Products Are More Important Than Customers: The Particular Audience Story | #154

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In this episode of Add To Cart, we are joined by James Taylor, the Founder and CEO of Particular Audience – an Australian technology platform helping eCommerce retailers create a […]

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Nathan Bush is a director at eCommerce talent agency, eSuite. He has led eCommerce for businesses with revenue $100m+ and has been recognised as one of Australia’s Top 50 People in eCommerce four years in a row. You can contact Nathan on LinkedIn, Twitter or via email.


Ten years working in the digital experience layer of eCommerce, James setup Particular Audience in 2017 to apply machine learning in place of rules based systems that were dominant at the time. Prior to Particular Audience, James had a career in investment banking and co-founded a betting exchange focused on user generated content. He moved to Australia in 2014 to launch Google Ventures company ‘Yieldify’ in the APAC region.

In this episode of Add To Cart, we are joined by James Taylor, the Founder and CEO of Particular Audience – an Australian technology platform helping eCommerce retailers create a more intuitive and personalized shopping experience through merchandise, search and advertising. And this is all done dynamically and without using any personally identifiable information or third party cookies. James’ theory is that product data and intention is more important than customer groupings and it is a fascinating one to explore. Particular Audience was founded here in Sydney, boasts clients including The Good Guys, Singapore Airlines and Calvin Klein across Australia, US, UK and Canada. Since 2019 they’ve raised $13.5m in investment, are set to continue this growth. Today, James and I follow this journey, dive into plans for their B2C product Similar and look at what the future of data privacy means for retailers. 

It’s kind of like the Netflixation of an eCommerce site.

James Taylor

Questions answered in this episode include
  • What role does AI play for Particular Audience?
  • What does the Similar plug in do?
  • What’s the future of eCommerce personalisation?

The netflixation of eCommerce

“I just started looking into machine learning software, which I didn’t know much about five, six years ago, and discovered basically how Amazon, Netflix, Spotify all go about their user experience and sort of thought, okay, well this is a way more sensible way to personalize an e-Commerce website. 

I sort of learned how collaborative filtering works, which is that wisdom of the crowd, that behavioral data to infer relationships between items and put my money into how we could build…it’s a little bit of infrastructure, right? So how can you handle all that data load and low latency request times, et cetera. 

So we built this platform and then it was beyond that, we realized that there was this issue where if an item didn’t have any behavioral data, it wouldn’t be benefited. So that’s where we started building the computer vision and the natural language processing to infer vectors or distance relationships between items.”


“The specific area that we care about is known by a few different names. Vectors, is one name that’s a pretty popular way to describe it at the moment, which is calculating the distance between things, and to calculate distance between things is to calculate the similarity or the complementarity between things and you can have different things that infer that. 

Computer vision for example, is a vector based technology that identifies the similarity between two images. This is a form of artificial intelligence, computer vision, the natural language processing, machine learning and collaborative filtering, again, uses machine learning to draw predictions from data cells.”


 “It’s an online community of shoppers that contribute a hundred percent anonymously… product price, product availability, data that we can then use to give us visibility outside of an individual e-Commerce website. So we can understand lots of interesting things, like 4% of people that don’t buy on your site, go onto this competitor site to buy this item that you don’t stock.

So we have real time pricing data for every single retailer on the internet. We do have B2B applications of Similar as well. So this is probably one of our most exciting products at the moment, which sort of falls within dynamic pricing, where if a customer is abandoning an item that this is set up on, we let the customer know there and then on the website, the price that all of these competitors are actually selling that item for and provide the ability to dynamically incentivize the customer to get a lower price for it on the website they’re on. 

And the impact is probably the most extreme uplift that I’ve seen in any conversion optimization take over the past 10 years. Eligible products are doubling conversion rates at a cost of less than 2% margin. So it’s a really big impact.”

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