GoDaddy · The Hub by GoDaddy Pro · 2024 · 3 months
How providing value upfront increased new client dashboards by 134%
I led the design of an AI document generator that helps Pros expedite early-stage client tasks. We narrowed a large vision to a focused MVP, used a multi-stage feedback strategy to build a business case for the full concept, and launched a tool that drove a 134% lift in new client dashboards.
Context
- Platform:Web. The Hub by GoDaddy Pro
- Role:Lead Designer (Growth). Team of 1 designer, 1 PM, 1 engineer.
- Timeline:2024, about 3 months
- How we defined success:More Pros creating client dashboards — the entry point to the wider client-management surface — and enough learning signal to earn buy-in for the full vision.
- Outcome:134% more new client dashboards, an 800% lift after a sample-client fast follow, and stakeholder buy-in to expand from MVP to the full concept.
- What I took away:Discovery on the tool was low because it sat inside a client dashboard we hadn't restructured yet. Front-loading a little bit of structural work would have let us scale faster after launch.
What we shipped
A tool that generates market research for the client project as part of “scoping the work.” A Pro enters a brief description of their client and our AI system returns research on the client’s industry that the Pro can use in the early stages of the engagement.
Concept work
For this project I explored what the client space could evolve into: a full client-project lifecycle management experience, with multiple tools supporting our users at every stage of their work.
The original dashboard had many features but no clear hierarchy, which made it hard to introduce new tools without adding to the confusion in the space.
I explored three directions. Each aimed to reduce cognitive load, centralize the most important existing features alongside the new AI tools, and structure the dashboard around the actual client project lifecycle.
Feedback strategy
As a small growth team that needed to move quickly we narrowed in on a few key tools and then ran a lot of user testing to help build a business case for the broader vision. The strategy had three main types of tests:
- Quick task-based prototype tests for signal.
- Live demo-account tests for the quality of the AI outputs.
- A feedback module shipped with the first launch for continued learning.
Momentum: by running signal tests early and often, then sharing back with stakeholders, we built momentum for the bigger vision and earned resources to expand.
Accelerated learning: testing with real AI outputs — not mocked data — caught quality issues we never would have found in a prototype, and gave stakeholders something concrete to react to before launch.
Outcomes
New client dashboards
+134%
After launch of the MVP AI tool.
After sample-client fast follow
+800%
Removing the “need a client first” barrier.
Stakeholder buy-in
Full vision
Resources to expand the MVP into the full concept.
Fast follow:a barrier to using the tool was needing a client dashboard first. As a fast follow, we added the ability to spin up a “sample client” so Pros without existing clients could try the tool immediately. That single change drove the 800% lift in new client instances, and became a key learning for future work on activation and entry points.
Reflection
Discovery on the tool was low, and it would keep feeling awkwardly placed until we could change more of the client dashboard around it. We knew this might happen in the early stages of planning but took the chance anyway.
In retrospect, a little bit of structural work up front would have let us scale faster post-launch. I’d push earlier to bundle the smallest set of dashboard changes with the MVP so the new tool had a place designed for it — not a spot it had to fit into.
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