1. How is your organization exploring the integration of AI-driven commerce within content to create new monetization streams and deepen consumer engagement?

At Shopsense, we’re focused on transforming content into commerce in a way that feels seamless, scalable and deeply tied to consumer passion. That’s because fandom drives purchases; whether it’s tied to a talk show, drama or live sports, viewers are inspired by what they watch on-screen. We built our AI engine to ingest video, image and text-based content to identify contextually relevant products and automatically generate curated storefronts around that very content. In fact, we’ve already ingested over 1 billion SKUs from more than 1,250 retail partners, so our system can quickly surface product matches—including exact matches or items that are stylistically aligned—without requiring a ton of labor and manual effort. This capability is what allows us to unlock monetization opportunities wherever content lives, and gives publishers, networks and creators tools to activate their IP in new and meaningful ways.

2. How do you plan to maintain editorial integrity while embedding commerce solutions within native content experiences?

When it comes to content, we’re not the creators—we’re the enablers. Here’s one way to think about it: our technology uses existing content as a creative input and pairs it with shoppable experiences that are authentic and aligned. But of course it’s critical that the content partners maintain full control of the experience. We work closely with each partner to respect their brand, editorial and audience guidelines, which could mean weeding out certain retailers, filtering by price point, or blocking entire categories. For example, a publisher may want to exclude big-ticket luxury items from a mass-market piece, and so our models adapt accordingly. Because we’re not authoring the editorial, but rather enhancing it with intelligently surfaced commerce, we’re able to preserve integrity while still driving value.

3. What role does personalization and product relevance play in improving conversion rates and retaining audience attention in a saturated content market?

Our whole ethos is that product relevance is everything. When we’re able to surface the exact lipstick worn by a talk show host or the really cool blazer featured in a scripted series, we see conversion rates jump—as much as 4 times higher compared to more generic thematically aligned recommendations. Audiences are emotionally invested in the content they consume. If someone connects with a character or host, they want that exact same item because it’s a tangible extension of their emotional connection to the content. That’s where our AI-powered personalization engine really shines. We’re able to tap into fandom and emotional connection at scale, and bridge the gap between the screen you’re watching and the shopping that’s inspired by that screen. In fact, nearly 63% of consumers say they discover new brands or products through TV content, which shows just how powerful entertainment has become as a driver of commerce.

4. How are you rethinking your monetization model to capitalize on the convergence of storytelling and shopping, particularly among Gen Z and Millennial audiences?

This is exactly why we prioritize the second screen experience. Gen Z and Millennials are fully accustomed to consuming content with a phone in hand—it’s their default behavior. We’ve seen this play out: if a show is shoppable but the call-to-action isn’t clear or well timed, even the most eager viewers might miss it. But when we lean into that phone-in-hand behavior—surfacing real-time shoppable moments on mobile, aligning products with the content they’re already immersed in—engagement rates spike when we meet them in the moment on the device they’re already using. For younger audiences, content-integrated commerce isn’t disruptive when it’s done right; it’s an experience they expect these days. The key is to meet them in the moment without asking them to shift context.

5. As commerce integration expands across editorial, video, and social content, how are you preparing your organization to scale AI-driven commerce across multiple formats and channels?

Even though we launched last year with broadcasters in our sights, we built Shopsense from day one to be format-agnostic. Whether it’s text, video, stills or live content, our models can ingest and interpret it—identifying relevant products, creating storefronts and tagging shoppable moments across channels. Because our product library is already robust and our AI is contextually trained, we can easily plug into new formats with very little friction. We’ve already proven we can do this across digital articles, linear TV, CTV and social posts. So as content continues to fragment across platforms, which it inevitably will, we’re positioned to scale along with it, and to ensure commerce is embedded natively and intelligently wherever audiences choose to engage.

6. How is your company managing the balance between leveraging contextual data for commerce personalization and maintaining user trust and data privacy?

