<![CDATA[Product Data Weekly]]>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.

]]>productdataweekly.comRiverside.fm (https://riverside.com)Mon, 15 Jun 2026 15:13:39 GMTFri, 13 Feb 2026 12:54:51 GMT60Ben Adams<p><b>Product Data Weekly</b> is a short, practical podcast about the realities of product data operations.</p><p>Each week, we explore the challenges that sit behind growing catalogues, evolving systems and ever increasing data demands.</p><p>No theory.<br />No vendor hype.<br />Just grounded conversations about what it really takes to manage product data at scale.</p><p>If you’re involved in ecommerce, product information, supplier onboarding, ERP, PIM, marketplace feeds or anything connected to product data, this is for you.</p><p>For more practical insight, sign up at <a rel="noopener noreferrer nofollow" href="http://productdataweekly.com" target="_blank">productdataweekly.com</a> and receive our weekly newsletter every Thursday.</p>episodicBen Adamsben@startwithdata.co.ukno<![CDATA[Episode 16: Mastering Product Data for Industrial Solutions & Electrocomponents]]>Manufacturers and distributors think they're dealing with different product data problems. They're not. It's the same problem, viewed from opposite sides of the fence.


In this masterclass episode, we focus on industrial solutions and electronic components, and the product data flow between manufacturers and distributors. We break down why the data nearly always exists but stays buried (in someone's head, an ERP, or a PDF nobody's digitised), why that bottleneck delays launches, frustrates sales teams and creates compliance risk, and what it's actually costing you.
We also share ten things you can do right now to fix it (none of which need a PIM).


If product data is the bottleneck between you and your trading partners, this will show you where it's slipping.

Episode Breakdown
00:00 – Why the data exists but stays buried
04:40 – Three real examples: RS Group, Varian Group, Pacific Automation
06:54 – Same problem, opposite sides of the fence
11:00 – What slow product launches really cost
14:19 – Why the data required keeps growing (and DPP is coming)
20:06 – Why supplier data onboarding isn't a PIM problem
28:19 – Ten things you can do now without a PIM

Keywords: product data, supplier data onboarding, industrial distribution, electronic components, data enrichment, data sheets, Digital Product Passport, compliance, new product introduction, e-commerce, B2B, digital transformation

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

]]>d1307e3b-3072-42c9-97ff-2d847f0903c2Fri, 12 Jun 2026 13:56:50 GMT<p>Manufacturers and distributors think they're dealing with different product data problems. They're not. It's the same problem, viewed from opposite sides of the fence.</p><p><br />In this masterclass episode, we focus on industrial solutions and electronic components, and the product data flow between manufacturers and distributors. We break down why the data nearly always exists but stays buried (in someone's head, an ERP, or a PDF nobody's digitised), why that bottleneck delays launches, frustrates sales teams and creates compliance risk, and what it's actually costing you.<br />We also share ten things you can do right now to fix it (none of which need a PIM).</p><p><br />If product data is the bottleneck between you and your trading partners, this will show you where it's slipping.</p><p></p><p>Episode Breakdown<br />00:00 – Why the data exists but stays buried<br />04:40 – Three real examples: RS Group, Varian Group, Pacific Automation<br />06:54 – Same problem, opposite sides of the fence<br />11:00 – What slow product launches really cost<br />14:19 – Why the data required keeps growing (and DPP is coming)<br />20:06 – Why supplier data onboarding isn't a PIM problem<br />28:19 – Ten things you can do now without a PIM</p><p></p><p>Keywords: product data, supplier data onboarding, industrial distribution, electronic components, data enrichment, data sheets, Digital Product Passport, compliance, new product introduction, e-commerce, B2B, digital transformation</p><p></p><p>Resources<br />Product Data Weekly Newsletter – <a rel="noopener noreferrer nofollow" href="https://productdataweekly.com" target="_blank">https://productdataweekly.com</a></p>no00:34:53116Episode 16: Mastering Product Data for Industrial Solutions & Electrocomponentsfull<![CDATA[Episode 15: Why Massive Retailers Still Run on Monstrous Spreadsheets]]>You'd assume the biggest retailers have moved past spreadsheets by now. Most haven't, and it's not because they're behind. In this episode, we break down why massive businesses still run their entire product catalogue off one giant spreadsheet Sharon could break on a Tuesday, why spreadsheets survive because they're bad and not despite it, and why a PIM so often just replicates the same mess in an expensive database. We also share what actually needs fixing before you swap the spreadsheet for a system. If you've ever felt behind for still running on spreadsheets, this will explain why so many others do too, and what really has to change first.

