For years,
product feeds have sat quietly behind some of the largest revenue-generating
advertising programs in retail and eCommerce.
They contain
the information required to advertise a catalogue: what a product is, what it
costs, whether it is available, which category it belongs to, what it looks
like and where someone can buy it.
Important?
Absolutely.
Strategic?
Historically, not always viewed that way.
For many
businesses, the feed has remained an operational layer somewhere between the
eCommerce platform, product catalogue, merchandising team and paid media
account. Teams improve titles, fix disapprovals, maintain attributes, update
prices and make sure products can be advertised correctly.
But the role of
product data is starting to become much bigger.
Shopping is
becoming more conversational. Advertising platforms are getting better at
interpreting detailed consumer intent. Product selection is becoming
increasingly automated. Retail media is expanding. Commerce experiences are
appearing in new environments.
This changes
the value of the information sitting underneath all of it.
For Retail,
eCommerce and D2C businesses, the product feed is no longer interesting simply
because it helps an advertising platform serve a product ad.
It is becoming
increasingly valuable because it helps the wider commerce ecosystem understand what
a business actually sells and which products may be relevant to a particular
customer need.
Shopping
Intent Is Becoming Richer
Traditional
digital commerce has often reduced intent to relatively compact signals.
A consumer
searches for “running shoes.”
Another
searches for “55 inch TV.”
Someone else
searches for “women's winter coat.”
Those signals
can be commercially powerful, but they represent only part of what the customer
actually wants.
The real
requirement might be considerably richer.
A customer
isn't necessarily looking for a television. They may be looking for a
television that fits a relatively small living room, works well in bright
daylight, supports their preferred gaming setup and stays within a specific
budget.
Someone
shopping for a coat may care about weather resistance, material, fit, length,
temperature, style, colour, occasion and price simultaneously.
These needs
have always existed.
What is
changing is the customer's ability to express them more naturally within
digital shopping environments.
Conversational
shopping experiences can handle longer questions, follow-up questions,
comparisons and much more context than a conventional product search. Research
into platform-based shopping assistants also suggests that consumers are
particularly likely to use conversational interfaces for exploratory tasks that
are difficult to compress into conventional keywords.
That has an
important consequence for commerce.
If the
customer's request becomes richer, the system trying to connect that request
with a product needs a richer understanding of the products available to it.
This is where
product feeds become much more interesting.
A Product
Feed Is Increasingly About Product Understanding
Consider what a
basic product record might tell an advertising platform:
Product: Women's Running Shoe
Brand: Example
Colour: Black
Price: €129
Availability: In stock
That
information is useful.
But compare it
with a product record that also contains meaningful information about fit,
material, cushioning, terrain, weight, waterproofing, intended use, available
variants, complementary products and other relevant characteristics.
The second
version gives a commerce system considerably more context.
This direction
is already visible in the market.
Google, for
example, has expanded Merchant Center with optional conversational attributes
covering product questions and answers, related products, variant information,
supporting documents and other details intended to provide additional product
context.
Amazon is
approaching the same broader challenge from within its own commerce ecosystem.
Its shopping experiences can interpret questions around purpose and use case,
compare products and provide recommendations, while its advertising products
increasingly use shopping signals and product context to determine which
products to surface.
The individual
implementations will differ.
The strategic
direction is more important than any particular platform feature:
The more
responsibility commerce systems take for interpreting intent and selecting
products, the more valuable accurate and detailed product information becomes.
Alpha Retail
and Beta Retail
Imagine two
fictional European eCommerce businesses: Alpha Retail and Beta Retail.
They compete in
the same category.
Both have large
product catalogues.
Both invest
significantly in paid media.
Their pricing
is competitive, their brands have similar market awareness and both have access
to sophisticated advertising technology.
From the
outside, their media capabilities look remarkably similar.
The difference
is in what those systems know about their products.
Alpha Retail
maintains a functional product feed.
Its catalogue
contains the required product identifiers, titles, categories, descriptions,
images, prices and availability information. The feed works. Products are
eligible for advertising. Campaigns run at scale.