This is such a great question. We focus on contextual signals, not behavioral ones. Our approach to personalization is rooted in the content itself, rather than tracking individual users or hoovering up personal data. What that means is that we can deliver highly relevant product recommendations while keeping user trust intact. In other words, our technology is privacy-first by design, offering our partners a compliant way to unlock commerce-driven value without the thorny complexity of data collection. And for consumers, the experience feels seamless and secure and something they don’t have to second-guess. We believe the smartest personalization simply requires intelligent alignment between content and commerce.

Get in touch with our MarTech Experts.

1. How is your organization leveraging AI to streamline online shopping experiences and reduce friction in the customer journey? 

Think of us as removing two different bottlenecks—one customer‑facing, one technical.

1. On‑site experience (the customer bottleneck):

• • Our Adaptive Transformer Search understands the intent behind a query, even if it’s vague, for example “red shoes for muddy trails” returns the exact SKU instead of a dead‑end results page.
  • A multimodal recommendation engine curates a storefront that’s unique to each shopper, surfacing the product you’re statistically most likely to buy next, boosting average basket value 30‑plus percent for some clients. 
  • Finally, a real‑time multi-objective bidding agent decides which sponsored product or organic product should fill each slot, so retailers monetise and grow margin without sacrificing relevance and conversion. The result is ~20 percent higher conversion rates and ~180 percent higher click‑through on sponsored slots vs. legacy keyword targeting approaches.

2. Retail‑MCP (the technical bottleneck):

• • Today, most AI assistants simulate clicks or scrape HTML. MCP lets any agent call the retailer’s stock, price, loyalty, incentives and media endpoints directly, securely and in milliseconds. 
  • OAuth‑style scope gives retailers control over which data an agent can read or write, solving 'bot blocking' and 'AI discoverability' in one move.
  • Because the protocol is open, the same purchase flow works for ChatGPT, an in‑car voice assistant, or a smart‑TV overlay. No new integrations each time a channel emerges or gets traction.

Put together, a shopper can say: “I need men’s trail runners under £120, size 11, by Friday” - the agent makes a single MCP request, finds three in‑stock options, applies a brand‑funded discount, and checks out in seconds. It collapses the entire funnel (awareness to purchase) into one conversation while generating incremental retail‑media revenue along the way.

For retailers, that means higher basket sizes, margin and media yield with virtually zero manual merchandising. For brands, it means their products surface when a high‑intent customer is ready to buy. And because the data never leaves the retailer’s environment, everyone stays privacy‑compliant and future‑proof as agents penetrate more of the commerce journey.

Particular Audience’s AI engine removes choice-paralysis for shoppers, while Retail-MCP removes integration-paralysis for every new AI interface. Together they turn ‘AI for eCommerce’ from buzzwords into a five‑second path to purchase.

2. What measures are being implemented to minimize the number of clicks and page loads required for customers to complete online transactions? 

Websites are sort of like human 'read and write' interfaces for humans. Agents promise to mitigate the need to navigate.

In a legacy journey, a shopper might type “trail shoes”, scroll 15 results, use filters, bounce, and start again (or not convert at all).

In a PA + MCP Journey, a shopper might say “I need men’s trail runners under 120 quid, size 11, Friday latest to Shoreditch”, the agent then calls Search, Reviews, Recommendations, Inventory, Payment & Shipping APIs via MCP finding 3 in-stock SKUs with relevant reviews, viable alternatives, accounting for brand-funded discounts, then take payment and organise shipping. If the customer is happy to permit it.

Whilst trail shoes make for a fine example, the early adoptions phase is mostly replenishment products with commodity products like electronics set to follow.

Ultimately this saves around ~15 clicks and 6-8 page loads per considered item.

3. What protocols are in place to ensure data privacy and security when AI agents access and interact with retail systems?

For PA's Adaptive Transformer Search and multi-modal recommendation engine, privacy has always been foundational. We built PA in a way that is designed to collect zero personally identifiable information and does not depend on any third-party tracking. We leverage internet-scale language data, real-time context and machine learning to improve relevance while preserving privacy.

