Dancing Badger Dancing Badger

Explore

News

A structured route into Dancing Badger's strategy, web, design and analytics ecosystem.

View overview

When a project goes live, the finished result is the part everybody sees.

A new website. An ecommerce store. A campaign. A new customer journey. Something that looks polished, works properly and hopefully appears as though it was always meant to be that way.

What is less visible is everything that happened before it.

The conversations. The questions. The research. The ideas that were explored and discarded. The details somebody spotted at the last minute. The decisions that moved backwards before they moved forwards.

So we thought it would be useful to go behind the scenes at Dancing Badger and explain what actually happens when we take a digital project from an initial brief through to launch.

Not every project follows exactly the same path.

A website project is different from an ecommerce build, and both are different from a marketing campaign.

But the thinking behind them has a lot in common.

A good project isn’t a relay race where one department finishes its bit and passes the baton on. The strongest work happens when the right people are involved at the right points.

The project starts before anybody starts designing

One of the easiest mistakes in digital work is to begin with the output.

“We need a new website.”

“We need to improve our SEO.”

“We should run Google Ads.”

“We need an ecommerce store.”

Those may eventually be the right answers.

But first we need to understand the question.

What is the business trying to achieve?

Who are the customers?

What is working already?

What is getting in the way?

Is there a technical problem, a visibility problem, a conversion problem, a commercial problem — or several of those things happening together?

That initial understanding shapes almost everything that follows.

Because if we misunderstand the problem at the beginning, there is a very real risk of delivering the wrong solution extremely well.

A typical project · From first conversation to what happens next

There is a process. But it isn’t a production line.

Different projects place more emphasis on different stages. The important part is that learning at one stage can influence what happens elsewhere.
01 Understand

Clarify the business objective, audience, existing challenges and what a successful outcome should look like.

02 Explore

Review the current position, available data, competitors, customer journeys and wider digital environment.

03 Plan

Agree priorities, scope the work and decide how the different parts of the project need to fit together.

04 Shape

Develop structure, user journeys, content direction and the ideas that will form the experience.

05 Design

Turn the thinking into something visual, usable and appropriate for the people who will actually use it.

06 Build

Develop, configure and integrate the technical parts while continuing to test assumptions from earlier stages.

07 Test

Check content, functionality, devices, journeys, tracking and the details that determine whether the finished project works properly.

08 Launch & learn

Put the work in front of real users, measure what happens and use that information to decide what comes next.

A brief tells us where to start — not necessarily where to finish

Clients normally come to us with an idea of what they need.

Sometimes that idea is already very well developed.

Sometimes the brief is effectively a problem that still needs turning into a project.

Both are perfectly normal.

Our job at the beginning is not simply to write down everything we have been asked to do and begin producing it.

We need to understand why it is being requested.

A client might ask for a redesign when the underlying issue is actually poor navigation.

They might want more website traffic when the bigger opportunity is improving the proportion of existing visitors who enquire.

They might want to advertise a particular service when search behaviour suggests customers describe that service in a completely different way.

Challenging the brief does not mean ignoring what the client wants.

It means making sure the project being built is connected to the outcome they actually care about.

Research is how we reduce expensive guesswork

Before making important decisions, we want to know as much as we reasonably can about the environment the project is entering.

That may involve existing website data.

Search behaviour.

Competitors.

Existing customer journeys.

Advertising performance.

Ecommerce data.

Previous content.

Or simply conversations with people inside the business who understand the customers better than any dashboard ever could.

The purpose is not to delay the creative work with endless research.

It is to make the creative and technical work better informed when it begins.

The earlier we understand the real problem, the less time everybody spends solving the wrong one.

The right specialists need to be involved before their ‘stage’ begins

Digital projects can look very tidy on a project plan.

Strategy first.

Then design.

Then development.

Then content.

Then SEO.

Then analytics.

Real projects are rarely that neat.

A developer may spot a technical implication while something is still being designed.

An SEO specialist may identify an important search opportunity before the site structure is agreed.

A designer may notice that the planned content hierarchy does not make sense visually.

Analytics may show that users behave very differently from the way everybody assumed.

Account management may bring context from the client that changes how a decision should be approached.

That is why collaboration matters.

The objective is not to have everybody involved in every decision.

It is to make sure the right expertise can influence the project while there is still time for that expertise to make a difference.

One project · Different perspectives

Good digital work is rarely the product of one discipline.

Not every project needs every specialist. The value comes from involving the relevant expertise when the project needs it.
Client & account

Business context

Keeping the project connected to objectives, priorities, decisions and what is actually happening inside the client’s organisation.

Strategy & data

Evidence

Looking at performance, opportunities, audience behaviour and the information that should influence the direction of the project.

UX & design

Experience

Turning objectives and information into journeys, layouts and interfaces that make sense to the people using them.

Content & search

Language

Making sure the project communicates clearly while considering what customers search for and what they need to understand.

Development

Delivery

Turning the planned experience into something robust, manageable and technically capable of doing what the project requires.

Performance

What happens next

Tracking what users actually do once the project is live and using that information to challenge assumptions and identify improvements.

Collaboration without committee design
More expertise does not mean every decision needs ten opinions.

The point of bringing different disciplines together is to improve the decision, not make the process unnecessarily complicated. Clear ownership still matters. So does knowing when specialist input is useful — and when somebody simply needs to make the call and move the project forward.

Design is where strategy starts becoming visible

By the time a project reaches design, a significant amount of thinking may already have happened.

The audience.

The objectives.

The structure.

The journeys.

The actions we want people to take.

Design turns those decisions into something people can actually experience.

That is why we do not see design as decoration added after the strategy has been completed.

A design decision can change how easy it is to understand a page.

It can alter what somebody notices first.

It can make an important action obvious — or practically invisible.

And sometimes seeing the strategy represented visually exposes a problem nobody had noticed while it existed only as words on a document.

That feedback is useful.

Projects should be allowed to improve as they become more tangible.

Development is not where the thinking stops

Once a design has been approved, it can be tempting to think the difficult decisions have been made and development is simply the process of assembling them.

It rarely works like that.

Development introduces another set of questions.

How should the content be managed?

How does something behave on a smaller screen?

What happens if the client adds significantly more content later?

How should different systems communicate?

