Ranking Discrimination in Tutoring Apps

1. Introduction

Ranking discrimination in tutoring apps occurs when a digital tutoring platform uses its ranking, recommendation, search, or visibility algorithms to systematically favour certain tutors, courses, institutions, or educational products over competing providers.

The discrimination may take several forms:

giving affiliated tutors higher search positions;

promoting tutors who pay higher commissions;

suppressing competing tutors;

favouring the platform's own courses;

manipulating ratings or recommendation scores;

giving preferred tutoring partners greater visibility;

reducing the visibility of independent tutors;

conditioning ranking on exclusivity;

using commercially sensitive tutor data to favour selected providers; or

changing ranking criteria in a discriminatory manner without transparent justification.

Ranking discrimination does not automatically constitute an infringement of competition law. The legal concern becomes stronger where the platform possesses substantial market power or dominance and the ranking practice forecloses competitors, disadvantages users, or leverages power from one market into another.

2. Indian Competition-Law Framework

The principal provisions are Sections 3 and 4 of the Competition Act, 2002.

Section 3

Section 3 concerns anti-competitive agreements.

Ranking discrimination can potentially fall within Section 3 where the platform enters into restrictive arrangements with tutors or educational institutions.

Examples include:

exclusive ranking arrangements;

agreements preventing tutors from using competing platforms;

restrictions on independent advertising;

discriminatory commission agreements;

arrangements that restrict access to competing tutoring services.

However, ranking discrimination by a platform acting unilaterally is more naturally analysed under Section 4 where the platform is dominant.

3. Section 4 and Ranking Discrimination

Section 4 prohibits abuse of dominant position.

Several provisions may be relevant.

Section 4(2)(a)

A dominant platform may impose unfair or discriminatory conditions or prices.

For example, the platform may give two similarly situated tutors materially different ranking treatment without objective justification.

Section 4(2)(b)

Conduct limiting or restricting:

technical development;

provision of services;

market access; or

production

may become relevant where ranking manipulation prevents competing educational providers from reaching students.

Section 4(2)(c)

Denial of market access is particularly important.

For an online tutoring platform, visibility can effectively determine whether a tutor reaches students.

If a dominant platform systematically pushes a competing tutor to pages or positions where students rarely see the tutor, the ranking mechanism may potentially operate as a form of market-access restriction.

Section 4(2)(e)

This provision concerns leveraging dominance in one relevant market into another.

For example:

A platform is dominant in online tutoring search and uses that position to favour its own tutoring courses over independent tutors.

This may raise leveraging concerns.

4. Why Ranking Is Economically Important

Traditional discrimination may involve:

Firm A receives a higher price than Firm B.

Digital ranking discrimination may instead involve:

Tutor A appears first; Tutor B appears on page 10.

The economic effect can be substantial because users frequently do not inspect every available tutor.

Ranking therefore controls:

attention;

clicks;

enrolments;

enquiries;

conversions;

reputation;

revenue; and

future ratings.

A small change in algorithmic visibility can therefore produce significant competitive consequences.

5. Types of Ranking Discrimination

A. Self-preferencing

The tutoring platform gives its own courses or tutors preferential placement.

Example:

Search: “Class 12 Mathematics”

The platform's affiliated course appears above independent tutors despite otherwise comparable relevance.

This is one of the most important forms of ranking discrimination.

B. Commission-based ranking

The platform ranks tutors partly according to the commission they pay.

A higher commission may therefore produce greater visibility.

This can raise concerns where the platform possesses substantial market power and competing tutors cannot realistically obtain comparable visibility without accepting commercially disadvantageous terms.

C. Affiliation discrimination

The platform favours:

affiliated coaching centres;

subsidiaries;

preferred educational institutions;

exclusive tutors; or

strategic partners.

D. Data-based ranking discrimination

The platform may possess extensive information about:

tutor conversion rates;

student searches;

lesson prices;

cancellation rates;

student preferences;

competitor performance.

