The product catalogue looks very much “done and dusted” in your primary channel. Customer search works OK. Filters are behaving themselves. Your category managers are satisfied that they trust what they see. All good.
So, you proceed to syndicate the same products to a marketplace, a distributor portal, trade partner, and your comparison engine of choice. It’s then that the workload explodes. Teams scramble to rebuild attributes lists, rewrite titles, split variants, reformat units, reclassify categories, and then sit back at the ready, biting their nails while babysitting feeds to all these channels.
What happened? The simple answer is that it isn’t a tooling failure. It’s just what happens when the business never reaches common agreement on a shared structural backbone for what a product is, regardless of which channel it’s sold through.
Telling signs: your channel launches feel like re-platforms
When a product structure scales satisfactorily to a new channel, there are mostly mapping and validation tasks to carry out. If it doesn’t scale, populating each channel feels like a small rebuild project, and bugbears abound:
- Marketplaces reject “valid” products (using your criteria!) because mandatory attributes aren’t present or aren’t correctly typed
- Variants which render neatly on your site arrive as ungrouped rows or in the wrong parent/child shape
- Category trees don’t align because your teams have maintained parallel hierarchies and “translation” spreadsheets
- Text gets cut off mid-description, images fail to render, and unit formatting breaks rules you didn’t even know existed
- Local market requirements (fundamentals like language, compliance, classification) end up cross-polluting the global model
If you recognise any of these patterns, your core issue is not likely to be missing data. It’s because the structure you’ve built only makes sense inside one consumption context.
Why this happens even if your PIM is live
A PIM which is technically ‘live’ can still be operationally contested if your product model is doing double duty in that:
- It’s being treated as a presentation model (built to drive web navigation, merchandising, SEO copy blocks)
- It’s also being expected to function as a portable product model (capable of projecting into marketplace feeds, partner schemas, B2B classification, print, and compliance)
Those are two fundamentally different jobs. If you attempt to get one model to satisfy both demands, every channel requirement becomes a debate about schema, and every debate inevitably ends up with manual rework.
The three structural failures which create channel-specific rework
1) Semantic failure: you don’t share meaning with the channel
Your internal vocabulary doesn’t align with the channel’s vocabulary. Colour vs “shade.” Collection vs product type. Outerwear vs Apparel > Jackets & Coats. A human can interpret the intent, but a channel schema cannot.
At scale, this lack of alignment shows up as:
- Inconsistent attribute usage across teams (Size vs Dimensions vs Measurements)
- Reduced trustworthiness in filters and faceting because values are free-text and non-comparable
- Constant circular arguments about whether the channel is being “picky” or your data is “fine”
You can’t solve these problems by renaming fields. The genuine solution is to agree on which concepts are canonical[1], and to treat translation as a governedcapability, not a ‘heroic’ activity by plucky team members.
2) Structural failure: your data shape can’t be projected cleanly
Channels don’t just use different labels; they expect different grammars. Common instances of common content clashes include:
- Your internal model relies on inheritance and nested structures; The channel expects flat SKU-level records
- Your variant logic assumes “parent product with selectable options;” the channel expects each variant as an item with its own attributes and images
- Your ERP logic groups product identity differently from how channels group sellable units
When projection is challenging, incidences of transforms (basically, the process of altering, converting, or cleaning product data) proliferate. Every transformation has a cost: someone has to design it, debug it, maintain it, and re-validates it when the channel’s requirements change.
3) Governance failure: no one owns cross-channel fitness
Most merchants distribute data ownership by channel:
- web team “owns” taxonomy and attribute labels
- marketplace team “owns” feed optimisation and compliance
- ERP team “owns” material groups and master data rules
- regional teams “own” localisation
Each decision is locally rational, but collectively, it tends to result in structural drift: exceptions get baked in, parallel hierarchies appear, and the “single source of truth” becomes yet another source of arguments. The PIM ends up getting the blame simply because it’s where the conflict becomes visible to all.
The hidden cost: Not only manual effort, but structural debt
Channel-specific rework Once you quantify what channel-specific rework involves, the costs to commercial performance become apparent:
- Longer time-to-revenue for new channels because onboarding is dependent on specialist mapping effort
- Higher run cost because feed stability requires ongoing manual intervention
- Lower optionality because cross-channel initiatives (analytics, personalisation, automation) can’t trust a coherent product model
- More fragile operations because every change ripples through bespoke transforms and exceptions
The organisation isn’t just continually paying for more people-hour efforts. It also pays in constrained growth: you can’t add channels at the speed which commercial teams expect, and you can’t change fast without breaking downstream outputs.
What “a lack of shared structural backbone” looks like in practice
You don’t need to dig down into the tech stack to see the tell-tale signs:
- Product types are apparently defined by whoever shouted loudest during the website build
- Attributes exist because a channel asked for them, not because the product needs them
- Variants are modelled for the current storefront experience, not for portability
- Global, local, and channel-specific values are mixed together because “it’s quicker to hit time-to-market deadlines that way.”
- Documentation does exist (spreadsheets, confluence) but enforcement is manual and inconsistent
At this point, a top-down, ‘forced’ adoption approach (think: more training, more governance meetings, more “data quality initiatives”) doesn’t stick. People work around the structure because the structure as it is doesn’t match the commercial reality of multi-channel selling.
A structural mismatch that keeps dragging down performance…permanently
Weak scalability across channels is usually a single, essentially simple, mismatch: a single-channel presentation structure is being used as the enterprise product backbone. It performs fine where you’ve optimised it (most likely your primary storefront), but it fails everywhere else because it can’t be reliably projected into the ecosystems of other channels without pushing up the cost of transformation.
It’s a chronic condition explaining rework never disappears, taking up valuable time and effort better deployed elsewhere. Add to that fact, channels constantly change and unless you do something about it, this structural debt keeps charging interest.
What next? Book a discovery call
If you’re finding that new channels keep triggering rework like variant remodelling, attribute firefighting, or feed exceptions, We’re ready at Start with Data to get you on the journey towards structural integrity. Get in touch with us today to book a discovery call. We’ll review where your product structure is acting like a storefront model, where it needs to function as a portable backbone, and how you can eliminate those constant channel-specific fixes.