ranging from hacked accounts in online multiplayer games to exploits in single-player experiences. Studies suggest that nearly 70% of gamers have encountered cheating and inauthentic behavior, leading to a decline in player trust, engagement, and ultimately, revenue. The loss can be roughly sliced: The loss of revenue from in-game stores due to IAP cheating, trading digital assets that were acquired fraudulently, and weakened player retention.

Developers and publishers suffer from reputational damage, direct and indirect revenue losses, increased churn, and more, due to the continuous surge of online game cheating.

To combat this growing threat, the industry invests in ever-evolving anti-cheat strategies, such as advanced detection algorithms, stricter penalties, and community-driven initiatives. The battle against cheating is a constant arms race, demanding continuous innovation and adaptation to keep pace with the evolving tactics of cheaters.

Cheating types plaguing online Games – A few examples:

• Automation: Scripts that automate gameplay, giving players unfair advantages such as enhanced skill level, higher win rate, or more resources by playing non-stop for longer periods.
• Farming: Exploiting automated gameplay to gain resources or experience bypasses the intended gameplay loop (e.g., Elden ring).
• Emulators: Running games on unauthorized platforms can potentially grant unfair advantages (e.g., Call of Duty).
• Hot Keys: Instant execution of complex actions removes the need for manual skill
• Input Devices/Modifiers: Rapid-fire controllers bypass human limits, offering impossible accuracy (e.g., Destini-2).
• Wintrading: Manipulating the matchmaking system for easy wins, often for personal gain (e.g., wintrading rings in League of Legends).

Additional cheating types are related to commercial cheating (vs. indie cheating):

– Account selling
– Grey market (selling assets outside of the game and trading them in-game)
– Account takeover
– API attack/backend spoofing

How wide is the cheating phenomenon?

Surveys conducted in recent years indicate that nearly 77% of players have encountered cheaters during their gaming sessions, leading to frustration and jeopardizing the integrity of the gaming experience.

Of the online gamers, only 12% have never had their multiplayer gaming experience negatively impacted by other players’ inauthentic behavior. In other words, about 9 in 10 players have had a negative experience because of cheaters.

Not surprisingly, 76% of respondents also stated that it is important for them that multiplayer games online are secured against players with unfair advantages. Source

Moreover, it is estimated that cheating costs the industry up to $29 Billion!

Traditional anti-cheat strategies: strengths and weaknesses

 

Pattern-based detection

Pattern-based detection identifies known cheating patterns and behaviors. This approach is similar to how antivirus software works. It relies on pre-defined patterns of known cheats to flag suspicious activity.

Strengths:

• Effective against known cheats.
• Relatively simple to implement.

Weaknesses:

• Reactive approach, unable to detect new cheats until they are identified and patterns are created.
• Vulnerable to modifications of existing cheats, requiring constant updates and pattern maintenance.
• Event Specificity: Limited to detecting cheats similar to the ones it was trained on.

Anomaly-based detection

Anomaly-based detection analyzes player behavior and game data to identify suspicious patterns that deviate from normal gameplay (Anomalies). This approach relies on algorithms that learn from known cheats and attempt to generalize to identify new ones.

Strengths:

• Can detect new cheats based on suspicious behavior patterns.
• More adaptable than pattern-based detection.

Weaknesses:

• Can be prone to false positives, flagging legitimate players as cheaters.
• Requires careful tuning to avoid false positives and false negatives. Hence it’s time-consuming and expensive.
• Constant Adaptation: Requires constant updates to keep up with evolving cheats.
• inability to detect cheats that closely mimic human behavior, as these cheats do not create anomalies. One example of such cheats is the “reply attack.”

Server-side validation

Server-side validation involves running game logic on the server side to verify the accuracy of player actions and game state. This approach aims to prevent the manipulation of in-game data by cheaters (also called moding).

Strengths:

• Provides a high level of security as game logic is not exposed to client-side manipulation.
• Can detect some sophisticated cheats that modify game data.

Weaknesses:

• High resource consumption on servers.
• Can introduce lag and performance issues for players.
• Effective against specific types of cheats, particularly those that exploit server-side vulnerabilities.

Summarizing the Limitations of Traditional Anti-Cheat Strategies

While traditional anti-cheat methods play a crucial role in preventing cheating, they face significant limitations:

• Reliance solely on limited pre-defined game events – This creates huge blindspots as it looks only at what actions that players took without the nuances that show how they take these actions- what strategies are they using (which is a key in cheating)
• Reactive Nature: These methods require identification and response to new cheating methods as they emerge, leaving a window of vulnerability for cheaters to exploit.
• Inconsistency in Effectiveness: The effectiveness of these methods can vary depending on the type of cheat and the skill of the cheater. Sophisticated cheaters may be easily able to bypass traditional detection methods.
• High Resource Consumption: Server-side validation and manual moderation require significant resources from game developers and publishers, potentially impacting performance and scalability.

