Attention Economy Metrics in Competition Analysis

1. Introduction

Attention Economy Metrics in Competition Analysis refers to the use of measurable indicators of user attention, engagement, visibility, interaction and time to assess market power and competitive effects in digital markets.

Traditional competition analysis often focuses on:

Price

Sales

Output

Market share

Costs

Profitability

Digital markets require additional metrics because many platforms provide services at zero monetary price.

For example:

Users → Attention → Engagement → Data → Advertising → Revenue

A platform may therefore possess substantial economic power even when users pay nothing.

Important: “Attention economy metrics” is an analytical framework, not a standalone competition-law doctrine. The metrics must be connected to an established legal theory such as dominance, exclusion, self-preferencing, tying, exclusive dealing, merger effects or foreclosure.

2. Meaning of Attention Economy Metrics

An attention economy metric measures some aspect of how effectively a platform attracts, retains, distributes or monetizes user attention.

Important metrics include:

Daily active users

Monthly active users

Time spent

Session frequency

Session duration

Engagement rate

Click-through rate

Search share

Recommendation share

Ranking position

Impressions

Conversion rate

User retention

Churn

Multi-homing

Switching costs

Advertising inventory

Share of user attention

These metrics can help competition authorities understand the actual importance of a digital platform.

3. Why Attention Metrics Matter

Suppose Platform A has:

50% of revenue market share.

Platform B has:

25% revenue share.

But Platform A controls:

80% of relevant searches,

75% of user time,

85% of recommendations.

A purely revenue-based analysis could fail to capture the platform's gatekeeper position.

Therefore:

Revenue share ≠ necessarily attention share.

Attention metrics can provide another perspective.

4. Zero-Price Markets

Many digital services are offered without direct monetary payment.

Examples:

Search engines

Social networks

Video platforms

Messaging services

Some AI services

The user pays through other forms of value:

Attention + Data + Engagement

This makes conventional price-based analysis less informative.

5. Major Attention Economy Metrics

5.1 Daily Active Users — DAU

DAU measures the number of users active on a platform during a typical day.

Competition relevance

A high DAU can indicate:

Strong user adoption

Network effects

Entrenched position

Attractive advertising inventory

But:

High DAU alone does not establish dominance.

6. Monthly Active Users — MAU

MAU measures users active during a month.

It helps determine:

Reach

User base

Platform scale

Potential network effects

Example

Platform A:

500 million MAU

Platform B:

20 million MAU

The difference may indicate significant scale, but competition analysis must still consider:

Geographic market

Relevant product

Multi-homing

User intensity

Substitutability.

7. Time Spent

One of the most important attention metrics is:

Total time users spend on the platform.

Example:

Platform A: 10 billion minutes/month
Platform B: 1 billion minutes/month

Platform A potentially controls a much greater quantity of user attention.

But time alone can be misleading.

A platform might have long sessions because users are forced to complete complicated processes.

Therefore, time should be combined with other evidence.

8. Average Session Duration

This measures the average length of a user's session.

Example:

User opens app → watches videos → closes app after 45 minutes.

A high average session duration may indicate strong engagement.

However, competition authorities should ask:

Is the engagement voluntary?

Is it driven by quality?

Is it driven by lock-in?

Is it generated by exclusionary design?

Can users easily switch?

9. Session Frequency

This measures how frequently users return.

For example:

8 sessions per day.

High frequency may indicate strong user dependence.

It can contribute to:

Network effects

Data accumulation

Advertising value

Switching costs.

10. Engagement Rate

Engagement may include:

Likes

Comments

Shares

Clicks

Saves

Purchases

Video completion

Searches

A platform with a large user base but low engagement may have less attention power than a smaller platform with extremely high engagement.

11. Click-Through Rate

CTR = Clicks ÷ Impressions × 100

Example:

1,000 impressions

100 clicks

CTR = 10%.

CTR can be relevant to:

Search ranking

Advertising

Recommendations

Sponsored results.

A large difference in CTR between ranking positions may demonstrate the economic importance of visibility.

12. Ranking Position

Ranking position is particularly important in digital competition.

