Most product data was written for humans. The buyers have changed.

The new buyer is an AI agent: ChatGPT, Gemini, Perplexity, Claude. When someone asks for "an IP65-rated, 5–10kW three-phase motor for an outdoor conveyor", the agent matches that against structured data. If your attributes aren't there, you get ignored.

In this episode, Ben breaks down the shift from descriptive data, to structured data, to intent-ready data, and what you need to win when machines are doing the shopping.

If AI search keeps surfacing your competitors instead of you, this will explain why.

Episode Breakdown
The query that breaks most catalogues
Why the machines are shopping now T
he new customer journey
Descriptive data isn't enough
Structured data: what good looks like
Intent-ready data: the future Intent = outcome + constraints + context
How to get your data AI-ready

Keywords: product data, e-commerce, structured data, AI search, agentic commerce, product schema, data enrichment, PIM, buyer intent, digital transformation

Resources Product Data Weekly Newsletter, https://productdataweekly.com

Most takes on the Australian e-commerce market assume it's just a smaller, slower version of the UK or US. That's half right, and the other half is where it gets interesting. In this episode, Ben's just back from two weeks in Australia and we unpack what's actually happening on the ground: the marketplace onboarding bottleneck nobody's solving, why the PIM vs DAM debate looks different over there, and why Aussie retailers may be further ahead on practical AI adoption than the UK.

We also cover what mid-size retailers are doing differently when they don't have the budget for big consulting groups or huge product data teams. If you're selling into Australia, partnering with Aussie retailers, or just want to know which markets are skipping the hype and getting practical, this one's for you.

Episode Breakdown
01:45 ‑ The marketplace catalogue arms race

03:44 ‑ Why onboarding sellers at scale is the real bottleneck

06:22 ‑ PIM vs DAM in a less saturated market

10:08 ‑ Real example: the bonsai specialist using AI for product imagery

12:22 ‑ The three types of AI vendor at Retail Fest

15:45 ‑ Small teams, big catalogues, why automation isn't optional

18:03 ‑ What's fundamentally different about the Aussie market

Keywords: product data, e-commerce, Australia retail, marketplaces, supplier onboarding, PIM vs DAM, digital asset management, AI adoption, product data operations, Retail Fest, mid-size retailers, data enrichment

Resources Product Data Weekly Newsletter ‑ https://productdataweekly.com

Most teams try to fix their product pages by jumping straight to descriptions or AI search tools. Which feels like progress but is usually why nothing improves.

In this episode, Clare walks through the five layers of product data optimisation and why doing them out of order is the reason so many product data projects stall. We cover what each layer does, what breaks when you skip ahead, and where the AI surfacing question actually fits in.

If your product pages aren't performing and you can't quite say why, start here.

Episode Breakdown

00:00 – Why this is a layered approach, not a checklist

02:04 – Layer 1: basic data and mandatory attributes

03:20 – Layer 2: readability and why descriptions need foundations first

05:30 – Layer 3: product relations, variants and substitutes 06:45 – Layer 4: structured data and why AI needs it

09:30 – Layer 5: product expertise as competitive advantage

12:30 – Where to find product expertise inside your business

13:08 – How to audit your catalogue against the five layers

14:30 – Common mistakes teams make with the five layers

Keywords: product data, e-commerce, product data optimisation, product attributes, structured data, product descriptions, AI search, agentic AI, product relations, PIM, conversion rate, digital transformation

Resources Product Data Weekly Newsletter – https://productdataweekly.com

In this masterclass episode, Ben and Clare break down five silent leaks costing eCommerce and B2B businesses sales every day through bad product data. Most teams assume fixing product data means a 12-month PIM project signed off by the board, but each of these leaks can be tackled in a week. Drawing from real customer work at Start with Data, Ben and Clare walk through the issues that actually move the needle: inconsistent specs across channels, missing info that kills the mid-sale, competitors outranking you with worse products, listings that neither humans nor AI can read, and the ownership gap that unravels every other fix.

They also share the 90-day action plan they use with customers to turn one-off fixes into a proper improvement programme. If product data keeps getting pushed down your roadmap, this episode will challenge how you think about it.