These limitations highlight the need for more advanced and proactive anti-cheat solutions to combat the evolving landscape of cheating in online games.

ML-based anti-cheat strategy with sensor-powered justice

As mentioned, traditional anti-cheat methods offer valuable tools, but their blindspots leave room for improvement. A new emerging approach is leveraging the power of ML and mobile device sensor data signals to combat cheating more effectively.

Sensor-based game analytics

Gaming devices come equipped with sensors like touch metrics, accelerometer, gyroscope, and more, tracking user interactions and movements. These sensors generate a unique dataset, offering insights into not just what players do, but how they do it with unprecedented granularity. Analyzed with the right tools, this exclusive dataset offers valuable insights into player behavior, uncovering hidden opportunities for anti-cheat detection.

ML-powered analysis

Machine learning algorithms analyze sensor data to identify in-game anomalies and recognize patterns that deviate from typical gameplay in real-time. This allows for the detection of suspicious behavior even if the specific cheat being used is unknown. There are plenty of products able to detect cheaters, but an ML-based solution learns from players and adapts itself to fit the specific game. Moreover, as it keeps being trained on the data, it is always kept up to date with no need for manual maintenance when new versions of the game are released or new cheats are deployed by the cheaters.

Most importantly, an ML-based solution that leverages device sensor signals allows an unprecedented understanding of human behavior, thereby providing a new level of accuracy in detecting unauthentic player behavior, including cheating. This enhanced accuracy makes its cheat detection much harder to fool.

Proactive approach

Unlike traditional methods that react to known cheats or are limited to pre-defined tracked events, AI-based anti-cheat solutions offer a proactive approach, simultaneously analyzing huge chunks of data signals.

Beyond the obvious: sensor-based analytics unsung advantages

Leveraging mobile device sensor data in conjunction with AI offers unique advantages for anti-cheat solutions:

• Unveiling Hidden Anomalies: Sensor data captures subtle movements and interactions uncovering intricate patterns that traditional methods can’t detect, hence enabling early identification of potential cheaters.

For instance, traditional methods struggle to detect automation scripts like rapid-fire controllers, which bypass human limitations and offer impossible accuracy. These scripts automate key presses and actions, simulating superhuman reflexes and aiming precision. However, sensor-based AI can analyze subtle variations in player input, such as touch durations and micro-movements, revealing patterns indicative of automated behavior. This allows for the detection of previously undetectable cheats, ensuring a fairer and more competitive environment for all.

• Adaptability to Evolving Tactics and Continuous Learning: Machine learning algorithms trained to analyze human behavior in diverse sensor data can adapt and evolve to recognize new cheating techniques, providing a more robust and future-proof solution. As cheaters are constantly innovating, devising new ways to exploit game mechanics and gain an unfair advantage, this benefit is crucial.

For example, imagine the emergence of a new hotkey script that allows players to instantly execute complex actions in a game similar to League of Legends. While traditional methods might not initially recognize this new form of cheating, sensor-based AI can detect subtle changes in touch patterns, such as the duration and frequency of touches, which can reveal the use of hotkey scripts. Alternatively, it can measure the time it takes for a player to react to in-game stimuli, which can help to identify automated scripts that provide superhuman reflexes.

In our hotkey script example, sensor-based AI would be able to detect the script by analyzing the changes in the player’s touch pattern. As the player presses the hotkey and the script executes the complex action, the AI observes a sudden change in the timing and sequence of touches. This deviation from normal behavior would be flagged as suspicious and could be investigated further.

Efficiency through Automation: Sensor-based AI automates cheat detection, reducing the need for manual reviews and freeing up resources for game development.

To illustrate this, let’s compare the identification processes:

Traditional anti-cheat example:

• A player reports suspicious activity to the game developer.
• The developer gathers evidence, such as gameplay footage and logs.
• Analysts manually review the evidence to determine if the player is cheating.
• If the player is found to be cheating, the developer takes action, such as banning the player from the game.

This process can be slow and inefficient, with several drawbacks:

• Slow detection (days or weeks).
• Limited scope.
• Human bias leads to false positives/negatives.

Sensor-based ML solution:

• Sensor data from a player’s device, such as touch metrics, gyroscope, and accelerometer, is collected.
• Machine learning algorithms analyze the data in real-time to identify patterns that are indicative of cheating.
• Once the AI detects suspicious activity, bias-free, the player is flagged for applying the relevant policy.
• As the false-positive rate is low, down to effectively non-existent, there is no need for manual review.
• If needed, provide flagging reasons and evidence for a quick manual review process (AI explainability).