Beta Retail has
treated its product information differently.
Alongside the
fundamentals, its catalogue contains much richer information about product
characteristics, variants, materials, intended uses, compatibility,
specifications, product relationships and other attributes that genuinely
distinguish one item from another.
Now imagine
both retailers sell 20,000 products.
A shopper
expresses a relatively specific requirement rather than searching for a generic
category.
Both retailers
may have several products capable of satisfying that requirement.
The difference
is not necessarily the quality of their products.
It is not
necessarily their media budget either.
The difference
is how much useful information the commerce system has available when it
tries to understand which products fit that customer's requirement.
Alpha has given
the system a catalogue.
Beta has given
it a richer description of the catalogue.
That
distinction becomes increasingly important as more product selection decisions
are handled automatically.
And this is
where the conversation moves beyond feed management.
The
Opportunity Extends Across Paid Media
It would be
easy to look at this purely through the lens of Shopping campaigns.
That would be
too narrow.
Product-level
advertising is already spread across multiple parts of the paid media
ecosystem.
Search and
Shopping environments use catalogue information to connect demand with
products.
Retail media
networks combine product, transaction and shopper information inside commerce
environments.
Paid Social
platforms use catalogues to dynamically select and advertise products across
large audiences.
Dynamic
remarketing and prospecting use product information to determine what someone
sees.
Marketplace
advertising uses product detail information alongside enormous volumes of
shopping behaviour.
New
conversational commerce environments are adding another layer where customers
can research, compare and evaluate products before making a decision.
Amazon, for
example, now allows automatic product selection within Sponsored Brands
collections, dynamically assembling relevant groups of products from an
advertiser's catalogue based on campaign objectives and shopping signals.
The important
point isn't that every platform will use feeds in exactly the same way.
They won't.
The point is
that product data is becoming useful across a broader set of paid commerce
decisions.
For a retailer
managing tens of thousands of products, that matters.
The question is
gradually becoming less about whether every SKU can technically participate in
advertising and more about whether the business has given its media and
commerce systems enough information to make useful distinctions between those
SKUs.
Product Data
Is Only One Part of the Opportunity
There is
another layer that makes this particularly interesting from a growth
perspective.
Knowing which
product best matches a customer's requirement is useful.
Knowing which
products the business actually wants to grow is even more useful.
Consider two
products that are equally relevant to a customer.
One has limited
inventory and a high probability of being returned.
The other has
healthy stock, stronger margin, lower return rates and historically attracts
customers with higher repeat purchase value.
From a pure
relevance perspective, both products might look attractive.
From a business
perspective, they are not equally valuable.
This is where
the opportunity extends beyond descriptive product information into commercial
intelligence.
Inventory
position.
Margin.
Promotional
priorities.
Product
profitability.
Return rates.
Customer
lifetime value.
Repeat purchase
behaviour.
Seasonality.
Geographic
availability.
Stock ageing.
These signals
answer a different question.
Product
information helps a system understand:
What should
I show?
Commercial
information helps the business answer:
What should
we grow?
Connecting
those two questions is potentially far more valuable than optimizing either one
independently.
Retailers
Already Have a Huge Amount of This Intelligence
Most
established Retail, eCommerce and D2C businesses are not starting from zero.
They have years
of information sitting across different systems.
Paid Search
contains evidence of how customers express demand.
Shopping
campaigns contain product-level performance histories.
Paid Social
contains information about which products and propositions attract attention
outside explicit search demand.
Analytics
contains behavioural and conversion patterns.
CRM systems
contain customer histories.
Commerce
platforms contain transactions, inventory and product relationships.
Merchandising
teams understand seasonality, stock pressure and promotional priorities.
Finance
understands margin and profitability.
Customer
service data can reveal common product questions, objections and reasons for
returns.
The opportunity
is not simply to collect more data.
In many
businesses, the more interesting challenge is connecting information that
already exists.
A product feed
can increasingly become part of that connection because it provides a
structured product layer around which other commercial information can be
organised and activated.
This is where
the conversation starts moving from product feed management toward product
intelligence.