Takeaways:

• 90% of buyers drop a purchase when product info is missing or wrong
• Inconsistent product data is a process failure, not a people failure
• Customer service teams hold the answers your product pages are missing
• Competitors outrank you because Google and AI understand them better, not because they're better
• Buyers spend 8 seconds scanning a listing, and AI tools spend zero on unstructured pages
• Blank attributes mean invisible products in comparison engines
• Without a single product data owner, every other fix unravels within a year
• You don't need a 12-month PIM project to start; pick one leak and fix it this week
  

Join the Product Data Weekly Newsletter here: https://productdataweekly.com

Check out Ben & Clare's agency, Start with Data, here: startwithdata.co.uk

In this episode, we break down the two big myths about PIMs, where a PIM genuinely is the right call, and what most retailers and distributors actually need instead (or alongside it).

If you're 3 PIM demos deep and still can't articulate the value, this will explain why.

Episode Breakdown
00:00 – The two big myths about PIMs
03:23 – What a PIM actually does well
05:45 – Why brands and manufacturers usually get clear ROI
07:06 – What a PIM can't fix
09:45 – Work out the problem before you go to market
11:16 – The two real problems
14:31 – When a PIM is genuinely the right call

Keywords: product data, e-commerce, PIM, PIM implementation, supplier onboarding, data enrichment, product syndication, data governance, product taxonomy, ROI, digital transformation

Resources Product Data Weekly Newsletter – https://productdataweekly.com

Clare and Ben discuss the four essential steps teams should take before committing to a PIM implementation or e-commerce re-platform, focusing on product data readiness.

They share practical insights on why so many projects run late and over budget, and how proper preparation around product data almost always costs less than fixing problems after go-live.

Keywords: product data, e-commerce, PIM implementation, data audit, data dictionary, data migration, channel mapping, data enrichment, ROI, digital transformation

Key topics:

• Auditing where your product data currently lives

• Mapping output channels before building your data model

• Defining what "good" data looks like with a data dictionary

• Filling data gaps before go-live — not after

• Why a PIM won't fix your data for you

Chapters:

00:00 Investing in product data: the challenge

02:49 Step 1 — Audit where your data lives

04:28 Step 2 — Map your output channels

06:57 Step 3 — Define what good data looks like

10:19 Step 4 — Fill the gaps before go-live

12:04 Listener challenge & close

Resources

Product Data Weekly Newsletter - https://productdataweekly.com

Ben and Clare discuss the importance of investing in product data, focusing on five key areas: conversion, organic search visibility, return rates, operational efficiency, and cross-team collaboration. They share practical insights on how to justify data investments and measure their impact to drive growth and efficiency.

Keywords: product data, e-commerce, conversion rate, organic search, return rates, operational efficiency, data investment, business case, ROI, digital transformation

Key topics:

• Justifying product data investment
• Impact of data on conversion rates
• Organic search and content optimization
• Reducing return rates with better data
• Operational efficiency and process automation

Chapters:

00:00 Investing in Product Data: The Challenge

03:20 Understanding Conversion Rates

07:52 Search Visibility and Organic Traffic

11:22 Managing Return Rates Effectively

14:57 Enhancing Marketing Efficiency

17:37 Operational Efficiency in Product Data Management

resources

Product Data Weekly Newsletter - https://productdataweekly.com

This episode explores the various methods of sourcing and enriching product data, highlighting their pros, cons, and practical considerations. It provides insights into manual processes, data scraping, data pools, AI-driven solutions, and internal reuse strategies, offering actionable advice for scaling and improving product data sourcing.

Key Topics

• Product data sourcing challenges
• Manual data entry limitations
• Data scraping and legal considerations
• Data pools and industry standards
• AI for data extraction and structuring
• Internal data reuse and efficiency

00:00 Introduction to Product Data Challenges

02:48 Understanding Product Data Sourcing

05:20 Manual Data Entry: Pros and Cons

08:04 Data Scraping: Risks and Rewards

10:42 Exploring Data Pools

13:28 Leveraging AI for Data Ingestion

16:33 Supplier Portals and Templates

19:12 Internal Data Reuse Strategies

22:11 Common Mistakes in Data Management

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