Engagement metrics are quantitative measures of how players interact with a game and with the studio. They show whether players return and how well the studio resolves the moments that frustrate them.
This guide covers ten KPIs in three groups, namely player activity, player value, and player experience. Most lists leave out the third group. Each KPI includes a definition, a plain-text formula, a benchmark where a sourced one exists, and a note on what to watch. Last updated on 28 October 2026.
What Are Engagement Metrics in Games?
Engagement metrics are quantitative measures of how players interact with a game and with the studio. A metric is any measurement, and a KPI is a metric tied to a goal. This guide groups the ten KPIs by what they show.
- Activity KPIs show how players play and return.
- Value KPIs show what that play is worth.
- Player experience KPIs show how well the studio resolves frustration, because support conversations reveal problems early.
Together, the ten KPIs give a studio a full view of player behavior.
The 10 Engagement Metrics at a Glance
Ten KPIs cover three groups, and each has a clear owner.
| KPI | Group | Formula | Who owns it |
| 1. DAU/MAU stickiness | Player activity | DAU divided by MAU | Product and LiveOps |
| 2. Session length | Player activity | Total play time divided by number of sessions | Product |
| 3. Session frequency | Player activity | Sessions divided by DAU | Product and LiveOps |
| 4. Day-N retention | Player activity | Players active on day N divided by players who installed on day 0 | Product and LiveOps |
| 5. Churn rate | Player activity | Players lost in a window divided by players at the start | LiveOps |
| 6. ARPDAU | Player value | Daily revenue divided by DAU | LiveOps and finance |
| 7. Player lifetime value (LTV) | Player value | Average revenue per player over their lifetime | LiveOps and finance |
| 8. Player CSAT | Player experience | Satisfied responses divided by total responses | CX |
| 9. Time to resolution | Player experience | Average time from issue raised to issue closed | CX |
| 10. Automation rate | Player experience | Issues resolved without a human agent divided by total issues | CX |
Player Activity KPIs
Activity KPIs show whether players come back and how they play while they are there. They form the base of the chain. Product and LiveOps teams usually own them, and they respond to onboarding changes and content updates. Read all five together, because each one covers a different part of the habit.
1. DAU/MAU Stickiness
- Definition: DAU/MAU compares daily active players with monthly active players.
- Formula: DAU divided by MAU.
- Benchmark: GameAnalytics’ 2026 benchmarks report a median DAU/MAU of 4% to 5% for PC games in 2025. The top 10% sat at 13% to 15%, and the top 1% was above 50%.
- What it tells you: The ratio shows how many monthly players return on a typical day.
- Watch-out: Logins without play can inflate the number. Count active play.
2. Session Length
- Definition: Session length is the average time a player spends in one visit.
- Formula: total play time divided by the number of sessions.
- Benchmark: GameAnalytics reports a median mobile session of 3.1 to 3.5 minutes in 2025, with the top 25% near 5.2 minutes. Median PC sessions ran about 18 minutes.
- What it tells you: The figure reflects pacing and depth.
- Watch-out: Longer isn’t always better, since a stuck or frustrated player can also play for a long time.
3. Session Frequency
- Definition: Session frequency is how often a player starts the game in a day.
- Formula: sessions divided by DAU.
- Benchmark: GameAnalytics reports a median of 3.8 to 3.9 sessions per day for mobile games in 2025 and 1.65 to 1.70 for PC games.
- What it tells you: The number shows how well the game fits into daily routines.
- Watch-out: Push notifications can inflate counts without real engagement. Review frequency beside session length.
4. Day-N Retention (D1, D7, D30)
- Definition: Day-N retention is the share of players who return on a given day after install.
- Formula: players active on day N divided by players who installed on day 0.
- Benchmark: GameAnalytics reports median mobile retention of about 22% on D1, just under 4% on D7, and 0.68% to 0.79% on D30 across 2025.
- What it tells you: D1 reflects first impression, D7 reflects the core loop, and D30 reflects long-term reasons to stay.
- Watch-out: Read the full curve, because three points can hide a steep drop between them.
5. Churn Rate
- Definition: Churn rate is the share of players who stop playing in a set period.
- Formula: players lost in the window divided by players at the start of the window.
- Benchmark: This guide cites no churn benchmark, because studios define an inactive player in different ways.
- What it tells you: Rising churn after a release points to a change worth investigating.
- Watch-out: State the inactivity window you use, such as 30 days, and keep it fixed to keep trends comparable.
