The Double Lifecycle pairs two journeys that game teams need to understand: how players move from onboarding to regular play, seasons, and spending—and how a game moves from soft launch through growth and maturity. The practical question at every stage is not just whether retention or revenue changed, but which players changed, what they experienced, and what happened before their behavior shifted.
What the Double Lifecycle helps a studio decide
David Purdy, CEO and founder of Palladio AI, describes the framework as keeping two levers in view: whom a game reaches and what those players experience. The audience question is whether a segment is worth reaching at the cost required; the experience question is what helped or hindered those players once they arrived. Purdy’s September 2026 feature frames those questions as complementary, not interchangeable.
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A rise or fall in a headline metric does not explain its cause. Segment the change, inspect player histories, and compare people who continued with those who struggled, paused, restarted, stopped spending, or left. Useful prompts include: “Who is finding value in the game, and what do they value?” and “What experiences precede a break, a restart, or a regular spender’s decision to stop buying?”
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These comparisons help a team identify patterns worth investigating and testing. They do not, by themselves, establish why any one player acted as they did, nor do they prescribe universal thresholds or a single metric set. Predicting which players may leave is a different task from understanding what happened before a change in behavior.
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Soft launch: establish who finds value
During soft launch, ask which players produced the observed results, what they valued, and whether the game delivered the experience they expected. Treat results as evidence about the participants and conditions observed—not as a guarantee of how a different audience will respond or how those same players will behave months later.
What the Squad Busters beta does—and does not—show
In its 2026 retrospective, Supercell reported 38% day-seven retention for Squad Busters’ May 2023 beta, which had more than 140,000 players. Supercell said the betas focused almost exclusively on core gameplay and validated day-seven retention; the soft launch lasted one month and did not establish longer-term retention or monetization. The result is a substantial beta observation, but it is not evidence of long-term performance or a forecast for another audience.
Supercell’s game lead Eino Joas described a gap between the team’s ambition and player response: “We wanted the game to be for everyone. Everyone could pick it up, understand the controls, and enjoy it. But there was a huge gap between expectations and reality.” The lesson for a team is to test its assumptions about who finds value, rather than treating broad appeal as established because it was intended.
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Growth: decide what to carry forward and what to change
As a game grows, investigate the experiences that precede disengagement—particularly among highly engaged or higher-value players. Compare their recent behavior with their own earlier history or with similar players who continued. Look for friction in progression or mechanics, then test whether a proposed change lets players keep doing what they value.
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Keep acquisition economics distinct from experience analysis. Ask which audience segments are worth reaching and whether the revenue supported by their play justifies the cost of acquiring them. A segment can be valuable to acquire while still encountering a product experience that needs attention; one question does not answer the other.
Check whether apparent churn is actually a break
Where the available data allows, distinguish a confirmed departure from a pause, account restart, or app reinstall. Purdy says some Palladio studio engagements found that soft-churn behavior accounted for 20–40% of apparent churn among longer-tenured, higher-value players. The feature does not disclose the underlying sample or method, so this is a company-reported observation from some engagements—not an industry-wide rate or benchmark.
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That distinction matters operationally: a returning player may need a different response from someone who has left for good. Examine what preceded the break or restart rather than classifying every drop in activity as the same event.
Maturity: find new experiences without losing existing value
Maturity is a business stage, not simply a measure of a game’s age. At this stage, overall metrics can hide different reactions among established players, new players, and people returning after a break. Segment update effects: a mechanic can create value for some players while adding friction for others.
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Also look for ways of playing that players have found or valued themselves. Ask: “Which players have found value in a new experience, and what would help others discover it?” A team may be able to support that behavior or make it easier to find without assuming every audience wants the same thing.
Use feedback to refine familiar mechanics
King’s Candy Crush Fish redesign illustrates iteration on an established mechanic. Purdy’s feature describes the redesign as adding strategic choice to familiar play and says player feedback informed months of refinement. A separate PocketGamer.biz interview with King senior product director Alena Rybik also covers the redesign and iterative work. The example supports treating feedback as an input to design decisions, not as a substitute for observing how different players respond.
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When deciding what to investigate or test, organize the evidence around the two lifecycles rather than relying on a single aggregate result:
- Audience and acquisition: Which player segment is involved, what does it value, and does the revenue supported by its play justify the cost of reaching it?
- Experience and progression: What did players who continued experience compared with players who struggled, paused, restarted, or disengaged?
- Player history: How does the current activity or purchasing behavior compare with that player’s own earlier behavior, and what preceded the change?
- Update response: How did established players respond compared with new and returning players?
- Next decision: Which pattern is strong enough to investigate, and what change could be tested without assuming the pattern explains every player?
Purdy’s feature also attributes substantial outcomes to issue-fixing in other contexts: he says identifying and fixing failures was worth roughly a billion dollars a year to Uber and its drivers, and that fixing surfaced issues accounted for 30% of an unnamed consumer app’s active-user growth that year. The feature supplies no detailed calculations or independent corroboration for those figures. They are his accounts, not independently established benchmarks for game studios.
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