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<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 535615784390975<|im_end|>
<|im_start|>user
rank the campaigns in from best to worst in terms of ROAS from 30th june-30th july<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 343748033303757<|im_end|>
<|im_start|>user
# CREATIVE FATIGUE ANALYSIS PROMPT
You are a creative performance specialist for Meta Ads optimization.
Today's Date: 2025-06-16 (1)
Maintain an internal list **options_accessed = []**.
• When the user selects 🎨 Option 1 or 📊 Option 2, append that option's label to the list.
• Whenever you show "Next-Step" choices, display only the options **not** yet in options_accessed.
────────────────────────────────────────
**TASK**
Identify ads experiencing creative fatigue within a campaign and generate targeted creative refresh recommendations.
────────────────────────────────────────
**STEP 1 – CONFIRM INPUTS**
• **campaign_name** or **campaign_id** (Campaign to analyze)
• **Time frame** (default: last 14 days)
• **Fatigue threshold criteria:**
- Frequency > 2.5 (default)
- CTR decline > 20% (default)
- CPM increase > 25% (default)
- Days running > 7 (default)
⟹ Pause until campaign is confirmed and thresholds are set.
**STEP 2 – FATIGUE DETECTION**
Run SIMPLIFIED FETCH WORKFLOW for the specified campaign:
• **A. Resolve Ad List** - Get all active ads in campaign
• **B. Performance Analysis** - Get insights with frequency, CTR, CPM, impressions, spend, conversions for current period (last 7-14 days) AND comparative period (previous 7-14 days)
**Then apply SYSTEMATIC FATIGUE DETECTION PROCESS:**
**Sub-Step 1: Calculate Week-over-Week Changes**
For each ad, calculate:
• CTR Change % = ((Current_CTR - Previous_CTR) / Previous_CTR) × 100
• CPM Change % = ((Current_CPM - Previous_CPM) / Previous_CPM) × 100
• Conversion Rate Change % = ((Current_Conv_Rate - Previous_Conv_Rate) / Previous_Conv_Rate) × 100
**Sub-Step 2: Apply Fatigue Criteria Filters**
Flag ads meeting ANY of these threshold criteria:
• Frequency > 2.5
• CTR decline > 20% week-over-week
• CPM increase > 25% week-over-week
• Days running > 7 days with same creative
**Sub-Step 3: Calculate Fatigue Severity Score**
For each flagged ad, calculate normalized scores (0-10 scale):
• Frequency_Score = MIN(10, (Current_Frequency / 2.5) × 2.5)
• CTR_Decline_Score = MIN(10, (CTR_Decline_% / 20) × 2.5)
• CPM_Increase_Score = MIN(10, (CPM_Increase_% / 25) × 2.5)
• Days_Running_Score = MIN(10, (Days_Running / 7) × 2.5)
**Fatigue Severity = (Frequency_Score + CTR_Decline_Score + CPM_Increase_Score + Days_Running_Score) / 4**
**Sub-Step 4: Rank and Select Top Fatigued Ads**
• Rank all flagged ads by Fatigue Severity Score (highest first)
• Select top 3-5 most fatigued ads for detailed analysis
• Store calculated scores and percentage changes for report
**Sub-Step 5: Categorize Fatigue Level**
• **CRITICAL (Score 7.5-10.0):** Immediate replacement needed
• **HIGH (Score 5.0-7.4):** Refresh within 24-48 hours
• **MODERATE (Score 2.5-4.9):** Monitor and prepare refresh
Then:
• Filter ads meeting ANY of these fatigue criteria:
- High frequency (>2.5) + performance decline
- CTR dropped >20% week-over-week
- CPM increased >25% week-over-week
- Same creative running >7 days with declining metrics
• Identify 3-5 most fatigued ads based on severity score
• Calculate fatigue severity: (frequency_score + ctr_decline_score + cpm_increase_score) / 3
As soon as detection finishes, automatically continue with STEP 3 in the same message.
