The Practical Guide to Optimize Product Data

Key Takeaways

Start with an audit. You can't optimize data you don't understand. Know what's missing, inconsistent, or wrong before touching anything else.

Once you know what you have, the priorities become clear:

1. Attributes first. They determine whether your product appears in search at all. Missing a filter attribute excludes a product silently, before anyone reads a word of copy.
2. Descriptions second. Write using words real customers search for, not internal terminology or supplier codes.
3. Channel adaptation third. The same copy rarely works everywhere. Same facts, different length, tone, and structure per channel.

Don't forget what most teams underestimate:

• Images, pricing, and availability are product data. Treat them with the same rigor.
• Associations between products drive cross-sells and upsells. Build them intentionally.
• Translations need glossaries. Fluent isn't the same as accurate.

On tooling and scale: AI generates faster, not better, so give it detailed instructions or plan to fix the output. A PIM system earns its cost when managing the spreadsheet becomes the job, not a tool for doing the job.

Product data degrades continuously. Build a process to maintain it, not just a project to fix it.

Start With a Data Audit

Before you optimize product data, you need to know what you actually have. Businesses don't do this, assuming the catalog is roughly correct, and focus on improving copy or adding channels. Then they connect a new marketplace feed and get a very high rejection rate on day one, almost entirely from missing or malformed attribute data that had been sitting unnoticed for months.

The audit is how you find out where the real problems are. Export your full catalog and go through it field by field. Look for:

• Missing required attributes
• Numeric values stored as free text
• Duplicate records
• Outdated specifications
• Products with no images
• Descriptions copied unchanged from a supplier sheet years ago

None of this is visible from the storefront. It only shows up when a feed breaks or a filter returns two results instead of two hundred.

The findings will tell you what to fix first. A missing filter attribute on your 50 best-selling products costs more than a weak description on a slow mover. Fix the high-impact gaps before touching anything else. A PIM system or feed management tool with a built-in data quality report makes this faster, but a spreadsheet export and a few hours of honest review gets you most of the way there.

The audit isn't a one-time task. Run it on a schedule. Data degrades faster than most teams expect.

Write Descriptions for Search, Not for Yourself

Internal product names and supplier codes mean nothing to a customer typing into a search bar. They're searching "waterproof hiking boots men size 11," not your SKU or brand model code.

Start with Google Search Console. Filter by product category pages and sort by impressions. You'll see queries driving traffic to your pages, and you'll also see queries with high impressions and low clicks. That gap is where your descriptions are failing. The customer found you, but the title or snippet didn't match what they were looking for.

Cross-reference with site search data. Queries typed into your own search bar that return weak results tell you exactly what customers expect to find and don't. Those terms belong in your descriptions and attribute values. Autocomplete on Google and Amazon shows you how real customers phrase their searches, which is often different from how product teams write about the same things.

Work the terms into titles and the first sentence of descriptions, but don't reverse-engineer copy around keywords. Write to answer the questions a customer has before buying: what's it made of, what does it fit, what's included, what's the difference between this and the cheaper version. Those answers usually contain the right terms anyway, and they convert better than keyword-stuffed copy written for a crawler.

One thing most businesses miss: negative search intent. A customer searching "wool blanket machine washable" is filtering out products that require hand washing. If your blanket is machine washable, that phrase needs to be in the description. If it isn't, it also needs to be there, because a customer who buys it and finds out at home will return it. Incomplete descriptions don't just underperform. They generate returns.

Match Your Copy to the Channel

The same product description rarely works everywhere. Each channel has different rules, different audiences, and different ranking factors.

Your own webshop gives you space. You can write longer, richer descriptions that build context and support the brand. A marketplace like Amazon or Bol.com has strict formatting rules, character limits, and its own search algorithm that weights title fields heavily. Google Shopping pulls from structured feed fields, not prose. A B2B catalog might need technical specs front and center, with commercial terms and lead times alongside product content.

The most common problem we encounter when auditing multichannel setups for our clients is product descriptions that were never adapted for each channel. The text written for a webshop where customers browse and take their time reading had been pushed unchanged to a marketplace like Amazon or Bol.com, where the first 80 characters of a product title decide whether it shows up in search results at all. The result was poor visibility with no obvious explanation, because nothing in the system had flagged it as an error.

What stays consistent across all channels:

• The facts. Dimensions, materials, compatibility, included components. Get those right once and carry them everywhere.

