Prompt

I'm building a mobile app and want to test onboarding flows with feature flags

Technology / AB Testing & Experimentation1 observationLast seen Oct 13, 2026

Latest observation

Oct 13, 2026OpenAI APIWeb search: off

A good way to test onboarding flows with feature flags is to treat each step as a controllable experiment rather than a hardcoded path.

Basic approach

  1. Identify onboarding variants

    • Full onboarding
    • Shortened onboarding
    • Skip onboarding
    • Different welcome screens
    • Different permission prompts
  2. Create feature flags Example flags:

    • onboarding_enabled
    • onboarding_variant_a
    • show_permission_step
    • skip_tutorial
    • new_home_experience
  3. Gate the flow Use the flag value at app startup to decide which flow the user sees.

    Example logic:

    • If onboarding_enabled = false → go straight to home
    • If onboarding_variant_a = true → show new onboarding
    • Else → show default onboarding
  4. Target specific users Test with:

    • Internal testers
    • Beta users
    • Specific cohorts
    • Random percentage rollout
  5. Measure outcomes Track:

    • Completion rate
    • Drop-off step
    • Time to completion
    • Conversion to signup / first action

Suggested setup

For mobile apps, feature flags are often managed with:

  • LaunchDarkly
  • Firebase Remote Config
  • ConfigCat
  • Statsig
  • Split

Example rollout strategy

  • Internal team: 100% new onboarding
  • Beta users: 50/50 split
  • Production: 5% rollout, then increase

Important testing tips

  • Make flags deterministic so the same user sees the same flow
  • Cache flags locally for offline support
  • Add a fallback flow if flag fetch fails
  • Avoid too many flags that can create confusing combinations

Example pseudo-logic

if featureFlag("onboarding_enabled") == false:
    showHome()
else if featureFlag("onboarding_variant_a") == true:
    showOnboardingA()
else:
    showOnboardingDefault()

If you want, I can also help you design:

  • a flag strategy for iOS/Android
  • a testing matrix for onboarding variants
  • or sample code in Swift/Kotlin/React Native.

Observation history

How did Obsurfable measure this prompt?

Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.

Which AI systems does Obsurfable collect answers from?

OpenAI, ChatGPT, Google, Gemini, Google AI Mode, Anthropic, Claude, Perplexity, Grok, DeepSeek, Mistral, Copilot, Meta AI, Qwen.

How can brands improve their AEO?

Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.

Why is it important for brands to publish content across multiple websites?

Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.

Want this interpreted for your brand?

Explorer is the free public corpus. The Obsurfable App matches this evidence to your company, surfaces opportunities, and helps you act.