small_Blacktesty.png

thumbnail_Blacktesty.png

Blacktesty.png

large_Whitetesty.png

medium_Whitetesty.png

small_Whitetesty.png

thumbnail_Whitetesty.png

Whitetesty.png

Like most websites, we store cookies for analytics, and well, that's it. You can read all about this in our privacy policy.

We help commercial leaders cut through complexity. Here's how we work.

Our approach

We use pattern recognition to diagnose what's genuinely blocking results versus what's just complex. We've led commercial functions at scale, so we know which stones to look under. And we use AI to find patterns in days that would take months with traditional analysis.

When we find what's blocking results, we design the fix and stay involved while you implement. We're there until it's working - checking progress, adjusting course, making sure the change sticks.

Years of transaction data, but no visibility into the patterns. A diagnostic across the estate - 100+ sites, millions of customers - revealed they were actually running two different businesses without knowing it. Competed sites and monopoly sites needed completely different commercial approaches, but were being managed the same way.

The analysis showed which customer segments were creating value and which were destroying it. Changed how they thought about pricing, marketing, and where to invest. The commercial approach got restructured around what the data actually showed.

Leadership knew something wasn't working but couldn't pinpoint it. Structured conversations about how decisions got made, what metrics each team used, where things got stuck.

Patterns emerged quickly. Teams were optimising for different things. Pricing decisions undermined marketing investments. Operations couldn't deliver what commercial had promised. The fix: identify the 2-3 metrics that actually mattered and redesign how teams worked together around them.

Trading decisions made on gut feel because they didn't trust their data. The starting point was a specific project - pricing strategy for a new market - but with a wider lens.

While solving the immediate problem, the team built capability to test decisions before committing to them. Turned instinct into evidence. By the end they had a methodology they could use themselves, not a dependency on external help.

An honest conversation about what might be blocking results and whether we can help.

Book a 30-minute call


## slim-hero
title: Our approach
subtitle: We help commercial leaders cut through complexity. Here's how we work.

## dual-section
leftLabel:
leftHeading: We find what's blocking results, then help you fix it
leftContent: We use pattern recognition to diagnose what's genuinely blocking results versus what's just complex. We've led commercial functions at scale, so we know which stones to look under. And we use AI to find patterns in days that would take months with traditional analysis.
rightLabel:
rightHeading:
rightContent: When we find what's blocking results, we design the fix and stay involved while you implement. We're there until it's working - checking progress, adjusting course, making sure the change sticks.

## sticky-scroll-section
heading: Every engagement is different
item1: {"heading": "From Data to Direction", "subheading": "National leisure operator", "tags": "Diagnostic • Customer segmentation • Commercial strategy", "content": "Years of transaction data, but no visibility into the patterns. A diagnostic across the estate - 100+ sites, millions of customers - revealed they were actually running two different businesses without knowing it. Competed sites and monopoly sites needed completely different commercial approaches, but were being managed the same way.\n\nThe analysis showed which customer segments were creating value and which were destroying it. Changed how they thought about pricing, marketing, and where to invest. The commercial approach got restructured around what the data actually showed."}
item2: {"heading": "From Friction to Focus", "subheading": "Growth-stage hospitality business", "tags": "Discovery • Operating model • Metrics alignment", "content": "Leadership knew something wasn't working but couldn't pinpoint it. Structured conversations about how decisions got made, what metrics each team used, where things got stuck.\n\nPatterns emerged quickly. Teams were optimising for different things. Pricing decisions undermined marketing investments. Operations couldn't deliver what commercial had promised. The fix: identify the 2-3 metrics that actually mattered and redesign how teams worked together around them."}
item3: {"heading": "From Instinct to Evidence", "subheading": "Multi-site retail group", "tags": "Tactical project • Pricing • Capability building", "content": "Trading decisions made on gut feel because they didn't trust their data. The starting point was a specific project - pricing strategy for a new market - but with a wider lens.\n\nWhile solving the immediate problem, the team built capability to test decisions before committing to them. Turned instinct into evidence. By the end they had a methodology they could use themselves, not a dependency on external help."}

## contact-banner
headline: Book a 30-minute call
body: An honest conversation about what might be blocking results and whether we can help.
buttonText: Book a call
buttonHref: https://calendar.app.google/kjXYPCcxjM66gzkP9


Approach

# Introduction: The Agent as Customer's Advocate

In my [last article](https://www.zebra.associates/blog/article/the-listening-gap) I set out some different routes that brands may take in response to the rise of agentic search. Here I’m going to stand back a little and consider what these changes may mean for brands and eCommerce specifically.

