Sucker for Love: Crush Landing

Akabaka · 2026 · $12.99 · Casual · deal grade A

Measured on 2026-09-24 05:37 UTC, 23 days ago. The whole catalog is re-measured daily; a game that just launched can move fast between passes.

Estimated net residual revenue: $9.7k to $14.5k per month
Opportunity: $15.7k per month at x1.30 dormancy
Estimated lifetime owners: 6.9k
89% positive across 195 reviews · 111.1 reviews/month

Why it's flagged

Developer active, last post 0 months ago
Studio active elsewhere (3 titles tracked), so this one may welcome a partner
Store page localized in only 1 language(s)

Analyst notes (AI-assisted)

Note written 2026-09-03; the live figures above take precedence over any numbers in it.

Sucker for Love: Crush Landing is the third entry in Akabaka's monster-dating-sim series, blending dating mechanics with physics-puzzle gameplay and cosmic horror.

This title sits at a strategic pivot point: it has real franchise equity (a loyal three-game fanbase) and solid monthly residuals, but the third installment divides its audience sharply. Core players cite departures from romance-focused design in favor of randomized puzzle-solving, while newer or less demanding players enjoy the shift. For a publisher or studio, this represents either a case study in how franchise iteration risks alienating incumbents, or a genuine opportunity to recalibrate the series around a clearer design philosophy and shore up what made games one and two resonate. Most realistic play: revival.

Risk: Series identity instability: players explicitly compare this entry unfavorably to 'Date to Die For' (the franchise peak), suggesting franchise fatigue or creative misalignment at the studio level.
Risk: Performance and stability issues reported at launch (frame-drop lag spike at front door, minimization crashes) suggest QA or optimization gaps that may require developer attention or internal fixes.
Risk: Gameplay loop relies heavily on randomization and brute-force puzzle solving, which testing feedback labels 'not fun'; core complaint is that solutions require trial-and-error across multiple runs rather than skill or intuition.

This case as Markdown · Estimates carry ±30-50% error per title.