Today, product titles and descriptions don’t just influence Shopping Ads in Google Ads, they also shape how your products surface across Performance Max, AI-generated search results, and even third-party shopping assistants. Simply put: better product content equals better discoverability, all around.
That’s where feed testing comes in. By strategically experimenting with your titles, descriptions, and attributes, you can not only improve ad performance but also optimize for relevance in AI-driven environments. Not sure where to start? Below, we’ll cover five proven feed testing strategies, complete with AI angles, pro tips, and best practices, to help you capture more qualified traffic in today’s evolving digital landscape.
#1: Source Testing Terms from Search Trends and Your Site
The best testing terms are the ones your customers are actively searching for but that aren’t yet in your feed. That could mean seasonality-driven phrases, trending styles, or attributes that align with your audience’s mindset. Incorporating these terms helps your products surface in more relevant searches, increasing qualified traffic without harming existing reach.
Best Practices:
- Layer in seasonality cues like “spring essentials” or “cozy winter styles”
- Use audience-specific terms like “teen,” “petite,” or “vintage-inspired”
- Pull inspiration from curated collections or “shop by style” pages on your site
- AI search tools rely heavily on descriptive context. By adding richer, trend-aware descriptors, your products are more likely to be selected in AI-generated product recommendations or overviews.
#2: Use KPIs to Prioritize Testing Terms
Not all testing terms are created equal. Some will be more profitable than others, so before rolling terms into your feed, evaluate their historical performance against non-brand traffic. This helps you focus on terms that are not just high-volume but also high-conversion.
Best Practices:
- Compare CPC, CVR, and ROAS for candidate terms against account averages
- Factor in context like seasonality and macro shifts (e.g., post-COVID shopping trends)
- Use last year’s performance data for season-specific terms; use more recent data for evergreen terms like “new” or “best”
- AI-powered shopping assistants may weigh product relevance differently than Google Ads. Testing which terms actually drive conversions gives you confidence that those descriptors will carry over value in broader AI-driven discovery.
#3: Run A/B Tests for Cleaner Insights
Feed testing works best when it’s methodical. A/B testing allows you to alternate terms in and out of your feed to isolate performance. This reduces noise from external factors like holidays or promotions, giving you a clearer read on whether a term is effective.
Best Practices:
- Rotate test and control periods (e.g., test day vs. control day)
- Avoid static weekly testing (like always testing on Mondays) to prevent skew
- Run tests for 4+ weeks to build confidence in results
- AI systems are still “learning” from feeds. Clean, controlled tests help you understand what data inputs AI finds most relevant, which can shape your long-term feed strategy.
#4: Test Titles vs. Descriptions Strategically
Where you place search terms matters. Titles and descriptions play different roles in visibility and click-through. Titles drive immediate visibility in Shopping Ads and eligibility for high-intent queries. Descriptions provide depth, context, and nuance for AI tools parsing your product data.
Best Practices:
- Use shorter, click-grabbing, relevant terms in titles (“New”, “Best-Selling”, “Summer”)
- Use fuller context in descriptions (“Designed for everyday comfort with a vintage 90s vibe,” “Father’s Day Gifts,” “Shop the best in airport styles”)
- Test prepends (“New – Product Title”) vs. appends (“Product Title – Shop Spring”)
- AI-driven search relies heavily on contextual descriptions. A balanced approach ensures your products are optimized for both quick-scan human clicks and in-depth AI parsing.
#5: Use Multiple Comparison Periods for Deeper Insights
Testing isn’t just about pre/post, it’s also about month-over-month, year-over-year, period-over-period, test vs. control and sale-over-sale. By comparing impressions and performance across different periods, you can identify whether lifts are due to your test or simply due to seasonal demand shifts.
Best Practices:
- Compare impressions and clicks pre- vs. post-test
- Layer in YoY data to normalize for seasonality
- Avoid overlapping tests with similar terms (“spring” vs. “summer”) to reduce noise
- As AI shopping surfaces continue to evolve, demand baselines may shift dramatically year over year. Multi-period comparisons give you a stronger, more reliable read on true performance.