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I remember when I first started working in retail how naive I was about UPCs. It was universal! I assumed it was a key that could easily unlock real product data. I quickly realized that none of that was the case. And worst they're easily corruptible because we're living in an Excel world where the first rule is leading zeros must be banished. We have some thoughts on making sure you've got the right UPCs. What are your UPC stress stories? https://lnkd.in/gGjwv6TE

A better model will not fix your product data generation. I say that as someone who has spent years building the models. Hundreds of product categories each carry their own vocabulary and unit conventions. A furniture category accepts walnut and rejects brown wood. A color rule that is correct for office furniture becomes a defect on writing instruments. That is not a context window problem. And no model solves the harder part. It returns a correct value and an incorrect value with the same fluency. Nothing internal separates them. Generation always returns something. Validation has to be allowed to say no. So we run them as separate systems, across more than 400 categories, each enforced on its own. Full argument here: https://lnkd.in/gJmkGvqV

In most industries, product content isn't about answering the question, "do I want this?" It's about answering the question, "Does it fit?" In HVAC especially the correct fitment data is far more important than even the part or product description. Missing fitment data loses the sale. Wrong fitment data comes back as a return. I wrote up how the distributors who avoid both are handling it: https://lnkd.in/ewGHmQdt

Many teams approach enrichment as merely a content issue, focusing on filling in gaps. However, a crucial requirement that often gets overlooked by most tools is that the data added must be accurate for the category it belongs to. Ensuring correctness is key to effective data enrichment. https://lnkd.in/gbCvT6SK

Getting the basics of your product data right is hard: accurate, complete, valid, and consistent. Most teams never fully clear that bar, but even flawless data can be worthless if it doesn't tell shoppers what they actually want to know. New piece on how to measure product data quality → https://lnkd.in/eNFnwevs Relevance to an agent or shopper is the last piece of the puzzle, and the most difficult to get right. It decays even when your data stays the same. Quality content isn't a project you finish. It's a pipeline you maintain.

The flexibility that product tags allow are also their Achilles heel. Meant to allow you to break out of the standard category taxonomy that governs your site, their unstructured nature lets them turn into their own beast. https://lnkd.in/ghZfWtdq