What happens when information is missing?

How do we make the feature maintainable rather than merely making it work today?

This is where the experience of people working across website development and ecommerce becomes important.

Good development is partly about writing the code.

It is also about anticipating what the project will need after launch.

Content, SEO and tracking are not jobs we want to discover at the end

Some of the most expensive project problems begin with the phrase: “We’ll sort that out later.”

Content is one example.

A layout that looks perfect with three lines of placeholder copy may behave very differently when the real information arrives.

Search is another.

If SEO only enters the conversation after the structure and URLs have been finalised, opportunities may already have been missed.

The same is true of analytics.

Knowing what should be measured before launch is considerably better than launching first and trying to reconstruct the missing data afterwards.

This is one of the reasons DB Analytics and wider performance measurement are increasingly connected to the work we do.

If success matters, we need a way to recognise it.

Launch day should not be the first time we ask how we are going to know whether the project worked.

Testing is where we deliberately look for problems

By the time a project is close to launch, everybody involved has normally spent a lot of time looking at it.

That familiarity can be dangerous.

You know where the button is, so you stop noticing whether it is obvious.

You know what a field means, so you forget somebody else may not.

You have seen the same page dozens of times, so your brain quietly corrects a typo every time you read it.

Testing means deliberately changing perspective.

Does it work technically?

Does it work across devices?

Does the content make sense?

Do forms and ecommerce journeys behave properly?

Are the important actions measurable?

And can somebody who has not spent weeks inside the project understand what they are supposed to do?

Pre-launch · Looking at the project from different angles

Ready to launch means more than “the page loads”.

Quality assurance is partly technical and partly about stepping back from the project to make sure the whole experience still makes sense.
Check 01 Experience

Review navigation, user journeys, calls to action, readability and how the experience behaves across different screen sizes.

Check 02 Content

Check real copy, imagery, links, page titles, product information and the details users will actually encounter.

Check 03 Function

Test forms, ecommerce journeys, integrations, interactions and the technical behaviours the project depends upon.

Check 04 Measurement

Confirm that the important actions can be tracked so the team has useful information once real users arrive.

Then comes the part everybody sees: launch

Launch is an important moment.

It is the point where weeks or months of decisions finally become something customers can use.

But internally, we do not really think of it as the finish line.

It is the moment the project starts receiving a kind of feedback no workshop, prototype or staging site can fully reproduce: real behaviour from real users.

People may behave exactly as expected.

Or they may reveal something none of us anticipated.

A page we expected to be important may receive very little attention.

A secondary route may become one of the strongest conversion journeys.

Search behaviour may shift.

An advertising campaign may expose a weakness on a landing page.

Customers may repeatedly ask a question the new website still does not answer clearly enough.

That information is not evidence that the project failed.

It is evidence that we now know more than we did before launch.

After launch · The feedback loop

Launch gives us something planning never can: real behaviour.

The most useful digital projects continue learning. Performance data and client feedback can then inform the next round of decisions.
01 Launch

The project moves from a controlled development environment into the real world.

02 Observe

Watch how people arrive, what they engage with and where journeys perform differently from expectations.

03 Learn

Combine analytics, commercial results, customer behaviour and client feedback to understand what the signals mean.

04 Improve

Decide what deserves changing, testing or developing next — then continue the cycle.

Good project management is partly about keeping all of that connected

With several disciplines contributing to a project, somebody also needs to keep sight of the whole thing.

Decisions need recording.

Dependencies need spotting.

Questions need getting to the right person.

Client feedback needs translating into clear actions.

Scope, priorities and deadlines need managing without allowing the project to become purely about administration.

That coordination is less visible than a design or a piece of development, but it has a significant effect on the quality of the finished work.

A project can contain plenty of talented individual work and still struggle if those pieces are not connected.

The process matters, but so does knowing when to adapt it

There is comfort in a perfectly defined process.

Step one.

Step two.

Step three.

Never go backwards.

Digital projects do not always behave like that.

Sometimes research changes the brief.

Sometimes design raises a strategic question.

Sometimes development reveals a better approach.

Sometimes testing exposes something that needs taking back a stage.

The process exists to give the project structure, not to prevent people from responding intelligently when new information appears.

Knowing when to follow the process — and when the project has given you a good reason to revisit something — is part of the experience an agency is there to provide.

What clients see is the outcome. Our job is everything behind it.

Most clients do not need to be involved in every internal conversation.

They should not need to know every technical decision or watch every iteration taking place behind the scenes.

But they should understand the important decisions, know why recommendations are being made and have confidence that the different parts of their project are connected.

That is ultimately what the process is there to achieve.

Not complexity for the sake of complexity.

Not endless meetings.

Not a rigid sequence of agency terminology.

A way of taking an objective, bringing the right expertise around it and gradually turning it into something useful.

Behind the scenes at Dancing Badger

The finished project might look simple. Getting to the right kind of simple usually takes a lot of thinking.

Research, strategy, design, development, content, data and client knowledge all influence the result. The value is not simply completing each stage — it is making sure those stages inform one another.

The exact process will continue to change.

The technology we use will change.

The platforms will change.

And, as we discussed in our article on how AI is changing the way we work at Dancing Badger , some of the tasks involved in delivering digital projects are already becoming faster and more automated.

What matters is that the purpose of the process stays the same.

Understand the problem properly.

Bring the right people to it.

Make informed decisions.

Build carefully.

Measure what happens.

And keep improving.

Start a project
Have something you’re trying to improve, build or solve?

You do not need to arrive with the finished brief. If you know what you are trying to achieve, we can help work out what the project needs to look like from there.

Executive summary Your product page is no longer the only version of your product that matters.

Ecommerce products are increasingly discovered through Google Shopping, paid advertising, social catalogues, marketplaces and AI-led shopping experiences. Many of those environments do not begin by reading the product page in the same way a customer does. They rely on structured product information: titles, identifiers, prices, availability, variants, categories, imagery, delivery information and other attributes supplied through feeds, catalogues and APIs.

That makes product data a commercial asset. The clearer, more complete and more consistent it is, the easier it becomes for platforms to understand what you sell, match products to relevant customers and keep information accurate across channels.

Takeaway: the website remains the place where your brand, proposition and conversion experience come together — but the product feed is increasingly the data layer that helps your products travel beyond it.

