Not all attributes are created equal. Some improve search visibility. Others drive clicks, boost conversions, or support emerging shopper values. But before investing time into enriching your product catalog, how do you know which attributes will actually move the needle?

At CatalogIQ, we believe better data leads to better outcomes—but only when it's backed by insight. That’s why our platform doesn’t just enrich attributes; it scores them and forecasts their impact. With the right signals, ecommerce teams can make smarter decisions about which attributes to prioritize, which to skip, and how to maximize return on enrichment efforts.

In this post, we’ll explore two proven approaches you can use to validate attribute performance potential—search volume analysis and predictive modeling—plus how CatalogIQ automates and amplifies both.

Method 1: Align Attribute Strategy with Real Search Behavior

Search queries are a goldmine. Whether typed into your site’s search bar or searched in Google, they reflect exactly what customers are trying to find. When a product attribute consistently shows up in those queries, it signals relevance and opportunity.

1. Identify High-Potential Attributes

Start with a list of new attributes you’re considering—things like:

• organic cotton
• rfid compatible
• vegan
• recycled plastic

These could be emerging product features, certifications, materials, or use-case descriptors. Choose ones aligned with your brand and category.

2. Mine Your Internal Search Data

Your own on-site search logs can tell you whether customers are already seeking those attributes. Are users typing “eco-friendly,” “machine washable,” or “made in USA” into the search bar? Those are attributes worth scoring and enriching.

3. Layer in External Keyword Data

Tools like Google Keyword Planner, Google Trends, SEMrush, or Ahrefs can help you validate broader demand:

• Search volume: Is the term trending up?
• Seasonality: Are there peaks in interest?
• Competitive usage: Are others optimizing for it?

This gives you directional insight into what shoppers expect to see when searching.

4. Estimate Visibility Lift

If an attribute has high interest across both internal and external channels, enriching it into your catalog can improve your product’s discoverability in both site search and SEO. With CatalogIQ, you can map those terms directly to your attribute fields and model projected lift.

Method 2: Use Predictive Modeling to Estimate Business Impact

Search data tells you what shoppers are looking for. Predictive modeling helps you estimate what will happen if you add it.

1. Analyze Historical Data

Look at past attributes you’ve added. Which ones improved click-through, conversion, or engagement? If “breathable fabric” boosted performance in the past, “moisture-wicking” may perform similarly.

2. Apply Regression Analysis

Use regression models to estimate the relationship between specific attributes and outcomes like:

• conversion rate
• search ranking
• average order value

CatalogIQ tracks attribute-level performance metrics to give you this view without the math.

3. Train Machine Learning Models

With large-scale catalogs, ML models can reveal deeper patterns—predicting how future attribute additions will affect KPIs. Our platform uses historical enrichment and performance data to prioritize which attributes are worth the investment.

4. Test, Track, Iterate

Once you add a new attribute, monitor its performance. Track engagement on enriched products. Watch filter usage. Compare conversion rates. CatalogIQ surfaces this feedback in real time—helping you iterate smarter and faster.

Real-World Example: Smarter Enrichment in Fashion Retail

A mid-sized fashion retailer was considering adding “sustainable fabric” to its product detail pages. Their internal search data showed frequent queries for “eco-friendly” and “recycled.” Google Trends confirmed growing interest in “sustainable fashion.”

They used CatalogIQ to model likely performance based on similar attributes like “organic cotton.” After rollout, they tracked a 12% increase in filter usage and an 8% lift in conversion rates on enriched product pages—clear evidence that attribute forecasting works.

Why This Matters Now

With thousands of SKUs and limited resources, merchandisers can’t enrich everything. But with AI-powered scoring and forecasting, they don’t have to. CatalogIQ makes it easy to focus on the fields that matter most—and measure their impact across discovery, conversion, and customer experience.

Want to know which product attributes will actually drive performance? Contact Kevin Jackson at kevin.jackson@magnetlabs.ai to schedule a demo of CatalogIQ, MagnetLABS’ innovative catalog quality scoring and enrichment platform.