At a glance: Get in-depth game engagement metrics with Level Analytics. Slice and dice your gameplay data to get the right insights to accelerate game-level optimisation.

Who should use this? Product team

About Level Analytics

Level Analytics supports in-depth analysis of game levels, helping developers better understand how users play the game, view ads, and make in-app purchases (IAP) at each level. This aids in better game optimisation for higher revenue, retention, and longer playtime.

For games or game modes without levels, Cost Center will automatically separate levels based on the number of days/hours played since the user installed the game.

Level Analytics is the feature that provides insights to the following questions for any game:

What are the Abandonment Rate, Retention Rate, Completion Rate, Play Time, and Level Starts for each level?
What is the Failure Rate at each level? Why do users fail? Which reasons cause users to fail the most?
How many Interstitial and Rewarded Video ads are shown at each level? From which location or ad network do they come?
What does ad revenue look like at each level?
Where do users make in-app purchases? Which products do users purchase at each level? What were the last scene, screen, or button users accessed before making a purchase?
What do users play on any given day (e.g. Day 3) or any hour after installing the game?
How does the above information differ between users who completed level 1000 and other users?
How does the above information differ between users who have seen 10 rewarded videos and those who have seen none?
How does the above information differ between users who made an in-app purchase and other users?
How does the above information differ between app versions?

Custom Firebase Tracking

A game needs to track the following events to Firebase to use Level Analytics:

Event	Parameter	Description
level_start	play_mode (legacy: level_mode)	Gameplay mode. i.e. Normal, Daily Challenge, Classic
	level	The current level
level_end	play_mode (legacy: level_mode)
	level
	success	"true" if the user passed the level. "false if otherwise.
	reason	Level failed reason
	ad_duration	Ad duration within the level
iap_sdk	play_mode (legacy: level_mode)
	level
	value	Total amount of an in-app purchase in local currency
	currency	Currency of the purchase
	product_id	Product ID of the in-app item
ad_revenue_sdk	play_mode (legacy: level_mode)
	level
	ad_format	Accepted values: interstitial / rewarded / banner / appopen / rewardedinterstitial / mrec / audio / native / collapsible
	value	Ad revenue in USD
	location (legacy: placement)	Location of the ad
	ad_network	Ad network that serves the ad

The level_start and level_end events are tracked whenever a level is started or completed.
The iap_sdk event is tracked whenever an in-app purchase occurs.
The ad_revenue_sdk event is tracked whenever an ad is served.

Note:

If a game or gameplay mode doesn't have a level mechanism, there's no need to track the level parameter. Cost Center will use the number of days or hours after the first open as the level.

Using Level Analytics

1. To run Level Analytics report, select the following filters to customise your report data:

Play Mode	Gameplay mode. i.e. Normal, Daily Challenge, Classic
Level	Based on gamelevel or no. of days/hours after first open
First Open date range	Get performance based on a period when users first open the game/app
Active Date	Get performance based on a period when users were active
Country	Get performance based on specific country
A/B Test	Compare Firebase A/B test version performance
App Version	Compare app version performance
Days after first open (D0, D1,...)	Identify performance based on Day X to Day Y engagement
Level Achieved	Filter for user performance based on "level achieved"
Users have events	Filter specific event to retrieve events based performance

2. Click on "Run Report"

3. Optional: Set filter parameters in "Compare with" to generate report for comparison.

4. Focus on a range of game levels by setting level range. This is especially useful if you have many game levels.

Level Analytics Graphs

Level Play

Abandonment Rate

Tracks the percentage of users who drop off for each level. Use this to identify levels with high abandonment rate to optimise for better player engagement.

Play Time

Shows the average time spent by users to play a specific level. Analyse game levels with long play time to gauge game level complexities. You can infer that a longer play time may mean more difficult gameplay which could increase abandonment rate.

Level Start per User

Tracks the number of users who started on a specific level and the average no. of times a user starts a level.

Completion Rate

Tracks the % of users who completed a game-level successfully (Completion Rate) and the % of users who continued the game to the next level (Continuation Rate).

Level Fail

Level Fail per User

Tracks the number of users who failed a specific level and the average no. of times a user fails a level.

Failure Rate

Tracks the % of users who failed at each level.

Ad LTV

Tracks the total Ad LTV for each game level including Banner, Interstitial and Rewarded Ad. Use left filters to view breakdown for Interstitial, Rewarded and Banner Ad LTV performance.

Use "Group By" to group performance based on "Location" or "Ad Network".

IAP

IAP LTV

Tracks the IAP LTV for each game level and Accumulated IAP LTV from Level 1.

Use "Group By" to group performance based on "Product". Hover on each bar to view breakdown of the IAP product and no. of users for each IAP product.

IAP Revenue per user

Tracks the average IAP Revenue per user for each level. Hover over the chart to see total no. of IAP users for a level.

Use "Group By" to group performance based on "Product" to view IAP Revenue breakdown by product.

Note

Level Analytics is an add-on feature based on credit usage. Detailed usage breakdown and top-up history are available in Big Data > Credit & Usage for each app, or Organisation Settings > Usage & Billing > Big Data for all apps.

Please ensure that your organisation has enough credit before generating a report.

User Acquisition Metrics