Personalised spend-optimisation
Silently using a player's behavioural data to tune offers, prices, odds, difficulty, or matchmaking to maximise that individual's spending.
- Code
- I10
- Category
- Informational / interface
- Severity
- Severe
- Evidence
- EmergingPrecautionary severe rating based on the potential harm of covert individual targeting; game-specific evidence remains emerging.
- Purpose served
- Serves businessPrimarily serves the provider's revenue, retention, or data — the most suspect.
- Mechanism family
- Sneaking / Hiding
- Platforms
- Mobile / F2P · Live-service
- Also known as
- dynamic spend optimisation, algorithmic targeting, personalised pricing
How it works
Dynamic systems informed by play and purchase history adjust what each player sees — offers, “frustration → sell-a-fix” difficulty, or matchmaking that surrounds them with spenders — optimised per person to increase spend, invisibly.
Why it can be harmful
Invisible, individualised targeting defeats informed consent and comparison, can exploit identified vulnerabilities (a data-protection concern), and manufactures false social proof; children and at-risk players cannot detect or resist it. The severity rating is precautionary: the harm could be severe when such targeting is deployed at scale, even though the public game-specific evidence base is still emerging.
Examples in the wild
- Behaviour-tuned offers and dynamic pricing
- Difficulty tuned to sell a fix
- Matchmaking that normalises spending
Illustrative genre examples to aid recognition — not allegations about specific titles.
References
- King, D. L.; Delfabbro, P. H. (2019). Unfair play? Video games as exploitative monetized services: An examination of game patents from a consumer protection perspective. Computers in Human Behavior. doi.org/10.1016/j.chb.2019.07.017 · citing patterns
- Helberger, N.; Sax, M.; Strycharz, J. (2021). Choice architectures in the digital economy: Towards a new understanding of digital vulnerability. Journal of Consumer Policy. doi.org/10.1007/s10603-021-09500-5 · citing patterns
- Strycharz, J.; Duivenvoorde, B. (2021). The exploitation of vulnerability through personalised marketing communication: Are consumers protected?. Internet Policy Review. doi.org/10.14763/2021.4.1585 · citing patterns
- van Rooij, A. J.; Birk, M. V.; van der Hof, S.; Oostenbach, K., et al. (2025). Game-check: Development, application and visualization of a classification system for behavioral design in games. Trimbos Institute, Eindhoven University of Technology & Leiden University (for the Dutch Ministry of the Interior and Kingdom Relations). osf.io/5qzda/ · citing patterns
- Gray, C. M.; Santos, C. T.; Bielova, N.; Mildner, T. (2024). An ontology of dark patterns knowledge: Foundations, definitions, and a pathway for shared knowledge-building. Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems. doi.org/10.1145/3613904.3642436 · citing patterns
Related patterns
Involuntary social ranking / identity labels
The system assigns relationship labels, closeness ranks, or social-cluster positions to people from behavioural data they did not choose to make socially meaningful.
Bad defaults / preselection
The provider-preferred option is already selected or treated as the normal path, so inaction becomes consent, spending, or data sharing.
Bait-and-switch / product not as expected
The advertised content or experience differs materially from what is actually delivered.
Collection & completionism pressure
A visible, incomplete collection — roster, index, grid — compels players to keep playing or paying to complete the set.
Disguised ads / content
Ads are styled as gameplay or rewards so the player cannot tell promotion from play.
Feedforward ambiguity / unclear consequences
The interface fails to make clear what a button, prompt, or action will actually do before the player commits.