While nascent it’s pretty clear that ‘Agent mode’ is here to stay, and from here user expectations for how it should feel are going to grow. Less Minority Report, more Her.

Today this agent layer is vanilla, objective, cold. 

As we get more expert at interacting with them - and they get better at listening to us - this will change.  

In this world the agent becomes not just a passive gateway, but an active **advocate and filter for the customer**. Consider this future interaction:

“Place a weekly shop at Tesco. Read me my favourites for me to choose which I want to add. Then let me know what’s on offer that I might consider adding”

Here the agent has native access to my account at [Tesco.com](http://tesco.com) (ideally not via an agentic browser). It then retrieves first a list of favourites, then polls a service for suitable offers, curating them for me.

This future of eCommerce may seem complex and perhaps far-fetched, but it will be with us soon. The question is how brands should equip themselves, moving beyond traditional marketing to a world where they are equipped to communicate with (and even negotiate through) the agent, at scale.

Let’s think through what this might entail for brands.

# The New Inbound: Engineering for Experience

I’ve previously suggested there may be a dividing line between experiential and functional eCommerce. On both sides of the divide however the brand will have a requirement to ensure their core content is agent optimised or at least agent readable. Content here could include product specifications, descriptions, reviews. Given advances in computer vision, images and video too.

We could define this as the inbound content strategy. Its purpose is to ensure the agent can effectively find and present the brand’s best qualities when the user is actively searching for a product that might match.

Part of this is undoubtedly the place of GenAI Engine Optimization (GEO) - a term that’s mildly horrific to anyone like me old enough to remember the days of doorway pages, the rise of social optimization etc.

But it’s also an engineering challenge, to build the pipelines and systems that allow for seamless and efficient communication between the brand's data and the AI agent:

1. API Development & Maintenance: Engineers will be responsible for creating and maintaining robust APIs that provide agents with direct access to a brand's product catalog, customer data, and content. These APIs must be fast, reliable, and well-documented.
2. Structured Data Implementation: While marketing defines the content, engineering implements the schemas (e.g., Schema.org) and data structures that an agent can parse. This ensures that the rich metadata created by the marketing team is correctly formatted and delivered.
3. Dynamic Negotiation Systems: the team will need to build systems that can receive requests from an agent and, based on predefined rules or machine learning models, offer dynamic pricing, bundles, or add-ons in real time.
4. Data Feeds & Integrations: Engineers will manage the systems that deliver updated product information, inventory levels, and pricing to agents in a timely and efficient manner, often through continuous data feeds.

By adopting these engineering practices, a brand becomes the most compelling option not just because of its products, but because it is the most discoverable and intelligently articulated choice for a user's agent.

# The New Outbound: Communicating with the Agent

I’ve referenced an idea for a ‘dynamic negotiation system’ above as an engineering challenge. It’s also of course a commercial one.

We’ll need to decide how brands should ‘market’ to the agent. In this new frontier of outbound I see two key modes:

1. Notifications: a brand could send a notification to a customer’s agent about a relevant offer or new product. Here the agent may be in ‘sentry mode’, and given a brief to minimise noise may simply decide not to let irrelevant communications through to the user. So brands will need to work much harder to personalise this kind of promotion to the right customer: personalisation will need to get, well, personal.
2. Negotiations: here the brand’s system could interface with the agent to dynamically offer a better price, or potentially even to intervene in real time to win a competitive transaction. This moves from a static offer to a dynamic, programmatic negotiation. The implication is a far more complex set of pricing/discounting rules, able to evaluate net revenue considering a variety of variables - including, probably more importantly, the customer value to the brand.

# The Metrics of Trust and Negotiation

In this world of fluid inbound and outbound communications between users, their agents and brands we’ll need to start considering new types of business metrics that reflect the success of the relationship:

- For instance we may want to monitor the offer acceptance rate: the % of agent-relayed offers that result in a purchase.
- Or the profitable negotiation success rate: % transactions negotiated between a brand’s system and an agent at sustainable margin
- Agents themselves may start to build metrics that allow them to make better choices for their users: a ‘trust score’ based on a brand’s ability to serve relevant inventory or provide fair pricing for example

These next generation metrics will by their nature be more complex to operate than today's eCommerce metrics. Unlike click-through rates or conversion percentages, they'll require sophisticated data pipelines that can track multi-step negotiations and evaluate the quality of agent relationships over time. Early adopters will need to experiment with proxy measures whilst the industry develops standardised benchmarks - much as we did in the early days of social media marketing.

