ACES and PIES Explained: A Practical Guide for Automotive Distributors
ACES and PIES are the two XML data standards that underpin automotive aftermarket cataloguing.
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Akeneo vs Bluestone PIM vs Plytix: An Honest Comparison (2026)
Most Akeneo vs Bluestone vs Plytix articles are written by people who have read the marketing pages, watched the demo videos, and assembled a feature comparison table from the vendor websites.
Best PIM for B2B Distributors in 2026: Four Platforms Compared
Most PIM vendor shortlists are built for retailers. The evaluation criteria assume a few thousand SKUs, consistent brand-owned product data, and a single primary ecommerce channel.
Best PIM for Shopify: 6 Options Compared
Shopify handles checkout, orders, and storefront design well. What it does not handle is complex product data at scale. Once your catalogue reaches a few thousand SKUs, or you start publishing to channels beyond your Shopify store, the standard product editor becomes a serious bottleneck.
Product Data Quality: How to Measure It (And What Good Looks Like)
Most teams cannot answer a simple question: “Is our product data good?” They have spreadsheets full of attribute counts, screenshots of empty fields, and a vague sense that things could be tidier.
PIM cost: What you actually pay for (and what vendors don’t tell you)
PIM cost rarely matches what the first vendor quote suggests. For a mid-market distributor running 50,000 to 100,000 SKUs, all-in first-year PIM pricing usually lands between £120,000 and £350,000.
PIM vs MDM vs ERP: Which does what, and where they overlap
Buyers ask us some version of the same question every month: do I need a PIM, an MDM, or do I just need to push my ERP harder? PIM vs MDM vs ERP is a confusing comparison because the three systems overlap in real ways, and vendor marketing has muddied where the lines sit.
PIM Selection Process: a 12-Stage Buyer’s Framework
Most failed PIM projects can be traced back to a flawed selection. The vendor was picked before the requirements were clear, demos showed scripted scenarios rather than real catalogue conditions, and the scoring framework was assembled after the favoured vendor had already emerged.
How Long Does a PIM Implementation Take? (Honest Timelines)
A typical PIM vendor demo ends with a slide that says twelve weeks to go-live. The number is real for a specific kind of project: a small catalogue, one channel, no integrations, no governance, no change management.
Product data cleaning 101: Steps to cleanse your catalogue
Dirty product data slows launches, weakens SEO, breaks filters, and increases returns. This eBook explains how to cleanse your catalogue step by step and build the controls needed to keep it clean.
Governance for taxonomy: Keeping category structures consistent over time
A well-designed taxonomy will still fail if no one governs it. This article explains how to maintain category consistency over time through ownership, change control, documentation, and continuous monitoring.
Calculating PIM ROI: How to measure the return on your investment
PIM ROI can be measured across four key areas: internal efficiency gains, data quality improvements, revenue impact, and time-to-market acceleration. Businesses typically recover implementation costs within 12–24 months, with ongoing returns compounding as product data scales.
Will AI eliminate the need for PIM systems?
AI can generate descriptions, extract attributes, and speed up enrichment. It cannot replace the governed structure, workflows, and single source of truth that PIM systems provide. This article explains why AI is making PIM more important, not less.
The PIM implementation checklist: 10 steps to a successful launch
PIM implementations fail when teams treat them like software installs rather than operating model change. This 10-step checklist covers the critical stages, from objectives and data audit to migration, training, and post-launch governance.
How AI Is transforming product information management
AI is changing Product Information Management from a static system of record into an active engine for enrichment, validation, localisation, and automation. This eBook explains what is changing, where the value lies, and what foundations organisations need to benefit safely.
Ethical considerations of AI-generated product content
AI can generate product content faster than any team. It can also mislead, overclaim, and create regulatory risk if left unchecked. This article explains the main ethical issues and how to manage them properly.
Classifying complex industrial products: Taxonomy tips for technical catalogue
Industrial buyers search by specification, compatibility, and standards, not broad category names. This article explains how to build a technical catalogue taxonomy that supports findability, filtering, and long-term scale.
Why PIM is a game changer for industrial component suppliers
Industrial component suppliers face complex data, strict compliance, and rising digital expectations. Discover why PIM is a game changer for managing technical specifications, reducing errors, accelerating time-to-market, and enabling scalable B2B and distributor-led growth
Top 5 product data quality issues (and how to fix them)
Product data problems rarely arrive alone. Missing fields, inconsistent values, duplicate records, weak taxonomy, and outdated information all damage performance. This article explains the five most common issues and how to fix them in a sustainable way.
PIM vs spreadsheets: Why Excel isn’t scalable for product information
Spreadsheets work for small catalogues and simple workflows. They do not scale to multi-channel product data, cross-team collaboration, or governed publishing. This article explains where Excel breaks down and why PIM becomes essential as complexity grows.