What changes per channel:

• Length, tone, structure, and which details you lead with.

Managing this at scale means building channel-specific templates and knowing which fields feed which output. A single master record with channel variants per field is more maintainable than separate product records per channel. Doing it manually per SKU doesn't scale past a few hundred products.

Attributes Are How People Find You

A customer on a furniture site looking for a dining table under 80cm wide doesn't browse. They filter. If your table doesn't have a width attribute, it doesn't exist in that search.

This is the most consequential part of product data work. Copy can be improved incrementally and its impact is gradual. Missing or broken attributes exclude products from search results entirely, immediately, and silently. Nobody tells you. The product just doesn't appear.

Attributes are the structured data fields behind your product: dimensions, weight, material, color code, compatibility, certifications, power requirements, age range, and whatever else is relevant to your category. The more complete and correctly typed they are, the more surfaces your product appears on.

The data type matters as much as the value:

• A numeric field for dimensions lets users filter by range.
• A controlled vocabulary for color lets users filter by exact value.
• Free-text fields do neither.

If someone entered "approx. 80cm" instead of "80" in a numeric field, the filter breaks. If color is entered as "dark navy blue" in one record and "navy" in another, they don't aggregate under the same filter value.

At AtroPIM, we regularly find during attribute audits that 20 to 30% of a client's catalog is effectively invisible on filtered search because key attributes are empty or incorrectly typed. That's not a copy problem. No amount of description work fixes it. The products are excluded before any copy is ever read.

Go deep on attributes for your core categories:

• Look at what filters your competitors' sites expose and make sure you have data for all of them.
• Look at what attributes marketplaces require and which ones are optional but recommended. Google's product data specification lists required and optional attributes per category, and optional attributes that improve discoverability are worth filling (source).

A practical way to find gaps: run your own site search for product types and look at what filters appear. Then check how many results each filter value returns. A filter value with 2 results usually means missing data, not a thin assortment. Fix the data, not the filter.

Standardize Attributes Across Similar Products

Inconsistent attribute naming is a quieter version of the same problem. If your product catalog has "seat height," "height of seat," "seat H," and "height from floor" across different chair listings, none of them filter together correctly. The damage is invisible in any individual product record and only shows up when filters aggregate across the category.

Build a defined attribute set for each product type. Every chair gets the same set of attributes, named identically, using the same units and value formats. Every power tool gets its own fixed set. Every skincare product gets its own. The set is defined once, reviewed periodically, and applied consistently from that point forward.

Resistance at this stage rarely comes from disagreement about the standard itself. It comes from the unglamorous work of applying it retroactively to an existing catalog. It's work that doesn't belong to anyone's KPI and doesn't show results until it's fully done.

Without named ownership, the catalog drifts back into inconsistency within 6 months. Someone adds a new supplier's products without mapping them to the standard. A category manager adds a new attribute that already exists under a different name. The standard erodes gradually, and the filter breakage follows.

When you onboard products from a new supplier, map their data structure to your taxonomy before importing. Suppliers name things for their own catalog logic, not yours. Letting their terminology become your attribute names is how the inconsistency starts in the first place.

Images and Media Are Product Data Too

Images drive conversion. Multiple angles, lifestyle shots, detail close-ups, and scale references all reduce purchase uncertainty. In categories like apparel, furniture, and electronics, customers expect to see the product thoroughly before buying. A product with one low-resolution image from a supplier PDF loses to a well-shot competitor regardless of price or copy quality.

File naming and alt text matter for SEO. An image named IMG_4821.jpg contributes nothing. One named black-leather-office-chair-armrest-detail.jpg does. Google's image SEO guidelines confirm that descriptive filenames and alt text help images rank in image search and contribute to overall page relevance (source).

Different channels have different image requirements:

• Amazon requires white backgrounds for main images and rejects listings that don't comply.
• Google Shopping has minimum resolution requirements and disapproves of feeds with low-quality images.

Know the specs per channel and meet them before publishing, not after the first rejection report.

3D and AR assets are becoming expected in furniture, home decor, and some apparel categories. They're not universal yet, but the gap between merchants who offer them and those who don't is increasingly visible in conversion data.

Build Product Associations Deliberately

Recommendation engines need data to work with. "Customers also bought" and "you might also like" aren't magic. They're driven by either behavioral data or manually defined product relationships, and behavioral data takes time to accumulate on new products or low-traffic pages.