While MCP provides a foundational shift towards a standardized, secure interface, it is important to note that it still inherits risks from the underlying LLM and needs constant refinement and broader industry cooperation to set and raise standards as applications proliferate. I can say that MCP directly addresses the privacy and security limitations inherent in traditional browser-based AI agents. Browser-based agents, which mimic human web interactions, struggle with security risks due to broad browser access, making fine-grained control difficult and risking exposure when injecting internal data. Shoppers are understandably hesitant to share sensitive information like credit card details with such agents. MCP is presented as a structurally superior alternative for AI agents interacting with retail systems, offering a more robust interface.

We follow a simple rule: right data, right purpose, right connection. The SaaS tools behind the APIs that an MCP considers 'tools' generally provide fantastic governance out of the box. That's what makes MCP such a compelling option to interact with AI agents.

4. What strategies are in place to integrate AI agents into the retail systems and data to enhance transaction efficiency and security? 

Instead of custom integrating a bunch of bespoke APIs, a retailer can expose a single doorway (a Retail-MCP endpoint). Think of MCP like a universal USB-C port for retail data: the agent can ask for stock, prices, loyalty points and even coupons through a quick call. 

Strategically speaking, it isn't too different to integrating a modular and composable set of services in eCommerce already. The main challenge is getting an LLM to reason in a way that makes best use of the tools we're giving it.

5. How is your organization preparing to adapt to the increase in AI-based traffic and its implications for retail operations? 

Imagine reading about SEO in 1995 so you could do something about it, and be early. If retailers knew what they know about Amazon today, how might they have taken eCommerce a little more seriously in the mid-90s?

Model Context Protocol (MCP) is a fundamental shift away from traditional browser-based AI agents that mimic human web navigation, which is inefficient, slow, costly, and faces significant security risks and indeterminacy. Retail-MCP, Particular Audience’s implementation, specifically focuses on enabling a multi-tool MCP architecture for retailers for actually awesome customer experience step ups. We're adopting manifests like .well-known/mcp/manifest.json which allows retailers to communicate accessible resources and available data to AI agents. 

As we open up to external applications, we will be encouraging retailers to selectively whitelist beneficial AI agents instead of using blanket blocking mechanisms. We're encouraging our customers to embrace MCP, starting with high-impact use cases, implementing phased rollouts, focusing on data readiness, building governance and security guardrails (like monitoring and logging all agent actions via MCP),

I think the single most exciting thing Retail-MCP is working on is the concept of 'Multi-Tool Agent Architectures', we are focused on enabling a multi-tool architecture where an agent has access to numerous tools simultaneously and can chain them to complete complex tasks. All by leveraging existing retail infrastructure like order databases, policy knowledge bases, search and CRM tools.

Particular Audience is addressing the rise of AI traffic by providing foundational technologies (ATS for semantic search relevance, MCP for efficient agent interaction) and advocating for adoption.

6. How is your organization addressing the demand for faster and efficient online shopping experiences through AI integration?

What Particular Audience contributes:

• Instant, relevant results
  • Our Adaptive Transformer Search understands the meaning of a query (“red shoes for muddy trails”) rather than filtering for exact words. Shoppers land on the right SKU quickly, no filter gymnastics or endless scrolling.
• Personalised storefronts
  • Real-time recommenders mix personalisation, similarity, cross-selling, intent capture, margin and promotions to create a different and relevant website for every visitor. Good personalisation is good prediction. 
  • Sponsored products are slotted in automatically, so retailers can monetise without jeopardizing customer experience.
• Dynamic pricing and competitor price beat automation
• • Saving customers from price comparing across the web. Giving them the confidence and/or incentive to convert.

What Retail-MCP adds on top:

1.One front door

• • Instead of stringing together separate calls for stock, price, shipping and payment, an AI agent achieves complex sequences via one MCP interaction. The retailer replies with a ready-to-pay basket, including any loyalty points, coupons or ads within a few hundred milliseconds. 

2.Built-in security and control

• • Each agent receives a time-boxed, purpose-specific key: “Read stock, place order, nothing else.” If behaviour looks odd, the retailer can revoke that key instantly.

