At a glance: Understand how players' level data and analysis tell stories about user experience, engagement, potential profitability, and overall success of a game. Level Analytics is essential for all game types - hypercasual, hybridcasual, puzzle, arcade, simulation games, and beyond.
Level playing is where users engage directly with the core of the game, and will impact its behavior as well as progression in the game.
When designing the level, we will have to balance between:
Easy vs Difficult: Too easy might cause user lose the appetite and feel bored when playing, but too hard might kill the joy
Force Ads (Banner, interstitial ads) vs User-generated Ads (Rewarded videos): In relation to user motivation, the higher adoption of user-generated ads means users are keen to finish the level to continue the progression/journey in the game.
Rewarded ads vs In-App Purchase: Mini monetisation (small item package, on the spot usage to survive the specific level) vs Bigger package that can be used across multiple levels
and more…
There is no right or wrong when it comes to level design and balancing, but it will need a lot of assumptions, iteration, and validation to make it better. That’s the reason why Cost Center's Level Analytics provides the data & insight to help you, as developers, make the best decisions.
Step 1: Verify user retention and Abandonment Rate
Metric Explanation
Abandonment Rate: Percentage of users stop playing the game after finishing the previous level
Remain Rate: Percentage of users that still play the game since the beginning
Ideally, we will have a 0% Abandonment Rate and 100% Retention Rate, but it would not happen in real life. There is no magic number but we should maintain the Abandonment Rate as low as possible, and the Retention Rate as high as possible. As long as users stay in the game, you will have a chance to monetise them, either through IAA or IAP, but this cannot happen if they leave the game completely.
A few elements that can impact these 2 metrics:
Level Difficulty: When the level is too hard to pass, the Retention Rate will be negatively impacted. Try keeping the leak rate at max 20% per level.
Effort vs Reward: When the reward is not comparable to the effort to finish the level, it can kill the motivation causing users to leave the game.
Storytelling: This gives the motivation for users to continue the journey, engaging them to prevent drop-offs. You can add more emotional elements to make it spicier.
Step 2: Verify the engagement / motivation via Replay action
Avg Time spent on each level per user
Metric Explanation
The average play time per user indicates the time that the user spends on each level
The higher the number is, the longer time that users stay on the level, due to:
Level is too hard and players need to play multiple times → Cross-check with the replay metrics later
The board is too complicated and players need more time to think before making the decisions (when the level is not limited by time)
Level start per user
Metric Explanation
Level Start Users: Total number of users who started playing at each level
Level Start per User: Average number of play per user, the minimum is 1 and the higher means the more time users replay each level
Typically, this metric will positively correlate with the time played on each level mentioned above, as it indicates the engagement level of users for each level. However, in some cases, for example, board games with unlimited time and requiring strategic moves, these 2 metrics can be separated and independent of each other.
Step 3: Failure tracking
Failure rate tracking
Metric Explanation
Failure Rate represents the percentage of users that cannot finish or pass at each level played
This metric reflects the difficulty level at each gameplay. The higher the number, the harder the level is.
For game designers, depending on the user motivation, we can design the level based on this metric, for example:
Relaxation Motivation: can keep it low (<10%) and provide the joy when finishing the level
Mastery Motivation: can keep it medium to high (20-40%) and provide pride when finishing the level, especially the hard ones.
Pro Tip
Do not make it flat but build it as a curve with some peak to enhance the surprise and challenge when playing the game.
Number of fails
Metric Explanation
This metric groups the data into the number of users by failure counts.
For example in the chart above, 8 users failed 2 times, and 2 users failed 11 times.
The metric will help us to better understand:
Distribution of skilled users in the game: if the bar is higher on the right, it indicates that the majority of users are less skilled to complete the level
Motivation of users: the higher the number of users level fail, the higher motivation that users have to complete the task.
However, we need to map this metric with Level Completion Rate and User Remain Rate above to ensure that users don’t leave the game because it’s too hard.
Pro Tip
Segment users by the number of fails to customise level difficulty at each retry to maintain user retention and engagement.
Step 4: Unlock the monetisation opportunity
Monetisation
Metric Explanation
The chart indicates the ad LTV contribution by ad format across the user journey, where
Banner ad, Audio Ads, Native Ads: Time spent at each level (correlate with time played at each level)
Interstitial ad, App open ad: the number of times the user changed screen, could be because of level completion (change to a new level) or retry/replay
Rewarded ad: Number of times users watch ads to continue playing
Metric Explanation
IAP LTV: The metric indicates the IAP occurring at each level.
This metric will typically correlate with the Failure Rate at each level.
Failure reason
Metric Explanation
The Failure reasons show why users fail at each level and the reasons behind, helping game designers to deeply understand players' behaviour to better balance level difficulty.
This will also provide the opportunity to improve monetise strategy in the game by providing:
Meaningful reward via Rewarded video: In the example above, by providing more space when users fail will motivate them to watch rewarded video than giving them extra time
IAP Offer: when looking at the pattern of 5-10 levels, we can understand the main reason why user fail, then design the IAP offer that they can use multiple times, for example Rewarded video bundle.