Episode Breakdown
00:00 – Why big businesses still run on one giant spreadsheet
02:57 – The three reasons teams get stuck
06:54 – Why "free" spreadsheets cost more than you think
12:20 – Why the PIM business case is so hard to prove
13:24 – Where big catalogues get dangerous
15:34 – What to fix before you move off a spreadsheet


Keywords: product data, e-commerce, spreadsheets, PIM, data governance, product data model, business case, product taxonomy, ROI, digital transformation


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

]]>b27a7a5a-4d41-4b1f-a3e0-f918eca039aaFri, 05 Jun 2026 15:48:13 GMT<p>You'd assume the biggest retailers have moved past spreadsheets by now. Most haven't, and it's not because they're behind. In this episode, we break down why massive businesses still run their entire product catalogue off one giant spreadsheet Sharon could break on a Tuesday, why spreadsheets survive because they're bad and not despite it, and why a PIM so often just replicates the same mess in an expensive database. We also share what actually needs fixing before you swap the spreadsheet for a system. If you've ever felt behind for still running on spreadsheets, this will explain why so many others do too, and what really has to change first.<br /></p><p>Episode Breakdown <br />00:00 – Why big businesses still run on one giant spreadsheet<br />02:57 – The three reasons teams get stuck <br />06:54 – Why "free" spreadsheets cost more than you think<br />12:20 – Why the PIM business case is so hard to prove <br />13:24 – Where big catalogues get dangerous <br />15:34 – What to fix before you move off a spreadsheet</p><p><br />Keywords: product data, e-commerce, spreadsheets, PIM, data governance, product data model, business case, product taxonomy, ROI, digital transformation</p><p><br />Resources Product Data Weekly Newsletter – <a rel="noopener noreferrer nofollow" href="https://productdataweekly.com" target="_blank">https://productdataweekly.com</a></p>no00:16:21Episode 15: Why Massive Retailers Still Run on Monstrous Spreadsheetsfull<![CDATA[Episode 14: Why AI Search Keeps Ignoring Your Products]]>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

]]>4c189fe8-0948-4d4d-a9da-e003b2f76a49Thu, 28 May 2026 11:28:35 GMT<p>Most product data was written for humans. The buyers have changed.<br /></p><p>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.<br /></p><p>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.<br /></p><p>If AI search keeps surfacing your competitors instead of you, this will explain why.<br /></p><p><b>Episode Breakdown <br /></b>The query that breaks most catalogues <br />Why the machines are shopping now T<br />he new customer journey <br />Descriptive data isn't enough <br />Structured data: what good looks like <br />Intent-ready data: the future Intent = outcome + constraints + context <br />How to get your data AI-ready<br /></p><p>Keywords: product data, e-commerce, structured data, AI search, agentic commerce, product schema, data enrichment, PIM, buyer intent, digital transformation<br /></p><p>Resources Product Data Weekly Newsletter, <a rel="noopener noreferrer nofollow" href="https://productdataweekly.com" target="_blank">https://productdataweekly.com</a></p>no00:26:12114Episode 14: Why AI Search Keeps Ignoring Your Productsfull<![CDATA[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

]]>8a2c66f1-53db-4ec5-8b85-6eca9553b88aThu, 21 May 2026 12:55:27 GMT<p>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. </p><p>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.<br /></p><p>Episode Breakdown <br />01:45 ‑ The marketplace catalogue arms race </p><p>03:44 ‑ Why onboarding sellers at scale is the real bottleneck </p><p>06:22 ‑ PIM vs DAM in a less saturated market </p><p>10:08 ‑ Real example: the bonsai specialist using AI for product imagery </p><p>12:22 ‑ The three types of AI vendor at Retail Fest </p><p>15:45 ‑ Small teams, big catalogues, why automation isn't optional </p><p>18:03 ‑ What's fundamentally different about the Aussie market</p><p></p><p>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</p><p></p><p>Resources Product Data Weekly Newsletter ‑ <a rel="noopener noreferrer nofollow" href="https://productdataweekly.com" target="_blank">https://productdataweekly.com</a></p>no00:19:45113Episode 13: Marketplaces, Product Data Acronyms, and Practical AI full<![CDATA[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

