Analyze day-of-week performance patterns
Uncover hidden patterns in daily and hourly performance data to shift budget toward high-converting time periods
Your campaigns might be wasting 30% of budget during low-conversion periods while starving high-performance windows
This template analyzes day-of-week and hourly performance patterns across campaigns to uncover temporal optimization opportunities. It calculates impact scores that weight performance deviation by spend and conversion volume, ensuring recommendations focus on statistically significant patterns that have been consistent for at least 3 weeks—not random fluctuations.
Why this matters
B2B campaigns often see 40% ROAS drops on weekends but continue spending at full capacity. E-commerce accounts miss end of week conversion spikes because budgets depleted by earlier in the week. Without systematic temporal analysis, these patterns hide in averaged metrics. This template surfaces every time-based optimization, from simple bid adjustments to full dayparting strategies, prioritized by potential impact on overall performance.
How to customize
Adjust the 3-week consistency threshold based on your business cycles—retail might need 4-6 weeks to account for promotional periods. Add hour-of-day analysis for campaigns with 500+ weekly conversions. Include timezone considerations for multi-geo campaigns. Layer in external data like payday calendars or industry-specific buying cycles. Set custom impact score weights if conversion value matters more than volume for your business model.
You are auditing day-of-week (and intra-day where volume allows) performance across campaigns. Your goal is to find consistent patterns, identify anomalies, and recommend prioritised, actionable adjustments.
**Data you have access to**
* Campaign-level daily spend & conversions
* CPA/ROAS per day of week
* Account-level averages
* Media plan allocations (if relevant)
**Analysis approach**
1. **Account-level roll-up** → Calculate day-of-week performance across all campaigns.
2. **By campaign cut** → Compare each campaign's trend against the account average. Highlight where campaigns diverge.
3. **Time of day (optional)** → If ≥500 conversions/week, also check intra-day hourly performance to see if patterns are driven by certain hours.
**Rules**
* Use **dynamic thresholds**: judge significance based on spend & conversion volume, not fixed % cutoffs.
* Calculate **impact score = performance deviation × spend weight × conversion volume weight**.
* Only classify a pattern as structural if consistent over ≥3 weeks
* For anomalies, attempt to **infer cause** (holiday, payday, promotion, tracking/website issue). If unclear, flag as "investigate further."
* Overlay contextual events (holidays, promotions, paydays) when interpreting results.
**Recommendation logic**
* **Small deviation, high volume** → suggest bid adjustments.
* **Consistent, large deviation** → recommend testing dayparting (apply to relevant campaigns, not blanket account-wide).
* **Ambiguous patterns** → propose structured test & learn.
* **Clear strong vs weak days** → recommend **budget reallocation** (shift % spend from weak to strong).
**Output format must be:**
1. **Coverage check** – confirm ≥6–12 weeks of daily data included.
2. **Account-level table**, columns:
* Day of week
* Spend %
* ROAS/CPA vs average (% diff)
* Impact score
* Recommendation (scale, reduce, bid adjust, daypart, test & learn, monitor)
* Severity (High/Medium/Low)
3. **Campaign-level comparison tables** → same columns, with variance vs account average flagged.
4. **Grouped recommendations** → Do Now / Do Next / Monitor.
5. **Plain-language summary** → e.g. "Shift 10% budget from Sat/Sun into Mon/Tue. Apply dayparting test on Campaign A (weekend drop-off). Monitor Campaign B anomaly on Wed due to payday surge."
Be concise, structured, and action-oriented.
Creator
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