3.Channel-agnostic speed

• • Because MCP is open, the same five-second checkout will work in ChatGPT, a voice assistant or a smart fridge, no new integration required for every time a new channel pops up. This interoperability given the rate of change we see each and every week is a critical investment.

It is still so early, many applications are at the education, inception and pilot stages. Everyone is learning, and gradually forging best practices as an industry. No profit seeking retailer wants to be late, so it's a super inspiring time to be at Particular Audience.

Get in touch with our MarTech Experts. 

1. What are the key features of RCS that enable more interactive and personalized customer engagements in mobile commerce?

RCS enables more interactive and dynamic experiences in many ways including:

1) A branded thread: all messages are sent from a verified thread with the  brand’s name and logo. RCS effectively pre-loads a brand’s contact card or profile into the messaging app; building immediate visual trust with the recipient. The days of needing to download a contact card are over. 

2) Interactive features like suggested replies and actions can enable conversations between brands and consumers as they shop at scale.

3) Rich media (carousels, videos, pdfs, files etc) give marketers exciting new options to engage with customers.

Today all marketing messages include a link sending users to the website to shop. The most critical part of the online shopping experience happens on the website, but websites are very hard to personalize. RCS's rich capabilities enable bringing shopping into messaging, extending the personalized experience through the entire journey. But RCS alone will not drive performance. Combined with AI, RCS Business Messaging creates a new interface of mobile commerce and communication where messages can now be personalized to every individual consumer's needs.

2.  In what ways can brands utilize RCS to create richer, app-like experiences within messaging platforms? ​

RCS can help create personalized shopping experiences along every path of the journey: from discovery, preference collection, understanding customers are shopping for, browsing, cart management and checkout in the messaging app directly.

Using a product drop campaign as an example, brands can now leverage carousels to highlight new products and identify customer preferences using suggested replies / actions.

Brands can also leverage functionality like RCS videos to highlight educational content, brand partnerships / collaborations and more!

3. What role does RCS play in the broader context of omnichannel marketing and customer communication?

RCS levels the playing field between text and other marketing channels, that are all media rich and more interactive.

Text has historically been the channel to unlock 1:1 personalization at scale, but SMS / MMS capabilities have always been limited to text and small images (<500KB). RCS enables sending media up to 100MB in size enhancing the text exchange from a stale, black & white experience, to multi-color full HD interactions aligning with other channels (i.e. email, social).

RCS also has the potential to unlock two-way messaging at scale, with suggested replies substituting typing for tapping. Given the real time nature of text, this would be much more difficult to enable on other marketing channels.

4. What metrics should brands focus on to measure the success of their RCS-based campaigns?

In our latest consumer survey, 90% of consumers said that they are more likely to make a purchase when interacting with a brand using at least one RCS feature. This can lead to higher response and conversion rates, reduced cart abandonment, and improved post-purchase experiences that build loyalty and ultimately higher revenue.

Attentive customers generated $29 billion in revenue last year alone, and on average they experience more than 2x return on investment with our AI-driven platform. We strongly believe, and early testing results confirm, that RCS Business Messaging will supercharge those results.

5. How does the integration of multimedia elements in RCS messages impact customer engagement and conversion rates?

We strongly believe that performance gains will be driven by unlocking more personalization using RCS rich media capabilities, and we are still in very early days of testing to determine the impact.

Many consumers have a higher affinity for rich media and consume content through videos, carousels etc already. Some people are heavy instagram users, others are heavy X users. RCS will finally enable catering to the former audience using the text channel.

6. How the evolution of RCS impacts the future development of mobile messaging standards and consumer expectations?

Currently, mobile messaging capabilities in the U.S. are trailing the experiences available in Asian markets facilitating shopping in messaging. RCS is a major step forward that will enable us to get closer to that experience. RCS is the next global standard for SMS and MMS, and as the only marketing company on the CTIA board, Attentive is playing a key role shaping the future of RCS alongside Google and major carriers. We’re thrilled to be in this unique leadership position to help our customers operate at the forefront of this exciting new technology. As our CEO Amit Jhawar said at our launch, "Attentive isn’t waiting for the future—we’re building it."