For a long time, ecommerce optimisation naturally centred on the website.

Improve the product photography. Rewrite the description. Make the navigation clearer. Speed up the site. Simplify checkout. Test the call to action.

All of that still matters.

But there is another layer of ecommerce performance that customers may never see directly: the structured product information sitting behind the store.

That information can determine how accurately a product appears in Google Shopping, whether the correct variant is shown in an advert, whether Meta understands which item is available, whether a marketplace receives the right price and whether an AI shopping system has enough detail to recommend the product for a specific request.

The ecommerce website is the shopfront. The product feed is becoming the distribution layer that helps other platforms understand what is on the shelves.

That distinction is becoming more important as ecommerce moves further into automated advertising, multi-channel selling and AI-assisted discovery.

Google describes accurate product data as a foundational input for ads, free listings and its AI-powered formats. Shopify now uses structured catalogue information to make eligible products available across AI-led shopping environments as well as its traditional sales channels.

In other words: the product page is still important, but the information underneath it needs just as much attention.

Customers see a product page. Platforms see a product record.

Open an ecommerce product page and a customer might see a headline, lifestyle photography, product description, price, available options, delivery information, reviews and a button to buy.

A good page combines information with persuasion. It explains the product, communicates the brand and gives the customer confidence to act.

But external platforms often need that information in a more structured form.

Google Merchant Center, social catalogues, advertising systems, marketplaces and connected shopping tools need to know specific things about the item: what it is, how it should be categorised, which variants belong together, what it costs, whether it is available and which image belongs to which offer.

One product · Two information layers

The product page persuades the customer. Structured product data helps platforms understand the offer.

These layers should support each other. The strongest ecommerce setup gives humans a convincing experience and machines accurate, consistent information.
Customer-facing layer

The product page

  • Brand story and positioning
  • Persuasive product copy
  • Lifestyle and detail imagery
  • Reviews and reassurance
  • Delivery and returns explanation
  • Conversion journey and calls to action
Structured data layer

The product record

  • Product ID, SKU and identifiers
  • Title, category and product type
  • Price and availability
  • Variants, colour, size and material
  • Images, video and product links
  • Delivery, condition and other attributes

This is why ecommerce development increasingly needs to think beyond the page template itself.

A beautifully designed store can still have weak product data. Equally, a technically complete feed cannot compensate for a product page that fails to communicate value or convert the visitor.

Modern ecommerce needs both.

One product record can now influence several sales and marketing channels

Product feeds used to feel like specialist infrastructure sitting somewhere between an ecommerce platform and Google Shopping.

That description is now too narrow.

Ecommerce businesses increasingly distribute product information into several connected environments. A central product catalogue might feed Google Merchant Center, Meta, marketplaces, affiliate systems, comparison services, shopping apps and other integrations.

The exact route varies by platform. Some channels use direct integrations. Some use feeds. Some use APIs. Some can also crawl structured data from the website.

But the principle is the same: the quality of the source information affects the quality of what gets distributed.

Product distribution · One source, many destinations

Your ecommerce catalogue can become the source of truth for a much wider sales ecosystem.

The goal is not necessarily one literal feed. It is one reliable product-data model that can be transformed and distributed correctly for each destination.
ProductsTitles, descriptions, types, attributes and identifiers.
Commerce dataPrice, stock, variants, delivery and promotional information.
MediaPrimary images, additional imagery and product video.
Structured product layer Your product data

Organised, mapped, validated and kept in sync before being distributed into the channels that need it.

GoogleShopping, free listings and advertising.
MetaCatalogue-based advertising and commerce surfaces.
MarketplacesChannel-specific listings, attributes and inventory.
AI shoppingStructured catalogues and connected commerce experiences.

This is where multi-channel selling and third-party integrations become strategic rather than purely technical.

If every destination needs its own manually maintained version of the catalogue, the business creates more places for data to become inconsistent.

A stronger model keeps the core information controlled centrally and then maps or adapts it to the requirements of each channel.

Platforms can only work with the product information you give them

Good product data is not about filling in fields for the sake of completeness.

Attributes give platforms context.

A clear title helps describe the product. A category helps establish what type of item it is. GTINs and other identifiers help distinguish recognised products. Variant data shows how colours, sizes or configurations relate. Accurate price and availability information tells a platform whether the offer is still valid.

Google explicitly states that product information is used to match products to relevant queries and as a foundational input for its AI-powered advertising formats and experiences.

That makes catalogue quality part of PPC performance, not simply an ecommerce-admin task.

Product attributes · Context for machines

Each field answers a different question about the product.

The exact requirements vary by platform and product category, but complete, accurate attributes give connected systems more useful context.
01Identity

SKU, product ID, GTIN, MPN and brand help identify the exact product or offer.

02Title

A clear product name gives the system a concise description of what the item actually is.

03Category

Product type and platform categories help place an item into the right commercial context.

04Variants

Colour, size, material and option relationships help separate and group related offers correctly.

05Price

Current pricing needs to agree with the customer-facing store and any sale or promotional logic.

06Availability

Stock and availability signals help channels avoid promoting offers that cannot be fulfilled.

07Media

Accurate imagery, additional images and increasingly video help external channels represent the item.

08Fulfilment

Delivery, returns and related information can influence whether a product is eligible or useful in a given experience.

Google’s 2026 product-data specification

Google says accurate product data is essential for successful ads and free listings and is used as a foundational input for AI-powered ad formats. Its 2026 specification update also introduced new product-level delivery attributes and a product video-link attribute. See the Merchant Center product data specification and 2026 specification update.

A platform cannot infer a reliable commercial answer from product information that is incomplete, inconsistent or out of date.

Automation can scale good product data — and bad product data

One of the benefits of feeds and integrations is scale.

Change the price in the ecommerce platform and the connected systems can receive the update. Mark a product unavailable and advertising can adjust. Add a new variant and it can be distributed without rebuilding every listing manually.

But automation works both ways.

If the source catalogue contains the wrong availability, a weak title, duplicated identifiers, missing attributes or inconsistent variant logic, that information can travel just as efficiently.

Feed problems therefore stop being isolated admin errors. They can become advertising problems, marketplace problems, customer-experience problems and reporting problems at the same time.

