Scaling through Self-Service
How empowering users with choice reduced support burden by 18%
CoachHub is a leading B2B2C platform connecting employees with professional coaches globally, enabling personalized development. The coach selection process represents a critical juncture in the end user journey, as coaching effectiveness heavily depends on strong coach-coachee relationships.
When users gain platform access through their employer, they complete a questionnaire to determine their coaching preferences, after which an algorithm generates up to six potential matches. However, this rigid process was creating friction for users and operational inefficiency for the business, as dissatisfied coachees needed to contact support to see alternative coaches—a process that delayed their onboarding and placed significant burden on the support team.
Client
Industry
SaaS, Human Resources, Personal Development
Headquarters
Berlin, Germany
Founded
2018
Role
Senior Product Designer
Team
Cross-functional team including product manager, developers, QA, and engineering manager
Challenge
My team identified a critical issue affecting both user satisfaction and business operations: 25% of users were abandoning the onboarding flow at the coach selection step, while requests for custom coach matches consumed 18% of the support team's capacity—requiring 3-6 hours per request.
My research revealed that users expected the ability to refine their coach match results independently, but instead were forced to contact support if they didn't like the coach matches chosen by the algorithm. This disconnect frustrated users at a pivotal moment in their journey while creating an unsustainable operational burden that threatened CoachHub's ability to scale efficiently. The business needed a solution that would improve the coachee experience while simultaneously reducing the support team's workload.
Results
My redesigned coach matching experience transformed a support-dependent process into an intuitive self-service solution. This change eliminated the previous 18% support team burden while maintaining high-quality matches for users.
Implementation data showed impressive adoption, with 50% of users proactively using the new capability to refine their preferences without contacting support.
Beyond immediate workload reduction, my solution incorporated strategic data collection points at key decision moments, providing the business with valuable insights about user preferences and match satisfaction. These insights continue driving algorithm improvements and supporting efficient scaling of the platform, directly contributing to enhanced user satisfaction and reduced operational costs.
50%
Adoption rate
48%
Fewer users abandoning onboarding at the coach matching step
70%
Decrease in manual matches
“One of the coachees for [big client name] was stuck in the matching process for quite some time. When our HR stakeholder spoke with him to check why, he said that he wasn’t happy with his matches, and didn’t think the coaches looked professional. When he couldn’t change the original inputs, he gave up.”
– Customer success team lead, describing the scope of our problem
Process
Research & Analysis: My discovery phase began with in-depth interviews with the Customer Support team to understand operational challenges and identify common edge cases. I then partnered with our Data & Insights team to analyze user behavior patterns and develop analytics dashboards that would guide our decision-making.
Quantitative analysis revealed critical friction points: 25% of users abandoned the matching process entirely, while rematch requests represented over 10% of all support tickets. Collaborating with our UX researcher, we conducted a comprehensive review of existing user feedback, uncovering a key insight: users approached the matching survey as an exploratory step rather than a permanent commitment, creating frustration when they couldn't later revise their preferences.
Building Cross-functional User Flows: With a clear problem definition, I facilitated a cross-functional ideation workshop with my PM and tech lead, as well as stakeholders from Customer Support, Data & Insights, and Coach Relations teams. We mapped potential solutions against technical constraints and business objectives, prioritizing approaches that would reduce support workload while enhancing user autonomy.
Prototyping & Usability Testing: I developed multiple design iterations in Figma, each addressing different user scenarios and edge cases. These were refined into an interactive prototype that we tested with six users. Their feedback—particularly around expectations about survey resubmission—provided valuable insights that informed copywriting and UX refinements to better set user expectations.
Development & Launch: The final solution elegantly balanced user needs with business requirements, creating a self-service flow that reduced operational burden while strategically incorporating data collection points to drive ongoing improvements to the matching algorithm.
Conclusion
This project exemplifies how strategic UX design can deliver measurable business impact. By transforming coach matching from a support-dependent process to a self-service experience, we eliminated a significant operational bottleneck while empowering users with greater autonomy.
The 50% adoption rate validated our approach, while the embedded data collection has created an ongoing feedback loop that continues to optimize the matching algorithm. Beyond operational efficiency gains, this solution directly supports CoachHub's core value proposition of creating effective coaching relationships – ultimately enhancing the platform's scalability and supporting client retention through improved user engagement.