Learn more about Sensor-based ML tech

Additional benefits include:

• Personalized Player Profiles: AI analyzes individual, non-aggregated sensor data patterns. This allows for more accurate identification of anomalies and suspicious behavior specific to individual playstyles.
• Scalability for Diverse Environments: Sensor-based AI solutions adapt to different game environments and player bases, making them effective for online games of varying sizes and genres.

What news does it bring for game publishers?

Sensor-based AI translates to catching cheaters within minutes (not days), achieving maximum recall, liberating developers from manual work, resulting in no false bans (happy players), and providing non-skewed clearer data insights for superior games. Developers no longer need to continually introduce new event logs to track down cheaters, adjust their anti-cheat solutions with the release of new versions, or grapple with a high false positive error rate.

 Conclusion

As cheating in online games evolves, anti-cheat mitigation strategies are not staying behind. Emerging methods go beyond traditional detection by scrutinizing subtle player behaviors and harnessing ML engines, thus uncovering concealed patterns and adapting to fresh tactics. This not only ensures a fairer gaming environment but also liberates resources for game developers to focus on improvements and innovation, ultimately enhancing the player experience.

 

We all know that in the online gaming sphere, where player loyalty is both a coveted currency and a constant challenge, being able to accurately predict churn is an important key.

Accurate churn prediction is essential to optimizing player retention, unlocking opportunities like precise cross-promotions, tailored re-engagement incentives, and ad-based monetization that does not interfere with experience or cannibalize retention.

In a nutshell, churn prediction analyzes player behavior patterns to identify those at risk of leaving, using advanced algorithms to trigger targeted interventions like re-engagement campaigns and game design optimization, ultimately boosting player retention and profitability

 

Online-gaming retention – Industry benchmarks

Recently, research by AppsFlyer found an average retention rate of less than 30% on day 1 for mobile games, dropping to less than 8% on Day 7, as you can see in the chart below (These numbers are also dropping year over year as you can see):

 

Another research by GameAnalytics reveals a retention rate of between 2% to 20% after 7 days:

Day 30 data shows, unsurprisingly, much lower retention rates:

Do you need extra proof – this table is based on Adjust research:

 

As it may look harsh in the eyes of outsiders, industry professionals are familiar with these trends and are hardly surprised. However, understanding these benchmarks is important when looking at the soaring UA costs.

Retention, hence churn, directly determines whether a user shows a positive or negative ROI, making accurate churn prediction crucial for extending engagement and maximizing lifetime value.

Moreover, retaining existing players is significantly cheaper than acquiring new ones. Researches show that acquiring new users can cost 6-7x more than retaining existing ones, and can increase revenues by over 25%!

 

The problem with today’s churn prediction strategies:

Traditional churn prediction methods, while providing a valuable starting point, struggle to keep pace with the dynamic reality of modern online gaming. Their reliance on historical data and static player profiles creates several key limitations that result in insufficient accuracy:

1 .Data acquisition lag, model stability issues, resource limitations, engineering bottlenecks, all these challenges result in models being trained on months-old data, therefore struggling to adapt to the ever-evolving landscape of games. Major releases, feature updates, and changing player behavior can render their predictions obsolete. Retraining on fresh data becomes a constant, resource-intensive effort that impacts scalability.

2 . Newbie Blind Spot: With a typical requirement of 6-12 months of player data, these models are blind to the nuances of new players. Early churn signals are missed, leaving valuable opportunities for engagement untapped.

3 .Frustration in the Shadows: Real-time behavioral patterns, crucial indicators of frustration and impatience, often go undetected by traditional methods. Subtle changes in gameplay, interaction with features, and communication patterns can be missed, leading to inaccurate churn predictions.

4 . The Blurry Lens of Granularity: Traditional models often paint players with a broad brush, failing to identify specific cohorts or sub-segments within the churn-prone user base. This lack of granularity damages targeted interventions and personalized re-engagement strategies.
As a consequence, game publishers, including tier-1 developers that are known to have enormous resources turned to technological innovation, are openly reporting their frustration driven by the relatively low accuracy of their churn prediction capabilities.

 

AI-based, sensor-based – Behavioral analytics for the rescue

As discussed, traditional churn prediction methods often grapple with static player profiles and data silos.

Enter behavioral analytics, a transformative approach that leverages the power of AI and mobile device sensor signals to paint a dynamic picture of player behavior right from the first session.

By analyzing touch gestures, screen tilt, and other subtle cues, behavioral analytics unlocks deep insights into player engagement, satisfaction, and even frustration with unmatched accuracy.

Unlike methods that rely on long-term historical data or must wait for specific events to trigger predictions, behavioral analytics operates in real-time and is based on immediate physical signals that aren’t predefined by the developers like logs.