It may use this data to identify successful independent tutors and then alter ranking parameters to favour its own competing services.

E. Rating manipulation

A platform may:

highlight favourable ratings;

suppress negative ratings;

change review visibility;

apply different review rules to affiliated tutors.

This can distort competition where students depend heavily on platform ratings.

F. Algorithmic demotion

The platform may reduce a competitor's ranking following:

refusal to accept exclusivity;

use of another platform;

lower commission payments;

competitive pricing;

advertising outside the platform.

6. Ranking Discrimination and Relevant Market

The relevant market may need to be defined carefully.

Possible markets include:

Market for online tutoring intermediation

The platform connects tutors with students.

Market for online tutoring services

Students directly purchase tutoring services.

Market for tutoring search/discovery

The platform provides search and recommendation functionality.

Subject-specific markets

Depending on the facts, separate markets might exist for:

mathematics;

science;

language tutoring;

competitive examination preparation;

professional education.

The market should not automatically be defined as “education” as a whole.

7. Network Effects

Tutoring apps frequently have two-sided or multi-sided characteristics.

One side consists of:

students.

The other consists of:

tutors;

coaching institutions;

course providers.

More students attract more tutors.

More tutors increase student choice.

This creates network effects.

Once a platform becomes large, an independent tutor may find it difficult to reach students elsewhere because students increasingly search where the largest user base exists.

Consequently, ranking discrimination by a powerful platform may have greater foreclosure effects than ordinary discrimination in a conventional market.

8. Case Law

1. Matrimony.com Ltd. v. Google LLC

CCI Case Nos. 07 and 30 of 2012

This is an important Indian authority concerning search-result prominence and alleged preferential treatment by Google.

The case involved complaints concerning Google's search and search-advertising practices.

The CCI examined Google's position in relevant online search and advertising markets and the competitive significance of search-result presentation.

Relevance to tutoring apps

The case provides a strong Indian analogy for algorithmic ranking.

A tutoring platform may similarly control:

search results;

sponsored placement;

visibility;

recommendation order.

The central question is whether ranking practices by a dominant platform distort competition rather than merely reflecting legitimate relevance or quality criteria.

9. Umar Javeed v. Google LLC

CCI Case No. 39 of 2018

This case concerned Google's practices in the online search and related digital ecosystem.

The CCI examined Google's conduct in relation to search and online services.

Relevance

The case is useful because it demonstrates that digital platforms may have competitive significance beyond traditional pricing.

In tutoring apps, ranking may be the principal mechanism through which a platform determines which tutors students discover.

Consequently, algorithmic prominence can itself be an important competitive parameter.

10. Google Shopping — Google and Alphabet v. Commission

Case C-48/22 P; General Court, T-612/17

The European Commission found that Google had given preferential positioning and display to its own comparison-shopping service while demoting competing comparison-shopping services.

The EU courts considered the competitive consequences of Google's search-ranking practices.

Relevance to tutoring apps

This is one of the closest major comparative authorities.

A tutoring platform may similarly:

operate a marketplace for tutors while simultaneously offering its own tutoring services.

If its algorithm systematically places its own services ahead of competing tutors, the conduct may resemble self-preferencing.

Important distinction:

Higher ranking is not automatically unlawful.

The competitive analysis must consider:

dominance;

discriminatory treatment;

actual or potential foreclosure;

effects on competition;

objective justification; and

the structure of the relevant market.

11. Microsoft Corp. v. Commission

Case T-201/04

The European Commission and EU courts examined Microsoft's conduct concerning interoperability and the interaction between Microsoft's dominant position and neighbouring markets.

Relevance to tutoring platforms

Although not a ranking case, it provides an important principle concerning dominant digital ecosystems.

A platform can use control over an important technological environment to affect competition in adjacent markets.

For tutoring apps, the equivalent could be:

dominance in tutoring discovery → control over ranking → advantage for the platform's own tutoring products.

