DMA Search Fairness: What SEO Teams Should Measure Now

If your organic click-through rate or direct conversions fell after DMA-related search changes, don’t assume your rankings failed. An extra comparison layer, a different result layout, a new intermediary, or a longer route to conversion can produce the same dashboard symptom.

The honest verdict on DMA search fairness is not proven. The rules were meant to curb gatekeeper self-preferencing, but reported outcomes include more user friction, lower click-through rates, fewer direct bookings, and no clear weakening of Google’s central position. To decide what is actually happening, you need to measure user utility, business access, competitive opportunity, and market power separately.

Search fairness is four questions, not one metric

The Digital Markets Act was passed in 2022 and came into force in March 2024. Its search-market logic was straightforward: a dominant gatekeeper should not give its own services an unfair advantage over competing services.

That principle addresses a real problem. Google has been accused of promoting services such as Google Shopping ahead of alternatives that may serve the user better. But restricting self-preferencing does not automatically produce a competitive market, a better user journey, or stronger outcomes for independent businesses. Those are different tests.

This distinction prevents two common analytical errors. A less convenient interface does not, by itself, prove that competition became less fair. A more competitive market can impose some short-term friction while users and businesses adjust. The reverse is also true: giving several services a place on the results page does not establish fairness if Google still controls the gateway, the rules, and most demand.

One survey involving 5,000 European consumers reported a more cumbersome online experience, with respondents even expressing willingness to pay to restore aspects of the previous integrated experience. That is an important warning about user utility. It is not, on its own, a complete measure of market contestability. The right response is to retain the warning while refusing to make it answer a different question.

Build a scorecard around the complete search journey

A DMA impact analysis should begin with a specific user task, not an account-wide traffic graph. Choose a query cohort tied to one decision: compare an offer, find a provider, reach a product page, start a booking, or complete a purchase. Then map every step from the search result to the final action.

1. Define matched query cohorts. Keep branded and non-branded searches separate. Split informational and transactional intent, and separate devices when their result layouts differ. An account-wide average can conceal the exact queries on which a new handoff appeared.
2. Record the visible search interface. For each cohort, capture result types, ordering, labels, proprietary modules, comparison services, organic links, and the domains receiving the first click. Preserve dated snapshots so later analysis does not depend on memory.
3. Measure the full funnel. Connect impressions and average visibility to clicks, landing sessions, qualified actions, conversion rate, direct conversions, and assisted conversions. A traffic metric tells you where attention moved; it does not tell you whether the business relationship survived the move.
4. Count handoffs and friction. Record how many domains and decisions sit between the result and the intended action. Look for repeated searches, backtracking, abandonment, and paths that send the user from Google to an intermediary before reaching the provider.
5. Segment destination ownership. Classify clicks going to Google-owned experiences, independent comparison services, publishers, marketplaces, and the provider’s own site. Without this classification, a declining organic CTR cannot reveal who captured the lost demand.
6. Use a credible comparison. Compare the same query cohorts before and after an observable interface change. Where possible, use comparable unaffected markets or journeys as controls, while accounting for seasonality, demand shifts, promotions, device mix, and unrelated ranking changes.
7. Set the interpretation rules first. Decide which combinations would indicate better user utility, stronger business access, or greater contestability before looking at the result. This reduces the temptation to label any favorable business movement as proof of fairness.

A simple before-and-after chart is rarely enough. Search demand, ranking systems, result features, brand activity, and conversion conditions can all move during the same period. If you do not control for those changes, the DMA becomes a convenient explanation rather than a demonstrated cause.

Your scorecard should also preserve trade-offs instead of averaging them away. If independent providers receive more qualified visits while users take an extra step, business access may have improved while user utility weakened. If users face more steps and independent providers receive fewer direct conversions, the implementation is failing both tests. If one large intermediary captures most displaced clicks, the market may have redistributed attention without becoming meaningfully more contestable.

Diagnose lower clicks and direct bookings before changing SEO

Reported declines in click-through rates and direct bookings are consequential, but neither metric explains its own cause. The same decline can originate at several points in the journey, and each one calls for a different response.

