Every year, retailers pour enormous effort into Black Friday, aligning categories, planning promotions and briefing channels. Yet the results are often underwhelming. You might think that’s because of weak pricing, but more often it’s because assortment data lacks transparency and isn’t ready.
“Data isn’t ready” can mean many things, like: a broken variant link, a product not surfacing in filters, a sold-out item still marked as available, incorrect product images or a bundle that fails because one component wasn’t mapped correctly. These are operational issues that every retailer faces in their online shop, especially during high-volume periods. (In case you doubt that, reach out and I’ll show you with the help of AI agents & one click on your website.)
Below, I’ll explain how unstructured assortment data quietly erodes performance and what category teams can do now to avoid these pitfalls.
1. When structure fails, margin leaks
You don’t always notice it during planning when a product is labeled inconsistently or another is tagged with a filter that doesn’t match the on-site logic. But that’s enough for entire parts of your assortment to become invisible to shoppers, pricing engines and promo logic.
And that invisibility is expensive. If a top seller can’t be found or included in the right promotion mechanic, it’s a direct hit to margin.
2. Substitute logic breaks without clear relationships
Let’s say your most promoted product sells out on Friday morning. In a well-prepared system, a defined alternative steps in. But in most setups, it doesn’t, because product relationships weren’t established, variant logic wasn’t clean, or the data doesn’t tell the engine what’s similar enough. This kind of failure often repeats across dozens of products, creating a domino effect of lost sales, frustrated shoppers and dead-end search results.
These are fixable gaps, but they need to be addressed before promotions start. And we all know how long it can take to clean just one category.
3. Bad data breaks promotion logic
Black Friday is about discount logic and as a numbers guy, I really wonder how anyone can rely on that logic if the product data isn’t clean.
A classic example: you plan a cross-category bundle, but half the items don’t share the same attributes, or one SKU is miscategorized. The bundle fails, or worse, creates a pricing inconsistency that confuses shoppers. Promotions that rely on faulty hierarchies or outdated product labels not only fail to convert, they also erode trust. And that’s much harder to fix than a data field.
4. The shopper experience suffers before conversion fails
Messy product data doesn’t just confuse tools or those setting up pricing logic. It frustrates humans. If shoppers can’t filter for the right size, can’t compare alternatives, or land on products that appear twice or not at all, they leave. And they don’t just leave empty-handed, they leave with the impression that your store or brand isn’t reliable.
What if I told you that AI agents can clean your whole assortment data with a single click, no matter how many millions of products your assortment contains?
What Category Teams Can Do Right Now
Here’s where to start:
- Audit your product hierarchies: Do filters, bundles and variants follow consistent logic?
- Review substitute logic: Are fallback options clearly defined and linked?
- Standardize key attributes: Especially those linked to pricing and promo engines
- Clean up inconsistencies: Remove duplicate listings, unify labels, check for outdated metadata
- Test live scenarios: Simulate common promo or filter logic to find breakpoints early
Small fixes here often outperform and can help fixing small things fast. For fixing all the data, you would need the help of AI for --> to solve all data problems with one click.
Put the one click claim to the test. Send one category from your online store and I will clean it and reply asap (non of your internal data needed): arber@zenline.ch