It is therefore useful as a comparative authority on ecosystem-based foreclosure.

12. Intel Corp. v. Commission

Case C-413/14 P

Intel concerned rebates offered by a dominant undertaking and the question of how exclusionary effects should be assessed.

The CJEU emphasized the importance of examining the actual or potential exclusionary effects of the conduct where such effects are contested.

Relevance

A tutoring platform might offer ranking benefits to tutors who agree to:

higher commissions;

exclusivity;

minimum spending;

preferential commercial arrangements.

The competitive assessment should therefore consider whether the arrangement actually restricts competitors' ability to compete.

The case supports an effects-oriented analysis rather than assuming that every commercially preferential arrangement is automatically abusive.

13. Slovak Telekom v. Commission

Joined Cases C-165/19 P and C-166/19 P

The case concerned access restrictions imposed by a dominant telecommunications operator.

The Court examined the circumstances in which exclusionary conduct by a dominant undertaking can infringe Article 102 TFEU.

Relevance

Visibility on a tutoring platform can function as an important form of access to customers.

If a dominant tutoring platform substantially controls student discovery and deliberately makes competing tutors effectively invisible, the situation can raise an access/foreclosure question.

The analogy is strongest where:

the platform is difficult to bypass;

alternative customer-acquisition channels are weak;

ranking determines a large share of student traffic; and

the ranking practice significantly reduces rivals' opportunities.

14. Bronner v. Mediaprint

Case C-7/97

The case concerned access to a newspaper distribution system controlled by a dominant undertaking.

The CJEU adopted a demanding test for compulsory access to infrastructure.

Relevance

This case helps prevent over-expansion of competition law.

A tutoring platform is not automatically required to give every tutor a preferred ranking.

The platform normally retains legitimate freedom to design:

search algorithms;

quality standards;

recommendations;

safety criteria;

student-matching systems.

Competition law becomes more significant where the ranking mechanism is used by a dominant platform in a manner that materially forecloses effective competition.

15. Eturas v. Lietuvos Respublikos konkurencijos taryba

Case C-74/14

Eturas involved an online travel-booking system and restrictions communicated through the platform's electronic system concerning discounts.

The CJEU examined the evidentiary implications of a platform-mediated restriction.

Relevance to tutoring apps

The case is highly relevant to the algorithmic dimension of platform competition.

A tutoring platform can potentially facilitate coordinated conduct through:

automated ranking rules;

commission adjustments;

pricing restrictions;

common algorithmic instructions.

It demonstrates that digital systems can be instruments through which competition-restricting conduct is implemented.

16. Amazon Marketplace / European Commission Proceedings

European competition proceedings involving Amazon examined the relationship between Amazon's marketplace activities and its use of seller-related data and the operation of its marketplace.

Relevance

A tutoring platform may similarly obtain extensive data concerning independent tutors and then use that data to compete against them.

For example:

Platform observes that Tutor A receives unusually high demand.

Platform identifies the successful subject and pricing model.

Platform launches an affiliated tutoring course.

Platform uses marketplace data to improve its own offering.

Platform gives the affiliated course preferential visibility.

This combination of data advantage + ranking control + vertical integration can substantially strengthen foreclosure concerns.

The exact legal assessment depends on the applicable jurisdiction and evidence.

17. Google Android

The European Commission's Google Android proceedings concerned restrictions imposed within Google's mobile ecosystem.

Although the case did not concern tutoring rankings, it illustrates how contractual and technical restrictions within a digital ecosystem can reinforce a platform's position.

Relevance

A tutoring platform could potentially combine ranking discrimination with:

default placement;

bundling;

exclusivity;

contractual restrictions;

preferential recommendation.

The cumulative effect can be more important than any individual restriction.

18. Ranking Discrimination and Self-Preferencing

Self-preferencing should be separated into two questions.