# 4 lessons for brands to get ahead of this curve

“Predictions are difficult, especially about the future” _Niels Bohr_

OK so some of this is a little guess work about where we are headed. But parts of it are happening right now, in beta features and early implementations. 

4 lessons for brands then:

1. Go early to get ahead: build these capabilities today and get ahead as consumer expectations evolve
2. Break down silos before they get broken down for you: view this transition not as a threat to traditional marketing channels, but as an evolution that requires new forms of partnership between marketing, engineering, and commercial teams
3. The human touch will be ever more valuable: while we may choose to hand off tasks to technology the brands that thrive will be those that can seamlessly blend human creativity with machine efficiency, creating experiences that feel both personal and scalable. The future belongs to brands that can speak fluent 'agent' whilst never losing sight of the human they're ultimately serving.
4. Product + trust + efficiency = value: in this new landscape, the strongest brands won't just be those with the best products, but those that become the most trusted and efficient partners for their customers' digital advocates. 

The question isn't whether this future will arrive, but whether your organisation will be ready when it does.


Go early or get left behind: four lessons for the agent economy

Last month, I needed to find a lightweight, battery-operated lawnmower for my elderly neighbours. Something easy to manoeuvre, reliable, and available for collection near their home within the week.

What followed was a depressing tour of generic ecommerce experiences. Endless filtering through "corded vs cordeless," scrolling past petrol-powered machines they'd never be able to start, comparing weights buried in specification tables, and manually checking store availability for each model. Not one site asked the obvious questions: Who's using this? What's your situation? What actually matters to you?

Twenty minutes later, I described the same need to ChatGPT. It immediately grasped the context—elderly users need lightweight, easy-start options—and provided three specific recommendations with reasoning, weights, and suggestions for where to find them locally.

The contrast was embarrassing. An AI with no memory of my previous searches understood my parents' needs better than retailers who've been serving customers like them for decades.

## UX has become lazy

This isn't an isolated failure. Somewhere along the way, we stopped asking "what does this user actually need?" and started asking "what colour should the button be?"

User experience design has been reduced to selecting from pattern libraries and copying successful templates. Teams now download Shopify themes, tweak the colours, and call it user-centred design. We've convinced ourselves that because ecommerce patterns are "established," we no longer need to understand our users.

The evidence is everywhere. Fashion retailers serve the same "new visitor" experience to someone buying their fifth black jumper. B2B suppliers make electricians navigate the same generic category pages as DIY enthusiasts. Luxury brands treat repeat customers identical to bargain hunters browsing for the first time.

We've traded user understanding for interface consistency, and called it progress.

## The death of user understanding

This laziness didn't happen overnight. As UI patterns became commoditised, product teams pushed user research further into the background. "It's just ecommerce" became the rallying cry—as if understanding your customers was optional once you had a search bar and some filters.

As Jakob Nielsen observes, "UX practitioners today echo the complacency of their predecessors in the mid-90s. They seem content to tinker with their personas and the deluge of deliverables from our constantly expanding methodology toolbox while designing yesteryear's UX instead of focusing on next-generation UX."

Take any major retailer. A customer buying winter coats gets the same faceted search experience whether they're a new parent looking for something practical, a cyclist needing weather protection, or someone replacing a coat they've owned for years. The system knows nothing about context, nothing about intent, nothing about the person.

We justified this by calling it "scalable UX." In reality, we'd simply stopped listening.

The principles of interaction design—understand the user, identify their mental models, create systems that match how they think—became afterthoughts. Personalisation became a "nice to have" bolt-on feature, not fundamental to how we design experiences.

Most ecommerce sites make no meaningful effort to understand users beyond remembering whether they shop in men's or women's clothing. That's the extent of our personalisation ambition.

## The cliff edge approaches

Here's the problem: users are about to remember what good service feels like.

AI agents are teaching people to expect systems that actually listen. When someone asks ChatGPT for help, it asks follow-up questions. It remembers context. It tries to understand what they actually need, not just what they typed.

These aren't abstract future possibilities. Right now, someone can ask an AI agent: "I'm an electrician. I need a consumer unit from Screwfix that's available for collection within 10 miles of SG13 8AZ tomorrow. It needs to be at least 10-way and IP2XC rated. Give me options and help me compare."

