What UM Has Learned From a Year of Full Colour Media — with Susan Kingston-Brown
Why It Matters
Full Colour Media shows how agencies can harness AI and analytics without sacrificing brand uniqueness, offering a blueprint for winning clients in an increasingly commodified media landscape.
Key Takeaways
- •Full Colour Media counters bland algorithmic media planning.
- •Research with Oxford revealed no universal advertising silver bullet.
- •Agency embeds "stand against bland" question at every workflow step.
- •New client wins demonstrate differentiated strategy across global markets.
- •Integration of AI requires balancing efficiency with creative distinctiveness.
Summary
The Media Litter podcast interview with Susan Kingston‑Brown focuses on M’s Full Colour Media, a global omni‑channel planning proposition launched last year to push back against homogenised, algorithm‑driven buying. The initiative was born from research with Oxford’s Saïd Business School and coincided with Interpublic’s sale to Omnicom, prompting a re‑evaluation of agency identity.
Full Colour Media is built on the insight that no single formula works for all 10,000 brands studied; instead, each brand follows distinct consumer‑journey patterns. M therefore re‑engineered its diagnostics, workflow and media‑plan development around a "stand against bland" mantra, asking teams at every stage to identify how proposals diverge from the algorithmic mean.
Kingston‑Brown cites concrete outcomes: Tourism Ireland’s globally consistent yet locally tailored campaign, Amazon Music India’s market‑entry win, and internal cultural shifts such as new murals and breakout spaces that reinforce the colourful brand ethos. Employees report heightened curiosity and ownership as they seek non‑standard solutions.
The approach signals a broader industry tension between AI‑driven efficiency and the need for creative differentiation. Agencies that embed brand‑centric thinking into data‑heavy processes may retain relevance and win new business, while those that default to pure algorithmic optimization risk becoming indistinguishable.
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