]]>a0f7893e-50a9-46ef-bc9e-a6cf8a249a1fThu, 14 May 2026 13:38:42 GMT<p>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.</p><p></p><p>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.</p><p>If your product pages aren't performing and you can't quite say why, start here.</p><p></p><p>Episode Breakdown </p><p>00:00 – Why this is a layered approach, not a checklist </p><p>02:04 – Layer 1: basic data and mandatory attributes </p><p>03:20 – Layer 2: readability and why descriptions need foundations first </p><p>05:30 – Layer 3: product relations, variants and substitutes 06:45 – Layer 4: structured data and why AI needs it </p><p>09:30 – Layer 5: product expertise as competitive advantage</p><p>12:30 – Where to find product expertise inside your business</p><p>13:08 – How to audit your catalogue against the five layers</p><p>14:30 – Common mistakes teams make with the five layers</p><p></p><p>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</p><p></p><p>Resources Product Data Weekly Newsletter – <a rel="noopener noreferrer nofollow" href="https://productdataweekly.com" target="_blank">https://productdataweekly.com</a></p>no00:11:12Episode 12: 5 Layers of product data optimisationfull<![CDATA[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 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



]]>2da0bf9c-504b-4267-884d-3087802664c1Thu, 07 May 2026 09:30:00 GMT<p>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. </p><p></p><p>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.</p><p></p><p>Takeaways:</p><ul><li>90% of buyers drop a purchase when product info is missing or wrong</li><li>Inconsistent product data is a process failure, not a people failure</li><li>Customer service teams hold the answers your product pages are missing</li><li>Competitors outrank you because Google and AI understand them better, not because they're better</li><li>Buyers spend 8 seconds scanning a listing, and AI tools spend zero on unstructured pages</li><li>Blank attributes mean invisible products in comparison engines</li><li>Without a single product data owner, every other fix unravels within a year</li><li>You don't need a 12-month PIM project to start; pick one leak and fix it this week<br /></li></ul><p>Join the Product Data Weekly Newsletter here: <a rel="noopener noreferrer nofollow" href="https://productdataweekly.com" target="_blank">https://productdataweekly.com</a><br /><br />Check out Ben &amp; Clare's agency, Start with Data, here: <a rel="noopener noreferrer nofollow" href="http://startwithdata.co.uk" target="_blank">startwithdata.co.uk</a></p><p><br /><br /></p>no00:34:46111Episode 11: Webinar Recording: 5 Ways to stop losing Sales to Bad Product Data full<![CDATA[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

]]>7de5f397-da3c-4e40-b6d4-6d3cda986452Thu, 30 Apr 2026 14:41:44 GMT<p>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).<br /></p><p>If you're 3 PIM demos deep and still can't articulate the value, this will explain why.<br /></p><p>Episode Breakdown <br />00:00 – The two big myths about PIMs <br />03:23 – What a PIM actually does well <br />05:45 – Why brands and manufacturers usually get clear ROI <br />07:06 – What a PIM can't fix <br />09:45 – Work out the problem before you go to market <br />11:16 – The two real problems <br />14:31 – When a PIM is genuinely the right call<br /></p><p>Keywords: product data, e-commerce, PIM, PIM implementation, supplier onboarding, data enrichment, product syndication, data governance, product taxonomy, ROI, digital transformation<br /></p><p>Resources Product Data Weekly Newsletter – <a rel="noopener noreferrer nofollow" href="https://productdataweekly.com" target="_blank">https://productdataweekly.com</a></p>no00:15:37110Episode 10: Do you actually need a PIM?full<![CDATA[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