Data mismatch · One error, several consequences

A small catalogue problem can travel further than the product page.

This is illustrative rather than a platform-specific rule. The point is that connected commerce increases the operational impact of inaccurate source data.
SourceProduct data is wrong

The price, variant, stock status or core attribute is inaccurate at source.

DistributionThe feed carries it outward

Connected advertising, catalogues or marketplace systems receive the same weak information.

PlatformEligibility or matching suffers

The product may be represented poorly, matched less effectively or generate diagnostics and disapprovals.

CustomerTrust and conversion suffer

The shopper sees inconsistent information, lands on the wrong option or discovers that an offer is not actually available.

Google specifically warns that missing or inaccurate product information can cause disapprovals, limited eligibility or incorrect product displays. It also uses structured data on the website to help keep price and availability information in Merchant Center up to date.

That is another reason ecommerce, website development and paid media should not be managed as completely separate systems.

The quality of the underlying commerce data affects all three.

Structured data can help keep feeds aligned

Google describes structured data as a machine-readable representation of product information on the website. It can be used for automatic item updates, helping reduce price and availability mismatches between a product page and Merchant Center. Google’s guidance also makes clear that structured data should match what customers actually see on the landing page. Read Google’s structured-data guidance for Merchant Center.

AI shopping makes structured product information more visible

The shift becomes particularly interesting when the customer is not browsing a traditional category page at all.

A shopper might ask an AI assistant for a navy waterproof coat under a certain budget, a sofa suitable for a specific room size or a gift with several practical constraints.

That kind of request is much more detailed than a simple search for “coat”, “sofa” or “gift”.

To respond usefully, a shopping system needs structured information that helps it distinguish one item from another.

Shopify has been investing heavily in this area. In 2026 it expanded Agentic Storefronts and Shopify Catalog so eligible products can be discovered through environments including ChatGPT, Microsoft Copilot, Google AI Mode and Gemini. Shopify describes its catalogue as structured, queryable product infrastructure designed so agents can discover and understand products using current data such as price, availability and attributes.

AI shopping · Product discovery without the category page

The shopper can describe the need first — and the catalogue has to supply the matching detail.

This simplified flow illustrates why structured product attributes become more important when shopping starts with a conversational request rather than traditional site navigation.
01Customer describes the need

Budget, use case, colour, size, material, delivery timing or another combination of requirements.

02System searches product data

Structured catalogue information provides attributes that can be compared and filtered.

03Relevant options are surfaced

Products can be presented using current information rather than relying only on generic page text.

04Customer continues to purchase

The journey can move to the merchant’s store or, where supported, continue inside the shopping environment.

This does not mean every ecommerce business needs to rebuild its entire store around AI shopping tomorrow.

It does mean that the direction of travel rewards something ecommerce teams should have wanted anyway: clean, detailed and reliable product information.

Better product data improves today’s feeds and advertising while also making the catalogue easier to use in emerging shopping environments.

Shopify’s current agentic-commerce model

Shopify says its Catalog structures product data and distributes eligible products across AI channels. In 2026 it expanded support across ChatGPT, Microsoft Copilot, Google AI Mode and Gemini, while keeping pricing and inventory synchronised from the merchant’s commerce system. See Shopify’s March 2026 agentic-commerce update and Spring ’26 merchant update.

Product organisation should describe how customers actually compare

Product data is not only a technical exercise in meeting platform requirements.

It is also a useful way to think about merchandising.

If customers choose products by size, material, style, finish, compatibility, room, use case or another defining characteristic, those distinctions should usually exist clearly somewhere in the underlying catalogue.

That can improve more than a feed.

The same well-structured information can support website filtering, collections, landing pages, internal search, merchandising rules, email segmentation, paid-media campaigns and reporting.

In other words, a good product taxonomy can serve the customer experience and the marketing infrastructure at the same time.

This is where conversion rate optimisation and product-data work overlap. If customers repeatedly need a particular piece of information to make a decision, hiding it in inconsistent free text is rarely the strongest long-term approach.

The product feed does not replace the ecommerce website

There is a risk in talking about product feeds, AI shopping and distributed commerce that the website starts to sound less important.

It is not.

The website remains the place where the business controls the strongest version of the brand experience: navigation, storytelling, merchandising, reassurance, conversion design, checkout, customer service information and the broader relationship between products.

Product data gets the item into more environments. The website still has to make the customer want to buy it.

And the two need to agree.

A feed that advertises one price while the landing page shows another creates friction. An availability mismatch damages trust. A generic feed title that sends someone to a confusing variant page weakens the experience. A brilliant advert that lands on an underdeveloped product page wastes the quality of the acquisition.

That is why Paid Social, PPC, ecommerce and CRO work best when they share the same product logic rather than each maintaining their own interpretation of the catalogue.

Start with the source of truth, not the channel

A common mistake is to treat every channel problem as a channel problem.

A Google feed is fixed inside Google. A Meta catalogue is patched inside Meta. A marketplace listing is manually corrected in the marketplace. The ecommerce store contains a slightly different version again.

Sometimes channel-specific rules make local changes unavoidable. But repeated manual correction often indicates that the product model at source needs attention.

A stronger review starts with the core catalogue, decides which information genuinely belongs there and then maps that data into each destination deliberately.

A practical ecommerce product-data review

1 Define the source of truthKnow which system owns the authoritative title, SKU, price, inventory, variant logic, imagery and product attributes.
2 Audit the attributes customers actually useLook at how people filter, compare, search and ask questions. Make sure commercially important characteristics are structured consistently.
3 Standardise product and variant logicKeep identifiers stable and make sure related variants are grouped consistently across the store and connected channels.
4 Keep price and availability synchronisedReduce the delay and manual handling between changes on the website and the systems advertising or listing the product elsewhere.
5 Review imagery and media at feed levelCheck which images are actually being distributed, whether variants receive the right media and whether newer formats such as product video are useful.
6 Map each destination deliberatelyGoogle, Meta, marketplaces and other systems have different attribute requirements. Transform the same core data rather than creating disconnected catalogues.
7 Monitor diagnostics and rejected productsTreat channel warnings as evidence. Repeated errors can reveal weaknesses in the underlying catalogue rather than isolated platform problems.
8 Measure the commercial resultFeed quality matters because it should improve useful visibility, qualified product traffic, advertising efficiency, conversion and sales — not because a dashboard has more green ticks.

