Making a 50-day setup feel like a self-serve one
Making a 50-day setup feel like a self-serve one
Seller onboarding at ManoMano took 50+ days, more than 10× longer than competing platforms, and ran on support tickets and account managers. I redesigned it around the single step generating 34% of all support contacts.
Team : 3 Product managers, 4 Engineers, Ops & Support, Legal, 2 Business owners
Challenge
Seller onboarding took 50+ days to complete. More than 10× longer than competing platforms.
Context
The journey depended heavily on support tickets, account managers and complex regulatory requirements, while the platform wasn’t built for a scalable self-serve experience.
The business impact was direct: slower onboarding meant sellers took longer to start selling, which put sales performance at risk.
Design principles
Conducted qualitative research with sellers and internal stakeholders to understand onboarding friction, support dependencies, and regulatory complexity.
Conducted qualitative research with sellers and internal stakeholders to understand onboarding friction, support dependencies, and regulatory complexity.
Synthesized research findings into key pain points and opportunity areas, aligning user needs with business and operational constraints.
Synthesized research findings into key pain points and opportunity areas, aligning user needs with business and operational constraints.
Defined the target experience by simplifying the onboarding journey, restructuring information, and translating complex requirements into clearer interactions.
Defined the target experience by simplifying the onboarding journey, restructuring information, and translating complex requirements into clearer interactions.
Sellers faced a high-touch onboarding journey involving support tickets, account managers, and complex regulatory requirements and transparency fees at the end of the journey.
I found out that the support team allocates 30% of its time to onboarding sellers manually, and that our users suffer from an average of 2 weeks for a support response.
Understood the existing experience using user journey mapping to identify key bottlenecks and proposed tactical solutions for specific areas.
Moreover, I faced a heavy technical constraint: product import has to move through a catalogue of 25+ million products, and migration takes time no matter how good the interface is.
Partnered closely with product managers, business owners, and the legal team to align on quick wins and mid-term wins.
As a result, I removed some legal requirements, provided more transparency on hidden fees, added more touchpoints and explanation, and used AI support to ease the load on the support team.
Prototype & Hi-fidelity design
In the meantime, I created a high-fidelity mockup to provide a less distracting experience, highlighting system status, showing incentives at the end of the flow, and providing a list of all the info sellers needed so they wouldn’t be surprised by legal requirements.
With the acceleration of generative AI, I reached more productivity in delivery by using AI to help with benchmarking, consistency checks, and handover with engineers — design refinement has never been easier.
In the meantime, I created a high-fidelity mockup to provide a less distracting experience, highlighting system status, showing incentives at the end of the flow, and providing a list of all the info sellers needed so they wouldn’t be surprised by legal requirements.
Impact
Days median Time to Onboard
Days median Time to Onboard
New inbound to join the platform compared to before launch
Impact
Quarterly business volume made from self-serve onboarding
One journey. No more redirects, no more scattered pages. Simple path towards business and always available AI support
One journey. No more redirects, no more scattered pages. Simple path towards business and always available AI support
The new experience : An educational and highly accompanied experience to guide users through all steps and subscription incentives
The new experience : An educational and highly accompanied experience to guide users through all steps and subscription incentives
What I trade away
Full self-serve is the ambition, and about 30% of sellers get through without human help today. Two things stood between V1 and that number, and I could only move one.
Import errors drive 34% of onboarding issues, and the import service cannot yet carry a 25M-product catalogue. I could not fix the pipeline, so I designed for failure: AI-assisted error messages that name what broke and what to change. A mitigation rather than a fix, and it should be retired when the service catches up.
The harder one was pricing. I wanted fees presented plainly. The business owner needed a persuasive framing to protect retention. Both are defensible. We settled on this: the fee and renewal date are always visible and never behind an interaction, and the card leads with persuasive pricing.
I traded how the number is framed, not whether it is there, because a surprised seller is a support ticket and a churn event, which costs more than a less flattering price card.
Pricing moved from a manual policy explained by support to a subscription page stating the fee, its scope and the renewal date. It targets the tickets that arrive on renewal day from sellers who did not expect the charge.
What I learned
Great product design is rarely about finding the perfect solution. It’s about navigating trade-offs.
Cross-functional teams bring different goals, constraints, and perspectives to the table. I learned to make those trade-offs explicit, understand the risks behind each direction, and align the team around the best outcome for users and the business.
