Product Data Weekly

Ben Adams
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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.

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  1. 6일 전

    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

    11분

  2. 5월 7일

    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

    35분

  3. 4월 30일

    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

    16분

  4. 4월 23일

    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

    13분

  5. 4월 16일

    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

    23분

  6. 4월 9일

    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

    24분

  7. 3월 27일

    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

    16분

  8. 3월 19일

    Episode 5: The Most Visible Data Problem on Your Website

    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 noticedWhy missing images are a data problem, not a marketing problemThe five ways images fall through the cracksWhat a placeholder actually signals to your buyersHow some B2B distributors are sitting on 20-30% of their catalogue with no imageryScore it, source it, gate it: what to do about itWhy images need to be part of supplier data submission, not a follow-up

    8분

  9. 3월 12일

    Episode 4: Why Supplier Data Is Never Usable - And Three Levers to Fix It

    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 doesThe real numbers: 10–15 interactions per SKU, up to a month to launch a single productWhy jumping straight to automation without the right foundation just automates the messLever 1: Structure — how to decouple supplier onboarding from your presentational taxonomyLever 2: Usability — how to remove friction without dropping data qualityLever 3: Automation — what it can actually do once the foundation is in placeHow to start small: one supplier, one category, one honest conversation

    21분

  10. 3월 5일

    Episode 3: If AI Can’t Read Your Product Page, It Doesn’t Exist

    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: Render core product data in static HTMLImplement and validate structured product schemaWrite question answering content with real contextThese are small technical adjustments with outsized impact. If AI can’t confidently understand your product, it won’t recommend it.

    13분

  11. 2월 26일

    Episode 2: Why Your Category Structure Is Quietly Hurting Ecommerce Performance

    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 dataIncremental approach to category structure improvementCustomer experience is influenced by category structures

    18분

  12. 2월 17일

    Episode 1: The 5 Questions Your Team Is Afraid to Ask About Product Data

    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: Where does your product data get manually reformatted today?Which attributes are always missing or wrong?How many versions of the same product exist across your systems?What slows product launches more — approvals or fixing data?Could you onboard 50 new suppliers this year without hiring? Subscribe for a new episode every week, and get more insights every Thursday at productdataweekly.com

    24분
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소개

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.

정보

  • 제작진
    Ben Adams
  • 방송 연도
    2026년
  • 에피소드
    12
  • 등급
    전체 연령 사용가
  • 저작권
    © 2026 Ben Adams