**STEP 3 – OUTPUT FATIGUE REPORT**
## 🚨 FATIGUED ADS IDENTIFIED
### Ad #1: [Campaign Name] - [Ad Name]
Fatigue Indicators:
Frequency: [X.X] (describe the implications (2 bullet points))
CTR Change: ↓[XX]% week-over-week (describe the implications (2 bullet points))
CPM Change: ↑[XX]% week-over-week (describe the implications (2 bullet points))
Days Running: [X] days with same creative (describe the implications (2 bullet points))
Fatigue Severity - [CRITICAL/HIGH/MODERATE] (do NOT show the fatigue severity score)
**Current Creative Analysis:**
- **Format:** [Single Image/Video/Carousel]
- **Visual Style:** [e.g., "Product on white background"]
- **Main Message:** [e.g., "Save 30% today"]
- **Color Scheme:** [Dominant colors]
#### 🎨 CREATIVE RECOMMENDATIONS
**OPTION 1: Visual Refresh**
- **Change:** [Specific visual change]
- **New Direction:** [Detailed creative suggestion]
- **Why It Works:** [Performance reasoning]
**OPTION 2: Message Pivot**
- **Current Angle:** [What's currently emphasized]
- **New Angle:** [Different benefit/emotion to focus on]
- **Copy Suggestion:** [Specific headline example]
**OPTION 3: Format Evolution**
- **Current:** [Current format]
- **Upgrade To:** [New format suggestion]
- **Execution:** [How to create it]
**🏆 RECOMMENDED ACTION:** [Which option to prioritize and why]
---
[Repeat format for each fatigued ad identified]
---
## 🎯 QUICK ACTION SUMMARY
### Immediate Actions (Next 24 Hours)
1. **[Ad Name]** → [Specific creative action]
2. **[Ad Name]** → [Specific creative action]
3. **[Ad Name]** → [Specific creative action]
### Creative Production Needed
- **[X] New Images:** [Specific requirements]
- **[X] New Videos:** [Specific requirements]
- **[X] Copy Variations:** [Specific needs]
### Expected Impact
- **Estimated CTR Improvement:** [X]%
- **Estimated CPM Reduction:** [X]%
- **Budget Optimization:** $[Amount] per week
— End of Fatigue Report —
**NEXT-STEP CHOICES**
🎨 Option 1 – Get Detailed Creative Briefs
**STEP 4 – FOLLOW-UP LOGIC**
🎨 **Option 1 – Detailed Creative Briefs**
• **First run Creative Analysis** for fatigued ads only:
- get_ad_creatives_by_ad_id_mcp_pixis-meta-ads → creative_id(s)
- get_ad_creative_by_id_mcp_pixis-meta-ads (full meta fields)
- analyze_creative_insight_mcp_analyze-image (add brand_id if provided)
- Store scores, heat-maps, attributes, creative tags, and thumbnails
• Then generate comprehensive creative briefs for each fatigued ad following this exact format:
## 📌 CREATIVE BRIEF #X: [Ad Name]
**Ad Fatigue Analysis:**
- Current performance issues and fatigue indicators
**Current Creative Analysis:**
- Visual style, format, messaging analysis
- Reference heat-map data and creative scores from analysis
**Copy Variations:**
- 3-5 specific headline/body text alternatives
**3 Creative Concepts:**
**CONCEPT 1: [Concept Name]**
- **Execution:** [Exact description of what the creative should be - specific visual elements, layout, colors, composition]
- **Data Justification:** [Reference heat-map analysis, creative scores, audience attention data, performance metrics that support this approach]
**CONCEPT 2: [Concept Name]**
- **Execution:** [Exact description of creative execution]
- **Data Justification:** [Reference creative analysis data and performance insights]
**CONCEPT 3: [Concept Name]**
- **Execution:** [Exact description of creative execution]
- **Data Justification:** [Reference creative analysis data and performance insights]
**DO NOT INCLUDE:**
- Execution plans, timelines, or success metrics
- Technical specifications or production details
- Implementation priority sections
• **End workflow**
Always reuse prior data (fatigue_ads_analyzed = true).<|im_end|>
<|im_start|>user
Campaign ID: 120226344806650689<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Solve this marketing analytics problem<|im_end|>
<|im_start|>assistant
<think>
Breaking down the problem into steps...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Audit the single campaign that has spent the most in my Meta-ad account over the past 14 days. For every active ad in that campaign, flag creative fatigue whenever its frequency tops 2.5, CTR has fallen more than 20 percent versus the prior period, CPM has climbed over 25 percent, or it has been live for more than a week. Give me a short campaign overview, then spotlight the three to five ads with the worst fatigue—rate each one as Critical, High, or Moderate and list the metrics that triggered the flag. Close with a concise root-cause summary (creative, audience, and structural factors) and a set of quick fixes I can apply within the next 48 hours.<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 2550727528
Can you pull the following brands:
Brilliant Earth
Blue Nile
Grown Brilliance
James Allan
Rare Carot
and then can you pull their traffic to site for 2024 and 2025 for Q2
Then can you show the % change for the following
Direct
Paid Search
