Macy's

Tilia Wong is Vice President of Strategy and Analytics for Macy’s Digital, leading operations and analytics to drive data-informed growth and enhance customer experience. Her career began in construction after earning a civil engineering degree, where she developed strong leadership skills. She later moved into consulting, focusing on analytics for the semiconductor industry in areas such as R&D, manufacturing and product strategy. Across her journey, she has championed the use of data to guide efficiency, innovation and strategic decisionmaking in complex business environments.

This feature explores Wong’s approach to building analytic and operational systems that deliver measurable impact and shares practical insights on how strategic thinking and cross-functional collaboration drive lasting business success.

Building Systems of Insight across Industries

In my current role, I ensure that Macy’s Digital teams have the analytic and operational frameworks necessary to optimize performance and customer experience, focusing on the core drivers of digital success: traffic, conversion and average order value. Transitioning across industries, from construction to consulting to e-commerce, I have leveraged my experience to establish clear baselines for operational efficiency and data-driven strategy. In every role, my priority has been strengthening analytics capabilities to ensure all decisions are driven by actionable insights and measurable outcomes.

Over the past decade, the evolution of data and technology has been remarkable. Early discussions focused on machine learning, followed by interest in blockchain and more advanced analytical models, before the rise of artificial intelligence (AI). Each phase expanded what could be achieved with structured data, though one truth remains constant. The quality of outcomes depends on disciplined data structures and human interpretation that provides context. Without that foundation, even the most advanced systems rest on fragile ground.

AI holds tremendous promise to enhance efficiency and accelerate decision-making. Innovation in the field moves rapidly, with new applications and emerging companies introducing creative solutions every few months. Retail provides an especially rich environment for experimentation. Its deep data ecosystem and quick customer feedback cycles create strong conditions to refine experiences and optimize performance with agility.

For organizations, the key challenge lies in deciding where automation ends and human oversight continues. Machines process vast volumes of information, while judgment and context remain essential to guide their direction. Building confidence in AI outputs requires thoughtful governance, clear standards and a balanced approach that keeps insight and integrity at the center of progress.

Personalization that Elevates the Shopping Experience

Effective digital growth pivots away from short-term Conversion Rate gains, centering instead on maximizing Customer Lifetime Value, with analytics informing every design choice to nurture that goal.

The team has focused on enhancing personalization to create a more curated and elevated shopping experience for Macy’s customers. The vision is to deliver a curated and elevated shopping experience that embodies value through inspiration, discovery and ease rather than price alone. This vision reinforces Macy’s dedication to meaningful customer engagement across every interaction.

A recent redesign of the website reflects this evolution, with a refreshed look and feel that better aligns with the brand’s vision. Every detail of the digital experience is guided by data-driven insight. Decisions around homepage design, navigation, visuals and personalized recommendations are thoughtfully shaped to make each interaction intuitive and engaging. Across the website and app, personalization informs product suggestions, promotions and curated experiences that make customers feel recognized and valued.

These initiatives are showing measurable success. Through the use of data and AI, Macy’s has seen stronger engagement, higher conversion rates and growing enthusiasm for premium merchandise. Critically, while many teams focus heavily on optimizing short-term Conversion Rate, our strategic lens prioritizes long-term Customer Lifetime Value. Cohort analysis and propensity modeling informs how we nurture high-value customer segments. The ability to excite customers about elevated products demonstrates the impact of thoughtful innovation grounded in data and human understanding.

Collaboration between Data and Business

Collaboration remains at the core of analytics. Building effective tools depends on human interpretation in understanding what data to collect, what it represents and how machines should apply it. This process requires close partnership among business teams where collective judgment shapes the structure of information and ensures insights that truly matter.

In a curated retail setting, human perspective continues to guide forecasting and creativity. Machines can recognize patterns but still find it difficult to anticipate future trends or preferences several seasons ahead. Envisioning what will capture the interest of shopper’s calls for imagination and intuition that strengthen the analytical process.

That same collaboration extends to communication with customers. Analytics can refine timing and delivery, while the core message and story remain driven by business insight. When data and human creativity work together, they build a thoughtful approach that deepens customer connection and sustains brand strength.

Leading with Agility and Foresight

Growth in analytics begins with agility and foresight. In a rapidly changing environment, staying adaptable is essential. What appears to be the right solution or partner may evolve within a few years, especially in large organizations where switching costs are high. Building systems that allow flexibility helps teams respond to shifts without disruption and ensures long-term stability.

Strong data architecture forms the foundation of that flexibility. It may not be the most exciting aspect of the work, but it is essential. Taking the time to organize, clean and strengthen the underlying data structures ensures everything built on top remains stable and reliable. It requires patience, steady investment and a long-term mindset that prioritizes sustainable systems over quick gains.

Talent development completes this cycle of growth. The nature of analytical work continues to evolve, requiring teams to stay familiar with new tools and approaches. Encouraging experimentation and continuous skill upgrades ensures readiness for future demands. As automation takes on routine tasks, leaders must consciously redirect their teams toward higher-value opportunities. This includes moving the team away from routine CVR optimization toward complex LTV modeling and design partnerships. This approach not only sustains productivity but also keeps people engaged in meaningful, forward-looking work.

Together, flexibility, strong data foundations and adaptive talent form the core of enduring excellence in analytics. Each reinforces the other, creating an ecosystem prepared for transformation and sustained success.