iOS and Web2App Onboarding That Learns User Needs, Engages Audiences, and Drives Sales

In short

For Transcriber Pro, the team used the JTBD framework to find the jobs people hire a transcription app for, answered fears about speed, quality and security, asked about settings and export formats during onboarding, and showed the work done using the labor illusion effect. Ads and funnel analytics on Web2App then guided changes before the App Store release.

Leo · CEO, Segmently Smart Flow

Published · 3 min read

The case: Transcriber Pro app

An app that converts video or audio to text. A developer who creates great apps approached us. He didn't know how to demonstrate the product's value to users or choose the best monetization strategy.

Our tasks were:

  • Identify the audience interested in such a product.
  • Communicate the product's value (the A-ha moment).
  • Choose a monetization strategy.
  • Prioritize the backlog (select features users will pay for).

💡 We used the Jobs To Be Done (JTBD) framework to determine the jobs for which this product could be hired.

✅ 1. Jobs and contexts for hiring a voice-to-text transcription app include:

  • Focusing on lectures, not on note-taking
  • Transforming meetings and calls
  • Patient interviews or consultations
  • Research interviews or focus groups
  • Legal proceedings or meetings

✅ 2. We identified and addressed fears/barriers that might prevent using the product:

2.1 Speed of transcription - "Fast transcribing. It takes several seconds to transcribe one minute."

2.2 Transcription quality - "Highly accurate. We use the latest AI technologies to ensure the best possible accuracy."

2.3 Data security - "Fully confidential. Transcription results secured with biometric protection."

✅ 3. We ask what settings they need directly during onboarding to prevent user confusion during app setup.

3.1 Need text result transformation?

  • Split dialogue by speakers
  • Get a summary
  • Receive follow-up information
  • Other options

3.2 Preferred export format:

  • Export as plain text.
  • Export as a .pdf file.
  • Export as a .docx file.
  • Export to Google Sheets.
  • Other formats.

💡 For users who selected "Other Option" on one of the screens, we asked:

  • How they plan to use the app
  • How they want to transform the text (what other features they need)
  • What other text export formats do they need?

🥸 This allowed us to get direct responses from users without investing in research and feature development.

✅ 4. When all the information was collected, we wanted to show the user the work we did to provide the service (using the labor illusion effect to make the price seem fair):

  • Tuning the AI model for your goals.
  • Building a private space for transcriptions.
  • Optimizing cloud storage for faster access.

✅ 5. We set up the iOS and Web2App funnel in 8 minutes

✅ 6. We launched ads for the Web2App funnel and collected all analytics on onboarding progression to check:

  • How accurately do we target the interested audience?
  • Which settings and features interest users?
  • Conversion rate, primary segments

✅ 7. Based on analytics, we changed the funnel, connected the SDK to the iOS app, and published the app in the App Store.

💡 After the SDK was added, all changes in onboarding and paywalls can be made without an App Store release.

Key takeaways

  • The JTBD framework revealed the contexts users hire a transcription app for, from lectures and meetings to legal proceedings.
  • Answering fears about transcription speed, quality and data security removes barriers to using the product.
  • Asking about settings during onboarding prevents confusion during setup, and “Other” answers show which features users want without separate research.
  • Showing the work done to prepare the service uses the labor illusion effect to make the price seem fair.
  • Ads and onboarding analytics on the Web2App funnel checked targeting, feature interest and conversion before the iOS app shipped with the SDK.