The agent delivers a complete report with recommendations, availability, pricing, and the option to place a click-and-collect order.

Compare that to the current Screwfix experience: search "consumer units," scroll through generic results, manually filter by IP rating (it's the 10th filter in a long list), check boxes for 10, 11, 12, 13, 14 ways, then click each product individually to check store availability.

The difference isn't just efficiency—it's that one system tries to understand what you need, while the other makes you navigate its database structure.

## The reckoning

Users' expectations are shifting rapidly for the first time in years. Google results that seemed perfectly adequate suddenly feel "comically irrelevant and one-dimensional" compared to conversational search. The same shift is coming for ecommerce.

When AI agents can provide genuinely personalised shopping experiences—understanding context, remembering preferences, asking clarifying questions—your generic category pages will look embarrassingly tone-deaf.

This isn't about technology replacing humans. It's about technology finally doing what we should have been doing all along: listening to users and responding to their actual needs.

## Know what game you're playing

The choice isn't simply "understand users or die." It's more nuanced: **decide what kind of commerce you're in, then optimise accordingly.**

**Functional commerce:** If you're selling solutions to problems—tools, supplies, utilities—accept that you're building for efficiency, not exploration. Users want to get things done and leave. AI agents will handle these transactions because they're fundamentally chores. Your job is to make your products and services easily accessible to these agents through APIs, structured data, and clear specifications.

**Experiential commerce:** If you're selling products that users enjoy choosing—fashion, lifestyle, luxury—then genuine user understanding becomes your only sustainable advantage. When users choose direct interaction over agent delegation, it's because they value the experience itself. Generic UX patterns won't compete with truly personalised, contextual design.

The Screwfix example reveals the deadly middle ground: they're clearly functional commerce—electricians aren't browsing for fun—but they've built consumer-facing UX that forces professionals to navigate irrelevant faceted search. They think they're in the "browsing" business when they're actually in the "procurement" business. That's why the AI agent easily outperforms their own website.

**The battleground:** Many purchases could swing either way. Brands with genuinely personalised, user-centric experiences might keep users engaged directly. Those with lazy, pattern-library UX will lose users to agents who understand their needs better than the brand's own website does.

The reckoning hits hardest in this middle ground—where brands pretend they're experiential while delivering commodity UX.

## Beyond the grid

The solution isn't to panic-buy AI features or bolt chatbots onto existing experiences. It's to honestly assess what game you're playing, then design accordingly.

**For functional commerce:** Stop pretending users want to "discover" your industrial equipment or professional supplies. Build efficient, data-rich interfaces that agents can navigate easily. Invest in APIs, structured product information, and clear specifications. Make it trivial for an AI to understand your inventory, pricing, and availability.

**For experiential commerce:** This is where user research and genuine personalisation become essential. When users choose to engage directly rather than delegate to an agent, they're telling you the experience itself has value. Generic UX patterns won't compete with brands that genuinely understand what makes each customer tick.

Start asking the right questions again. What does this specific customer need right now? Are they here to get something done or to enjoy the process of choosing? How can we serve them better than a generic experience—or better than an AI agent could?

The brands that survive the next wave won't be those with the most sophisticated AI—they'll be those that understand which game they're playing and design brilliantly for it.

The grid was never the problem. Our willingness to accept it was.

The reckoning is coming. The question isn't whether AI will change customer expectations—it's whether you'll be ready when it does.


The Listening Gap: how brands forgot their customers, and why AI is about to call their bluff


The hospitality and leisure sector is in a stage of great change, with rapidly developing consumer habits transforming how businesses and customers interact.

So how can the leaders of these businesses navigate the evolutions whilst continuing to drive improved commercial performance?

The key, says Kevin Styles (CEO of The Jamie Oliver Group) is to not over-complicate things. 

Kevin joins Mark for the second webinar in our ‘Enablers of Commercial Growth’ series to share and explore what he sees as the three key elements of commercial growth; value disruption; great people and a strong bias for action.
 
During the discussion Mark and Kevin debate the merits of keeping things simple, explain how you can apply this approach in your own organisation, and deep dive into some common innovations, activities and capabilities that can help you deliver commercial success at speed. 

But it’s not just theory. Kevin brings to life real-world examples of how these principles have been applied at The Jamie Oliver Group to great success over the last two years - and shares the professional and personal lessons he’s learned along the way. 