]]>3667d637-ed03-425b-bed0-300c5c1eb55fThu, 23 Apr 2026 07:13:52 GMT<p>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. </p><p></p><p>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.</p><p> </p><p>Keywords: product data, e-commerce, PIM implementation, data audit, data dictionary, data migration, channel mapping, data enrichment, ROI, digital transformation</p><p> </p><p><b>Key topics:</b></p><p>• Auditing where your product data currently lives</p><p>• Mapping output channels before building your data model</p><p>• Defining what "good" data looks like with a data dictionary</p><p>• Filling data gaps before go-live — not after</p><p>• Why a PIM won't fix your data for you</p><p> </p><p><b>Chapters:</b></p><p>00:00 Investing in product data: the challenge</p><p>02:49 Step 1 — Audit where your data lives</p><p>04:28 Step 2 — Map your output channels</p><p>06:57 Step 3 — Define what good data looks like</p><p>10:19 Step 4 — Fill the gaps before go-live</p><p>12:04 Listener challenge &amp; close</p><p> </p><p><b>Resources</b></p><p>Product Data Weekly Newsletter - <a rel="noopener noreferrer nofollow" href="https://productdataweekly.com" target="_blank">https://productdataweekly.com</a></p>no00:12:4219Episode 9: Stop. Do These 4 Things Before Your PIM Projectfull<![CDATA[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 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

]]>735b47d3-1c77-42e9-8ada-642f8cc3a323Thu, 16 Apr 2026 09:54:58 GMT<p>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.</p><p><br /><b>Keywords:</b> product data, e-commerce, conversion rate, organic search, return rates, operational efficiency, data investment, business case, ROI, digital transformation<br /><br /><b>Key topics:</b></p><ul><li>Justifying product data investment</li><li>Impact of data on conversion rates</li><li>Organic search and content optimization</li><li>Reducing return rates with better data</li><li>Operational efficiency and process automation</li></ul><p></p><p><b>Chapters:</b></p><p>00:00 Investing in Product Data: The Challenge</p><p>03:20 Understanding Conversion Rates</p><p>07:52 Search Visibility and Organic Traffic</p><p>11:22 Managing Return Rates Effectively</p><p>14:57 Enhancing Marketing Efficiency</p><p>17:37 Operational Efficiency in Product Data Management</p><p> resources<br /></p><p>Product Data Weekly Newsletter - <a rel="noopener noreferrer nofollow" href="https://productdataweekly.com" target="_blank">https://productdataweekly.com</a></p><p></p>no00:23:0618Episode 8: Show me the Money - 5 Areas Product Data Investment Pays Off full<![CDATA[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 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

]]>5cb85397-09cc-4778-837d-c5b91c3529feThu, 09 Apr 2026 13:08:10 GMT<p>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.</p><p></p><p><b>Key Topics</b></p><ul><li>Product data sourcing challenges</li><li>Manual data entry limitations</li><li>Data scraping and legal considerations</li><li>Data pools and industry standards</li><li>AI for data extraction and structuring</li><li>Internal data reuse and efficiency</li></ul><p></p><p><b>00:00 </b>Introduction to Product Data Challenges</p><p><b>02:48 </b>Understanding Product Data Sourcing</p><p><b>05:20 </b>Manual Data Entry: Pros and Cons</p><p><b>08:04 </b>Data Scraping: Risks and Rewards</p><p><b>10:42 </b>Exploring Data Pools</p><p><b>13:28 </b>Leveraging AI for Data Ingestion</p><p><b>16:33 </b>Supplier Portals and Templates</p><p><b>19:12 </b>Internal Data Reuse Strategies</p><p><b>22:11 </b>Common Mistakes in Data Management</p>no00:23:447Episode 7: Mastering Product Data Sourcingfull<![CDATA[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