To win back lapsed players, read Helpshift’s re-engagement playbook for mobile games.
Player Value KPIs
Value KPIs show how much revenue engaged players generate. They sit downstream of activity, because players who stay longer have more chances to spend. LiveOps and finance teams usually own them. Review them with segment views, since new players and high-value players behave very differently.
6. ARPDAU
- Definition: ARPDAU is the average revenue generated per daily active player.
- Formula: daily revenue divided by DAU.
- Benchmark: This guide cites no ARPDAU benchmark, because monetization models differ widely between games. Trend your own history instead.
- What it tells you: The figure shows how well the game converts daily attention into revenue.
- Watch-out: Use a seven-day average to smooth event spikes.
7. Player Lifetime Value (LTV)
- Definition: LTV is the average revenue a player generates over their whole time with the game.
- Formula: average revenue per player over their lifetime, estimated from cohorts.
- Benchmark: This guide cites no LTV benchmark, because estimates depend on cohort age and genre.
- What it tells you: LTV links retention and spend, which makes it a key input for acquisition decisions. Player experience also influences how long players stay. Resolved issues can support LTV over time.
- Watch-out: Early LTV estimates change as cohorts age. Label each estimate with its cohort window.
Player Experience KPIs
Player experience KPIs measure how well the studio resolves the moments of frustration that lead players to quit. Support conversations surface bugs and purchase failures early, which makes these KPIs a leading signal. Treat them as signals worth investigating, since many factors affect retention. A rise in contacts about one issue can show a problem before cohort charts move. These KPIs also connect support teams to product and LiveOps teams.
Helpshift’s guide to Gaming Support KPIs covers these metrics in more depth.
8. Player CSAT
- Definition: Player CSAT is the share of players who report satisfaction after a support conversation.
- Formula: satisfied responses divided by total responses.
- Benchmark: This guide cites no CSAT benchmark. Trend your own score by issue type and channel.
- What it tells you: The score shows whether resolutions feel good to players.
- Watch-out: Review CSAT by issue type, because a strong overall score can hide a weak area such as purchase disputes.
9. Time to Resolution
- Definition: Time to resolution is the average time between a player raising an issue and the issue closing.
- Formula: total time from issue raised to issue closed divided by number of resolved issues.
- Benchmark: This guide cites no benchmark. Compare your own results by issue type.
- What it tells you: The figure shows how quickly players get back into the game.
- Watch-out: Track time to first response beside it, because a fast first reply can still sit beside a slow close.
See how AI agents shorten the wait in Helpshift’s guide to average resolution time.
10. Automation Rate
- Definition: Automation rate is the share of support issues resolved without a human agent.
- Formula: issues resolved without a human agent divided by total issues.
- Benchmark: Helpshift’s 2024 Benchmark Report found that 58% of all support interactions were fully automated.
- What it tells you: The rate shows how much routine volume AI handles, which frees human agents for complex cases.
- Watch-out: Review CSAT beside automation rate. Speed does not come at the cost of quality. Helpshift’s launch post says the Care AI analytics dashboard tracks Issues Resolved and CSAT side by side.
How to Read Engagement Metrics Together
No single KPI explains a game. Read them as a chain from activity to value, with player experience as an early warning. Two vanity traps show why. Installs can rise while active players stay flat, and ARPDAU can rise while the share of paying players falls. Both numbers look healthy and hide a weak base.
A support example shows the early warning at work. If contact volume rises while CSAT falls, investigate before retention drops. The cause might be a bug or a payment failure. Fixing it early protects the players who were about to leave. Segment the chain by cohort. New players and high-value players show different chains, and a blended view can hide a problem in one group. Set a monthly review where product, LiveOps, CX, and finance read the chain together.
For a practical segmentation framework, see Player Segmentation in Games.
What Are Good Engagement Metrics for Games?
Good engagement metrics depend on genre and platform, so compare against similar titles and trend your own history. GameAnalytics’ 2026 benchmarks cover more than 16,000 mobile games from 2025 and do not include genre-level cuts this cycle.
