Worklife Coaching: Turning a low-adoption product into a $2M revenue driver
Context
RiseSmart is a career transition platform acquired by Randstad, traditionally focused on outplacement services for employees affected by layoffs. The company launched a new product line, Worklife Coaching, to provide career coaching for current employees.
My Role
Business Model
Worklife Coaching operated on a pay-as-you-go model. Companies did not pay for access, only per coaching session completed by their employees. This created a direct link between user behavior and business outcomes:
Conversion to a first session wasn’t just a UX metric, it was critical to how we made money, especially considering the potential for repeat calls from an individual user once their coaching relationship was established.
The Problem
After initial launch, employees were signing up but most never booked a coaching session. Only 25% of registered users converted into their first call. This surprised us because employees already were showing intent by registering. Where were we losing people?
The business set a goal of 70% conversion from registration to first call, as that’s what we needed in our financial modeling to maximize the contracts we had in place with customers.
Through a combination of product redesign, research, and messaging improvements, we increased conversion from 25% → 60%+
These improvements helped drive over $2M in annual revenue for the Worklife Coaching product line.

Phase 0 – Proving Value Without Product
The service delivered value before the product existed. The earliest version of Worklife Coaching had no real onboarding experience:
Despite this, users still engaged. The core value of coaching was strong, even with a poor product experience. This validated the opportunity and justified investing in scaling the product.

We optimized for perceived value, but accidentally created decision paralysis. To support growth, we introduced self-service onboarding. A key feature allowed users to choose their coach. We added it because:
However:
Despite designing the feature to only show up to three coaches at a time, we created a difficult and sometimes overwhelming choice for users.
Inflection Point: Remove Decision Friction
I led an investigation into whether coach selection was actually beneficial. We studied users who had been automatically assigned a coach in the earliest version of the product to try to understand whether not having coach selection materially affected their experience.
Findings
Decision
We redesigned onboarding to:
Result
Conversion improved from 25% → ~50%.
Phase 1 – Scaling Introduced Friction

Phase 2 — Understanding the Broader System
Even after improving onboarding, conversion plateaued again. To understand the remaining gap, I led research with one of our largest customers, Cisco:
Inflection Point #2: Conversion happens before the product
Through interviews and analysis, we uncovered a critical insight: Users already had intent before reaching the product.
The product had limited ability to change that intent.
Major barriers existed outside the product
1. Weak brand recognition
2. Misaligned expectations
3. Poor follow-up experience
Decision
We expanded beyond product design and worked with stakeholders to improve:
Result
Conversion improved to ~60% at Cisco without product changes.
Phase 3 — Fixing Messaging at the Product Level
With system-level issues better understood, we returned to the product experience. I led a rapid research study using look-alike enterprise users to evaluate the landing page.
Findings
Even when users understood the product, key gaps remained:
Inflection Point #3: Designing for trust, clarity, and relevance
We redesigned the conversion experience around these principles:
Outcome Across all phases
This work fundamentally changed how we approached product design from feature driven to systems / behavior driven.

Looking forward
Based on these insights, we began exploring personalized onboarding experiences:
The Hypothesis
If users see themselves in the experience, they are more likely to convert.


Key Takeaways