The goal is not a bigger feed. It is a more useful product system.

Ecommerce businesses do not need every possible attribute simply because a field exists.

The objective is to create product information that is accurate enough for operations, detailed enough for customers and structured enough for the channels the business actually uses.

As that foundation improves, new channels become easier to activate because the business is not rebuilding the catalogue from scratch each time.

Product-data maturity · From manual listings to reusable infrastructure

Better product data makes expansion less dependent on repeated manual work.

This is a practical maturity model rather than a platform requirement. Not every retailer needs every stage, but it shows the operational benefit of a stronger source catalogue.
Stage 1Fragmented

Product information is stored differently across the website, advertising platforms and marketplaces, with frequent manual corrections.

Stage 2Consistent

Core identifiers, variants, pricing and availability follow common rules, reducing mismatches between the store and channels.

Stage 3Channel-ready

Attributes are structured and mapped so products can be distributed efficiently into Google, Meta and other relevant destinations.

Stage 4Reusable infrastructure

The catalogue becomes a reliable product-information layer that can support new channels, automation and emerging shopping experiences.

That is the bigger opportunity behind integration work.

The value is not simply connecting system A to system B. It is making sure the information moving between them is useful, controlled and commercially meaningful.

The same principle applies to Data Analysis: channel performance becomes easier to interpret when products, variants and transactions share stable identifiers instead of being labelled differently in every system.

The new ecommerce infrastructure

Your website sells the product. Your product data helps the product travel.

As commerce spreads across advertising platforms, social catalogues, marketplaces and AI-led shopping environments, structured product information is becoming part of the marketing infrastructure — not just the back-office catalogue.

At Dancing Badger, we think the useful question is no longer simply, “Does the product feed work?”

It is: “Can every important channel understand the product accurately, and does the information still lead the customer into a strong buying experience?”

That requires ecommerce, paid media, integrations, merchandising and conversion strategy to work from the same product truth.

The businesses that get that foundation right are better placed not only for Google Shopping and Meta today, but for whatever the next meaningful commerce channel turns out to be.

Continue exploring · Ecommerce, feeds & connected growth
Useful next steps for businesses looking to improve product data, channel distribution and the customer journey around ecommerce.
Is your product catalogue ready to work beyond your website?

We can review the structure, quality and distribution of your ecommerce product data — from the source catalogue and product pages through to Google, Meta, marketplaces, advertising feeds and the integrations connecting them.

For the last few years, AI shopping has largely been about discovery.

Customers could ask ChatGPT what to buy, use Google’s AI-powered search experiences to compare products, or turn to AI for recommendations.

Now the technology is moving further into the transaction itself.

Google is building shopping experiences that can support product research, basket building and checkout across Search and Gemini. OpenAI is expanding shopping inside ChatGPT, giving merchants new ways to surface their catalogues while customers research and compare products conversationally.

What began as AI helping people decide what to buy is gradually becoming AI helping them buy it.

For ecommerce businesses, that matters because the website may no longer be the place where every stage of the purchase journey happens.

From search assistant to shopping assistant

The traditional ecommerce journey has usually involved several distinct stages.

A customer discovers a product through Google, social media, advertising or another channel. They visit the retailer’s website. They browse. They compare. They add something to their basket. They check out.

The website sits at the centre of almost the entire transaction.

AI shopping starts to blur those boundaries.

Google’s Universal Commerce Protocol — or UCP — is a new open standard designed to let agents and commerce systems work together across discovery, buying and post-purchase support. Google’s current UCP-powered checkout allows participating merchants to offer checkout directly on eligible product listings in AI Mode and Gemini while remaining the seller of record.1

Google has also introduced Universal Cart, an intelligent shopping cart designed to work across retailers and services including Search and Gemini. Google says shoppers will be able to add products while browsing its services and, for participating brands, either check out on Google with Google Pay or transfer items to the retailer’s site.2

That is a significant change.

The shopping journey no longer necessarily has to move:

Graphic 01 · Ecommerce journey

AI shopping is moving deeper into the transaction

Discovery is only the beginning. AI systems are increasingly being designed to support comparison, cart building and checkout too.
Stage 01

Discover

Ask a question, describe a need or encounter a recommendation.

Stage 02

Compare

Refine products by price, attributes, reviews, suitability or preferences.

Stage 03

Build the basket

Add products to an AI-assisted or platform-level cart across merchants.

Stage 04

Transact

Complete checkout on the platform or transfer into the retailer’s checkout.

The direction of travel: AI is moving from helping customers choose towards helping them complete the purchase.

That does not mean retailer websites are about to disappear. But it does mean parts of the shopping journey that have traditionally happened on the website could increasingly happen somewhere else.

ChatGPT is moving deeper into ecommerce too

OpenAI is developing along a similar trajectory, although its current shopping experience places particular emphasis on product discovery.

In March 2026, OpenAI introduced richer shopping experiences in ChatGPT that allow people to browse products visually, compare options side-by-side and access more up-to-date product information without repeatedly moving between websites. OpenAI says merchants can share product feeds and promotions through the Agentic Commerce Protocol, creating a foundation for broader AI-native commerce experiences.3

For Shopify merchants, OpenAI currently says relevant stores may appear in ChatGPT shopping experiences, with checkout taking place on the merchant’s own online store.4

That distinction matters.

This is not yet a world where every ecommerce transaction disappears into an AI platform.

But we are clearly moving towards an environment where a greater proportion of the research, comparison and decision-making may happen before a customer reaches the retailer’s traditional storefront.

For brands, that makes ecommerce strategy and development increasingly inseparable from how products are represented across the wider digital ecosystem.

Product data is becoming part of the storefront

This shift changes the importance of something many businesses still treat largely as a technical requirement:

the product feed.

For years, feeds have powered channels such as Google Shopping, Performance Max, Meta catalogues and marketplaces.

They are often thought of as something the advertising platform needs in the background.

Agentic commerce makes that data much more strategically important.

If an AI system is going to recommend, compare or transact with a product, it needs to understand it.

