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As Senior Product Designer at Klaviyo, I was responsible for making sophisticated customer data accessible and actionable for our user base. I specifically worked in the Audience product area translating dense and abstract data into actionable insights.
The Problem
Modeling Revenue Potential at Scale
From Static Metrics to Proactive Recommendations
Recognizing that marketers were spending the most time diagnosing performance issues and exploring solutions, I saw an opportunity to streamline the journey through personalized AI-powered insights. I partnered cross-functionally with customer success, data science, and analytics teams to design a system that helps users interpret data faster, identify actionable opportunities, and make confident decisions sooner.
By integrating machine learning with behavioral and historical context, we transformed static metrics into proactive recommendations, reducing the time from insight to execution. These images illustrate how we reimagined the customer journey into a more personalized data-driven experience, that helps marketers optimize performance in real time.
Closing the Gap Between Insight and Execution
Through user research, I discovered that while marketers valued predictive insights, they often hesitated to act without understanding the “why” behind AI recommendations. To close this gap, I facilitated cross-functional workshops with product, engineering, and data science teams to design a transparent, action-oriented experience.
The resulting flow enabled users to see not just the insight but why it mattered—surfacing the opportunity, supporting data, and pre-generated content using Klaviyo’s existing media and templates. With this, marketers could move from insight to execution in minutes, creating campaigns, emails, and messages that once took hours. This focused, AI-assisted experience built confidence and accelerated the path from discovery to impact.
Impact
We hypothesized that transforming Klaviyo's analytics from static dashboards into proactive, AI-driven insights would increase engagement and unlock new revenue levers. Early results validated this: we drove a 25% increase in Klaviyo-attributed value, a 14% boost in engagement, and a measurable 6 point NPS lift tied to data trust and clarity.
For customers, what once took hours of manual analysis was reduced to minutes through AI-assisted workflows. This shift from reactive analytics to guided, insight-led action reshaped how marketers leveraged data.
For me, the initiative marked a moment of leadership and systems thinking. I collaborated cross-functionally with data science, engineering, and product to model revenue as a connected metric, and led design strategy from prototype through executive alignment. The key learning was that design-led experimentation grounded in business outcomes not only accelerates innovation but builds organizational confidence in scaling AI responsibly.
These outcomes shaped the AI Insights roadmap, creating a scalable framework for how Klaviyo uses AI to turn data into growth.