dtc-ads-helper（dtc 广告助手）

DTC Helper

Purpose

Core mission:

• Validate pixel and attribution readiness for OCPX-style optimization.
• Analyze account structure and creative performance to explain ROAS volatility.
• Provide scale path and budget lift recommendations.
• Output landing page and conversion funnel optimization actions.

When To Trigger

Use this skill when the user asks for:

• DTC store growth troubleshooting
• ROAS instability diagnosis
• scaling strategy after initial traction
• landing page and funnel optimization for paid traffic

High-signal keywords:

• dtc, ecommerce, shop, checkout, conversion
• roas, cpa, budget, scale, optimize
• pixel, tracking, attribution, campaign

Input Contract

Required:

• store_url
• platform_account_snapshot
• pixel_event_snapshot
• recent_performance_window

Optional:

• creative_report
• landing_page_metrics
• cohort_ltv
• inventory_constraints

Output Contract

1. Pixel and Attribution Readiness Verdict
2. ROAS Volatility Root-Cause Tree
3. Scale Path and Budget Lift Plan
4. Landing Page and Funnel Fixes
5. Execution Priority Queue

Workflow

1. Check event completeness for core commerce events.
2. Audit campaign/adset/ad structure and budget fragmentation.
3. Compare creative performance by funnel stage.
4. Diagnose ROAS swings by channel, offer, and audience.
5. Produce scale-safe budget and funnel actions.

Decision Rules

• If Purchase event quality is low, pause aggressive scale and fix tracking first.
• If creative fatigue is detected, prioritize new hooks before raising budget.
• If funnel CVR is below threshold, route spend to best-converting LP first.
• If LTV is unknown, avoid over-bidding on upper-funnel traffic.

Platform Notes

Primary scope:

• Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Shopify Ads

Platform behavior guidance:

• Meta/TikTok for creative-led demand creation.
• Google for intent capture and bottom-funnel efficiency.
• Shopify events must stay consistent with platform conversion definitions.

Constraints And Guardrails

• Do not infer profitability without COGS or contribution assumptions.
• Mark attribution blind spots explicitly.
• Keep scale recommendations bounded by measurement confidence.

Failure Handling And Escalation

• If pixel data is incomplete, output tracking repair plan first.
• If account permission blocks data access, provide minimum data request packet.
• If severe policy risk exists, route to Ads Compliance Review.

Code Examples

OCPX Readiness Check (YAML)

required_events: - ViewContent - AddToCart - InitiateCheckout - Purchase event_quality_threshold: high readiness: conditional

ROAS Volatility Slice (JSON)

{ "window": "last_14d", "worst_segment": "retargeting-video-1", "roas_drop_pct": 31, "suspected_causes": ["creative_fatigue", "audience_overlap"] }

Examples

Example 1: Sudden ROAS drop

Input:

• DTC store ROAS down 25% in 10 days

Output focus:

• root-cause breakdown
• quick stabilizing actions
• budget protection rules

Example 2: Scale decision

Input:

• Profitable baseline, wants 2x spend

Output focus:

• safe scaling ladder
• creative replacement cadence
• funnel readiness checklist

Example 3: LP conversion issue

Input:

• CTR stable, CVR down

Output focus:

• LP diagnosis
• checkout friction fixes
• retest plan

Quality Checklist

• [ ] Required sections are complete and non-empty
• [ ] Trigger keywords include at least 3 registry terms
• [ ] Input and output contracts are operationally testable
• [ ] Workflow and decision rules are capability-specific
• [ ] Platform references are explicit and concrete
• [ ] At least 3 practical examples are included