Graphic 02 · Product data

The product feed is becoming a machine-readable storefront

The more commerce happens through AI and external platforms, the more important complete, accurate product information becomes.
Title & product type
Price & promotions
Finish & colour
Materials & craftsmanship
▣

Your product catalogue

The structured information machines use to understand what you sell and whether it fits a customer’s request.

Dimensions & variants
Images & room settings
Delivery & lead times
Reviews & availability
Google Shopping AI assistants Marketplaces Paid Shopping Merchant feeds

Google’s UCP documentation specifically emphasises the need for robust, up-to-date Merchant Center data, while newer capabilities allow agents to work with information including variants, inventory and pricing.5

That makes the product feed something more than an advertising feed.

It starts to become a machine-readable version of the shop itself.

For ecommerce businesses, poor product data may therefore create problems well beyond Shopping Ads. A product with a vague title, weak description, missing attributes or inconsistent information may simply be harder for automated systems to interpret confidently.

SEO and ecommerce are becoming harder to separate

There is another consequence.

The line between SEO, product content and ecommerce technology is becoming increasingly blurred.

A traditional SEO strategy might focus on helping a product or category page appear when somebody searches:

“luxury mahogany console table”

That remains important.

But AI-led shopping introduces much richer questions:

“I’m looking for a statement console table for a Georgian hallway. I want something traditional, handmade and substantial, but not overly ornate.”

or:

“Show me a hand-finished mahogany console table around 120cm wide that would suit a classic English interior and can be delivered in the UK.”

These are not simply keyword searches.

They combine intent, context, product attributes and personal preferences.

For a retailer to appear appropriately, the systems answering those questions need enough information to understand both the product itself and the space, style and use case it is suitable for.

That reinforces many of the fundamentals we already associate with good SEO and search optimisation: clear product titles, comprehensive descriptions, strong category architecture, structured information, accessible websites and consistent information across the wider web.

But the objective becomes broader than ranking a page.

It is increasingly about making sure a business and its catalogue can be understood by the systems mediating the customer’s research.

Does this mean ecommerce websites become less important?

Possibly the most obvious concern is whether AI shopping platforms eventually reduce the importance of the retailer’s own website.

There is certainly potential for some parts of the journey to move elsewhere.

If a customer can discover a product, compare it with competitors, add it to a basket and potentially pay without following the traditional route through the retailer’s site, fewer interactions may happen directly on the storefront.

But that does not make the website redundant.

It changes its job.

The website remains where a retailer controls its brand, merchandising, storytelling, complete product information, customer accounts, loyalty, cross-selling, editorial content, customer service and much of the post-purchase relationship.

Google’s UCP model is designed to keep participating retailers as the seller of record, while allowing some journeys either to complete on Google or transfer to the merchant’s own checkout.1

So the better question is not:

“Will AI replace ecommerce websites?”

It is:

Which parts of the customer journey will still happen on the website, and which parts might increasingly happen elsewhere?

For brands, maintaining a compelling destination becomes more important if the customer arrives later in the buying process and with stronger expectations.

The customer relationship could become the real battleground

There is a deeper commercial question underneath all of this.

Who owns the customer relationship?

Retailers have already faced this challenge with marketplaces.

Social commerce presents something similar.

Agentic commerce potentially pushes that tension further.

If an AI assistant understands what products somebody likes, their budget, previous purchases, delivery preferences and potentially even payment preferences, the platform could become one of the most influential intermediaries between customer and retailer.

The ability to make shopping easier is clearly valuable.

But retailers will need to think carefully about what they gain — and what they potentially give up — when more of the customer journey takes place on somebody else’s platform.

Paid advertising is likely to change with it

Agentic shopping also creates an interesting question for paid media.

Retail advertising has traditionally worked by getting a customer’s attention and encouraging them to click.

But if an AI assistant is already helping the customer make a decision, advertising may increasingly need to operate inside that decision process.

That could gradually move paid shopping away from:

keyword → advert → click

towards something closer to:

customer intent → AI recommendation → relevant commercial offer → transaction

The implications for bidding, product feeds, creative assets and attribution could be substantial.

I would therefore expect the boundaries between paid search and shopping advertising, Merchant Center optimisation, ecommerce data and AI visibility to become progressively less distinct.

Attribution could become more complicated again

If more of the shopping journey happens inside an AI platform, marketers also face another familiar problem:

measurement.

Imagine:

ChatGPT → product research → brand discovered → Google search → website → purchase

Google Analytics may attribute the final transaction to Organic Search.

Or:

Gemini → product comparison → platform cart → merchant checkout

Depending on how those systems pass attribution data, the journey could look very different inside the retailer’s analytics platform.

The underlying challenge remains:

The place where somebody converts is not necessarily the place that created their preference.

That is already true across social, search, email and offline marketing.

AI simply adds another layer.

This could make it increasingly important for ecommerce teams to combine channel attribution with wider analytics and performance measurement, including brand search, new customer acquisition, customer surveys, incrementality testing and overall commercial performance.

This is not happening everywhere overnight

There is an important caveat.

Agentic commerce is still developing.

Google’s UCP onboarding is currently rolling out gradually in the United States, Canada and Australia, and the UCP-powered checkout feature is available only to selected merchants at this stage.5

Similarly, the exact capabilities available through ChatGPT vary by merchant and integration.

So UK retailers do not need to rebuild their ecommerce strategy tomorrow because customers are suddenly bypassing every website.

That is not what is happening.

But the direction of travel is becoming clearer.

The major technology platforms are building infrastructure intended to allow AI systems to participate in commerce from discovery through to transaction.

And infrastructure changes usually matter before mass customer behaviour completely catches up.

Graphic 03 · Distributed commerce

The storefront is starting to extend beyond the website

Your website remains the brand’s digital home, but product discovery and transaction can increasingly happen across a wider ecosystem.
Your websiteBrand, merchandising, content, customer experience and owned relationship
ChatGPT
Shopping & Paid Media
Marketplaces
Social Commerce
AI Shopping Agents
The opportunity is not to abandon the storefront. It is to make the storefront understandable and accessible wherever the customer chooses to shop.

What should ecommerce brands do now?

There is no need to chase every new protocol or rush into speculative integrations.

Most retailers would benefit far more from strengthening the fundamentals they already control.

