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Ensure consistent event tracking across platforms (Meta, Google Ads, TikTok) using platform-specific pixel/SDK + server-side tracking.

Validate that purchase events report with parameters like value, currency, content ID, and product category for optimal optimization.

Cross-check that deduplication is configured correctly where both pixel and server events are sent (especially Meta CAPI).

Confirm Enhanced eCommerce is fully implemented: product impressions, add-to-cart, checkout steps, purchases.

Verify correct event parameter mapping (item_name, item_id, value, currency, coupon), especially when syncing GA4 with Google Ads audiences or conversions.

Set up event-based audiences (e.g., cart abandoners, product viewers without purchase) for remarketing segmentation.

Validate feed for accuracy, freshness, and policy compliance—ensure no GTIN or image disapprovals.

Leverage custom labels (e.g., profit margin tier, seasonal product, clearance) to structure PMAX or Standard Shopping campaigns.

Audit supplemental feeds for promotions or local inventory ads if applicable.

Check Meta Catalog sync frequency, field mapping (ID, title, price, availability), and use of product sets for dynamic ads segmentation.

For TikTok Shop, verify direct product sync accuracy, SKU availability, and price accuracy—ensure TikTok events align with product-level data.

Evaluate campaign segmentation: ensure split by product category, performance tier, or margin group where appropriate.

Avoid over-aggregation—limit product diversity per campaign/ad group to preserve bidding efficiency.

Run separate PMAX campaigns by product group or performance level (e.g., Bestsellers, New Releases, Clearance).

Use asset group segmentation by audience signal, not by creative variant. Each asset group should have unique intent (e.g., retargeting vs. discovery).

Leverage insights tab to analyze search categories and asset performance.

Use Standard Shopping in tandem with PMAX for tighter control over high-margin or strategic SKUs.

Apply priority settings in Shopping to control query flow between campaigns.

Separate high-intent and mid-funnel keywords into distinct campaigns to align with conversion-focused bidding strategies.

Employ ad customizers (e.g., dynamic pricing, inventory levels) to increase urgency and relevance.

Use exact match for proven terms and test broad match with smart bidding only if sufficient conversion volume supports it.

Transition low-volume campaigns to tCPA or Max Conversions once enough data exists; tROAS for high-volume, profit-focused segments.

Monitor bid strategy learning phase—avoid frequent bid strategy switches that reset optimization cycles.

Segment campaigns by funnel stage (TOF/MOF/BOF) and by objective (e.g., Purchase, ATC, Video Views) to preserve signal quality and audience integrity.

Use Advantage+ Shopping Campaigns (ASC) for large catalogs or DABA-style prospecting, and layer in remarketing through traditional campaigns.

Ensure deduplication between custom audiences and lookalikes by excluding lower-funnel groups from TOF.

Leverage high-signal audiences: purchasers by AOV tier, high-frequency site visitors, loyalty program members.

Retarget with dynamic creatives using product sets filtered by behavior (e.g., viewed but not purchased, added to cart).

Consolidate ad sets unless there's a clear reason for separation (e.g., region, language, product line) to aid algorithm learning.

Monitor delivery breakdowns by placement and device. If spending heavily on low-performing placements (e.g., Audience Network), consider placement exclusion selectively.

Compare in-platform performance with GA4 or third-party analytics to spot discrepancies—especially with post-iOS14 signal degradation.

Use Meta’s Conversion Lift Tests or platform-delayed attribution to better understand contribution beyond last-click.

Analyze each campaign and platform for user journey bottlenecks: ad click → PDP view → ATC → checkout initiation → purchase.

Use platform-specific tools (e.g., GA4 path analysis, Meta breakdowns, Google Ads conversion paths) to identify where users exit.

Ensure full alignment between product feed data (title, price), ad messaging (offer, CTA), and landing page content to reduce bounce and increase CVR.

Consistency is especially critical for PMAX and DABA campaigns where creative and feed merge dynamically.

Blend platform-reported ROAS with modeled data (e.g., from Triple Whale or Northbeam) to capture actual performance.

Use post-purchase attribution surveys to fill in gaps caused by signal loss—especially important on Meta and TikTok.

Tie product-level ad spend to gross margin to filter out low-ROAS products that are actually profitable—or vice versa.

Build margin-informed exclusions or targeting logic into Shopping and PMAX campaigns.

Adjust conversion windows on platforms to reflect actual average path-to-purchase duration, ensuring correct attribution.

Understand lag especially when running seasonal campaigns or using long consideration-cycle products (e.g., high-ticket items).

Limit dynamic remarketing to recent visitors (e.g., 7-day vs 30-day) to avoid diminishing returns and ad fatigue.

Segment by product price tiers or cart value to serve different incentives (e.g., free shipping vs. 10% off).