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The Double Lifecycle pairs two questions: where the game is in its business life—from soft launch to growth and maturity—and where each player is in their own journey—from onboarding and regular play to seasons, spending, breaks, or departure. Teams make better decisions when they study both: which players they reach and what those players experience.
That means looking beyond headline retention and revenue. Ask: “Who is finding value in the game, and what do they value?” Then examine what happened before players progressed, paused, restarted, stopped spending, or left. Those patterns help a team decide what to investigate and test; identifying who may leave is not the same as understanding what led to disengagement.
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What the Double Lifecycle helps a team decide
A game’s business stage does not tell the whole story. A soft-launch build may attract one kind of player while failing to deliver the experience another audience expects. A mature game can still bring in new players, welcome returning ones, and serve established players whose needs differ. The framework keeps those audience questions alongside questions about the game itself.
- Audience: Which player segments are worth reaching, and what does it cost to reach them relative to the revenue their play supports?
- Experience: What helped players progress or continue, and what preceded difficulty, a break, a restart, reduced spending, or disengagement?
- Context: How does observed activity—such as sessions or purchases—compare with a player’s own history and with similar players who continued?
There is no universal threshold or single metric set for these questions. The useful comparison depends on the decision the team is making.
#1 Best Overall
Soft launch: learn who finds value
In soft launch, the priority is to understand who is responding to the game, what they value, and whether the experience delivers what they expected. A large sample can provide evidence about its participants, but does not guarantee that another audience will respond in the same way or that those participants will behave similarly months later.
Supercell’s 2026 retrospective reports that more than 140,000 players took part in a May 2023 Squad Busters beta, which had 38% day-seven retention. Supercell says the beta focused almost exclusively on core gameplay and validated day-seven retention; its one-month soft launch was not enough to validate longer-term retention or monetization. The figure is a result from that beta, not proof of long-term performance.
Rank #2
Useful questions at this stage include:
- Which kinds of players are finding value, and what parts of the game provide it?
- Are players struggling with onboarding, progression, or core mechanics?
- Does the observed response represent the audience the game is intended to reach?
- What evidence would be needed before making a claim about longer-term retention or monetization?
Growth: decide what to preserve and what to change
As a game grows, investigate the experiences that precede disengagement, especially among highly engaged or higher-value players. Compare a player’s behavior with their own earlier activity or with similar players who continued. Look for friction in progression and mechanics, then test whether a proposed change removes that friction without taking away what players value.
Keep the audience and experience questions distinct. A segment may be reachable and active, but its play must support enough revenue to justify its acquisition cost. Separately, a change to the game should be evaluated by whether it improves the experience for the players it is meant to help.
Rank #3
Ask, “What experiences precede a break, a restart, or a regular spender’s decision to stop buying?” A fall in activity can look like churn without confirming that a player has left. In some studio engagements, Palladio AI CEO and founder David Purdy says the company found breaks, account restarts, and app reinstallations among behavior initially mistaken for churn. He reports that soft-churn behavior accounted for 20–40% of apparent churn among longer-tenured, higher-value players in those engagements. The feature does not disclose the sample or method, so this is a company-reported observation, not an industry benchmark.
Maturity: evaluate updates by player segment
Maturity is a business stage, not simply a measure of a game’s age. At this stage, an overall metric can hide sharply different responses to an update. Segment results for established players and for people who are new or returning; a new mechanic may create value for some while adding friction for others.
Rank #4
Also look for ways players already choose to play that the studio could support or make easier to discover. Ask, “Which players have found value in a new experience, and what would help others discover it?” This frames an update as a question about both the experience and the audience it serves.
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King’s redesign of the Fish mechanic in Candy Crush illustrates iterative refinement of a familiar feature. David Purdy’s feature describes adding strategic choice and using player feedback over months of refinement. A separate PocketGamer.biz interview with King senior product director Alena Rybik also discusses the redesign and iterative work.
Best Value
How to investigate a change without confusing prediction with explanation
Start from the decision, not from a dashboard total. If players are pausing, restarting, or buying less, a prediction that a player is likely to leave does not explain what happened before that behavior. Trace the player’s experience and compare it with a relevant baseline.
- Define the outcome of interest. Specify whether the concern is slower progression, a break in play, a restart, reduced purchases, or confirmed departure where the data permits that distinction.
- Choose the comparison. Compare affected players with their own earlier behavior or with similar players who continued. For an update, compare established, new, and returning players rather than relying only on the overall result.
- Inspect the preceding experience. Look for what happened before the change in behavior, including progression and mechanic-related friction. Treat a pattern as a reason to investigate, not automatic proof of cause.
- Separate reach from experience. Assess the cost of reaching a segment against the revenue its play supports, and assess whether the game experience helped that segment find value.
- Test a focused change. State which experience should improve and for whom, then examine the response in the relevant segments. Keep the evidence tied to the population and period observed.
This process helps teams decide what to test. It does not supply a universal formula for retention, acquisition economics, or update success.
What reported figures can—and cannot—show
Several figures in David Purdy’s September 2026 Mobidictum feature are attributed to his account of work at Palladio AI or earlier experience; the feature does not provide detailed calculations or independent corroboration for them.
- Purdy says that fixing failures at Uber and for its drivers was worth roughly a billion dollars a year. This is his account, not a directly sourced statement from Uber.
- He attributes 30% of an unnamed consumer app’s active-user growth that year to fixing surfaced issues. The company and methodology are not disclosed, so this is not an industry benchmark.
- He reports the 20–40% soft-churn observation from some studio engagements. The underlying sample and method are not disclosed, so the figure should not be generalized.
These accounts can illustrate why teams investigate player experience, but they do not establish how much another studio would gain. Supercell’s beta statistic has a different boundary: it describes day-seven retention in the specified 2023 beta, while Supercell’s retrospective explicitly says the short soft launch did not validate longer-term retention and monetization.
Quick Recap
Sources
- David Purdy, “The Double Lifecycle: choosing what to improve as your game grows,” Mobidictum, September 28, 2026.
- Supercell, “The Best Games Haven’t Been Made Yet,” 2026 retrospective.
- Aaron Astle, “How player feedback, memes, and rebuilt architecture led to Candy Crush’s ‘Fish 3.0’,” PocketGamer.biz, December 4, 2025.
- “The Game Life Cycle & Game Analytics: What metrics matter when?”, Casual Connect / HoneyTracks presentation hosted on SlideShare.
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