Study of Deceptive Patterns in top Indian apps
Study of
Deceptive Patterns
in top Indian apps
Research on the prevalence, industry trends, and
common user flows of deceptive patterns
Objective of Research
To assess prevalence and use of Deceptive
Patterns in top Indian apps, encouraging the use
of conscious designs, and promoting a shift
towards ethically conscious online interfaces.
12k+
Screens
50+
Apps
across
belonging to
9
Industries
Snapshot of the industries and apps analysed
Cab Booking
Delivery & Logistics
E-commerce
Ed-tech
Fintech
Gaming
Health Tech
Streaming Services
Travel Booking
Key Research Findings
1
52 / 53 apps studied exhibited at least 1 Deceptive Pattern
~90% of apps showed 1-4 Different Deceptive Patterns
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2
4 / 12 Deceptive Patterns accounted for 78% of the total occurrences
Note: Disguised Advertisement was not conclusively observed in any of the apps involved in the study.
This does not imply that it does not exist but highlights the difficulty in distinguishing between genuine organic content and paid content in a closed environment.
3
79% of the apps studied showed instances of Privacy Deception
4
9 / 12 Deceptive Patterns were observed in Travel Booking Industry
The industry with the second highest number of Deceptive Patterns observed was Delivery & Logistics (8/12).
5
9 / 9 E-commerce apps made it difficult for users to delete their account (Difficult Deletion)
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6
4 / 5 Health Tech apps relied on Time-based Pressure (False Urgency) to rush users into decisions
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7
Basket Sneaking was observed 4X more in Delivery & Logistics compared to other industries
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8
9 / 9 instances of Confirm Shaming were observed in Travel Booking
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9
Highest instances of Deceptive Patterns were observed in Purchasing / Subscribing / Booking flows
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10
43 / 53 apps contained a Deceptive Pattern in Settings / Profile
Of the 43 applications that exhibited at least one Deceptive Pattern in settings or profile, 40 were related to data privacy and security settings.
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“Pioneering research in India, exploring
the prevalence of deceptive patterns in
widely-used consumer apps.”