Regulation & Policy
Self-preferencing
Also known as: self-preferencing rules
Self-preferencing is when a dominant platform gives its own products or services more favourable treatment than rivals on the surfaces it controls, for example in rankings, layout or default placement. It is a competition law concern because the platform sets the rules for a market it also competes in. In the EU, the Digital Markets Act restricts self-preferencing by designated gatekeepers.
What it is
Self-preferencing covers a range of behaviours: ranking your own comparison service above equally relevant third-party ones, giving your own answer box the top slot, setting your own service as a default, or applying stricter rules to competitors than to yourself. Regulators look at whether the platform is a gatekeeper for access to users and whether the preferential treatment harms competition rather than simply reflecting a better product. The European Commission's Google Shopping case is the best known example of enforcement against this kind of conduct.
Why it matters
For marketers, self-preferencing determines how much of a results page or feed is genuinely available to independent publishers and retailers. When platform-owned modules, answers and shopping units take the most valuable positions, organic click share falls even when rankings look unchanged. Policy changes and regulatory remedies can therefore move traffic more sharply than anything you do on your own site.
How it works
Practitioners track how much of the visible page is taken by platform-owned features for their priority queries, compare click-through rates before and after layout changes, and diversify demand across channels that the platform does not control. Some businesses and trade bodies also engage directly with regulators through consultations and complaints. Compliance teams at large platforms respond by publishing ranking and fairness commitments, which are worth reading because they hint at future layout changes.
When it applies
It applies when you compete with a platform's own service, such as comparison shopping, travel, local listings, app distribution or AI answers, or when you are forecasting the traffic impact of regulatory remedies.
Examples
- A price comparison site sees a platform's own shopping unit placed above all organic comparison results for commercial queries.
- An app developer finds the platform's first-party app is pre-installed and set as the default, while rivals require manual setup.
- A travel publisher tracks how a platform-owned hotel booking module changes click share on high intent searches.
How it is measured
- Share of above the fold pixels or slots held by platform-owned features for tracked queries
- Organic click-through rate before and after a layout or policy change
- Proportion of category sessions arriving from platforms you do not control
- Visibility in third-party comparison or aggregator units versus first-party ones
Insights on Self-preferencing
Related terms in Regulation & Policy
- AI complianceAI compliance is the work of making sure AI systems meet the laws, regulations, standards and internal policies that apply to them. It spans data protection, transparency, risk classification, documentation, human oversight and record keeping across the life of a system. In practice it combines legal interpretation, engineering controls and ongoing evidence gathering.
- AI governanceAI governance is the set of policies, roles, controls and review processes an organisation uses to manage how AI systems are built, bought and used. It covers risk assessment, documentation, human oversight, data handling and accountability. It applies both to AI a company develops and to third-party AI tools used by staff.
- AI privacyAI privacy is the set of practices, rights and obligations that govern how personal data is collected, used, stored and exposed when building or using AI systems. It covers training data, prompts and outputs, retention by AI vendors, and the transparency and control offered to the people whose data is involved. For marketing teams it shapes what customer data can safely be put into AI tools and what must be disclosed.
- AI safetyAI safety is the practice of designing, testing and operating AI systems so they cause less harm, behave predictably and resist misuse. It covers alignment with intended behaviour, evaluation and red teaming, content guardrails, and monitoring once a system is live. For marketers it shapes what models will say, how assistants handle brands, and what compliance teams expect before AI tools go into production.
- AI transparencyAI transparency is the practice of making clear how an AI system works, what sits behind it, and when content or an interaction involves AI. It covers regulatory disclosure duties as well as voluntary explanations such as model cards, labels on synthetic media and citations in AI generated answers. For publishers and marketers it sets expectations about when AI involvement should be declared and how clearly.
- Competition and Markets AuthorityThe Competition and Markets Authority (CMA) is the United Kingdom's competition and consumer protection regulator. It investigates mergers, anti-competitive conduct and market practices, and it holds specific powers over large digital firms under the Digital Markets, Competition and Consumers Act 2024. Its decisions shape how search engines, app stores and AI assistants operate in the UK market.
Primary research · August 2026
How ChatGPT Shortlists Software Brands
An audit across 10 categories and 60 buying questions. I recorded what ChatGPT reads, throws away and links to when a buyer asks it which software to buy, and what that decides.
- 60
- Questions asked
- 10
- Software markets
- 2,680
- Results read
- 367
- Links shown
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