For example:

If a dominant platform moves its own service from position 20 to position 1, the change can materially affect traffic.

Therefore:

Ranking can be an economic asset.

13. Impression Share

Impression share measures how frequently a product, advertisement or service is displayed relative to possible opportunities.

Example:

A competing advertiser receives:

5% of available impressions

while the dominant platform's own service receives:

70%.

This may become relevant where discriminatory allocation is alleged.

14. Search Share

Search share measures the percentage of relevant searches handled by a platform.

For example:

Platform A = 80%
Platform B = 10%
Platform C = 10%

High search share can indicate a significant discovery gateway.

This was particularly important to the reasoning surrounding Google Shopping.

15. Recommendation Share

In recommendation-driven markets, an important question is:

How frequently does the platform determine what the user sees?

For example:

70% of videos watched originate from algorithmic recommendations.

If the platform also owns competing content, recommendation power can become particularly relevant.

16. Conversion Rate

Conversion rate measures how many users exposed to a product ultimately perform a desired action.

For example:

100,000 impressions
→ 5,000 clicks
→ 500 purchases.

Conversion metrics can help establish the economic value of attention.

17. Retention Rate

Retention measures how many users continue using a service.

A high retention rate may demonstrate:

Product quality

Network effects

Switching costs

User dependence.

Again, high retention does not automatically mean anticompetitive conduct.

18. Churn Rate

Churn measures users leaving a platform.

Low churn can indicate strong competitive position.

Competition authorities can compare:

Ease of entry + ease of switching + actual user movement.

This can help determine whether competitors genuinely constrain the platform.

19. Multi-Homing Rate

Multi-homing asks:

How many users simultaneously use competing platforms?

Example:

60% use Platform A only

30% use A and B

10% use B only

If most users use only one platform, switching barriers may be stronger.

If users frequently use several platforms, competitive constraints may be greater.

20. Switching Cost Metrics

Switching costs can include:

Loss of followers

Loss of playlists

Loss of transaction history

Loss of reputation

Learning costs

Data migration

Contractual restrictions.

These costs can make attention markets less contestable.

21. Share of User Attention

A broader metric is:

What percentage of a user's relevant digital time is captured by the platform?

For example:

A user spends:

30% of entertainment time on Platform A

20% on Platform B

10% on Platform C.

Platform A has a significant share of the user's attention in that category.

This is conceptually different from traditional sales market share.

22. Attention Concentration Ratio

A competition authority could theoretically calculate an attention concentration ratio.

For example:

Top 4 platforms control 90% of relevant user attention.

This resembles traditional concentration measures but uses attention rather than revenue.

However, such a measure would be an analytical indicator rather than a legally established replacement for conventional market definition.

23. Attention-Based HHI

The traditional Herfindahl-Hirschman Index (HHI) measures market concentration based on market shares.

A similar analytical exercise could theoretically use:

Attention share instead of revenue share.

For example:

A = 50%

B = 30%

C = 20%

Attention HHI:

50² + 30² + 20² = 3,800

This could indicate high concentration in attention terms.

But competition authorities would need to establish that attention share is an appropriate measure for the particular relevant market.

24. Attention Metrics and Market Definition

The first legal question remains:

What is the relevant market?

Possible markets:

General search

Online video

Social networking

Digital advertising

App distribution

Online marketplaces

Music streaming

Attention metrics should therefore be used within a properly defined market, not as a substitute for market definition.

25. Case Law 1 — Google Shopping

Google and Alphabet v Commission, Case C-48/22 P

This is one of the strongest examples for attention-based competition analysis.

Facts

Google operated a general search engine and its own comparison-shopping service.

The Commission found that Google gave more favourable positioning and display to its own comparison-shopping service than to competing comparison-shopping services.

Competition principle

The case demonstrates the importance of:

Search ranking

Visibility

Traffic

Self-preferencing

Platform power.

Attention metric relevance

The relevant economic chain is:

Ranking → Visibility → Clicks → Traffic → Commercial opportunity

Therefore, metrics such as:

Search share

Ranking position

Click-through rates

Traffic diversion

can help quantify competitive effects.

