Why some studios are releasing "similar" games

If you look at the release lines of many studios, it seems that these are the same games with different names and skins. Behind this is not "laziness," but the economics of risk, portfolio strategy and production pipelines, where each change should be predictable in terms of metrics and costs. Below is an analysis of why this happens, where it is justified, where it is harmful and how to make "similar" games that still move the genre forward.

1) Economics: risk, payback and cost of change

Expensive traffic and uncertain LTV. The cost of attraction (CAC/CPIs) is growing faster than the accuracy of pLTV forecasts. Repeating proven mechanics reduces the risks of a cash gap.

Success curves with "tail stitching." 1-2 hits out of 10 pay for the portfolio. A series of similar games is an attempt to increase the likelihood of a "hit" due to the frequency of releases.

Scalability of assets and code. Reuse of art, code, matemlists and tools reduces CapEx and TTM (time-to-market).

2) Portfolio approach: variations around the working core

Core Loop as a platform. Studios build a "core" (mechanics of bets/progress/bonuses) and change the setting, pace, visual, frequencies of features - variations are obtained, not complete reinventions.

Differentiation matrix. Change 2-3 axes: theme/style, volatility/rhythm, intensity of visual effects, UX pragmatics (buttons/timings), metaprogress.

Controlled experiments. Similar games allow A/B at the product level: one variable - one release (for example, "the same cascades, but a different multiplier and bonus length").

3) Pipelines and reuse

Assets and templates. Common atlases, shaders, Spine/DragonBones animations, sound banks. Changing the palette, layout and effects is cheaper than a full release.

Mathematics. The same RTP breakdown and exposure caps with "microdosing" of feature frequencies. This reduces the risk of tail "explosions" and simplifies certification.

Toolbox. Feature editors, DSL configs, seed/step replays - speed up layout and simplify support.

4) Marketing, storefronts and recognition

Showcase algorithms love stability. Frequent, predictable releases support positions in collections and among partners.

Brand awareness. Series with recognizable visual DNA ("signature" effects, fonts, UI) are easier to promote.

Audience clusters. Different "skins" cling to different niches (mythology/retro/fiction), keeping the same UX.

5) Jurisdictions, licenses and compliance

Feature limitations. Auto-spins, buy-feature, speeds, minimum RTP, age gates - all this forces us to collect variations of "allowed sets."

IP and audio licenses. Sometimes the setting/music is dictated by licensors, the frames are rigid - it is easier to change the "surfaces" without touching the kernel.

Certification. Reusing already certified math speeds up rolling out in markets.

6) Players and expectations

Conservative preferences. Part of the audience has "new = familiar + slightly better": they want recognizable mechanics, but fresh design and convenience.

The effect is "I understood in 5 seconds." Simplicity of onboarding increases D0/D1 retention - similarity helps to "enter the game" faster.

7) Pros of "similar" releases

Fast time market and lower risk of failure.

Reuse of QA/SIM/replays - fewer bugs.

System iteration is an understandable increase in metrics, it is easier to train teams.

Easy localization for regions without changing the core.

8) Cons and risks

Audience fatigue. Engagement and rating drops if the differences are decorative.

Cannibalization. The new variation takes traffic away from the successful "original."

Reputational losses. Clone Factory → less attention from the press and platforms.

Technology debt. Delay in introducing new engine approaches if everything rests on the old core.

9) Ethics and transparency

You cannot disguise the same mathematics as a "new" game if a new mechanics is announced. Honestly describe what has changed (pace, visual, frequencies).

Unified mathematics demo/real. Banning "demo boosts."

Responsible Gaming. No "dark patterns" of retention: understandable timings, skip, limits.

10) How to turn similarity into development (practice)

1. Rule 30-30-30. At least 30% of the content is new (visual/sound), 30% is tuning math/tempo, and a maximum of 30% is pure release.

2. One big hypothesis per release. For example: "the multiplier grows for significant events, and not for each cascade." The rest is invariable.

3. Two performance LODs. The new graphics should have an "easy" mode for budget devices.

4. Seasonality. Similar games should be released as part of a thematic event (season), strengthening the "reason" for the player.

5. Social layer. Light "sociality" (leaderboards/events) adds differences with the same core mechanics.

6. What's New document. For each release - a list of measurable differences and KPI goals.

11) Success metrics of a "similar" release

Product: D1/D7, average session length, share of skip, share of returned from fluffs.

Monetization: ARPDAU/ARPPU, purchase/bid conversion, buy-feature contribution (if allowed).

Quality: crash/ANR, p95 network/render, complaints/1000 sessions, rating.

Cannibalization: overlapping audiences, net uplift portfolio (not just games).

Ethics: RG limit coverage, "night" sessions, time to reality checks.

12) "Variation" Release Checklist

Math/Integrity

RTP breakdown and volatility described; changes against the "base" version are visible
Separate RNG streams; prohibition '% N'; replay by '(seed, step, mathVersion)'
Demo = prod by chance; rules and sample calculations updated

UX/Graphics

At least one new visual feature (effect/animation/framing)
Readability of numbers/characters ≥ basic; "skip" and "less movement" available
LOD/profiles on budget devices are green

Content/Localization

New lyrics/voice acting; ICU currency/date formats
Seasonal/regional versions of assets without kernel edits

Compliance/RG

Ficheflags by jurisdiction, buy/auto-spin/speeds correct
Reality checks, quick access to limits/pauses/self-exclusion

Start-up/Observability

Canary 1→5→25→50→100%; Rollback-plan
Dashboards: uplift to basic D1/D7/ARPDAU game, cannibalization
Alerts on RTP drift, p95/p99 network and render

13) Anti-patterns

"Ruskin for Ruskin." Zero differences in tempo/mechanics - fast burnout and "minus to the brand."

Latent deterioration in odds. You cannot change mathematics without explicit communication - loss of trust and compliance risks.

Transferring bugs. Copying the kernel without refactoring and tests - accumulation of defects.

Perpetual bonuses. Increasing the length of scenes without caps - overheating of devices and complaints.

14) "reasonable similarity" cases (reference scenarios)

"Thematic Troika." One core with three settings (mythology/Asian/retro) and different bonus rates; general seasonal event and cross-promo.

"Mechanics + 1." On top of the working Cluster - introduce a "smart" multiplier (grows for clearing the column), leave everything else the same.

"Light version." The same slot, but lightweight FX, is lower than the bundle size, the target market is budget Android and web.

"Similar" games are a tool for managing risk and speed, not an end in itself. They are justified when:

1. built on a proven core and use a portfolio strategy, 2. contain real, measurable differences (tempo, visual, seasonality, UX), 3. maintain integrity of outcomes and transparency of rules, 4. respect Responsible Gaming and jurisdictional limitations, 5. rolling out neatly, with canaries and a rollback plan.

The similarity does not interfere with progress if the team systematically "accumulates" innovations - one hypothesis per release, with clear metrics and respect for the player. This is how a line of similar games turns into a sustainable quality factory, and not into a stream of disposable reskins.