First question: Is the platform favouring itself?

This is a factual question.

For example:

Platform-owned course receives first position.

Second question: Does the practice harm competition?

This requires analysis of:

market power;

ranking criteria;

competitor dependence;

foreclosure;

consumer choice;

alternative platforms;

quality differences;

efficiencies.

Therefore:

Self-preferencing ≠ automatic competition-law violation.

The competitive effects must be established under the applicable legal framework.

19. Legitimate Ranking Criteria

A tutoring app can have legitimate reasons for ranking a tutor highly.

Examples include:

student ratings;

tutor qualifications;

subject expertise;

verified credentials;

response time;

attendance;

student completion rates;

safeguarding compliance;

language compatibility;

availability;

geographical proximity;

demonstrated teaching performance.

A platform should ideally apply these criteria consistently.

20. Discriminatory Ranking Criteria

Greater concern may arise where ranking depends on:

ownership by the platform;

undisclosed payments;

refusal to use rival platforms;

acceptance of exclusivity;

discriminatory commission structures;

unrelated commercial relationships;

retaliation against competitors;

suppression of rival services.

The more disconnected the ranking factor is from legitimate consumer benefit, the stronger the competition concern may become.

21. Ranking and Sponsored Listings

Paid ranking requires special care.

A tutoring app may legitimately sell advertising space.

For example:

“Sponsored Tutor”

is not necessarily problematic.

Problems may arise if:

sponsored results are disguised as organic results;

non-paying competitors are systematically suppressed;

the platform represents paid placement as objective ranking;

the platform uses dominance to make paid visibility effectively unavoidable.

Transparency can therefore reduce consumer confusion, although transparency alone does not necessarily resolve a competition-law problem.

22. Ranking Manipulation Through Ratings

Suppose a platform gives its affiliated tutors:

greater weight for positive reviews;

faster review publication;

more prominent testimonials.

Independent tutors receive:

delayed reviews;

reduced visibility;

stricter verification.

This may distort competition because students use ratings as a proxy for quality.

The investigation should compare:

identical or similarly situated tutors → identical ranking criteria → different treatment.

23. Algorithmic Opacity

An algorithm does not need to be publicly disclosed in full.

Platforms have legitimate interests in protecting:

trade secrets;

security;

anti-manipulation mechanisms.

However, competition authorities may require sufficient evidence to determine whether discriminatory treatment exists.

Relevant evidence can include:

ranking logs;

A/B tests;

algorithmic change histories;

internal emails;

product documents;

ranking experiments;

commission data;

traffic allocation;

click-through rates;

conversion rates.

24. Counterfactual Analysis

A useful competition-law approach is to ask:

What would the market look like if the discriminatory ranking mechanism did not exist?

For example:

Actual situation

Platform-owned tutors appear first.

Counterfactual

Independent tutors are ranked according to the same relevance and quality criteria.

If independent tutors receive substantially greater visibility in the counterfactual and the difference affects enrolments, this may provide evidence of competitive foreclosure.

25. Effects on Consumers

Ranking discrimination may affect students through:

Reduced choice

Students may see only preferred tutors.

Higher prices

Reduced competition may permit higher tutoring fees.

Lower quality

Students may be diverted away from higher-quality independent tutors.

Reduced innovation

Independent tutors may have fewer incentives to develop new teaching methods.

Information distortion

Students may mistakenly believe that the highest-ranked tutors are objectively the best.

26. Defences and Objective Justifications

A tutoring platform may argue that ranking differences result from legitimate factors.

Possible justifications include:

better tutor quality;

verified qualifications;

student safety;

fraud prevention;

better completion rates;

availability;

faster response;

relevance to the student's query;

lower cancellation rates;

technical compatibility;

improved matching efficiency.

The platform should be able to demonstrate that the ranking criteria are genuinely connected to those objectives.

27. Cumulative Conduct

Ranking discrimination becomes more significant when combined with other practices.

