Product discovery is increasingly happening before customers ever reach a retailer’s site. Social platforms, search engines, marketplaces, and AI tools are surfacing answers instantly, often without a click. This Retail Pressure Test tests two competing views: invest heavily in content and brand presence to win discovery organically, or accept that visibility increasingly belongs to paid ecosystems and optimise for it. Both approaches can drive traffic and sales. Both reshape how retailers allocate marketing investment. Come ready to test whether your discovery strategy is building long-term advantage or simply paying to stay visible.

Most retailers know their conversion rate. Almost none of them know how much their payments setup is costing them.

Approval rates, local payment methods, and checkout friction are shaping revenue every single day, whether anyone is actively managing them or not. Most payments strategies were set once, optimised for cost and reliability, and haven't been seriously revisited since.

The retailers treating payments as infrastructure are leaving money on the table. The question is how much, and whether the complexity of fixing it is worth what's being lost.

This session puts two positions on the table: keep payments simple and cost-controlled, or treat the payment layer as a commercial system that deserves the same attention as any other growth lever in the business.

Every retailer in this room has an approval rate they haven't optimised, a market where local payment methods are underperforming, or a checkout flow losing customers nobody is tracking. The opportunity is there. Most businesses just haven't picked it up yet.

Loyalty programs are consuming budget, margin, and IT resources across the industry, yet most retailers cannot confidently say the return justifies it. This Retail Pressure Test tests two competing views: double down on traditional rewards that drive scale and repeat purchase, or rebuild loyalty around relevance, value exchange, and trust that customers cannot get from a competitor’s app. Both approaches can work. Both carry real implications for margin, retention, and long-term customer value. Come with a view on what your program is actually delivering and be prepared to have it tested.

Retailers have invested heavily in data and automation to deliver one-to-one personalisation. Yet many teams are discovering that complexity, cost, and operational strain can outweigh the gains. This Retail Pressure Test tests two competing views: pursue true one-to-one personalisation powered by AI and real-time data, or focus on fewer high-impact segments that deliver clarity and measurable results. Both approaches promise better customer engagement. Both reshape how teams invest in data, marketing, and automation. Come ready to test whether personalisation in your business is driving real value or simply adding complexity.

Omnichannel retail now means feeding your own site, marketplaces, retail media networks, and social channels with a constant flow of content on tighter budgets and faster timelines. Most governance models were built for a simpler environment and haven't kept up.

The result is familiar. Assets rebuilt from scratch. Channel teams bypassing approval because waiting costs more than the risk. Brand consistency eroding quietly while everyone debates process.

This session puts two positions on the table: tighten centralised governance to protect consistency and efficiency, or give channel teams the autonomy to move faster and own the consequences.

Both have worked. Both have cost retailers more than expected. Come ready to be honest about which problem your business actually has, because most teams are solving the wrong one.

Most retailers don't have a data ownership problem on paper. They have one in practice. The central team controls the model but can't move fast enough. The business units move fast but work from different numbers and reach different conclusions. Somewhere in the middle, decisions get delayed, duplicated, or made on instinct because getting the right data takes too long.

This session puts two positions on the table: centralise data ownership for consistency and control, or push accountability to the teams closest to the customer and the commercial decision.

The question isn't which model is correct. It's whether your current structure is actually speeding decisions up or quietly slowing them down.

AI shopping agents are rapidly changing how customers discover and purchase products. Instead of browsing storefronts, shoppers increasingly rely on AI to recommend what to buy and where to buy it. This Retail Pressure Test tests two competing views: embrace AI agents as a powerful new acquisition channel, or treat them cautiously as intermediaries that could erode brand control and margin. Both futures are plausible. Both carry major implications for pricing, merchandising, and customer relationships. Come ready to test whether AI agents represent your next growth engine or your next margin challenge.

Last-click attribution was never the full picture. Most retailers knew that and used it anyway. MMM is now back in the conversation as a more credible alternative, but a harder question is emerging:  how much should it actually control?