• Visibility loss: Impressions, positions, or eligible appearances decline for the affected query cohort. Investigate relevance, technical eligibility, content quality, competitor movement, and result-layout changes before blaming regulation.
• SERP interception: Visibility remains broadly stable while CTR falls and a different result type captures attention. Identify whether the click moved to a Google-owned surface, an independent service, or another publisher. Those movements have very different fairness implications.
• Handoff friction: The user clicks but must pass through an additional service before reaching the provider. Measure the completion rate at every transition. A new competitive option is not useful to the business if qualified demand repeatedly disappears at the handoff.
• On-site conversion loss: Landing sessions remain stable while conversion rate falls. Check page experience, message consistency, availability, offer changes, and measurement integrity. That pattern is less likely to be explained by search-result fairness alone.
• Attribution loss: The final conversion still occurs, but the added intermediary changes how the journey is credited. Reconcile search clicks, referral sessions, assisted conversions, and transaction records before declaring that demand vanished.

The destination of a lost click matters as much as the loss itself. If your page loses traffic to an independent service that better satisfies the query, your business performance fell while procedural competition may have improved. If the click moves into a gatekeeper-owned unit, weaker performance may coincide with continued self-preferencing. If the click moves to a dominant intermediary, the result could replace one dependency with another.

Direct bookings need the same care. A lower direct-booking count can reflect lower demand, weaker visibility, an interrupted handoff, an attribution change, or transactions migrating to an intermediary. Report those causes separately. Otherwise, a single metric will mix an SEO problem, a user-experience problem, and a market-structure problem into one number no team can act on.

Act on the layer that actually failed

What search and content teams can change

You cannot optimize away a gatekeeper problem, but you can make your own part of a fragmented journey easier to discover, understand, and measure.

• Maintain query-level evidence. Keep a recurring record of high-value result pages, their features, and their click destinations. Interface evidence is essential when traffic moves without an obvious ranking loss.
• Preserve destination data. Classify referrals and assisted paths by surface and intermediary. Do not combine direct, organic, comparison-service, and marketplace journeys into a single acquisition bucket.
• Reduce post-click uncertainty. Make the landing page complete the promise made in the result. Put the decision-critical information and next action where the visitor can find them without another search.
• Keep structured data aligned with visible content. Accurate schema can reduce ambiguity about the entity, offer, page purpose, and relationships represented on the page. It will not reverse a DMA-induced layout change or prove that a market is fair.
• Design for both direct and assisted discovery. Give intermediaries and AI-driven answer systems clear, consistent facts while preserving a strong path to the provider’s own page. Measure whether those external surfaces introduce qualified users or merely absorb the relationship.
• Report performance and fairness separately. Your executive dashboard should distinguish what happened to your business from what happened to the market. A regulation can hurt one company without reducing competition, or help one company without creating a fair system.

What regulators would need to demonstrate

A credible fairness claim requires more than evidence that Google changed a layout or exposed additional links. Regulators would need to show that independent services can acquire qualified demand, users can still complete tasks at an acceptable level of friction, and challengers can become viable without remaining dependent on the same gatekeeper.

Enforcement also has to change incentives. A fine that leaves the gateway, behavior, and economic advantage intact can become an operating cost rather than a competitive remedy. Structural options, including breaking up a monopoly, address a different layer of the problem than interface rules do. They also carry much larger consequences and require a stronger evidentiary case; they should not be treated as a cosmetic extension of search-result regulation.

The practical decision rule is simple: if a remedy changes presentation but does not reduce dependency, expand viable entry, or improve independent access to demand, it is managing the symptom. If it improves supplier access while adding user friction, it has created a trade-off that must be measured and refined. Calling either outcome an uncomplicated success hides the work still required.

Key takeaways

• The DMA’s equal-treatment goal is a rule for gatekeeper conduct, not proof that search outcomes became fair.
• User convenience, business performance, procedural neutrality, and market contestability are separate dimensions. A single CTR or satisfaction metric cannot represent all four.
• The survey of 5,000 European consumers is a meaningful warning about added friction, but consumer sentiment alone cannot establish whether independent competition improved.
• Lower CTR and fewer direct bookings should trigger a journey diagnosis: visibility, SERP interception, handoff friction, on-site conversion, and attribution each require a different response.
• A fairer result would let independent services gain qualified demand and become viable without simply shifting dependency from Google to another powerful intermediary.
• SEO teams should preserve query-level SERP evidence, classify click destinations, connect discovery to final outcomes, and keep fairness reporting separate from company performance.

Your next move is to choose one commercially important query cohort and map it from result page to completed action. Record who receives each click, how many handoffs the user encounters, and where qualified demand disappears. Repeat that measurement after material interface changes. You will then know whether you are facing an SEO issue, a user-experience issue, a distribution shift, or a gatekeeper problem – and you can stop asking one metric to answer four different questions.

References

• Search Engine Land – Is the Digital Markets Act Improving Search Fairness?