Product
Discovery Is Becoming a Business Question
There is
another reason senior marketing teams should care about this development.
Product
discovery is no longer confined to a retailer's website or a traditional search
results page.
Consumers can
discover, evaluate and compare products across marketplaces, social platforms,
advertising environments and conversational interfaces.
Amazon's
shopping assistant, for example, can help customers explore products by
activity, purpose and other use cases, while conversational shopping is
increasingly being embedded directly into large commerce environments.
At the same
time, retailers are paying close attention to where the customer relationship
ultimately sits. Recent reporting shows major retailers embracing traffic from
conversational shopping experiences while still wanting transactions and
customer relationships to remain within their own ecosystems, where first-party
data, loyalty and repeat purchasing can be developed.
That makes
product discovery more than a media question.
It connects
acquisition with merchandising, customer ownership, CRM, loyalty and long-term
customer value.
For a D2C
brand, the objective isn't simply to have a product selected.
It is to
acquire a valuable customer.
For a retailer,
the objective isn't simply to maximize product impressions.
It may be to
grow a category, accelerate particular inventory, acquire new customers,
increase basket value or improve contribution margin.
The product
feed sits much closer to these commercial decisions than its traditional
reputation suggests.
Back to
Alpha and Beta
Return to our
two retailers.
Alpha and Beta
both have access to increasingly capable advertising platforms.
Both can
automate bidding.
Both can use
sophisticated audience signals.
Both can
generate and test creative at scale.
Both can use
first-party customer data.
Both can access
increasingly sophisticated commerce technology.
Those
capabilities are becoming widely available.
Beta's
advantage isn't access to some secret advertising platform.
It is that the
business has created a richer connection between its products, its
customers, its media and its commercial priorities.
Its systems
have a better understanding of what each product represents.
Its media teams
can understand which products generate demand.
Its commercial
teams know which products create the most valuable outcomes.
Its customer
data provides another layer of information about who buys those products and
what happens after acquisition.
The product
feed becomes one of the structures connecting those pieces.
That doesn't
guarantee Beta wins.
Advertising
will never be that simple.
Brand strength,
pricing, product quality, customer experience, creative, distribution,
competition and dozens of other factors still matter.
But if both
companies increasingly rely on automated systems to make millions of small
decisions about customers and products, the quality of the information
behind those decisions becomes commercially significant.
What This
Means for Retail, eCommerce & D2C Growth
The biggest
opportunity here isn't better feed hygiene.
It is better
decision-making.
A richer
product layer can help businesses think more intelligently about the connection
between:
Customer
intent → Product relevance → Media investment → Transaction → Customer value
That creates
several commercially interesting possibilities.
Paid media can
become more closely connected with merchandising priorities.
Product
selection can reflect more than historical conversion volume.
Media
investment can become more sensitive to inventory and profitability.
Customer
acquisition can be evaluated against the products and customers that generate
longer-term value.
Large
catalogues can become easier for increasingly automated systems to interpret.
And businesses
can make better use of information that is currently fragmented across media,
commerce, CRM and operational systems.
None of this
means the product feed suddenly becomes the centre of the marketing
organization.
It shouldn't.
But it does
mean that treating it purely as the technical file required to run product
advertising increasingly understates its value.
The Bigger
Opportunity
For years,
performance marketing teams have invested heavily in improving the intelligence
around the customer.
Audience
segmentation became more sophisticated.
Measurement
improved.
First-party
data became more important.
Bidding
incorporated more signals.
Customer value
entered optimization strategies.
The product
side of the equation deserves the same attention.
A retailer can
understand its customers exceptionally well and still provide advertising
systems with relatively limited information about the thousands of products it
wants those customers to buy.
As commerce
becomes more conversational and product selection becomes more automated, that
imbalance becomes increasingly difficult to ignore.
The opportunity
for Retail, eCommerce and D2C businesses is therefore bigger than optimizing a
feed.
It is about
building a richer connection between what customers want, what the business
sells and what the business wants to grow.
Product feeds
are becoming an increasingly important part of that connection.
And that is
what makes them strategically interesting for the next phase of paid commerce.