26. Case Law 2 — Google Android

Google and Alphabet v Commission, T-604/18

Facts

The Commission examined Google's Android ecosystem and contractual practices involving mobile devices.

Competition principles

The case illustrates the importance of:

Defaults

Distribution

Network effects

Ecosystem power

User behaviour.

Attention metric relevance

A default service may receive substantially greater exposure.

The analytical chain is:

Default → User exposure → Usage → Data → Reinforcement

Therefore, default-selection metrics can help demonstrate the competitive importance of platform placement.

27. Case Law 3 — United States v Microsoft

United States v Microsoft Corp., 253 F.3d 34 (D.C. Cir. 2001)

Facts

Microsoft's conduct concerning its operating system and competing browser technology was examined under U.S. antitrust law.

Principle

The case illustrates how control over a dominant technological platform can influence distribution and competitive access.

Attention-metric relevance

Metrics could include:

Browser distribution

Default settings

User adoption

Switching

Distribution channels.

The broader lesson is:

Control over a technological gateway can affect what users actually encounter.

28. Case Law 4 — Microsoft v Commission

Microsoft Corp. v Commission, T-201/04

Facts

The European Commission examined Microsoft's conduct involving interoperability information and Windows-related products.

Principle

The case concerned dominance, interoperability and potential foreclosure.

Attention-metric relevance

Competition analysis can examine:

Access rates

Compatibility

Distribution

User adoption

Switching.

A technically incompatible rival may receive less effective access to users even if the rival's product is competitive.

29. Case Law 5 — Ohio v American Express

Ohio v American Express Co., 585 U.S. 529 (2018)

Facts

The case concerned anti-steering provisions in American Express's two-sided payment-card network.

Principle

The Supreme Court emphasized that two-sided platforms can require analysis of interactions between both sides.

Attention-metric relevance

Digital attention platforms are frequently two-sided:

Users ↔ Advertisers

Metrics therefore may need to consider:

User engagement

Advertiser demand

Advertising prices

Transaction volume

Cross-side effects.

A metric focusing only on one side may give an incomplete picture.

30. Case Law 6 — Epic Games v Apple

Epic Games, Inc. v Apple Inc., 67 F.4th 946 (9th Cir. 2023)

Facts

Epic challenged Apple's App Store rules concerning distribution and payment.

Principle

The litigation examined control over an important digital distribution ecosystem.

Attention-metric relevance

Relevant metrics could include:

App-store search share

Ranking position

Featured placement

Downloads

Developer traffic

User conversion

Alternative-store usage.

The case demonstrates why discoverability and distribution can have economic significance.

The Ninth Circuit did not accept all of Epic's antitrust theories, so the case should not be presented as establishing that App Store ranking or payment restrictions are inherently unlawful.

31. Case Law 7 — United Brands

United Brands v Commission, Case 27/76

Principle

United Brands provides foundational guidance concerning dominance and abuse under EU competition law.

Attention-metric relevance

Attention metrics can help supplement traditional evidence of market power.

For example:

User reach + engagement + switching costs + distribution control

could collectively provide evidence about the practical strength of a digital intermediary.

But attention metrics cannot by themselves establish abuse.

32. Case Law 8 — Bronner

Oscar Bronner GmbH & Co. KG v Mediaprint, Case C-7/97

Principle

Bronner establishes a demanding framework for refusal-to-deal situations involving infrastructure.

Attention-metric relevance

Suppose a platform is the primary channel through which consumers discover a particular service.

Metrics could help answer:

How much traffic does the platform control?

Are alternatives realistically available?

Can consumers reach competitors elsewhere?

Is the infrastructure indispensable?

Thus, attention metrics may provide factual evidence for a traditional legal test.

They do not replace the legal test.

33. Case Law 9 — IMS Health

IMS Health GmbH & Co. OHG v NDC Health GmbH & Co. KG, Case C-418/01 P

Principle

The case concerns exceptional circumstances surrounding compulsory access to intellectual property.

