PRODUCT STRATEGY • UX RESEARCH • AI WORKFLOWS
How understanding user motivation boosts revenue.
Babbel | Q1 2025 | Product Designer
In this case study, I show how I identified a way to reduce time-to-value during onboarding, helping users connect to the product faster and increasing revenue as a result.
This discovery led to a 7% increase in a key revenue metric and an 8% boost in visit-to-sale conversion.
🏆 This project was recognized with Babbel’s internal BOLD award for its measurable impact
Summary
Problem
The onboarding flow was too long and didn’t reflect users’ personal goals, which led to high drop-off rates.
Solution
Redesigned onboarding questions to highlight product value earlier in the journey.
My role
UX Research • Product Strategy
Impact: +7% in revenue metric, +8% in visit-to-sale 📈
Context
When I joined Babbel in Q2 2023, my first task was to evaluate and redesign the onboarding experience. After analyzing the current flow, benchmarking competitors, reviewing research, and wireframing, I presented the first proposal for a revised onboarding journey.
One of the key opportunities I identified was to reframe the onboarding questions to reflect user motivations, helping users connect with the product on a personal level and experience value sooner.
Chapter 1
Researching motivations
Using existing research, we identified different types of motivations that drive people to learn with Babbel. What hadn’t been done before was grouping these motivations to understand how they could be leveraged in the product experience.
I synthesized the insights and grouped motivations into three categories:
Skill motivations - the smallest building blocks of communication, like speaking or vocabulary
Tangible motivations - the things people want to do in real life, like doing groceries or talking to a neighbour
Aspirational motivations - deeper emotional drivers tied to identity, like connecting with family or travelling
These motivations build on one another to create a complete picture of why someone wants to learn a language.
The goal
Our goal was to validate and expand on motivational groupings to enable a more personalized and meaningful onboarding experience.
Chapter 2
AI-supported analysis
To validate the motivation clusters, we launched an in-app survey to ensure the questions were shown at the most impactful point in the onboarding flow. I analyzed the results in a CSV format using advanced Google Sheets queries and quantitative techniques.
One task exceeded the limits of what I could do manually. Instead of waiting for analyst support, I explored whether I could bridge the gap with AI. Through a mix of trial-and-error and rapid iteration, I completed the task independently using ChatGPT.
At the time (early 2023), few designers in our org were experimenting with AI. This initiative was one of the first applications of AI-supported workflows in our team, and I was later invited to present it during our internal Design Day.
Chapter 3
Survey results & final screens
Once our research validated the three motivation groups, we decided to add all three screens into the onboarding flow.
It was a bit of a controversial move since the general direction at the time was to cut screens, not add more. But we had a hunch (backed by data), and as it turns out, it paid off.
I can’t share the full survey results here, but one key insight stood out:
The motivation clusters naturally formed user profiles. Aspirational goals were often tied to tangible needs and specific skills. For example, someone learning for their career (aspirational) usually wanted to improve writing emails (tangible) and grammar (skill).
This gave us a really strong starting point for personalization in the future projects.
Chapter 6
Results & learnings
The motivation screens led to a 7% increase in revenue and 8% increase in visit-to-sale. Through this we learned that understanding the motivations of the user can help us design experiences that support our revenue goals.
Chapter 7
Personal learnings
This project will always hold a special place in my designer heart as it was one of our first big wins at Babbel. Beyond learning to trust my intuition, I took away two key lessons:
Take risks - Sometimes it’s worth going against the grain, especially when you have evidence to support your bold design decision.
Leverage AI - This was the first time I used ChatGPT in a project, and it quickly became an essential part of my process. I now use it for ideation, learning, feedback, and navigating design challenges.
Up next
How identifying a UX gap helped us improve the lead experience and drive conversion.
If you’re interested in Product Led Growth, this case study is for you!