How to Improve Shopping Ads Performance with Better Product Data

Product data quality is the real bottleneck in shopping ads performance

Most ecommerce teams focus on bidding strategy, budget allocation, and campaign structure when shopping ads hit a performance plateau. But the real constraint is usually the product feed: generic titles, incomplete attributes, and no meaningful way to segment by profitability or inventory health.

Research shows that stores with 99.9% attribute completion see 3-4x higher visibility in AI recommendations compared to stores with sparse data. Product titles under 150 characters that include size, color, and material attributes increase match relevance significantly. Real-world optimization results demonstrate that agencies have increased clients' sales by approximately 70% after campaigns plateaued, simply through feed improvements.

In 2026, shopping platforms rely almost entirely on structured product data for matching, ranking, and creative generation. Generic or incomplete feeds mean your products never enter the consideration set, regardless of how aggressively you bid.

This guide covers the four highest-impact data improvements for shopping ads:

1. Title optimization for query matching and click intent
2. Attribute completeness for eligibility and relevance
3. Custom label structure for margin-aware bidding
4. Campaign segmentation aligned with business value

If your feed has basic quality issues, start with our product feed optimization guide to stabilize core fields, then return here for performance-focused improvements.

Build structured product titles that match queries and drive clicks

Your product title is the single most important field in your feed. It appears directly in shopping ads, heavily influences which searches trigger your listings, and determines whether shoppers click. Platforms recommend titles under 150 characters with decisive information at the front because truncation happens in many placements.

Generic titles kill performance. A title like "Outdoor Gear" or "Coffee Beans" gives neither the platform's matching algorithm nor real shoppers enough information to understand what you're selling, who it's for, or why it matters.

A practical title format for most categories:

Brand + Product Type + Key Variant + Core Spec + Quantity or Format

Examples that work:

• Weak: Outdoor Gear
• Better: Columbia Men's Waterproof Hiking Boots, Size 10, Black, Leather
• Weak: Coffee Beans Dark
• Better: Lavazza Espresso Whole Beans 2.2 lb, Dark Roast, Italian Blend

Execution rules your team can standardize:

• Put decisive information first because truncation happens frequently
• Remove promotional language that doesn't help matching or filtering
• Use consistent unit formats appropriate to your market
• Validate title logic by category, not one universal template
• Front-load specifics that differentiate your product from similar items

For catalogs with thousands of SKUs, manual title writing becomes an impossible bottleneck. Lasso helps ecommerce teams apply category-aware title generation rules at scale while preserving brand tone, maintaining quality without slowing catalog velocity.

If your team needs a more structured approach, define category-specific title templates and enforce them with automated validation checks in your feed pipeline before data reaches shopping platforms.

Complete high-impact attributes to unlock reach and relevance

Missing attributes hurt shopping ads in two ways: they can trigger disapprovals that block products entirely, and even approved products match less precisely to shopper intent, resulting in lower impression share and weaker click-through rates.

Build your attribute enrichment roadmap in priority order instead of treating all fields equally:

Tier 1: Eligibility and matching foundation

Fix these first because they control basic participation:

• Stable product id or SKU
• Accurate brand value
• GTIN (UPC/EAN) or brand+MPN where applicable
• Current price and availability with real-time accuracy
• Complete product_type or category hierarchy at least 2-3 levels deep
• High-quality primary image meeting minimum resolution requirements

Tier 2: Query and filter attributes

These improve long-tail matching and shopper confidence:

• Color, size, material, pattern
• Dimensions, weight, capacity
• Compatibility or fitment details
• Condition, pack count, or format specifics

Platform algorithms increasingly use these attributes to match products to conversational queries and filter-based searches, which are expanding rapidly across shopping experiences.

Tier 3: Conversion-supporting attributes

These reduce post-click hesitation:

• Included accessories or components
• Warranty or return details
• Care or usage instructions
• Clarifying technical specifications

Channel-specific considerations:

• Multi-channel feeds: Use standardized product taxonomy and structured category mapping to support filtering, sorting, and ad segmentation consistently across channels
• Marketplace platforms: Required and recommended attributes vary by product type and update frequently, so validation should be ongoing
• Shopping platforms: Both core feed attributes and category-specific optional fields influence listing quality and discoverability

If your source data is inconsistent across suppliers or channels, normalize first so campaigns aren't built on conflicting values. For a deeper look at normalization workflows, see our guide on product data cleansing, enrichment, and normalization.

Use custom labels to segment by margin, lifecycle, and inventory health

Custom labels are one of the highest-leverage features in shopping platforms because they let you group products by business intent, not just taxonomy or category. Most platforms support multiple custom labels, and each represents a strategic dimension you define.