Social
Organice Search
Referrals
Displays Ads
Email
So we can see which of the channels above have shown the most growth YoY for driving website traffic<|im_end|>
<|im_start|>user
Please pull the afaible aversting peromance data for Brilian Earth's google ads account<|im_end|>
<|im_start|>user
I'm looking for something like this<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 13382884<|im_end|>
<|im_start|>user
which ads are showing a negative CPA trend, use a min spend of $50 in the past 7 days.<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
2877489478 : CID<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 388717271583472
Integrate backend sheet: https://docs.google.com/spreadsheets/d/1hg80kvoYsL4wofCdZYDLY2PoKx32iRwZCroOW3YbvXY/edit?gid=1052242436#gid=1052242436
Metrics to look at CPM, CPC, CTR, Spends, Purchases, Purchase Conversion Value, BE Transactions, BE Revenue, CPO (BE), ROAS (BE), ROAS
Campaign Id filter: 120218653793760478
120216436767540478
120216436399880478
120216435656260478
120214276243730478
120214276152630478
120210306256730478
120209451808680478
120204719411610478
120204402696950478
120218722232090478
120218168152110478
120218168152090478
120218168152080478
120218114937050478
120218114937040478
Date Range: Last 7 days
Share insights & scope of optimisations<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 1825934574<|im_end|>
<|im_start|>user
share performance of ads as per demo, geo, creative type for last 90 days<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
add a cup<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Solve this marketing analytics problem<|im_end|>
<|im_start|>assistant
<think>
Breaking down the problem into steps...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Solve this marketing analytics problem<|im_end|>
<|im_start|>assistant
<think>
Breaking down the problem into steps...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 914474208608497<|im_end|>
<|im_start|>user
run a geographical analysis on my account to see what states/regions are top performers over the last 30 days<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 1962976640593246<|im_end|>
<|im_start|>user
run meta get attribution configuration for this account<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Solve this marketing analytics problem<|im_end|>
<|im_start|>assistant
<think>
Breaking down the problem into steps...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Solve this marketing analytics problem<|im_end|>
<|im_start|>assistant
<think>
Breaking down the problem into steps...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 10153523400186539<|im_end|>
<|im_start|>user
Can you review the following ads from the campaign titled '2.0 - MM // [Editorial] - 2025 Purchases'. Please provide a performance update on these ads and advise whether I should keep all/some of them running or turn them off
6792101514508
6792101514308
6791495077308
6791497230308<|im_end|>
<|im_start|>user
why do you think static 1 is performing great in the warm ad set but poorly in the cold ad set and why do you think static 2 is performing better in the cold ad set than the warm ad set? What sets these ads apart in terms of creatives/copy etc?<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
act_1948234372346972<|im_end|>
<|im_start|>user
going forward i need you to mask all the data so that my brand or acount infromation is not revealed, ensure you mask the campaign and creatives and any other asset name as well, ensure that no information shown by you can be corelated with my account<|im_end|>
<|im_start|>user
# **Creative Recommendations From Past Ads**
**SYSTEM**
You are a **Meta-Ads Creative-Pattern Analyst**.
Today’s Date 2025-06-25 (3)
### Rules
* Follow every numbered step **exactly**.
* If any required input is missing, **ask and pause**.
* Show raw JSON only when it truly clarifies a point; otherwise summarise.
---
## WORKFLOW
────────────────────────────────────────
### STEP 0 Collect prerequisites
0.1 **act\_id** — Ad-account ID
0.2 Either explicit **ad\_ids** **or** instruction to “Pick top N ads by Spends / Impressions / Clicks”
0.3 **time\_frame** — `start_date`, `end_date` (YYYY-MM-DD, default last 30 days)
⟹ Pause until all inputs are confirmed. *Do not proceed further until confirmed.*
---
### STEP 1 DATA FETCH — run STANDARD FETCH WORKFLOW
Execute the **STANDARD FETCH WORKFLOW** to:
• Validate the account
• Resolve the reference-ad list
• Pull full performance metrics (incl. video)
• Pull creative metadata & thumbnails
---
### STEP 2 Pattern mining
• Run `analyze_image_patterns_tool_mcp_analyze-image` on all cached reference images.
• Combine pattern clusters with performance data to identify the **4 – 5** highest-impact patterns.
---
### STEP 3 Generate creative recommendations
For each critical pattern, craft **one** actionable creative concept (max 5 total).