]]>c866a041-b3d4-4145-ad06-66c2123950efFri, 27 Mar 2026 10:00:55 GMT<p>Most product data enrichment projects start the same way.</p><p>Which sounds like the right move but it’s usually why nothing improves.</p><p><br />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.</p><p><br />We also share the approach we see actually working.<br /></p><p>If your enrichment project isn’t moving the needle, this will explain why.<br /><br /><b>Episode Breakdown</b><br />00:00 – Why most enrichment projects start with bestsellers<br />01:10 – The problem with product-by-product enrichment <br />03:30 – How customers actually shop (and why this matters) <br />05:00 – What broken filters and facets look like in practice<br />06:30 – Why patchy data is worse than no data<br />08:15 – The category-led approach (what works instead)<br />10:30 – Real example: why nothing improved… then everything did <br />13:50 – How to choose your first category<br /></p>no00:15:5916Episode 6: Why Top-Seller Enrichment Doesn’t Workfull<![CDATA[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 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
]]>37f4583b-0cb9-4049-bf50-301439bf0921Thu, 19 Mar 2026 16:21:48 GMT<p>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.<br /><br />In this episode, Clare breaks down why this keeps happening and what to do about it.<br /><br />In this episode:</p><ul><li>The grey placeholder test, and why more than 10% means your customers have already noticed</li><li>Why missing images are a data problem, not a marketing problem</li><li>The five ways images fall through the cracks</li><li>What a placeholder actually signals to your buyers</li><li>How some B2B distributors are sitting on 20-30% of their catalogue with no imagery</li><li>Score it, source it, gate it: what to do about it</li><li>Why images need to be part of supplier data submission, not a follow-up</li></ul>no00:08:11Episode 5: The Most Visible Data Problem on Your Websitefull<![CDATA[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 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
]]>0b71059d-579e-455f-8499-7dba0cf7e07eThu, 12 Mar 2026 09:30:51 GMT<p>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.</p><p>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.</p><p><b>In this episode:</b></p><ul><li>Why the process assumes data arrives in a usable state — and why it never does</li><li>The real numbers: 10–15 interactions per SKU, up to a month to launch a single product</li><li>Why jumping straight to automation without the right foundation just automates the mess</li><li>Lever 1: Structure — how to decouple supplier onboarding from your presentational taxonomy</li><li>Lever 2: Usability — how to remove friction without dropping data quality</li><li>Lever 3: Automation — what it can actually do once the foundation is in place</li><li>How to start small: one supplier, one category, one honest conversation</li></ul>no00:20:424Episode 4: Why Supplier Data Is Never Usable - And Three Levers to Fix Itfull<![CDATA[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:

  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.

]]>9a5f55b5-f084-4ee7-8d9a-42d0f4c0e929Thu, 05 Mar 2026 11:10:53 GMT<p>Generative Engine Optimization is the new SEO.</p><p></p><p>In this practical episode, we share three simple changes that can dramatically improve how AI systems interpret and surface your products:</p><p></p><ol><li>Render core product data in static HTML</li><li>Implement and validate structured product schema</li><li>Write question answering content with real context</li><li></li></ol><p>These are small technical adjustments with outsized impact.</p><p></p><p>If AI can’t confidently understand your product, it won’t recommend it.</p>no00:12:413Episode 3: If AI Can’t Read Your Product Page, It Doesn’t Existfull<![CDATA[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 data
  • Incremental approach to category structure improvement
  • Customer experience is influenced by category structures

]]>91b6aaf4-f730-4fd2-80dd-cf49d81003c6Thu, 26 Feb 2026 07:39:07 GMT<p>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.</p><p></p><p>Takeaways</p><ul><li>Category structures impact product data</li><li>Incremental approach to category structure improvement</li><li>Customer experience is influenced by category structures</li></ul><p></p>no00:17:502Episode 2: Why Your Category Structure Is Quietly Hurting Ecommerce Performancefull<![CDATA[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:

  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?

Subscribe for a new episode every week, and get more insights every Thursday at productdataweekly.com

]]>ade3d8d8-c6e8-4f0b-93db-f9095cc63a95Tue, 17 Feb 2026 20:46:08 GMT<p>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.</p><p>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.</p><p>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.</p><p></p><p><b>The five questions:</b></p><ol><li>Where does your product data get manually reformatted today?</li><li>Which attributes are always missing or wrong?</li><li>How many versions of the same product exist across your systems?</li><li>What slows product launches more — approvals or fixing data?</li><li>Could you onboard 50 new suppliers this year without hiring?</li></ol><p></p><p>Subscribe for a new episode every week, and get more insights every Thursday at <a rel="noopener noreferrer nofollow" href="http://productdataweekly.com" target="_blank"><b>productdataweekly.com</b></a></p>no00:23:301Episode 1: The 5 Questions Your Team Is Afraid to Ask About Product Datafull