A practical setup for most ecommerce teams:

• custom_label_0: Margin tier (high_margin, mid_margin, low_margin)
• custom_label_1: Product lifecycle (new_arrival, core_catalog, clearance)
• custom_label_2: Price band (0_50, 50_150, 150_plus)
• custom_label_3: Seasonality (evergreen, seasonal_spring, holiday_q4)
• custom_label_4: Stock status (healthy, low_stock, overstock)

This structure creates immediate operational benefits:

• Set bids and budgets by profitability, not just by category
• Analyze ROAS and contribution margin together in one reporting view
• Protect low-stock items from aggressive spend that causes stockouts
• Scale winning segments faster with controlled expansion rules
• Allocate ad spend based on business value, maximizing return

Custom labels enable sophisticated bidding strategies as campaigns expand. You can bid more aggressively on high-margin products where you can afford higher acquisition costs, while conserving budget on clearance or low-margin items.

Common mistakes to avoid:

• Encoding multiple meanings in one label, making segmentation ambiguous
• Letting label values drift across teams without a controlled vocabulary
• Never refreshing seasonal or stock-based labels after initial setup
• Creating too many product groups that spread data too thin for statistical significance

Treat custom labels as controlled vocabularies with clear ownership and regular refresh cycles tied to your inventory and pricing systems. Document what each custom label and its values mean for team collaboration and long-term maintenance.

Segment campaigns by business intent, not just product type

Segmentation is where strong product data becomes measurable performance. Without it, even well-structured titles and complete attributes get averaged into broad groups with mixed economics and diluted results.

Use campaign and product group structure to express business intent directly.

A practical starting model:

1. Campaign families by objective: growth, efficiency, liquidation
2. Product group splits by custom labels: margin tier, lifecycle stage, seasonality
3. Exclusion and suppression rules: low stock, policy risk, incomplete data
4. Measurement by segment: CTR, conversion rate, ROAS, and contribution margin per segment

Better segmentation leads to cleaner performance signals and more confident budget decisions. Operationally, maintain a weekly review cycle:

• Refresh custom label logic based on current stock levels and margin data
• Review segment-level performance for winners and losers
• Promote top-performing segments to higher budget tiers
• Demote or suppress segments with weak margin-adjusted returns
• Identify new segmentation opportunities based on emerging patterns

This approach shifts teams from reactive bidding adjustments to predictable merchandising strategy. For broader merchandising strategy beyond paid ads, see our guide on merchandising with attributes.

Build a repeatable feed workflow that improves over time

The fastest way to lose shopping ads gains is to treat feed optimization as a one-time project. Winning teams run it as an operating system with validation gates, automated quality checks, and regular refresh cycles.

A reliable workflow:

1. Ingest supplier files and channel exports into one canonical schema
2. Normalize values for units, naming conventions, allowed vocabularies, and duplicates
3. Enrich missing high-impact attributes using rules, AI assistance, and taxonomy mapping
4. Generate structured titles and channel-specific outputs
5. Validate policy compliance and field completeness before publish
6. Publish and measure segment-level performance weekly, then iterate

You can run this manually for small catalogs, but it becomes fragile as SKU count grows and supplier data changes frequently. Automated workflows clean, standardize, and validate data across channels, reducing operational load while enhancing performance and trust.

Lasso helps teams automate this loop across ingestion, enrichment, and publishing so campaign decisions use cleaner, more consistent inputs. The platform combines AI-powered attribute extraction with deterministic validation rules, giving you the speed of automation with the safety of human oversight.

If you're building your 2026 roadmap, combine this playbook with a phased enrichment rollout and define clear ownership across catalog operations, paid media, and merchandising teams. When you want to scope rollout effort and evaluate team fit, review our pricing options or book a demo to discuss your specific feed challenges.

Align shopping ads data with evolving platform requirements

Shopping platforms continue to emphasize feed quality as a core ranking signal. In 2026, several trends make data quality even more critical:

• AI-driven matching relies more on structured attributes and less on exact keyword matches, so completeness and accuracy matter more than keyword stuffing
• Visual search and image extensions require high-quality product images and accurate color, material, and style attributes
• Conversational commerce experiences pull from the same product feed as traditional shopping, so feed improvements lift performance across multiple surfaces
• Automated bidding strategies learn faster and perform better when feed data is clean, consistent, and segmented meaningfully

According to industry research, optimizing product feeds opens up the single most powerful lever for e-commerce growth. Teams that invest in feed quality now see compounding benefits as platforms lean more heavily on structured data for matching, ranking, and creative generation.

For teams managing large or complex catalogs, this means treating product data as a strategic asset, not just a technical requirement. The same feed improvements that lift shopping ads performance also improve on-site search, category pages, and marketplace listings. For a broader view of catalog quality, see our product data quality checklist.