This session puts two positions on the table: use MMM as the primary engine guiding where marketing budget goes, or use it as a guardrail that informs decisions without replacing instinct and experimentation.

The risk with the first is over-indexing on a model that reflects the past. The risk with the second is using it as cover for decisions you were going to make anyway. Come ready to be honest about which one your team is actually doing.

Physical stores are under pressure to justify their role in the retail model.

Some are being optimised for fulfilment speed, cost, and proximity to support online demand. Others are being reimagined as experience-led environments that build brand and drive engagement. Few are doing both well.

This session brings that choice into focus by pressure-testing two opposing approaches: stores as fulfilment hubs versus stores as experience destinations. It will examine the trade-offs in cost, labour, space, and return, and explore what happens when retailers try to balance both within the same footprint. Both paths carry real implications for margin and long-term competitiveness. Come ready to test whether your store strategy is building advantage, or eroding it

Generative AI is transforming how retail content gets created.

What once required studios and approvals can now be produced instantly and at scale, enabling retailers to generate imagery, video, and personalised creative across every channel.

The upside is clear: more content, faster, drives visibility, relevance, and conversion.

But the trade-off is harder to ignore.

Without proper oversight, AI-generated content can misrepresent products, erode brand standards, and create liability through returns, complaints, and margin pressure.

Retailers now face a real decision: scale AI to maximise speed and reach, or constrain it to protect accuracy, trust, and long-term value.

This session pressure-tests where velocity drives impact, where it introduces risk, and what level of control is required to get the balance right.

Free, easy returns drove online conversion for years. They still do. But the cost profile has changed and so has customer behaviour. A growing share of returns are not genuine changes of mind. They are the policy working exactly as designed, for customers who were never going to keep the product.

This session puts two positions on the table: keep returns generous to protect conversion and customer confidence, or tighten controls to reduce abuse, logistics cost, and margin pressure.

Loosen the policy and you fund behaviour that erodes profitability. Tighten it and you risk penalising customers who had no intention of abusing it. Most retailers are currently running a policy designed for a different customer. Come ready to test whether yours still makes commercial sense.

Every retailer in this room is declining legitimate customers. Most don't know how many or what it's costing them. Fraud controls are set to protect margin, but when they're too aggressive they  create a different problem: real customers blocked at checkout, abandoned carts, and revenue that never shows up in any fraud report because it was never counted as a loss.

The tension runs both ways. Loosen controls and fraud exposure grows. Tighten them and good customers pay the price.

This session puts two positions on the table: tighten fraud controls to protect margin and minimise chargebacks, or pull back restrictions to protect checkout flow and stop penalising legitimate customers.

There's no clean answer. But most retailers are currently sitting on one side of this trade-off without having made a deliberate choice about it. Come ready to find out where your line actually sits, and whether it's costing you more than you think.

Amazon, Temu, and Shein are resetting what customers expect from price, assortment, and delivery speed. The pressure is real and it's not easing. Many retailers are responding by competing harder on price and operational efficiency. Others have concluded that race is unwinnable and are betting on brand, experience, and trust instead.

This session puts two positions on the table: match global competitors on price and efficiency, or build the parts of your business they structurally can't replicate.

The harder question isn't which strategy sounds right. It's whether the one your business is currently running is a deliberate choice or just what's happened by default.

AI is no longer just a support tool. In many retail environments it now matches or outperforms human agents in product discovery, delivering more consistent, more personalised, and more scalable  interactions. Most retailers are already moving this way and the results are hard to argue with.

But not all moments are equal.

When customers are browsing, AI guides and converts at scale. When something goes wrong, expectations shift fast. Customers want resolution, not a process. Getting that moment wrong doesn't just lose the interaction. It loses the customer.

This session puts two positions on the table: build a service model that scales on AI with humans as the exception, or design deliberately around the moments where removing the human costs you more than keeping one.

Where you draw that line is a commercial decision, not a technology one.