菜单
Design multi-stage retargeting funnels with recency windows, sequential messaging, frequency caps, and exclusion lists across platforms. Use when a user wants to build a retargeting or remarketing funnel, segment audiences by recency, or stop over-serving ads to the same people.
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基于 SOC 职业分类
| name | retargeting-sequence-designer |
| description | Design multi-stage retargeting funnels with recency windows, sequential messaging, frequency caps, and exclusion lists across platforms. Use when a user wants to build a retargeting or remarketing funnel, segment audiences by recency, or stop over-serving ads to the same people. |
Retargeting works when recency drives the message and exclusions stop the waste. One "all visitors 30 days" audience with one ad is not a funnel; it is a frequency complaint generator.
Confirm the account and existing audiences first: list_connected_accounts, list_audiences.
Define the conversion goal, then split visitors by how close they got:
Budget flows to intent: roughly 60% to Stages 3-4, 25% to Stage 2, 15% to Stage 1. Verify the pixel is actually populating these audiences before building on them — get_pixel_destinations and conversion-tracking.
Each window must exclude every shorter window, or the hot audience gets triple-served by every campaign at once.
Escalate instead of repeating: reminder with the product (day 0-1), social proof (day 2-4), offer with urgency (day 5-10), fresh angle or new feature (day 11-20), final win-back offer (day 21-30). After day 30, suppress or move to a cold pool. Generate stage variants with generate_image_ad / generate_text_ad and validate winners with creative-testing before scaling a sequence.
Mandatory, on every retargeting campaign:
Sync suppression lists from your customer data with sync_audience and attach exclusions alongside targets with attach_audience. Refresh weekly; a stale suppression list quietly re-targets last month's buyers.
Assign roles so platforms complement instead of pile on: Google Display/YouTube for reach, Meta for social proof, LinkedIn for B2B decision-makers, TikTok for short-form. Cap total cross-platform exposure around 5-8 impressions/day per user by capping each platform individually (Meta and Reddit 2-3/day, LinkedIn 1-2/day, Display 3-5/day). Upload the same suppression list everywhere so a converter disappears from all platforms, not just one.
Stand up per-stage campaigns with create_campaign_for_audience, run campaign-preflight before enabling, and watch weekly with pull_<platform>_ads_performance:
get_attribution and, when spend is material, an incrementality test (see attribution).Expand targeting without wrecking CPA — lookalike ladders, seed quality, audience layering, and exclusion hygiene across platforms. Use when a user wants to scale beyond exhausted audiences, build lookalikes, fix audience fatigue, or set up exclusion lists.
Build a post-cookie first-party data strategy — Enhanced Conversions, server-side tracking, Consent Mode v2, and activating owned data for targeting. Use when a user wants to recover lost conversion data, set up Enhanced Conversions or CAPI, implement consent mode, or plan for cookie deprecation.
Verify that ad campaigns actually target the geographies they claim — location criteria exist, presence-only matching is set, and click geography matches intent. Use when auditing campaigns, investigating cheap clicks or missing conversions, or reviewing any campaign before and after launch.
Diagnose and improve Google Ads Quality Score — Expected CTR, Ad Relevance, and Landing Page Experience — to cut CPCs and lift Ad Rank. Use when CPCs look high for the vertical, impression share is capped by rank, or keywords sit at QS 5 and below.
Optimize Google Shopping — product feed quality, titles, Merchant Center disapprovals, supplemental feeds, custom-label segmentation, and Shopping vs Performance Max structure. Use when Shopping impression share is low, products are disapproved, or feed titles and labels need work.
Design and run incrementality tests that measure true lift from ads — geo holdouts, platform lift studies, significance math, and iCPA decisions. Use when a user wants to know whether ads actually cause conversions, design a holdout test, or decide budget based on real lift.