1. What’s the single most game-changing innovation in commerce or fulfilment that decision-makers should embrace today? Why is it critical for their success?

There are many exciting innovations in commerce, however, what is more important for retail success is having the ability to critically evaluate your tech strategy, and understand which tools can truly help maximise business effectiveness. 

For example, something that at face value may be ‘boring’  is unified commerce. A unified commerce infrastructure makes it possible for merchants to bring together sales channels, inventory management, order fulfilment, and other retail activities under one platform. This not only lowers costs significantly but gives retailers access to a holistic view of their operations. 

Additionally, with today’s consumers often toggling between online and offline channels as part of a single transaction, having the tools to facilitate orders seamlessly across online, offline and social is vital.  For example, Aussie brand  Bared Footwear found that by adopting solutions like Shopify POS and Ship-to-customer, they were able to consolidate and unify their commerce stack to save time, optimise resources, and streamline their customer service. JB Hi-FI took a similar approach, establishing a 90-minute delivery service with Uber. In just over 12 weeks, the retailer integrated its inventory management system with Shopify via API, including the checkout page, enabling stores to pack and ship online orders to customers in less than two hours from the time of purchase. 

While a unified commerce infrastructure might not sound like the most exciting innovation, it is one of the most game-changing when it comes to business effectiveness. And with so many tools and innovations at retailers’ doorsteps, focusing on the tech that streamlines the foundations of a customer’s journey is crucial. 

2. What emerging trend in commerce and fulfilment should businesses prepare for in 2025, and how can they best position themselves to stay ahead of the curve?

Last year’s Black Friday Cyber Monday weekend saw  Shopify POS sales in Australia grow by 29%, proving that the future of commerce is offline as much as online.  With brick-and-mortar sales likely to continue playing an important role in 2025, merchants need to equip their floor staff with POS systems that support efficient workflows. For example, maintaining cart visibility on the home screen while scanning barcodes or using the search function helps reduce unnecessary steps. 

The checkout also remains one of the best areas for merchants to collect and leverage valuable first-party data. Shopify recently made metafields available through POS, aiming to improve clientelling and make data capture a natural part of the checkout flow. Teams can now record specific details directly in the customer’s profile, such as a pet store noting a customer’s pet type for future interactions. This enriched data not only enables a more personalised checkout experience but can be used to deliver highly segmented marketing communications that feel authentic and relevant to each customer.

3. What is the biggest challenge decision-makers face today in optimizing fulfilment or commerce experiences, and how should they tackle it with the help of technology?

The 2024 Australian Retail Report found that 61% of businesses struggle with efficiency-related challenges, from complex systems and manual processes to inefficient supply chains. Staffing pressures have added another layer of strain as cost-of-living increases and a tight labour pool drive up wage demands and impact retention. 

In light of these challenges, it’s no surprise that many retail leaders are prioritising operational improvements that help them do more with less. Automation can be particularly helpful in this regard, with tools like Shopify Flow allowing merchants to set up custom workflows to automate repetitive tasks related to inventory management, loyalty programs and discounts. We’ve also made it possible to automate more of the returns process, cancel returns if products aren’t sent back, and automate communication of abandoned cart or welcome emails using ready-made templates. 

Technology can also improve fulfilment efficiency. Today’s consumers want products delivered quickly with frequent updates on their order status and at little or no additional cost,  which puts pressure on merchants to fulfil orders swiftly and cheaply. Tools like Shopify Shipping make this process much simpler. With our built-in shipping software, businesses can access pre-negotiated low rates with global carriers, ship from up to 1,000 fulfilment locations, easily display different shipping options at checkout, and track the status of all orders in one place. We’ve also added time-savers like bulk printing of labels and packing slips, automations, and address validation to help retailers save precious time and money.

