Co-Founder & CEO
Maya Reid
Leads Fawna's product vision, customer insight strategy, and company direction around conversation-led product decisions.
Maya Reid is the co-founder and CEO of Fawna. She leads the company’s vision for helping SaaS teams turn customer conversations into clearer product decisions, stronger cross-functional alignment, and more confident roadmap priorities.
Her work sits at the intersection of product strategy, customer research, company building, and category design. Maya is especially focused on the systems companies need when the volume of customer feedback grows faster than the team’s ability to interpret it.
Background
Before starting Fawna, Maya worked with high-growth software teams on product strategy, research, and customer insight. Across those roles, she saw the same pattern repeatedly: companies had thousands of support tickets, sales calls, reviews, chat transcripts, and customer success notes, yet important product decisions were still shaped by scattered anecdotes and manually assembled summaries.
That experience shaped the original idea behind Fawna: customer feedback should not live as a disconnected archive. It should become a dependable operating layer that helps teams understand recurring problems, inspect the evidence, and decide what to do next.
Role at Fawna
Maya leads company strategy, product direction, positioning, and customer development. Her responsibilities include:
Defining the long-term vision for the Fawna product and category.
Working directly with customers to understand feedback workflows, planning habits, and organizational friction.
Translating recurring customer problems into product opportunities and company priorities.
Shaping Fawna’s positioning, messaging, and go-to-market narrative.
Connecting product, engineering, design, customer success, and commercial decisions around one strategy.
Building the operating principles that guide how Fawna grows.
How Maya approaches product strategy
Maya believes strategy should combine three sources of evidence:
A clear view of the market and the customer segment the company intends to serve.
A continuous understanding of recurring customer problems and changing expectations.
The discipline to choose which problems the company will solve exceptionally well - and which it will not pursue.
This approach prevents customer feedback from becoming a feature-voting system while ensuring important decisions remain connected to real customer context.
“The goal is not to collect more feedback. The goal is to create enough clarity that teams can make a better decision with the feedback they already have.”
What she focuses on with customers
Maya spends time with product leaders, product managers, support teams, customer success, and GTM leaders to understand where feedback workflows break down.
Common areas of focus include:
How customer evidence is prepared for roadmap discussions.
Where signal becomes lost between customer-facing and product teams.
Which themes are difficult to validate across multiple channels.
How teams distinguish recurring problems from high-profile anecdotes.
How decisions are communicated back to the people who contributed the evidence.
Leadership principles
Maya’s leadership style emphasizes clarity, direct communication, and thoughtful ownership. She encourages teams to work from shared context rather than excessive process.
Start with the customer problem, not the proposed feature.
Make important assumptions visible.
Preserve source evidence behind summaries and decisions.
Prefer a small number of meaningful priorities over a crowded roadmap.
Communicate decisions clearly, including why something is not being pursued.
Build trust through consistency rather than presentation.
Perspective on feedback intelligence
Maya sees feedback intelligence as a company capability rather than a product-team repository. The strongest systems help every function contribute signal while keeping decision ownership clear.
What Maya is building toward
Her long-term goal is to make customer evidence as accessible and dependable as product analytics. Teams should be able to understand what customers are struggling with, which themes are changing, who is affected, and what decisions were made - without beginning another manual research cycle.
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