Case Study: Flixmedia

Strategic Re-architecture for AI

How I led a 1-year initiative to transform a monolithic "black box" into a modular, data-first platform, unlocking the insights needed for personalization and AI.

Context

As a marketing tech company syndicating brand content to over 1,600 retailers globally, we faced a critical challenge: personalization was no longer a feature, but the standard. To remain competitive and drive performance, we had to evolve beyond a one-size-fits-all model. The risk of falling behind was immense.

The Problem: A Multi-Million Dollar Blind Spot

Our platform was built on a monolithic architecture, treating content as a single, indivisible "black box." This meant we couldn't personalize, A/B test, or attribute performance to specific content elements (e.g., a video vs. a feature grid).

We were data-blind, and our marketing manager users couldn't answer the critical questions needed to justify spend or optimize strategy: **"Which part of my content actually drove the sale?"**

[MONOLITHIC CONTENT BLOCK]

OUTPUT: Impressions, Clicks

No Granular Insight

We had no idea what was working.

The Solution: From "Black Box" to Modular

The only way forward was a complete transformation. I led a massive, 1-year strategic initiative to re-architect our entire content platform to be modular and data-first. This was a high-spend, high-reward project requiring buy-in from the C-suite and our parent company. After months of planning, our technical teams began an 8-month build to modularize everything.

BEFORE: The "Black Box"

[MONOLITHIC CONTENT BLOCK]

Impressions

Clicks

AFTER: The Modular World

[Hero Video]

[Feature Grid]

[USP Bar]

... and ~37 other modules

The Results & Impact

With the new platform live, we could finally see inside the black box. I personally led the analysis, collaborating with our shopping insights team. We quickly discovered our old "impression" metric was flawed and had to redefine our success metrics to tell a truer story of engagement. Our findings were transformative:

Proved Behavior Varies

Proved that user behavior differed significantly across regions. High-interactivity content that drove sales in one market had no effect in another.

Proved Context is Key

Performance changed based on retailer context (e.g., 'premium' vs. 'value' sites).

Proved Position Matters

Found a high correlation between where a module was positioned on the page and its impact on Add-to-Cart rates.

Found the "Content Sweet Spot"

Early research found the optimal amount of content was ~4 modules, after which the increase in ATC rates plateaued on average.

The Roadmap: Unlocking AI

This foundational project was never just about analytics; it was about building the engine for our future. The next steps on the roadmap I developed were to use this powerful new data stream to:

Segment Shoppers: Group users into behavioral cohorts based on our new, granular data.
Enable Personalization: Begin dynamically re-ordering content modules to push the most effective sections to the top for each shopper segment.
Deploy AI: Ultimately, use AI to fully automate this process, dynamically changing the content order and even copy to create a truly one-to-one experience.