Think of it as having a window into the player’s mind, offering an immediate understanding of their emotional state and potential churn risk. This allows for timely interventions, like personalized incentives or targeted re-engagement campaigns, before players reach the point of departure.

The accuracy of AI-powered behavioral analytics is surpassing industry standards by a significant margin due to richer data sets, real-time processing, and of course the advantages that come with AI technology.

Studies have shown models trained on mobile sensor data can predict churn with much higher accuracy compared to all other tools, even within the first 24 hours of gameplay.

This opens up a world of possibilities, especially when it’s about new players, where churn prediction is no longer a guessing game.

Also, by capturing the nuances of player behavior, behavioral analytics offers a significant advantage over traditional methods that are dependent on pre-defined events. It tracks subtle shifts in engagement, pinpointing potential churners before they become lost causes and right when frustration starts, for example.

In essence, behavioral analytics isn’t just about predicting churn, but about truly understanding players’ emotional state as reflected in their behavior.

 

Specific advantages:

Insights as early as 24 hours from the initial session: Game professionals are struggling to gain insights from new players. Waning playtime, hesitant gestures, and frustrated taps –all these are gold-worth indicators when discovered before they escalate into full-blown churn. Sensor-based early warning system allows developers to intervene before players reach the tipping point. It can therefore predict churn for fresh players, within the first 24 hours of their journey.

Churn risk segmentation – A laser-focused approach: Gone are the days of one-size-fits-all interventions. Behavioral analytics segments players based on their churn risk and playing behavior, enabling developers to craft targeted strategies. Players on the brink receive immediate attention, while others at moderate risk benefit from personalized nudges to keep them engaged.

Engagement Optimization: By analyzing player in-game behavior within specific game features, developers can pinpoint what sparks joy and what breeds frustration in means that are much more accurate compared to event-based logs. This data-driven insight allows product teams to optimize in-game elements, balancing challenge with reward, and ultimately creating experiences that keep players hooked.

 

The future of churn prediction: AI at the forefront

As the gaming landscape evolves, the ability to predict and prevent player churn becomes ever more critical.

The reliance on static data and limited ability to capture real-time emotions is no longer enough. As in many other industries, AI and machine learning step into the spotlight here as well. Continuous advancements in AI and machine learning algorithms are already paving the way for more accurate and nuanced churn prediction. By leveraging advanced neural networks and deep learning techniques, these models can analyze vast amounts of data, including mobile sensor signals, in-game behavior, and even player communication patterns. This allows them to identify subtle shifts in player engagement, frustration, and satisfaction, providing early warning signs of potential churn before it becomes irreversible.

Their ability to analyze ultra granular data signals at scale and in real-time allows them to accurately recognize patterns that otherwise would have stayed hidden, just because such behavioural patterns are not limited to the human mind.

Today, the standard in the industry is rapidly changing and churn models can predict a player’s risk of leaving within hours of their first login, supporting developers, product, and acquisition teams to exceed their KPIs.

This is about deeper connections with players and getting to know them better, just like humans do with each other, fostering a more engaging and satisfying gaming experience for all.

By understanding the player’s emotions, motivations, and frustrations, developers can create games that truly resonate and keep players coming back for more. In this way, AI becomes not a tool for manipulation, but a bridge to a healthier, more sustainable gaming ecosystem.
Why it makes so much sense? Think about human beings. We all tend to “learn” other people that we interact with. What’s their style? What makes them happy? Annoyed? Impatient? We are not simply leaning on explicit communication, but also on nuanced gestures. Eye movements, body position, hand movements, and more.

That is because we all know that body language tells a story that is worth a thousand words. Behavioural analytics is harnessing AI-based device sensor data in order to do the same when humans interact with software, just like in online gaming.

The future of churn prediction and retention optimization will be about crafting games that will “read’ the player and will instantly adapt to minor behavioral changes, as reflected in the device sensor signals.

 

Conclusion

In the dynamic world of online gaming, predicting and preventing player churn is paramount for sustained success. Current churn prediction methods face challenges such as data lag and a lack of granularity, making them inaccurate and therefore prompting frustration among industry leaders.

Enter behavioral analytics—a transformative, AI-powered approach that advances churn prediction capabilities. By analyzing mobile device sensor signals, this method provides real-time insights into player emotions and behaviors, predicting churn with much higher accuracy compared to all other methods, and as fast as within the first 24 hours of gameplay.

Its advantages include early insights, precise churn risk segmentation, and data-driven optimization of in-game features.

Looking ahead, AI and machine learning take the spotlight, using advanced neural networks to decode nuanced player emotions. This shift towards predicting churn risk within hours of a player’s first login heralds a transformative era, fostering deeper connections with players and creating a healthier gaming ecosystem.