Sinch
Led the unification of fragmented customer signals into a scalable, AI-assisted data platform, improving targeting accuracy and driving measurable ROI for enterprise marketing teams.
The Problem
Customer data was fragmented across multiple internal systems, leading to inconsistent targeting, limited personalization, and unclear ROI. Marketing teams struggled to confidently allocate budget or measure campaign effectiveness at enterprise scale.
This was fundamentally a decision-making and trust problem, not just a data integration issue.
My Role & Scope
Owned product discovery and definition for a unified customer data platform. Worked cross-functionally with engineering, data, and business stakeholders to align technical feasibility with enterprise-grade reliability and measurable business impact.
Operated within real enterprise constraints including legacy systems, multiple stakeholders, and high reliability expectations.
Key Product Decisions
Prioritized high-impact signals over full data completeness: Focused on signals that directly influenced targeting and ROI instead of attempting exhaustive data unification upfront.
Chose incremental rollout over large migrations: Delivered value in phases to reduce operational risk, validate assumptions, and build internal trust.
Balanced model sophistication with explainability: Ensured outputs and recommendations were interpretable by business teams, enabling adoption and confident decision-making.
Execution Highlights
Defined customer data models and ingestion strategies in partnership with platform and data teams. Enabled downstream targeting, attribution, and measurement use cases that directly influenced campaign performance. Supported enterprise adoption through clear success metrics and stakeholder alignment.
Outcomes
- +23% ROI improvement across enterprise campaigns
- Improved targeting accuracy and confidence in budget allocation
- Scalable foundation established for future personalization and AI-driven use cases
Key Learnings
- Enterprise data products succeed only when trust and adoption are explicitly designed
- Clear problem framing unlocks more value than broad feature coverage
- Incremental delivery is essential for progress in complex enterprise environments