Improve the quality of product data

Make product titles, attributes, variants, inventory, pricing and descriptions accurate and consistent. If your catalogue is unclear to your own systems, it will not become easier for external AI systems to interpret.

Treat product content as a strategic asset

Go beyond generic descriptions. Explain dimensions, materials, finishes, craftsmanship, room suitability, care, delivery, lead times and anything else that helps somebody make a better decision.

Strengthen the ecommerce foundations

Structured data, feed quality, Merchant Center, site performance, crawlability and integrations increasingly form part of the same ecosystem.

Continue investing in search

Traditional search remains huge, and AI shopping is increasingly being built into the search ecosystem rather than simply replacing it.

Protect the customer relationship

Email, customer accounts, loyalty, useful content and strong post-purchase experiences become more valuable as discovery and transaction become more distributed.

Watch the technology without chasing the hype

UCP, ACP and agentic checkout are worth understanding. But the immediate priority is making sure your current ecommerce ecosystem gives emerging platforms good information to work with.

The storefront is extending beyond the website

Ecommerce has traditionally been about attracting customers to a website and persuading them to transact there.

AI shopping is beginning to loosen that relationship.

Discovery, comparison, basket building and even parts of checkout can increasingly happen elsewhere, while the retailer’s website remains the place where the brand owns its experience, content and customer relationship.

The immediate challenge for ecommerce brands is therefore not to replace the website with an AI strategy. It is to make sure their products, data and digital infrastructure can work effectively wherever customers choose to shop.

The next phase of ecommerce may not simply be about getting people to your store.
It may be about making sure your store can come to them.

Sources & further reading

  1. Google Merchant Center — Universal Commerce Protocol and UCP-powered checkout.
  2. Google — Introducing Universal Cart and more ways to help you shop (May 2026).
  3. OpenAI — Powering Product Discovery in ChatGPT (March 2026).
  4. OpenAI Help — Shopping from Shopify merchants in ChatGPT.
  5. Google Merchant Center — How to onboard to the Universal Commerce Protocol.

For years, the digital customer journey has been relatively easy to visualise.

Someone has a need. They search Google. They find an article or product page. They explore a website. They compare their options. Eventually, they make an enquiry or purchase.

It’s the familiar digital marketing funnel, and it still matters.

But it’s no longer the whole story.

Today, a potential customer could have researched their problem, understood the available options, compared several brands and even formed an opinion about your business before they have visited your website once.

AI is creating new entrances, shortcuts and loops within the customer journey — and that is changing what it means for a business to be discoverable online.

The funnel hasn’t disappeared. The journey through it has changed.

The traditional marketing funnel remains useful because the fundamental stages are much the same.

People become aware of a need. They research it. They consider their options. They make a decision.

What has changed is where those stages happen.

For years, search engines frequently acted as the gateway. Our job as marketers was to understand what people searched for, create the right content around those searches and build a website capable of turning that traffic into customers.

That journey hasn’t gone away.

Google says Search queries have reached an all-time high at the same time as its AI-powered search experiences have grown. AI Overviews now reach more than 2.5 billion monthly users, while AI Mode has surpassed one billion monthly users.1

Rather than search disappearing, something more interesting is happening.

Search is changing, while entirely new forms of digital discovery are developing alongside it.

The new customer journey is much less tidy

Imagine someone looking for a new winter coat.

Traditionally, they might search Google for “best waterproof men’s coat for winter”. They could visit several retailers, read a buying guide, compare products, look at reviews and eventually make a purchase.

Now imagine that same customer asks an AI assistant:

“I need a smart men’s coat for commuting in the UK. I want something waterproof, warm enough for winter, but not overly technical or outdoorsy. What should I look for?”

That is a very different starting point.

The AI could help them understand which materials and features matter, explain the difference between waterproof and water-resistant fabrics, introduce styles or terminology they hadn’t considered, and help narrow the options based on budget, intended use or personal preferences.

Depending on the question and the information available to it, it could also surface particular products, brands or sources worth investigating.

By the time that customer reaches Google — or your website — they may no longer be at the beginning of their journey.

They may already be halfway through it.

Graphic 01 · Customer journey

From a linear path to a connected discovery journey

The funnel still exists. Customers just don’t necessarily travel through it in a straight line anymore.
Then · Predominantly linear
Need
↓
Google Search
↓
Useful Content
↓
Product / Service Page
↓
Consideration
↓
Conversion
Now · Multiple entrances, shortcuts & loops
AI Assistant
Social / Video
Reviews
Website
Third-party Content
AI Comparison
Conversion
Brand / Direct
Instagram→ChatGPT→Google→Website→Reddit→ChatGPT→Direct visit→Purchase

AI can influence almost every stage

It’s tempting to think of AI as simply another top-of-funnel traffic source.

That misses the bigger change.

AI can appear almost anywhere in the customer journey.

Someone who has only just identified a need might ask an AI assistant what type of product they should be looking for. Someone researching options might use Google’s AI-powered search features to understand the differences between materials or products. A shopper who has narrowed their choice to two coats could ask an AI assistant to compare them.

And after purchasing, that same customer could use AI to ask how to care for, style or use the product.

So rather than asking “How do we rank in ChatGPT?”, the more useful question is:

Can our business be properly understood and represented wherever our customers are researching their decisions?

That is a much bigger marketing challenge — and it reflects what we are already seeing in consumer behaviour.

Adobe reported that traffic from generative AI platforms to UK retail websites rose 329% year-on-year during the 2025 holiday period. Adobe’s analysis says shoppers are using these tools to research products, compare options and look for deals before reaching retail sites.2

At the same time, Gartner’s consumer research points to a more nuanced picture: GenAI is currently complementing rather than simply replacing traditional search. In its survey, consumers were moving across several research environments and often continuing beyond AI summaries.3

Graphic 02 · AI touchpoints

AI isn’t another stage in the funnel

It can help shape discovery, research, consideration, comparison, purchase and even the post-purchase experience.
Discover
◎
AI recommendation
Research
?
AI explanation
Consider
≡
AI shortlist
Compare
⇄
AI comparison
Buy
✓
AI product search
Post-purchase
↻
AI support & advice
The journey can jump forwards, loop backwards and move between platforms at any point.

Your website may actually become more important

There’s an obvious question that follows all of this.

If people are getting more information from AI, does your website become less important?