[Click here to watch now.](https://zoom.us/webinar/register/2217242384137/WN_l_fiahyrT2-L9yan5Ai6iA) 


Webinar - Delivering commercial growth; keeping it simple with Kevin Styles, CEO at The Jamie Oliver Group


Every business is striving for greater commercial performance. But what happens when the received wisdom, opinions and corporate playbook no longer work?

Hosted by Mark Fells and Andy Foote, the first webinar in our ‘Enablers of Commercial Growth’ series looks at why experimentation should be the overriding philosophy for all business.

Why is this a vital enabler? It’s simple - the more things you can test at low risk, the faster you can scale and drive growth, breaking the constraints of planning cycles, strategy reviews and forecasts.

With markets becoming more competitive and growth ambitions rising, the traditional planning cycles and big project approach struggle to deliver in a fast paced digital driven business environment.

To stay ahead, businesses need to develop and embed a more scientific approach to growth-testing, launching and scaling quickly.

Across not just conversion, but the broader commercial agenda, experimentation offers a way to test theory and shape growth. It’s a game-changing growth enabler, but most companies don’t have the internal expertise to deliver it.

In this session Mark, Andy and Nick Young (Chief Customer Experience Officer - RS Group) share how you can create a future proof, more cohesive approach to experimentation that can respond to real world evidence and make use of innovative techniques to provide clarity around which activities are most effective and why.

Finally, and perhaps most importantly, they outline how you can use this approach to increase effectiveness, drive and convert demand, and ultimately grow revenues.

[Click here to watch now.](https://zoom.us/rec/play/U4YJPwa5FVbej9LJP_GHIc_FqCj49ajWT0xQc287NXkAIptFUzYLYAgvbBxUm3mA8O64jq59bpe9MOY.21obtRx_rFOEdnEx)


Webinar - Experimentation as a growth engine: 'If it disagrees with experiment, then it's wrong'


You’ll remember that we talked in the first article in this series about the marketers dilemma, and the search for tools to make informed decisions. We zeroed in on two key tools: Media Mix Modelling (MMM) and Incrementality testing. MMM allows marketers to gain a holistic understanding of performance, and can be great for long term planning. But it’s less useful for identifying specific real-time insights and opportunities – for this we favour incrementality testing.

Incrementality testing however has its own downsides. It often takes place in highly controlled environments that don’t replicate real-world conditions. This means that extrapolating and scaling tests can as a result be resource intensive.

So to arrive at a plan driven by both forward-looking data that can simultaneously be driven by real-time adjustments we favour combining MMM and incrementality testing.

 
## Zebra’s 3-step process to combining MMM and incrementality testing 
 
### Use MMM for strategic allocation
Start with MMM to understand how to distribute your budget across various channels on a macro level.
### Refine and validate with incrementality testing
Implement incrementality testing for specific campaigns or channels to fine-tune the strategy based on real-time data.
### Create a feedback loop

Use the insights from incrementality tests to continually refine and update the MMM, creating a dynamic, data-driven approach to budget allocation. 

## Benefits of this approach
### Strategic & tactical insights
MMM provides the strategic framework, offering insights on how different marketing channels contribute to overall goals. Incrementality testing adds the tactical layer, pinpointing the exact value of specific campaigns or initiatives.
### Comprehensive coverage
While MMM covers broad trends and external impacts, incrementality testing drills down to the effectiveness of individual actions, offering a holistic view of marketing performance.
### Adaptability & precision
This blend makes space for adaptable strategies that can pivot based on precise, actionable insights, ensuring marketing budgets are invested where they yield the highest returns.
 
Our methodology is designed to navigate the complexities of modern marketing, providing clarity and confidence in budget allocation decisions. By utilising the right approach, the easier it becomes to realise the true commercial potential of your marketing budget.

## Specific marketing opportunities this approach unlocks 

Where we’ve introduced this approach we’ve seen marketing teams start to deliver significant efficiencies - some examples: 
#### Better allocation of investment across channels:
- Optimise sub-channel allocations, for example ‘Prospecting’, ‘Retargeting’ within Social.
- Understand diminishing returns and thus more accurately invest up to the returns threshold

#### Better understanding of the incremental impact of in-channel optimisations such as:
- Audience targeting i.e. bid-adjustments
- Different copy/creative
- Keyword value

In the next article, we'll showcase this approach in action through one of our case studies. 



Our approach

We find what's blocking results, then help you fix it

Every engagement is different

From Data to Direction

From Friction to Focus

From Instinct to Evidence

Book a 30-minute call

Company Details

Registered Address

Cookie Preferences