为什么使用此模板
通过识别人口统计和版位的新兴趋势和效果模式,在趋势达到峰值或消退前提早让受众定向保持新鲜,避免受众疲劳。
适用对象
需要基于数据(而非猜测)演变定向策略,并希望保持领先于受众疲劳的策略师和增长营销人员。
使用方法
每周与效果审查一起运行。使用趋势信号调整受众定向。与广告系列效果数据交叉参考以验证机会。
INPUT
- Campaign-level performance data, two queries: one for the last 7 days, one for the prior 7 days (used as baseline)
- Audience targeting details
PROCESS
SECTION 1 — Trend Analysis
Step 1.1 — Baseline Calculation
Using the two insight calls above, calculate the 7-day rolling average for each campaign. Use the prior 7-day period as the baseline to compare against the most recent 7 days.
Step 1.2 — Flag Declining Campaigns
Flag campaigns where ANY of the following is true for 3+ consecutive days:
- CPA rose more than
{{{CPA_RISE_PCT}}}% vs the prior 7-day rolling average AND current CPA exceeds 1.5× TARGET_CPA (absolute floor safeguard) - CVR dropped more than
{{{CVR_DROP_PCT}}}% week-over-week - ROAS declined more than
{{{ROAS_DECLINE_PCT}}}% vs prior period (only if revenue data available)
(Do not verify whether creative or audience was changed — apply the metric condition and flag it.)
Step 1.3 — Root Cause Classification
For each flagged campaign, classify the most likely root cause based on available metrics:
| Observed pattern | Suspected cause |
|---|---|
| Frequency rising + CTR declining | creative_fatigue |
| Reach plateaued + frequency high | audience_saturation |
| CPM rising + no CTR improvement | auction_competition |
| Cannot explain with above patterns | external_factor |
When classifying, state the evidence clearly (e.g., "CTR dropped from X% to Y% over 7 days while frequency climbed from Z to W, suggesting creative_fatigue"). Do not present the classification as a definitive fact — frame it as the most likely explanation given the data.
Step 1.4 — Output Trend Report
For each flagged campaign output:
- Campaign ID
- Metric trend line (daily values for CPA, CVR over the 7-day window; ROAS included if revenue data is available)
- Days in decline
- Suspected root cause with supporting evidence
- Recommended early intervention
SECTION 2 — Audience Overlap Analysis
Step 2.1 — Overlap Risk Assessment
Meta does not expose a direct audience overlap percentage via API. Use targeting data (age/gender/location) to identify overlap risk by comparing targeting parameters across active ad sets. Flag pairs where targeting parameters indicate likely significant overlap and no detected exclusion logic exists.
This is an approximate assessment — exact overlap % is not available via current tools.
Step 2.2 — Flag and Quantify
For each flagged pair output:
- Ad set pair IDs
- Overlap risk level (High / Medium / Low, based on targeting similarity)
- Estimated budget waste from internal competition:
- Low — less than 5% of combined spend
- Medium — 5–15% of combined spend
- High — more than 15% of combined spend
- Recommended fix:
add_exclusion/consolidate/adjust_targeting
OUTPUT
📈 SECTION 1: TREND ANALYSIS
| Campaign ID | Metric | Daily Values (7d) | Days in Decline | Root Cause | Recommended Intervention |
|---|
If no campaigns flagged: ✅ No campaigns showing significant decline trends this week.
🔍 SECTION 2: AUDIENCE OVERLAP
| Ad Set A | Ad Set B | Overlap Risk | Est. Budget Waste | Recommended Fix |
|---|
If only one campaign with one ad set active: ✅ No overlap risk — single ad set structure.
If targeting data is insufficient to assess overlap: ⚠️ Cannot assess overlap — targeting data not sufficient via current tools. Manual review recommended.
GUARD
- If fewer than 7 days of historical data available: output
⚠️ Insufficient historical data for trend analysis (need 14 days minimum). Output available data only. - Do not execute any changes automatically regardless of findings. Output recommendations only.
CONFIG (user-configurable)
- CPA_RISE_PCT: 10
- CVR_DROP_PCT: 15
- ROAS_DECLINE_PCT: 10 (only applicable if revenue data available)
- TARGET_CPA (required): user-defined target CPA used for absolute floor safeguard