4. In what ways does technology directly improve the experience for end users and customers? What should decision-makers prioritise to enhance customer satisfaction?

Technology has a vital role to play in creating a frictionless shopping journey. By harnessing the right tools and unified software, retailers can adapt to the changing needs and habits of today’s shoppers, engaging with them seamlessly when and where they want to shop. 

However, while it can be tempting to keep adding flashy features to your website, or invest in another piece of software in the name of customer satisfaction, merchants should first prioritise refining the basic touchpoints in the customer journey. These core aspects – customer relationships, inventory and stock management, and operations – are crucial for building a consistently positive customer experience.

For example, retailers should make sure customers can effortlessly log into their accounts, easily track and return orders, and view their progress on any loyalty programs. Providing a smooth checkout experience is also key. In-store, this might involve offering multiple payment options and instant refunds, while online shoppers benefit from fast load speeds and features like draft orders, guest checkout, chatbots for last-minute questions, and estimated delivery dates.

By getting the fundamentals right, retailers can strengthen customer loyalty and be prepared to tackle today’s thrilling retail storm head-on.

As we know, Elastic Path Composable Frontend accelerates building modular, digital commerce, Next.js frontends based on users’ responses to a few questions. How do you see brands embracing this new experience?

Brands are eager to embrace a Composable Commerce approach as it enables them to deliver high-performance commerce experiences that exceed customer expectations and drive revenue. But complexity and risk are a concern for brands—with so many options available regarding frontends, the paradox of choice often presents an issue.

Composable Frontend offers brands an advanced starting point for their frontend build. Leveraging Next.js, it connects to our Composable Commerce APIs and is full of best practices for frontend development. This provides a high-performance frontend of the box without the plumbing, allowing businesses to focus on what they do best—building extraordinary, high-performing digital experiences.

What was your vision behind the launch of Composable Frontend?

More and more brands are pursuing a Composable Commerce approach to create the differentiated brand experiences that digital commerce businesses need to evolve.

The Composable Frontend extends the core principles of composable to the frontend experience. It enables brands to break free of the rigidity imposed by all-in-one monolithic applications without swinging the pendulum to a completely custom frontend that is expensive to build and maintain.

The Composable Frontend is a modular, extensible approach to frontend development, helping customers reduce time to market for new commerce experiences without the tradeoffs that all-in-one solutions impose. 

What, in your view, is the biggest strength of Composable Frontend?

Composable Frontend accelerates the development of frontend experiences, taking care of the necessary blocking and tackling needed to launch new digital experiences. It also removes much of the risk of the frontend build for a Composable Commerce application as it’s connected to Elastic Path Commerce Cloud’s APIs and leading third-party technologies, including Algolia and Builder.io.

Composable Frontend equips companies with a high-performance frontend with commerce best practices built right in, enabling teams to focus on launching and generating revenue ASAP.

How user-friendly is this solution from the end-user perspective?

Composable Frontend is for use by developers building frontend experiences. Built in Next.js, it takes advantage of development best practices to accelerate the time to launch. Developers can hit the ground running with Composable Frontend, focusing on their unique digital experience requirements. Many frontend components come pre-integrated with preferred third-party providers (search, CMS, payments, shipping) in addition to Elastic Path Commerce Cloud’s APIs.

How do you see Composable Frontend changing the market dynamics?

• Accelerate time-to-market: cuts launch times in half
• Speeds frontend experiences thanks to no extra bloat
• Reduces implementation risk

How does Composable Frontend – a business-ready solution help brands deliver high performance?

Composable Commerce enables brands to compose “best-of-breed” technology into one business-ready solution. Composable Frontend, however, automatically and quickly builds a modular, Next.js frontend based on users’ responses to simple configuration questions. It provides a high-performance combination of static pages and dynamic data on the frontend. Faster page load speeds result in better SEO since static sites are fast, light, and easy to scan, resulting in better conversion rates.

What’s in store for the future of Composable Frontend?

Going forward, we expect to add more elective components to Composable Frontend, broadening the range of prospective use cases it can address from the same high-performance framework.