Product Data Weekly

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 advantage12:30 – Where to find product expertise inside your business13:08 – How to audit your catalogue against the five layers14:30 – Common mistakes teams make with the five layersKeywords: product data, e-commerce, product data optimisation, product attributes, structured data, product descriptions, AI search, agentic AI, product relations, PIM, conversion rate, digital transformationResources 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 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 weekJoin the Product Data Weekly Newsletter here: https://productdataweekly.comCheck 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 callKeywords: product data, e-commerce, PIM, PIM implementation, supplier onboarding, data enrichment, product syndication, data governance, product taxonomy, ROI, digital transformationResources 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 challenge02:49 Step 1 — Audit where your data lives04:28 Step 2 — Map your output channels06:57 Step 3 — Define what good data looks like10:19 Step 4 — Fill the gaps before go-live12:04 Listener challenge & close ResourcesProduct 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 transformationKey topics:Justifying product data investmentImpact of data on conversion ratesOrganic search and content optimizationReducing return rates with better dataOperational efficiency and process automationChapters:00:00 Investing in Product Data: The Challenge03:20 Understanding Conversion Rates07:52 Search Visibility and Organic Traffic11:22 Managing Return Rates Effectively14:57 Enhancing Marketing Efficiency17:37 Operational Efficiency in Product Data Management resourcesProduct 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 TopicsProduct data sourcing challengesManual data entry limitationsData scraping and legal considerationsData pools and industry standardsAI for data extraction and structuringInternal data reuse and efficiency00:00 Introduction to Product Data Challenges02:48 Understanding Product Data Sourcing05:20 Manual Data Entry: Pros and Cons08:04 Data Scraping: Risks and Rewards10:42 Exploring Data Pools13:28 Leveraging AI for Data Ingestion16:33 Supplier Portals and Templates19:12 Internal Data Reuse Strategies22:11 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 Breakdown00:00 – Why most enrichment projects start with bestsellers01:10 – 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 practice06:30 – Why patchy data is worse than no data08:15 – 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 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

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

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.

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.TakeawaysCategory structures impact product dataIncremental approach to category structure improvementCustomer 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: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