Enshittification testing is a structured way to detect whether a product or platform is drifting from serving users toward extracting value from them. The idea comes from observing a common arc in digital platforms: early delight, then gradual user friction, then aggressive monetization that erodes trust and utility. Testing gives teams an evidence-based method to spot that drift early and correct course.

Why platforms enshittify

• Incentive tilt: leadership goals shift from user growth and satisfaction to near-term revenue targets.
• Market power: switching costs rise and competitive pressure falls, which lowers the penalty for adding friction.
• KPI myopia: teams optimize local metrics that harm system health, such as ad impressions over task completion.
• Privacy decay: data capture expands faster than user benefit, which breaks the social contract.

What the testing aims to answer

1. Is value still flowing primarily to users, or has it flipped to the company or third parties?
2. Are core workflows getting slower, more confusing, or more paywalled?
3. Are long-term users measurably worse off than new users or business partners?
4. Are we adding friction that would make a rational, informed user leave if switching were easy?

Core signals and metrics

• Task friction index: steps, taps, scroll distance, time-to-complete for top 10 user jobs. Track month over month.
• Ad and promo load: percent of screen time or requests spent on ads, promos, or upsells during core tasks.
• Default capture rate: percent of defaults that favor the company over the user’s stated preferences.
• Dark pattern count: number of nudges that exploit confusion, not consent. Classify and trend.
• Price and fee creep: effective price for the same outcome after adjusting for shrinkflation of features.
• Trust balance sheet: ratio of trust-building events to trust-withdrawals in release notes and UX changes.
• Portability and exit: time to export data, completeness of export, and ease of account deletion.
• API and interop health: latency, quotas, deprecations, and restrictions that reduce third-party value.
• Tenure split retention: retention curves by user tenure. Watch for veteran erosion masked by new-user inflow.
• Support burden: median time to human help, ticket closure rates, refund friction.

The Enshittification Test Suite

Run these as automated checks plus quarterly human audits.

1. Core Job Degradation Test
   Compare the current flow for the top 5 user jobs with the same flow 3, 6, and 12 months ago. Flag if time, steps, or interruptions increased by more than 10 percent without offsetting benefits.
2. Ad and Upsell Intrusion Test
   Measure interruptions during core tasks. Flag if a user sees more than one non-user-serving element before completing the task.
3. Default Honesty Test
   Change each major default to align with clearly expressed user interests. If business KPIs drop sharply while user satisfaction rises, defaults were self-serving.
4. Paywall Regression Test
   Map features that moved from free to paid or from paid to higher tiers. Flag if core jobs now require payment for what was previously included.
5. Consent Integrity Test
   Audit permission prompts and data flows. Flag any collection that lacks a specific user-facing benefit within the same session or week.
6. Switching Cost Index
   Compute time to export, rehydrate data elsewhere, and close the account. Flag if this grows over time or exceeds a competitive benchmark.
7. Recommendation Drift Audit
   Track diversity and novelty of recommendations versus ad density and affiliate bias. Flag when relevance drops while monetized items rise.
8. Veteran User Health Check
   Compare NPS, complaints per active user, and churn for cohorts older than 12 months versus new cohorts. Flag divergence beyond predefined thresholds.
9. Interop and API Continuity Test
   Score rate limits, feature parity, and stability. Flag sudden deprecations without viable alternatives.
10. Release Note Plain Speak Test
    Review release notes for clarity about tradeoffs. Flag euphemisms that hide removals, new fees, or privacy-impacting changes.

Scoring rubric

• 0 to 2 flags: healthy with minor risks
• 3 to 5 flags: early enshittification risk; require mitigation plans
• 6 to 8 flags: acute drift; executive review and roadmap correction
• 9 to 10 flags: user harm phase; freeze monetization changes and run a recovery sprint

Data sources and methods

• Telemetry: task times, click paths, error rates, latency.
• UX studies: recurrent time-and-motion tests on fixed scripts.
• Surveys: quarterly NPS split by tenure and segment.
• Support analytics: topics, time to resolution, refund outcomes.
• Pricing and packaging diffs: structured changelog of every paywall move.
• Privacy and data map: inventory of data collected with stated user benefits.

Guardrails that prevent enshittification

• User-first OKRs: pair every revenue KPI with a user-outcome KPI that must not regress.
• Reversible experiments: ship behind flags and roll back on user-harm triggers.
• Portability pledge: fast export, clear deletion, and documented APIs.
• Default ethics: defaults should match the user’s likely best interest, not the company’s.
• Tenure advocacy: designate a product owner for veteran cohorts with veto power on core-job regressions.
• Sunlight rule: if you would not describe a change plainly in release notes, do not ship it.

When to run the tests

• Before and after any monetization change or packaging shift.
• Quarterly on a fixed schedule to build clean baselines.
• After leadership changes or new revenue targets.
• When support tickets spike or social sentiment turns.

Common failure modes

• Focusing on averages while veterans suffer.
• Counting clicks but not interruptions or cognitive load.
• Treating consent as a one-time checkbox instead of ongoing clarity.
• Hiding removals or fees in vague release language.
• Replacing user value with ad density and calling it engagement.

One-page checklist

• Have core tasks gotten longer, costlier, or more interrupted?
• Did defaults shift away from user benefit?
• Are long-time users doing worse than new users?
• Is data collection growing faster than visible user value?
• Are exit and export getting harder?
• Are APIs and integrations being throttled or removed?
• Do release notes say plainly what changed and why?
• Would a rational user stay if switching were easy?

Bottom line

Enshittification testing turns a fuzzy fear into concrete practice. By measuring friction, honesty, portability, and veteran health, teams can catch early drift and realign incentives before trust is lost. The cost of testing is small compared with the cost of recovering a betrayed user base.