Define associations explicitly in your product data:

• Accessories that fit this product
• Replacement parts
• Compatible items
• Bundle components
• Upgrades
• Alternatives for when a product is out of stock

On new product launches, manually defined associations are especially important because algorithm-driven recommendations have no behavioral data to learn from yet. The associations fill that gap and ensure customers are guided toward relevant add-ons from day one, particularly for replacement parts and accessories that customers wouldn't discover through browsing alone.

Be deliberate about the type of association. A cross-sell is a complementary product. An upsell is a higher-value version of the same thing. A replacement part is a different relationship entirely. Mixing them up produces irrelevant recommendations, and irrelevant recommendations get ignored faster than no recommendation at all.

Pricing and Availability as Live Data

Pricing data in feeds needs to reflect what the customer actually pays. That includes promotional prices with correct start and end dates, tiered pricing for B2B customers, and currency variants for international channels. Google Merchant Center disapproves of products where the feed price doesn't match the landing page price, and repeated mismatches can result in account suspension.

A common mistake during promotional campaigns is underestimating how quickly a price mismatch creates downstream damage. A feed updated once a day during a flash sale that changes prices every few hours means the feed is wrong for most of the promotion. Disapprovals arrive after the sale ends. The suspension risk accumulates quietly. Updating feeds in near real-time during active promotions isn't optional if the promotion involves frequent price changes.

Stock status affects more than fulfillment:

• On marketplaces, out-of-stock products lose ranking fast.
• On Google Shopping, they stop showing entirely.
• If you sell across multiple warehouses or regions, a product in stock in one country but not another needs correct regional flags in the feed.

Accurate availability data, updated at least daily, is not optional if you're running paid campaigns or relying on organic marketplace visibility.

Translate Accurately, Not Just Fluently

A grammatically correct translation can still be wrong. If your translation system doesn't know that a specific brand name should never be translated, or that a technical term has an approved equivalent in the target market, you end up with product listings that confuse or mislead in ways that are hard to catch without a native speaker reviewing every record.

Glossaries solve this. A translation glossary is a controlled list of terms with their approved equivalents in each target language. Brand names, product category terms, technical specifications, and trademarked language all belong in it. Any translation system worth using, whether human, machine, or AI-assisted, should apply the glossary before output, not as a post-edit check.

A glossary applied after translation catches errors. A glossary applied before prevents them.

Translation memory stores previously approved translations so identical or similar strings translate consistently across your catalog. It speeds up the process and reduces cost on large catalogs where the same phrases appear across hundreds of products. Without it, the same product feature can be translated four different ways across four categories, none wrong enough to flag, but all inconsistent enough to undermine brand credibility in that market.

Translation is a data quality problem, not just a language problem. A mistranslated product name in a feed causes the same filter breakage as a wrong attribute value: the product either doesn't appear in the right search results or lands in the wrong category because the translated term maps to the wrong taxonomy node.

Machine translation has improved significantly. For high-volume, lower-stakes content like attribute values and spec lists, it's often good enough with glossary support. For product titles, key descriptions, and anything customer-facing in a high-revenue market, human review still matters. Applying machine translation uniformly to everything, or insisting on human translation for everything, are both the wrong defaults.

AI Can Help, But Only If You Direct It

AI can generate product descriptions at scale. That's useful. It can also produce generic, inaccurate, or off-brand copy at scale, which is worse than having no copy at all, because it creates the appearance of complete data while the quality undermines conversion and trust.

The output quality depends entirely on the instructions you give. A prompt that says "write a product description for this chair" will produce something passable and forgettable. A prompt that specifies the channel, the audience, the tone, the character limit, the keywords to include, the claims to avoid, and provides an example of approved output will produce something usable.

A practical structure for a product description prompt includes:

1. Channel
2. Audience
3. Tone
4. Length
5. Required keywords
6. Forbidden phrases
7. One example of approved output
8. The product attributes as a structured data block

Most people provide two of these eight inputs and wonder why the output needs heavy editing. Investing time in prompt design and testing on a small batch before scaling to the full catalog saves substantially more editing time overall than running everything first and fixing afterward.

Give AI your product attributes as structured input, not a narrative. Tell it what format the output should follow. Tell it what it should not say. If your brand doesn't use superlatives, say so. If there are compliance restrictions on certain claims in your category, include them explicitly. AI has no way to know your regulatory environment, your brand voice, or the difference between a claim that's legally safe in one market and one that isn't.