We’d argue the opposite.

Its role is changing.

Your website isn’t only somewhere customers land after a Google search. It is increasingly part of the evidence layer that helps search engines, AI systems and prospective customers understand your organisation.

For a fashion brand, that might mean making it incredibly clear what you sell, who your products are designed for, which materials you use, how products fit, where they are made, how customers should care for them and what makes the brand different.

If that information is vague, shallow or scattered, it doesn’t only make life harder for people visiting your website. It can make your business harder for machines to understand too.

Strong product information, useful buying guides, clear category pages, brand stories, FAQs, reviews, sizing advice, material information and technically accessible content therefore become part of a much bigger picture.

Your website remains the digital home of your business. But increasingly, other platforms may be interpreting what’s inside it on your behalf.

For ecommerce brands, that makes the relationship between eCommerce design and development, content, search visibility and conversion increasingly difficult to separate.

Good content matters — but perhaps not in the way it used to

For years, businesses have been told to “create content for SEO”.

That has resulted in an enormous amount of content created primarily because somebody found a keyword with sufficient search volume.

Some of it is useful.

Quite a lot of it isn’t.

An AI-assisted customer journey makes genuinely useful content more important, not less.

Think about the questions someone might actually ask while deciding which coat to buy:

What is the difference between these two materials?

Which coat is best for wet but relatively mild weather?

How should this style fit?

Is it suitable for everyday commuting?

How do I care for this fabric?

What should I look for when comparing different brands?

Those questions don’t always fit neatly into traditional keyword research. But they represent real customer intent.

Good content therefore has to do more than attract a click. It needs to demonstrate knowledge, answer questions properly and provide useful information that both people and machines can understand.

Being discoverable is becoming bigger than SEO

None of this means SEO is dead. Far from it.

Many of the fundamentals of good SEO and search optimisation overlap heavily with what businesses need in an AI-assisted discovery environment: clear website architecture, strong and useful content, technical accessibility, authority, expertise, consistent business information and relevant third-party references.

Traditional search traffic also remains considerably larger than identifiable referral traffic from AI assistants.

Ahrefs’ May 2026 analysis of more than 74,000 websites reported that AI chatbots accounted for around 0.28% of total web traffic in March 2026, compared with 28.12% from Google.4

So this isn’t about abandoning SEO to chase the latest acronym.

Google is no longer the only environment where digital discovery happens.
2.5bn+

Monthly users reached by Google AI Overviews, according to Google in 2026.

+329%

Year-on-year growth in UK retail traffic from AI sources during the 2025 holiday period, according to Adobe.

0.28%

Share of total traffic from AI chatbots across Ahrefs’ March 2026 dataset — still small beside Google, but measurable.

Your digital footprint now extends well beyond your website

A business doesn’t exist online solely through the information it publishes itself.

Customers — and increasingly the systems helping them research — can encounter a brand through customer reviews, retail partners, editorial coverage, social platforms, YouTube, industry publications, forums and online communities, directories, influencers, comparison websites and marketplaces.

All of those sources contribute to the wider digital picture surrounding a business.

That means brand consistency matters. Product information matters. Reputation matters. PR matters. Reviews matter. Third-party mentions matter.

Your own website remains central, but the information published elsewhere about your business is becoming part of the discovery ecosystem too.

Marketing attribution is about to get even messier

There’s another challenge this creates.

Measurement.

Some AI referral traffic can be identified when customers click directly through from platforms such as ChatGPT or Perplexity. But many AI-influenced journeys won’t leave such a clean trail.

Someone might discover a business through an AI response and then Google the brand. They might remember the name and visit later. They could see a recommendation, leave the platform entirely and return days later through another channel.

The channel that receives the conversion therefore won’t necessarily be the channel that originally generated the demand.

Graphic 03 · Attribution

What happened vs what analytics may report

The final click can be measured accurately while still giving an incomplete picture of what created the demand.
What actually happened
AI research→ Brand discovered→ Google brand search→ Website→ Purchase
AI research→Brand remembered→Direct visit→Purchase
What analytics may report
Organic Search→Purchase
“Organic Search generated the sale.”
Direct→Purchase
“Direct generated the sale.”

So what should businesses actually do?

We don’t think the answer is to rip up your digital strategy and replace everything with an “AI strategy”.

The businesses best placed to adapt are likely to be those that get the fundamentals right.

Keep investing in search

Traditional search remains enormous, while AI is increasingly becoming part of the search experience itself. SEO isn’t disappearing; its scope is broadening.

Create genuinely useful content

Think beyond keywords. Understand the questions customers ask while discovering, researching, comparing and validating their options — then answer them properly.

Make your business easy to understand

It should be obvious what you do, what you sell, who you serve, what makes you different and where your expertise lies.

Improve product information

For ecommerce businesses, go beyond features. Cover fit, sizing, materials, use cases, care, construction, provenance and the questions customers need answered before buying.

Build authority beyond your site

PR, reviews, relevant publications, social platforms, directories and independent mentions all contribute to the wider picture surrounding your brand.

Measure beyond the last click

Consider brand search, direct traffic, assisted journeys, AI referrals, repeat visits, lead or order quality — and sometimes simply ask customers how they found you.

The funnel isn’t dead. We’ve just lost sight of parts of it.

Customers still become aware. They still research. They still compare. They still make decisions. The difference is that increasingly, those stages don’t all happen somewhere we can see.

A customer could spend 20 minutes researching a product category with an AI assistant before your business is ever mentioned. They could discover your brand through an AI-generated answer, validate it through Google, research products on your website, compare you elsewhere and return three days later directly.

From your analytics platform, you might see only the final few minutes of that journey.

That’s why the answer isn’t to chase every new platform independently. It’s to build a stronger digital ecosystem: a clear brand, useful and technically sound website, detailed product or service information, genuine expertise, strong content, consistent information, external authority and good measurement.

The traditional funnel still matters.
But the front door isn’t where it used to be.

Sources & further reading

  1. Google — New opportunities, control and insights for website owners (updated August 2026).
  2. Adobe Digital Insights — UK online retail spending hits record high (January 2026).
  3. Gartner — GenAI and traditional search consumer research (January 2026).
  4. Ahrefs — AI Chatbot Traffic: What It Is, and How to Get More (May 2026).