The Missing Piece: Particular Audience
‘The Missing Piece’ is a series where we ask the retail/commerce media tech platforms how they are addressing the challenges facing the ecosystem.
This time, we speak to James Taylor, Founder and CEO of retail technology company Particular Audience.
What excites you about the retail media landscape as a whole?
What excites me most is that retail media is finally entering its unbundling and optimisation phase. We saw this happen in ecommerce around 2020 – monolithic, all-in-one platforms gave way to specialised, high-performance components. Retail media is only now making that jump.
We’re moving from long-term exclusivity agreements with single vendors to a world where retailers can choose the best tools for each part of the stack. That shift unlocks innovation.
Technically, we’re evolving from rules-based configuration and audience segments to true machine learning – understanding intent, meaning, product attributes, and relevance in real-time. It’s very similar to the conversion-rate-optimisation boom of the 2010s: experimentation, iteration, and evidence-based improvement.
Retail media is nowhere near a solved problem, and that’s what makes the moment so exciting.
What are the biggest challenges facing the ecosystem?
The biggest challenge is the industry’s lack of education around search and recommendation systems. Everyone uses them – very few people understand why they behave the way they do.
Because many RMNs have been tied into single ad-tech platforms under one-to-three-year exclusive agreements, they’ve never had a basis for comparison. Innovation has been artificially restricted.
People often talk about fragmentation being a problem, but I see it differently. Fragmentation creates space for specialisation, and specialisation is exactly what retail media needs. This ecosystem isn’t mature enough for consolidation to be the answer.
Another major challenge is buyer experience. Brands want consistency, transparency, and outcomes they can confidently measure.
Finally, channels continue to proliferate from DTC to AI. This brings challenges and opportunities.
How does your business aim to solve those challenges?
We solve them through vertical integration and interoperability.
Search, recommendations, and sponsored placements shouldn’t be separate problems – they influence each other constantly. We treat them as one continuously learning system.
Our transformer-based search and multi-modal recommender models understand meaning, intent, imagery, copy, and metadata, so the same foundations that improve organic discovery also improve retail media performance.
Our reporting tool is now free to use and open-source via Github. A major win for transparency and measurement in retail media.
The result is better discovery for shoppers, higher-performing ad inventory for retailers, and a simpler, more transparent buying experience for brands.
It may also be worth mentioning that through our sub-brand, Retail-MCP, we’re helping retailers participate in the rise of agentic commerce. Something we view as an important but unpredictable new channel.
What has been your biggest success so far?
Solving the search problem.
43%+ of website users use onsite search, 94% of consumers say they receive irrelevant results. This costs retailers over $300 billion a year in the US alone.
For years, retail search has been dominated by keyword matching and rules-based ranking. We replaced that with transformer-based (the same ‘T’ in ChatGPT) semantic search that actually understands the shopper’s intent and the product’s meaning. It’s called ATS, or “Adaptive Transformer Search.”
Retailers using our approach have seen a step-change in product discovery and retail media performance – be it 20%+ lifts in engagement or the massive time savings AI offers from manual processes – we’ve validated and refined it across enterprise partners in the US, UK, EU, and Australia. It’s become the foundation for the rest of the retail media stack with downstream impacts, such as the ‘Automated Ad’Set Builder’ we announced last month, and is woven into our ML-powered hyper-personalisation capabilities for the customer experience layer.
What do you think the future holds for both your business and the wider world of retail media?
Technically, we’re focused on the underlying intelligence layer – understanding shoppers and products, predicting what people want next, and optimising and automating both organic discovery and paid placements across the full retail media surface.
Thematically, I think we’re looking at the end of the monolithic retail media platform.
RMNs have been stuck in exclusive agreements with slow and sluggish legacy platforms for too long, it’s not how the broader ecom and digital world operates. It stifles innovation. We’re changing that and the market is changing with it.
The future is modular, transparent, and machine-learning driven. Retailers will assemble the best components for search, recommendations, ad serving, measurement, and creative decisioning – rather than relying on one slow-moving provider to do everything.
For Particular Audience, the future is about powering that flexibility.
Also Published in: Retail Media Age
