Help new founders onboard

Pulley was initially designed and marketed for experienced founders: folks who know why a clean cap table matters and come to Pulley because they want a better way to "calculate dilution." Pulley quickly grew in revenue and found success with this persona.

But our next most ideal customer profile is the newbie founder: folks who never started a company before and want guidance on what they need to do regarding equity.

We launched a free plan to attract these new entrepreneurs.

We didn't have a way to know which lead and signup corresponds to which persona, causing us to serve a UX that is optimized for the experienced founder, alienating newer founders.

Even if we did already segment signups, we didn't have an onboarding UX designed for newer founders.

1. Audited manual processes - I wanted to know what kind of communications was working well vs. not. I worked with Customer Success (CS) and Sales to understand this and shortcut learnings.

2. Iterated on UX with my team - We took advantage of regular CS calls to test out new UX content. Earlier iterations were over-designed. By measuring speed and engagement from sign up → onboarding, we validated that less is more for our users.

My customer success teammate, Fly, spoke to 26+ founders in 2 weeks! These founders signed up for our free plan.

She was a better facilitator for these discovery calls since she already had a warm relationship with many of them. I reviewed and analyzed the calls to pull out patterns and insights and to validate our assumptions.

Luckily, we had a Data teammate who put together a fantastic dashboard of metrics we can use!

But because this dashboard was new, I worked with my Data teammate to fix some bugs within the data by cross-referencing other data sources.

Competitive analysis

I analyzed onboarding experiences from Angellist Stack, Carta, and WithCompound.

We planned to ask 2 simple questions that would segment founders.

For new founders, we designed a guided experience that educates them before throwing them in the deep-end of cap table management.

Running too many experiments at once on production - Pulley's avoidance of A/B testing meant we had to be strategic about what we tested.

Data pipeline being built in parallel - This meant more reliance on qualitative insights while quantitative infrastructure was being set up.