Attention-metric relevance

Metrics could potentially help establish:

Market dependence

Competitor access

Customer usage

Availability of alternatives

Again:

Metric evidence → factual foundation

not:

Metric → automatic liability

34. Case Law 10 — Commercial Solvents

Commercial Solvents Corp. v Commission, Joined Cases 6/73 and 7/73

Principle

The case concerns the use of dominant upstream market power to disadvantage downstream competitors.

Attention-metric relevance

A dominant digital intermediary can similarly operate as an upstream gateway.

Possible measurements include:

Traffic supplied to competitors

Search referrals

Ranking exposure

Conversion rates

Access rates.

This can help establish the practical effect of exclusionary conduct.

35. Attention Metrics and Self-Preferencing

Consider:

Before intervention

Competitor:

40% of relevant impressions

Platform's own service:

20%

After ranking change

Competitor:

5%

Platform's own service:

70%

Such evidence may help demonstrate a change in competitive visibility.

The legal question remains:

Was the ranking change objectively justified, or did it constitute exclusionary self-preferencing by a dominant undertaking?

36. Attention Metrics and Foreclosure

A useful analytical sequence is:

Ranking change

↓

Visibility reduction

↓

Traffic reduction

↓

Conversion reduction

↓

Revenue reduction

↓

Competitor expansion becomes harder

This provides a measurable theory of foreclosure.

37. Attention Metrics and Raising Rivals' Costs

Suppose a platform reduces competitors' organic visibility.

Competitors may respond by purchasing more advertising.

Therefore:

Lower organic visibility → higher advertising expenditure

This can be evidence relevant to a raising-rivals'-costs theory.

The authority would still need to establish the appropriate legal elements.

38. Attention Metrics and Data Advantages

Attention generates behavioural information.

Metrics can therefore measure:

Search history

Watch time

Clicks

Purchases

User preferences

Engagement patterns

A platform with substantially greater attention may acquire a data feedback advantage.

Feedback mechanism

More users → more attention → more data → better targeting → more users.

This can contribute to durable market power.

39. Attention Metrics and Network Effects

Attention metrics can also help demonstrate network effects.

For example:

Users ↑

→ creators ↑

→ content ↑

→ engagement ↑

→ users ↑

A competition authority can examine whether this feedback mechanism makes market entry increasingly difficult.

40. Attention Metrics and Merger Analysis

Attention metrics can become relevant to merger control.

Suppose:

Platform A = 40% of user attention
Platform B = 30%.

Their merger could create a platform controlling approximately 70% of attention in a relevant market.

But a competition authority would still consider:

Market definition

Substitutability

Multi-homing

Entry

Innovation

Network effects

Data

Efficiencies.

Attention share alone does not decide a merger.

41. Attention Metrics and Killer Acquisitions

A small startup may have:

Low revenue

Few users

but potentially possess:

High user engagement

Rapid growth

Unique technology

Strong recommendation technology.

Therefore:

Revenue may understate competitive significance.

Attention and engagement metrics can help identify emerging competitive threats.

42. Attention Metrics and AI Platforms

AI introduces new metrics.

Possible measures include:

Prompt share

Percentage of relevant queries handled by an AI system.

Recommendation share

Percentage of purchasing or information decisions influenced by the AI.

Referral share

Traffic sent by AI to particular providers.

AI answer visibility

How often a business is mentioned or recommended.

User dependency

How frequently users rely on the same AI system for important decisions.

This could become increasingly important as AI assistants act as digital intermediaries.

43. AI Self-Preferencing

Suppose an AI assistant:

User asks for "best travel service."

The AI systematically recommends its owner's service.

Possible analytical chain:

AI recommendation
→ attention
→ consideration
→ transaction.

If the AI provider has substantial market power, competition authorities could investigate whether the recommendation system is being used to disadvantage rivals.

44. Attention Metrics and Consumer Welfare

Attention metrics can be connected to consumer effects such as:

Reduced choice

Reduced quality

Less innovation

Higher advertising burden

Reduced privacy

Worse recommendations

But competition authorities should avoid assuming:

More screen time = better consumer welfare.

A platform can increase time spent while reducing user welfare.

Therefore, metrics require contextual interpretation.