The table shows mobile performance tiers across all genres. Treat the tiers as a map of the market, and avoid treating any one number as a target for every game. On PC, the median game retained about 7% of players on D1 and about 0.2% to 0.25% on D30. Use the tiers to set a realistic goal for the next quarter, such as moving from the median toward the top 25%.
| Performance tier (mobile, 2025) | D1 retention | D7 retention | D30 retention | Session length |
| Median (P50) | About 22% | Just under 4% | 0.68% to 0.79% | 3.1 to 3.5 minutes |
| Top 25% (P75) | Just above 30% | 6% to 7% | 1.6% to 1.8% | About 5.2 minutes |
| Top 10% (P90) | About 40% | 11% to 12% | Not stated globally | Just over 8 minutes |
| Top 1% (P99) | 64% to 68% | Above 25% | 13% to 15% | Above 22 minutes |
Who Owns Each Engagement Metric?
Product, LiveOps, CX, community, and finance each own a slice, so name an owner and an action for every group.
| KPI group | Owning team | Typical action when it moves |
| Player activity | Product and LiveOps | Adjust onboarding and events |
| Player value | LiveOps and finance | Review offers and pricing |
| Player experience | CX and community | Update knowledge and route high-value players to priority care |
Shared ownership works best with a monthly review. Each team brings one chart and one proposed action. Community teams add the voice of players from Discord and forums. Finance confirms how the value KPIs line up with revenue reports. A written owner for each KPI removes confusion when a number moves. Record each decision in a shared log. The next review can see what changed and why.
How AI-Native Platforms Improve Player Experience KPIs
An AI-native player engagement platform improves player experience KPIs by resolving routine issues instantly and passing complex cases to human specialists with full context. Helpshift follows three pillars in order, namely Technology, AI, and Human Services.
Care AI is the flagship agentic AI. Helpshift reports that Care AI resolves 70%+ of conversations autonomously, and human agents take over the rest. Helpshift’s native dashboard tracks Issues Resolved, CSAT, Time to Resolve, Time to First Response, Reopen Rate, and Issue Touches.
The Engagement solution adds proactive outreach based on churn risk and VIP status, which links support data to retention work. The Support solution keeps resolution inside the game. Human expertise stays essential for sensitive cases. Studio stories appear on the Case Studies page.
How to Build an Engagement Dashboard
Start with five to seven KPIs across the three groups, and define every term before the first review.
- Pick the KPIs that match your goals, with at least one from each group.
- Fix definitions for an active player, a session, a churned player, and a retention window, and write them down.
- Segment by cohort and by player type, such as new players and high-value players.
- Join in-game data with support data. Player experience sits beside activity and value.
- Set a review rhythm, such as a weekly check and a monthly deep review.
- Name an owner for each KPI and a first action for when it moves.
The Technology pillar of Helpshift supplies the in-game SDK and unified player context that make step four easier.
Conclusion
The ten KPIs cover three groups, and each group adds a different view of the game. Read them together and trend them against your own history. Start with one action. Audit which of the three groups your studio does not yet measure, and add one KPI from that group to your next review. Review the three groups together each month, and record what changed.
| SummaryTen KPIs fall into three groups, and the third group, player experience, is the one most lists leave out.Read the KPIs as a chain from activity to value, with player experience as an early signal.Benchmarks depend on platform and game age, and you should compare your game against similar titles and your own history. |
FAQs
1. What is a KPI for engagement?
A KPI for engagement is a metric tied to a goal that shows how players interact with a game. Examples include DAU/MAU stickiness, session length, Day-N retention, and churn rate. Choose KPIs that match your goals, and review them on a fixed cadence with named owners.
2. What are good engagement metrics?
Good engagement metrics depend on genre and platform. GameAnalytics’ 2026 benchmarks put the median mobile game at about 22% D1 retention and about 3.1 to 3.5 minutes per session in 2025. Compare against similar titles and track your own history.
3. What are some examples of engagement metrics?
Examples include DAU/MAU stickiness, session length, session frequency, Day-N retention, and churn rate. Value examples include ARPDAU and player lifetime value. Player experience examples include CSAT and time to resolution. This guide covers all ten with formulas.
4. How can I measure engagement?
Measure engagement with in-game telemetry plus support platform data. Telemetry shows activity such as sessions and retention. Support data shows player experience, such as CSAT and time to resolution. Join the two in one dashboard, with fixed definitions for an active player and a session.
5. What are the three types of metrics?
This guide groups the ten KPIs into player activity, player value, and player experience. Activity KPIs show how players play and return, and value KPIs show revenue per player. Player experience KPIs show how well the studio resolves player issues.
6. How does player support affect engagement metrics?
Player support affects engagement because unresolved issues can push players away. Support conversations also show bugs and purchase failures early. Treat a rise in contacts or a fall in CSAT as a signal worth investigating, and review it beside activity and value KPIs.
