Mistake 1: Treating Enrichment as a One-Time Project

This is the most universal mistake. A new ecommerce manager joins, sees the state of the catalog, commissions a data clean-up project, gets it done, and moves on. Six months later, the quality has degraded back to its previous state. The cycle repeats.

Product data is not static. Supplier specs change without notification. New products are added under time pressure with incomplete data. Channel requirements update quarterly. Keyword intent shifts as market conditions evolve. Each of these is a data decay event. Without a systematic maintenance process, the quality you fought for deteriorates continuously and silently, because data decay does not show up as a visible failure in your analytics. It manifests as slightly lower organic traffic, slightly higher CPC, and slightly worse conversion rates that get attributed to seasonal trends or competitive pressure.

Mistake 2: Treating Enrichment as a Copywriting Task

When enrichment gets resourced as a content project, the brief inevitably becomes “write better product descriptions.” This is the wrong brief. Better copy matters, but it is the least leveraged part of enrichment and the part least likely to move the metrics that matter most.

The highest-leverage enrichment work is structural and attribute-level: normalizing inconsistent values, adding missing structured attributes, completing GTINs, and fixing taxonomy classifications. None of these require writing. They require data management. When enrichment is owned by a content team rather than a data or commerce team, this structural work systematically gets deprioritized in favor of copy improvements that are more visible but less commercially impactful.

Mistake 3: Enriching Only the Top 50 SKUs

Hero products get the attention. The top 50 SKUs by revenue get rich descriptions, multiple images, complete attributes, and optimized titles. The other 4,950 get whatever the supplier provided. This is understandable given resource constraints, but it is strategically backwards.

The long tail is where organic search traffic is richest. Long-tail queries are more specific, have higher purchase intent, are less competitive, and are more precisely matched to what the shopper wants. A product with a very specific attribute profile, such as “recycled polyester waterproof hiking jacket 680g packable,” may only receive 80 searches per month. But it will convert at 10–15% from those searches, because the shopper knows exactly what they want and your product matches exactly. Sparse data on that product means it is invisible to those high-intent, high-conversion searches.

Mistake 4: Managing Each Channel's Data Independently

The feed specialist manages the Google Shopping feed in a spreadsheet. The marketplace manager manages Amazon listings in Seller Central flat files. The digital merchandiser manages website product data in the CMS. No one connects these three systems. When a product gets a price change, a description update, or a new size option, three separate people have to update three separate places, if they remember, if they know the correct format for each, and if there is no ambiguity about which version is correct.

The consequence is fragmentation: different versions of the same product data in different places, maintained by different people, drifting out of sync. A stale Google feed price triggers a Merchant Center disapproval. An outdated Amazon listing contributes to “not as described” returns. Website attributes do not match Google Shopping attributes, creating inconsistency for shoppers who switch between channels. Each gap is individually manageable. Together, at catalog scale, they represent a systematic quality problem that cannot be solved by working harder.

Mistake 5: Confusing Completeness with Accuracy

A product with all fields populated is not necessarily a product with good data. “Approximately 2kg” in a weight field is complete but not precise. “Great for all occasions” in a use_case field is populated but machine-unreadable. “Various shades of blue” in a color field passes a completeness check while being useless for filtered search and entity matching.

Accuracy and precision are separate quality dimensions. Completeness means the field is populated. Accuracy means the value reflects reality. Precision means the value is expressed in a way that machines can filter, compare, and query without ambiguity. A systematic enrichment program tracks all three, not just whether fields are filled, but whether the values in them are correct and queryable.

Mistake 6: Skipping Normalization

Normalization is the most tedious and least visible part of enrichment, which is why it is most often skipped or deferred. The result is catalog fragmentation: a retailer with 3,000 apparel SKUs that has 14 distinct values in its color attribute field, including Blue, blue, BLUE, Navy, Navy Blue, Dark Blue, Midnight Blue, Cobalt, Cobalt Blue, Ocean, Denim, Ink, Slate, and Deep Blue.

To a human merchandiser, these are variations on a theme. To a faceted search filter, they are 14 completely separate options, each generating its own filter facet, fragmenting the shopper experience and diluting each value's ranking signal.

The same fragmentation problem exists for materials, sizes, fit descriptions, certifications, and virtually every repeating attribute across a catalog. Each unnormalized value is a data quality debt that compounds with every new product added. After 18 months, the normalization backlog becomes the project that nobody has the budget to fix properly. That is exactly when most teams finally invest in AI tooling to solve it.

Mistake 7: Not Measuring Enrichment Performance

You cannot improve what you do not measure. Most ecommerce teams measure revenue, traffic, conversion rate, and ROAS. Very few measure the data quality metrics that drive those outcomes. If attribute completeness is not a tracked KPI, it will not be prioritized when resources get tight, and resources always get tight.

The metrics that matter for enrichment are: