A game update can generate impressive engagement during its first week, but what happens three months later? Players might initially love a new progression system, only to lose interest once the excitement disappears.
Traditional A/B tests help measure immediate reactions, yet they don’t always reveal lasting changes in player behavior. That’s why Designing Holdout Groups has become a valuable approach to long-term game product measurement.
By keeping a carefully selected group of players on an unchanged experience, studios can compare outcomes over time and better understand whether their updates actually create sustainable value.
1. Understand Why Long-Term Holdout Groups Matter
Live-service games constantly introduce new events, features, and gameplay improvements. Measuring the impact of each release separately can become difficult when multiple changes influence the same players.
A holdout group provides a longer-lasting comparison population that doesn’t receive the specific changes being evaluated.
How Holdouts Differ From Traditional A/B Tests
A standard A/B test usually compares different feature versions during a defined experiment. A long-term holdout measures whether an update, or collection of updates, produces meaningful benefits over an extended period.
For example, a studio might launch several progression improvements while withholding them from a small control group.
Comparing both populations after several months can reveal whether those improvements genuinely increase engagement rather than simply creating temporary excitement.
2. Define Exactly What the Holdout Will Measure
Before creating a control group, product teams need to identify the changes they want to evaluate.
A poorly defined experiment can produce confusing results because players may recieve different combinations of features.
Establish Clear Experiment Boundaries
Imagine a mobile RPG introducing a new crafting system, daily missions, and achievement rewards.
The studio could create a holdout that excludes selected players from all three updates.
However, this experiment would measure their combined impact rather than the individual contribution of each feature.
Teams should document which changes are included, when they become available, and which populations can access them.
Clearly defined boundaries make later comparisons easier to interpret and reduce disagreements between analysts and product managers.
3. Choose Stable Random Assignment Methods
Random assignment is essential because experiment groups should be comparable before the tested features become available.
If experienced players are disproportionately assigned to one population, retention differences may reflect existing behavior rather than the product changes.
Keep Player Assignment Consistent
For games with authenticated accounts, stable account identifiers are often more suitable than temporary session identifiers.
A player who switches devices should ideally remain in the same experimental group.
Statsig’s experimentation documentation explains how different randomization units can affect assignment consistency.
Studios should also check whether their identifier strategy fits their intended measurment period.
A device-based identifier may become unreliable when players reinstall the game or frequently switch devices.
Maintaining consistant assignment helps protect the validity of results collected over several months.
4. Select an Appropriate Holdout Size and Duration
Bigger control groups can improve statistical precision, but withholding useful features from too many players has costs.
The ideal allocation depends on active player counts, expected effect sizes, and the metrics being evaluated.
Balance Statistical Power With Player Experience
Statsig recommends small holdout populations, commonly around 1% to 2%, and suggests three-to-six-month periods for many cumulative measurement programs.
These are practical examples rather than universal requirements.
Consider a hypothetical game with 200,000 eligible players. A 2% allocation would reserve approximately 4,000 players for the control experience.
However, teams should conduct statistical power calculations before choosing that percentage.
If a game has limited traffic or wants to detect extremely small retention changes, a larger sample or longer observation period may be necessary.
5. Monitor Experiment Quality Throughout the Study
Long-term measurement becomes unreliable when player assignments, feature exposure, or event tracking behave unexpectedly.
Even a technically successful rollout can create misleading analytics if the experimental data is incomplete.
Detect Problems Before Interpreting Results
Microsoft Research identifies sample ratio mismatch as an important warning sign in controlled experiments.
This occurs when observed group sizes differ significantly from the allocation that the experiment was supposed to produce.
Teams should regularly inspect assignment counts, missing events, feature exposure, and telemetry consistency.
They also need to distinguish players who were assigned to a treatment from those who actually encountered it.
A clean exposure log makes it easier to investigate technical issues without accidentally excluding inactive users and biasing the comparison.
These checks should happen throughout the study rather than only during the final analysis.
6. Measure Outcomes Beyond Short-Term Engagement
A feature can increase session frequency immediately without improving the game’s long-term health.
That makes it important to combine engagement metrics with retention, monetization, and player experience indicators.
Create a Balanced Measurement Framework
A game studio might monitor Day 30 retention, weekly active users, average revenue per assigned player, and progression completion.
Crash rates and support complaints can provide additional safeguards.
Imagine a control group showing 25% Day 30 retention while the updated population reaches 27%.
That represents a two-percentage-point difference, but it doesn’t automatically establish a reliable causal effect.
Analysts still need to examine uncertainty, sample size, implementation quality, and potential biases.
Evaluating several indicators together provides a clearer view of whether product changes are actually beneficial.
7. Plan How the Experiment Will Eventually End
Long-running holdouts create operational responsibilities that shouldn’t be ignored.
Every additional feature can increase the complexity of maintaining two different experiences.
Define Exit Rules Before Launch
Product leaders should establish when the experiment will end and what evidence will determine its success.
They should also decide how held-out players will eventually recieve the newer experience.
Removing access to previously available features can create frustration, so assigning players before the tested changes launch is generally preferable.
Teams can document the experiment’s final findings, retire outdated feature configurations, and restore a unified experience.
A clear exit strategy prevents temporary research infrastructure from becoming a permanent maintenance burden.
Designing Holdout Groups allows gaming studios to measure whether product improvements deliver lasting benefits rather than temporary engagement spikes.
Reliable results depend on stable assignment, clear experiment boundaries, appropriate sample sizes, and continuous data-quality checks.
Start by identifying one important long-term product question, defining its control population, and building a measurement plan that supports confident development decisions.