For example:

Ranking preference + exclusivity + higher commissions + data advantages + contractual restrictions

may create substantially greater foreclosure than ranking preference alone.

The CCI or another competition authority may therefore need to consider the overall commercial strategy rather than examining each mechanism in isolation.

28. Practical Indian Legal Test

A useful framework for analysing a tutoring-app ranking case is:

Step 1 — Identify the platform

Determine whether the undertaking operates:

tutoring marketplace;

search service;

course platform;

payment service;

advertising service.

Step 2 — Define the relevant market

Identify the relevant product/service and geographic market.

Step 3 — Establish market power

Consider:

market share;

user base;

network effects;

switching costs;

entry barriers;

data advantages;

access to tutors.

Step 4 — Identify discriminatory treatment

Compare ranking outcomes for similarly situated tutors.

Step 5 — Identify the ranking variable

Determine whether ranking depends on:

quality;

relevance;

payment;

affiliation;

exclusivity;

commission;

platform ownership.

Step 6 — Examine foreclosure

Determine whether competitors lose meaningful access to students.

Step 7 — Examine consumer effects

Consider:

price;

quality;

variety;

innovation;

student choice.

Step 8 — Consider objective justification

Assess legitimate explanations and whether less restrictive methods were available.

Step 9 — Examine cumulative conduct

Look for:

tying;

exclusivity;

self-preferencing;

discriminatory commissions;

data exploitation.

Step 10 — Determine appropriate remedy

Possible remedies may include:

stopping discriminatory ranking;

equal treatment requirements;

transparency obligations;

non-discrimination rules;

monitoring;

behavioural commitments;

structural remedies in exceptional cases.

29. Evidence Particularly Important in Tutoring-App Cases

The following evidence can be highly significant:

algorithmic ranking rules;

internal ranking documents;

ranking-change histories;

A/B testing records;

tutor commission agreements;

affiliation agreements;

exclusivity clauses;

search-result logs;

click-through rates;

conversion rates;

student enrolment data;

tutor complaints;

ranking before and after contractual changes;

internal communications concerning competitors;

data showing preferential treatment of affiliated tutors.

A particularly useful comparison is:

Ranking position → student clicks → enquiries → enrolments → revenue

This can help establish whether the discriminatory ranking actually affects competitive opportunities.

30. Key Distinction: Ranking Preference vs Ranking Discrimination

These concepts should not be confused.

Ranking preference

A tutor is ranked highly because the tutor has:

better ratings;

greater availability;

stronger qualifications;

better student outcomes.

This can be legitimate.

Ranking discrimination

A competing tutor receives lower visibility because:

the tutor refuses exclusivity;

the tutor pays a lower commission;

the tutor competes with the platform;

the tutor is independent rather than affiliated.

The second situation raises significantly stronger competition concerns.

31. Case-Law Synthesis

32. Conclusion

Ranking discrimination in tutoring apps represents a modern form of platform competition problem because ranking determines which educational providers are visible to students.

The strongest competition concerns arise where a platform:

has substantial market power;

controls a significant gateway to students;

vertically integrates into tutoring services;

favours its own tutors or courses;

uses commercially sensitive data from independent tutors;

conditions visibility on exclusivity or high commissions;

systematically demotes competing providers; and

causes measurable foreclosure or harm to competition.

Under Indian law, Sections 3 and 4 of the Competition Act, 2002 provide the principal framework. Section 4 is particularly important where the tutoring platform is dominant and uses its control over ranking to discriminate against competitors or deny them meaningful access to students.

At the same time, ranking differentiation by itself is not unlawful. Digital platforms require the ability to rank tutors based on legitimate factors such as qualifications, relevance, safety, availability, student outcomes, and quality. The central competition-law inquiry is therefore whether the ranking system is objectively justified and competitively neutral, or whether it is being used by a powerful platform as an instrument of exclusion or self-preferencing.