Always review AI-generated content before publishing. AI will confidently state wrong specifications. A wrong dimension, a false compatibility claim, or an incorrect material description creates returns and erodes trust. The cost isn't the time to fix it. It's the return rate and the customer who doesn't come back.

Use a PIM System Once You Pass a Certain Scale

A spreadsheet works when you have 50 products and sell on one channel. The signs it's stopped working are specific:

• Two people edit the same file and overwrite each other's changes.
• A channel goes live with last month's prices because someone forgot to update that tab.
• A new marketplace requires an attribute that's inconsistently filled across 40% of your catalog, and there's no clean way to bulk-fix it.
• You spend more time managing the spreadsheet than improving the data in it.

That's the tipping point. Not a product count, not a channel count. It's when data management becomes the job instead of a tool for doing the job.

A Product Information Management system is a central repository for all your product data. It stores attributes, descriptions, media, pricing rules, and channel-specific variants in one place. It connects to your webshop, marketplaces, feed management tools, and translation systems.

In our experience at AtroPIM, the first measurable improvement after migrating to a PIM is usually feed error rates. The act of importing data into a system with validation rules forces a cleanup that spreadsheets never enforced. Teams that expected content improvements in the first months got data hygiene improvements instead, which turned out to be more valuable because it unblocked the channel expansion they'd been planning for months.

The core things to look for in a PIM:

• A flexible attribute model that fits your taxonomy without forcing a rigid structure
• Channel-specific output templates
• Translation workflow support
• Clean import/export tools for onboarding supplier data

The onboarding tooling matters more than most buyers realize. You'll import new supplier catalogs repeatedly. If that process is painful, data quality problems start at the first import and compound from there.

Don't buy for features you won't use in the first year.

Measure and Maintain

Product data isn't a project with an end date. It degrades continuously. Suppliers update specs without telling you. Products get discontinued. Marketplaces change their attribute requirements. A field that was optional last year becomes required this year, and you find out when the rejection rate spikes.

Set up regular automated data quality checks. Most PIM systems and feed management tools can flag missing required fields, identify values outside expected ranges, and catch duplicates on a schedule. Run them weekly. Don't wait for a channel to tell you something is wrong.

Feed rejection rates from Google Merchant Center, Amazon, or other channels are a direct and measurable signal of data quality problems. A sudden spike in rejections almost always points to a specific field or product category where a requirement changed or a batch import introduced bad data. Review rejection reports after every major feed update, not just when campaign performance drops.

Return rates tied to descriptions are worth tracking separately from overall returns. A high return rate in a specific category where customers cite "not as described" is a data problem, not a logistics one. The description set a wrong expectation. Fixing the copy fixes the return rate. This is one of the clearest direct connections between data quality and revenue, and most teams never make it because they look at returns and product data in separate systems without linking them.

Search analytics on your own site show where customers filter and what they don't find. A filter that gets heavy use but produces few results points to missing attribute data. A query with traffic but no clicks points to weak titles or missing products. Both are fixable with data work, and both are invisible if nobody is looking at the reports.

Assign ownership. Someone needs to be responsible for data quality as an ongoing function with clear metrics, not a cleanup project that gets resourced when things break badly enough to notice.

Where to Start

The right starting point is whichever problem is costing you the most right now, and that's usually visible in data you already have.

High impressions and low clicks in Search Console mean your titles and descriptions aren't matching search intent. Fix the top 20 products by impression volume, measure the click-through change over 4 weeks, and use that as the baseline for the rest of the catalog.

High bounce rate on product pages means customers are landing, but the page isn't answering their questions. Check image count and description depth first. Both are fixable without any infrastructure changes.

High feed rejection rates mean attribute data is incomplete or wrongly formatted for that channel. Pull the rejection report, identify the top 3 error types, and fix those fields across the full catalog before anything else. One systematic fix beats a hundred manual corrections.

Inconsistent filter results on your own site means attribute standardization hasn't happened yet. Pick your highest-traffic category, define the correct attribute set, clean that category completely, and measure filter usage before moving to the next. The improvement in filter engagement is usually immediate and easy to demonstrate to stakeholders who weren't convinced the work was worth doing.

Going international without glossaries means every translation project adds compounding risk. Set up the glossary infrastructure before adding a third language, not after you've published thousands of incorrect product names across a new market.