Product Data Weekly

Ben Adams

Product Data Weekly is a short, practical podcast about the realities of product data operations. Each week, we explore the challenges that sit behind growing catalogues, evolving systems and ever increasing data demands. No theory. No vendor hype. Just grounded conversations about what it really takes to manage product data at scale. If you’re involved in ecommerce, product information, supplier onboarding, ERP, PIM, marketplace feeds or anything connected to product data, this is for you. For more practical insight, sign up at productdataweekly.com and receive our weekly newsletter every Thursday.

  1. 10H AGO

    Episode 13: Marketplaces, Product Data Acronyms, and Practical AI

    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

    20 min

  2. MAY 14

    Episode 12: 5 Layers of product data optimisation

    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

    11 min

  3. MAY 7

    Episode 11: Webinar Recording: 5 Ways to stop losing Sales to Bad Product Data

    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 wrongInconsistent product data is a process failure, not a people failureCustomer service teams hold the answers your product pages are missingCompetitors outrank you because Google and AI understand them better, not because they're betterBuyers spend 8 seconds scanning a listing, and AI tools spend zero on unstructured pagesBlank attributes mean invisible products in comparison enginesWithout a single product data owner, every other fix unravels within a yearYou 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

    35 min

  4. APR 30

    Episode 10: Do you actually need a PIM?

    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

    16 min

  5. APR 23

    Episode 9: Stop. Do These 4 Things Before Your PIM Project

    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

    13 min

  6. APR 16

    Episode 8: Show me the Money - 5 Areas Product Data Investment Pays Off

    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 investmentImpact of data on conversion ratesOrganic search and content optimizationReducing return rates with better dataOperational 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

    23 min

  7. APR 9

    Episode 7: Mastering Product Data Sourcing

    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 challengesManual data entry limitationsData scraping and legal considerationsData pools and industry standardsAI for data extraction and structuringInternal 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

    24 min

  8. MAR 27

    Episode 6: Why Top-Seller Enrichment Doesn’t Work

    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

    16 min
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About

Product Data Weekly is a short, practical podcast about the realities of product data operations. Each week, we explore the challenges that sit behind growing catalogues, evolving systems and ever increasing data demands. No theory. No vendor hype. Just grounded conversations about what it really takes to manage product data at scale. If you’re involved in ecommerce, product information, supplier onboarding, ERP, PIM, marketplace feeds or anything connected to product data, this is for you. For more practical insight, sign up at productdataweekly.com and receive our weekly newsletter every Thursday.

Information

  • Creator
    Ben Adams
  • Years Active
    2K
  • Episodes
    13
  • Rating
    Clean
  • Copyright
    © 2026 Ben Adams