Most product data enrichment projects start the same way.

Which sounds like the right move but it’s usually why nothing improves.

In this episode, we break down why enriching products one-by-one (based on sales) leads to broken filters, patchy categories, and a frustrating customer experience.

We also share the approach we see actually working.

If your enrichment project isn’t moving the needle, this will explain why.

Episode Breakdown
00:00 – Why most enrichment projects start with bestsellers
01:10 – The problem with product-by-product enrichment
03:30 – How customers actually shop (and why this matters)
05:00 – What broken filters and facets look like in practice
06:30 – Why patchy data is worse than no data
08:15 – The category-led approach (what works instead)
10:30 – Real example: why nothing improved… then everything did
13:50 – How to choose your first category

Missing images are one of the most visible data quality problems on any eCommerce site, and one of the least likely to be treated as a data problem.  The result is grey placeholders that quietly kill conversion, suppress marketplace listings, and tell your buyers you don't care enough about the product to show them what it looks like.

In this episode, Clare breaks down why this keeps happening and what to do about it.

In this episode:

• The grey placeholder test, and why more than 10% means your customers have already noticed
• Why missing images are a data problem, not a marketing problem
• The five ways images fall through the cracks
• What a placeholder actually signals to your buyers
• How some B2B distributors are sitting on 20-30% of their catalogue with no imagery
• Score it, source it, gate it: what to do about it
• Why images need to be part of supplier data submission, not a follow-up

Supplier data is one of the biggest operational headaches in distribution, retail, and marketplaces. The data arrives late, incomplete, and in the wrong format. Teams spend weeks transforming it manually. And yet most businesses keep buying portals and templates that don't actually solve anything.

In this episode, Clare and Ben get into why the problem keeps getting worse — and lay out the three levers that actually move the needle.

In this episode:

• Why the process assumes data arrives in a usable state — and why it never does
• The real numbers: 10–15 interactions per SKU, up to a month to launch a single product
• Why jumping straight to automation without the right foundation just automates the mess
• Lever 1: Structure — how to decouple supplier onboarding from your presentational taxonomy
• Lever 2: Usability — how to remove friction without dropping data quality
• Lever 3: Automation — what it can actually do once the foundation is in place
• How to start small: one supplier, one category, one honest conversation

Generative Engine Optimization is the new SEO.

In this practical episode, we share three simple changes that can dramatically improve how AI systems interpret and surface your products:

1. Render core product data in static HTML
2. Implement and validate structured product schema
3. Write question answering content with real context

These are small technical adjustments with outsized impact.

If AI can’t confidently understand your product, it won’t recommend it.

This episode explores the critical importance of category structure and attribute management in e-commerce. Hosts Clare Adams and Ben Adams discuss how proper product categorisation impacts data quality, customer experience, and operational efficiency, offering practical steps for improvement.

Takeaways

• Category structures impact product data
• Incremental approach to category structure improvement
• Customer experience is influenced by category structures

Most product data problems don't show up as data problems — they show up as delayed launches, supplier headaches, and e-commerce teams drowning in fixes. In this first episode, Clare and Ben cut through the noise to ask the five questions that expose where your product data operations are actually breaking down.

From manual reformatting at ingestion, to 3,500-entry brand lists nobody can navigate, to the real reason new product launches take forever — this episode is a no-BS audit of where large-catalogue businesses are quietly losing time and money.

Whether you're a distributor, retailer, or marketplace, these five questions will tell you more about the health of your product data ops than any system audit ever could.

The five questions:

1. Where does your product data get manually reformatted today?
2. Which attributes are always missing or wrong?
3. How many versions of the same product exist across your systems?
4. What slows product launches more — approvals or fixing data?
5. Could you onboard 50 new suppliers this year without hiring?

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