45. Problems with Attention Metrics

45.1 Time does not equal quality

More time may result from:

High-quality content

Addictive design

Inefficient interface.

45.2 Users may multi-home

A user may spend significant time on several platforms.

Therefore, high attention share does not automatically establish monopoly power.

45.3 Different users have different value

One minute spent by a high-value advertiser target may not equal one minute spent by another user.

45.4 Metrics can be manipulated

Platforms may change:

Definitions

Measurement methods

Bot detection

Engagement calculations.

Authorities therefore need reliable evidence.

46. Combining Metrics

The best analysis generally combines several indicators.

For example:

User share + time share + search share + ranking share + switching costs + multi-homing

is more informative than any single metric.

A simplified evidence matrix:

47. A Competition-Law Analytical Framework

Step 1 — Define the relevant market

Identify the actual competitive service.

Step 2 — Measure traditional market power

Consider:

Market share

Revenue

Sales

Prices.

Step 3 — Add attention metrics

Examine:

Users

Time

Search

Ranking

Recommendations

Engagement.

Step 4 — Examine network effects

Does additional usage strengthen the platform?

Step 5 — Examine switching and multi-homing

Can consumers realistically move?

Step 6 — Identify conduct

For example:

Self-preferencing

Exclusivity

Tying

Bundling

Refusal to deal.

Step 7 — Quantify effects

Measure:

Traffic diversion

Lost impressions

Reduced clicks

Reduced conversions

Increased advertising costs.

Step 8 — Examine objective justification

Consider:

Quality

Security

Privacy

Technical necessity

Fraud prevention.

48. Hypothetical Example

Suppose Platform X has:

70% search share

75% relevant user attention

80% recommendation share

85% advertising impressions

It introduces an algorithm that reduces competitors' visibility.

After the change:

Competitor traffic ↓ 40%

Competitor impressions ↓ 50%

Competitor conversions ↓ 35%

Platform's own service traffic ↑ 45%

This evidence could be highly relevant to a competition investigation.

But the legal conclusion would require additional analysis concerning:

Dominance

Relevant market

Causation

Objective justification

Competitive effects.

49. Traditional Metrics vs Attention Metrics

The two groups should generally be combined, not treated as mutually exclusive.

50. Ultra-Basic Revision Keywords

Attention Economy — economy based on attracting user attention.

DAU — daily active users.

MAU — monthly active users.

Time Spent — amount of time users spend on a platform.

Engagement — user interaction with content/service.

CTR — clicks divided by impressions.

Ranking — position of a product/content item.

Discoverability — ease with which users find a product.

Impression Share — proportion of available exposure obtained.

Search Share — percentage of relevant searches handled.

Recommendation Share — proportion of exposure determined by recommendations.

Retention — users continuing to use the platform.

Churn — users leaving the platform.

Multi-Homing — using multiple competing platforms.

Switching Cost — cost/difficulty of changing platforms.

Attention Share — percentage of relevant user attention captured.

Attention HHI — conceptual concentration measure using attention shares.

Self-Preferencing — platform favouring its own products.

Foreclosure — making competition more difficult.

Network Effect — value increases with user participation.

Gateway Power — control over access to users.

51. Final Conclusion

Attention economy metrics provide competition authorities with an additional way to understand digital market power where price and revenue do not tell the whole story.

The important analytical chain is:

Users → Attention → Visibility → Engagement → Data → Monetization → Market power

Metrics such as user share, time spent, engagement, ranking position, search share, recommendation share, CTR, retention, churn and multi-homing can help establish the factual significance of a digital platform.

Cases including Google Shopping, Google Android, Microsoft, United States v Microsoft, Ohio v American Express, Epic Games v Apple, United Brands, Bronner, IMS Health and Commercial Solvents provide legal frameworks that can be applied to these metrics.

The central competition-law principle is:

An attention metric is evidence, not liability.

A platform having large amounts of user attention is not automatically violating competition law. The critical inquiry is whether the platform possesses substantial market power and whether its conduct uses that power to unlawfully exclude competitors, restrict competition, or otherwise produce legally relevant competitive harm.