Unique to MIKROS is the concept of “Scores”. Scores is a set of metrics provided about any specific user in comparison to all other users. The Scores will compare actions, behaviors, spending habits, gaming etiquette and more in relation to other users.
What lacks in other existing analytic services is comparable data. It is not good enough to only know what users are doing in your product, you should also understand user actions and behaviors while in other products, and the interconnecting relationship between users. Armed with this sort of comparative data allows you to make better decisions about how to engage your users, and how to improve user experiences. Your insights, understanding, and conclusions about your users are more accurate and complete because the evaluation of user behavior extends beyond your own product tracking.
Additionally, Scores are not monolithic data points such as averages, they are specific individual data points provided on a user by user basis. This is the powerful difference between MIKROS and others. Let’s walk through some of the Scores we provide you.
Note: All Scores operate on a scale of 1-10.
The Activity Score is a metric that distinguishes casual gamers from hardcore gamers. The Activity Score algorithm takes into consideration session duration and session frequency of any individual user and compares these metrics to all other users, forming a hierarchy. If a user has an Activity Score of 10, for example, this means they play games longer and more often than most everyone else.
Below is how we categorize and breakdown the Activity Score scale:
| Title | Score Range | Description |
|---|---|---|
| Legendary | 8-10 | Legendary gamers, otherwise known as hardcore gamers, devote a large amount of their leisure time to gaming. Because of their long lifetime value, this audience is a much sought after type of gamer. |
| Core | 5-7 | Core gamers have a wider range of interests than casual gamers. And are more likely to enthusiastically play different types of games, but without the amount of time spent and sense of competition of a Legendary gamer. |
| Casual | 1-4 | Casual gamers enjoy games without investing significant time to it, playing spontaneously, irregularly, or infrequently. |
The Activity Score is great for understanding your user makeup. If your target audience are casual gamers, for example, you can use the Activity Score to measure your targeting success and make adjustments as needed.
The Spending Score is a metric that distinguishes Whales from Freeloaders. The Spending Score algorithm takes into consideration the number of transactions for purchases, as well as the amount of money spent on in-app purchases for any individual user and compares these metrics to all other users, forming a hierarchy. If a user has a Spending Score of 10, this means they spend money more frequently and make larger in-app purchases than most everyone else.
Below is how we categorize and breakdown the Spending Score scale:
| Title | Score Range | Description |
|---|---|---|
| Whales | 8-10 | Whales are the biggest spenders. They make up the group of gamers that drive most of the revenue for games. |
| Sharks | 5-7 | Shark gamers spend occasionally. |
| Guppies | 1-4 | Guppies are gamers who spend money unpredictably, not very often, or not at all. |
The Reputation Score addresses the growing trend of toxic user behaviors and hacking in games. Billions of dollars are lost each year due to toxic user interactions and hacking. The methodologies used for catching hackers are flawed and oftentimes result in invalid bans and other punishments issued to gamers. It is costly for developers to investigate reported incidents of hacking and take action. Tools for catching toxic players don’t really exist at all, everything is word of mouth. Using our patented M2M (mobile-to-mobile) technology, MIKROS is a solution that can help curb these behaviors and amass a portfolio of offenses.
Today, offenders have no real punishment, because the extent of prevention occurs on a product by product basis. The result of this is that once punishment is issued, for example a ban, the offender moves on to the next product and does it all over again. Our M2M technology is the only technology in the world that maintains a history of toxic behaviors no matter what product the offender uses; safely and securely tracking users from one app to the next. This provides developers with options, allowing them to prevent offenders from joining their product based on their history, issuing warnings, triggering flags so that you can monitor that user or group of users more carefully, or ensuring that users follow appropriate gaming etiquette guidelines.
The Reputation Score is what makes it possible to know how toxic a user is, even if it is their first time playing your game, and even if they have not yet done anything wrong in your product. The Reputation Score algorithm takes into consideration a myriad of factors such as social interactions, sportsmanship, teammate compliments or complaints, trolling, afk, offensive language, hacking and more.
If a user has a Reputation Score of 10, this means they are the most ideal user for you. They get along with everyone, they do not cheat or hack, and they showcase amazing sportsmanship! If a user has a Reputation Score of 1, however, this user is undoubtedly a cheater, hacker, troll and constantly exhibits offensive behaviors. Generally speaking, you should be leary of users that have a Reputation Score less than 7.
The Tendency Score is a very fascinating metric. This Score represents the affinity any specific user has to playing and enjoying your product based on that user’s gaming history. Some factors we look at are game genre, gameplay and relevant game content.
Objectively, the Tendency Score analyzes all games played by a particular user and produces a metric that indicates the likelihood a user will either enjoy your product, spend significant or minimal time in your product, or even uninstall your product.
With this Score, you are able to target users that have a history of playing games like yours versus wasting time and money on users who are most likely only testing out your product, but have no intention of playing many hours or spending a lot of money. Generally speaking, you should not have high expectations of user retention for users who have a Tendency Score less than 7.