Include: Concept Details · Data-Backed Rationale · Implementation Guidelines.
---
## STEP 4 OUTPUT
### 1 · Executive Snapshot *(3 bullets max)*
* **Goal**: *e.g., “Maximise ROAS from quiz-based lead gen”*
* **Time-frame analysed**: *YYYY-MM-DD – YYYY-MM-DD*
* **Key unlock**: *≤ 12-word insight*
---
### 2 · Top-10 Ad Quick View
| Rank | Ad ID | Spend | ROAS | CTR | Conv. | Core Hook |
| ---: | ----- | ----- | ---: | --: | ----: | --------- |
| 1 | … | … | … | … | … | … |
| … | … | … | … | … | … | … |
---
### 3 · Winning Creative Patterns
| # | Pattern Title | Evidence (lift / frequency) | Psychological Mechanism |
| - | ------------- | --------------------------- | ----------------------- |
| 1 | … | … | … |
| 2 | … | … | … |
| … | … | … | … |
---
### 4 · Creative Recommendations
1. **\<Recommendation 1>**
* **Concept Details**: …
* **Data-Backed Rationale**: …
* **Implementation Guidelines**: …
2. **\<Recommendation 2>** …
3. **\<Recommendation 3>** …
4. **\<Recommendation 4>** …
5. **\<Recommendation 5>** …
---
**End of report.**<|im_end|>
<|im_start|>user
pick top 5 ads by spends, default 30 days<|im_end|>
<|im_start|>user
my website is Cookunity.com, find all the competitor insights you can find for me, mask each asset or business or campaign or etc to ensure, no name is revealed<|im_end|>
<|im_start|>user
can you do competitor analysis beneficial for facebook ads, be as extensive and detailed and critical as you can be.
use tools not realted to keywords<|im_end|>
<|im_start|>user
An error occurred while processing your request. Please contact the Admin.<|im_end|>
<|im_start|>user
An error occurred while processing your request. Please contact the Admin.<|im_end|>
<|im_start|>user
An error occurred while processing your request. Please contact the Admin.<|im_end|>
<|im_start|>user
An error occurred while processing your request. Please contact the Admin.<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 10154364921761054<|im_end|>
<|im_start|>user
I need to create some more ads. What kind of creative is best performing in the account over the past 3 months?<|im_end|>
<|im_start|>user
can you try again<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 84837325<|im_end|>
<|im_start|>user
please give me the insights for this table
Metric "Meta LW
(June 23-29, 2025)" "Meta LW1
(June 16-22, 2025)" LW-1 VAR "Meta Last Year
(June 23-29, 2024)" LY Variance
Impressions 120,524 127,324 -5.30% 279,410 -56.90%
Reach 39,556 43,309 -8.70% 79,316 -50.10%
Frequency 3.05 2.94 0.037 3.52 -13.40%
Clicks 4,191 3,893 0.077 7,141 -41.30%
Purchases 135 150 -10.00% 264 -48.90%
"Purchase Conversion
Value" $32,902.05 $32,946.57 -0.10% $35,564.82 -7.50%
Spend $1,622.89 $1,686.41 -3.80% $3,058.19 -46.90%
ROAS 20.27 19.54 0.037 11.63 0.743
CPC $0.39 $0.43 -9.30% $0.43 -9.30%<|im_end|>
<|im_start|>user
sorry can you please fill out the table with the same metrics<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 43312463<|im_end|>
<|im_start|>user
for the last 7 days, share spends, leads, CPL day on day<|im_end|>
<|im_start|>user
for the last 56 days, share spends, leads, CPL week over week<|im_end|>
<|im_start|>user
for last 30 days
share region wise spends, spend split %, leads, cpl<|im_end|>
<|im_start|>user
share a table with campaigns, adsets and audiences targeted in the respective adsets<|im_end|>
<|im_start|>user
share age analysis for last 30 days<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 1261382201574550<|im_end|>
<|im_start|>user
For the account 1261382201574550, Bucket the account into four groups - ads launched less than 7 days ago, ads launched 7-14 days ago, ads launched 14-30 days ago and ads launched 30 days plus. Contrast the performance based on those four buckets. Look at the spend share of those 4 buckets and call out if certain buckets are trending higher. Also, Contrast surface average CTR, CPM, frequency, CVR and CPA for each group. Create a table where the column headers include <7days, 7-14 Days, 14-30 Days, 30+ days, and the row headers include Number of Ads, Avg. CTR, Avg, CPM, Avg. Frequency, Avg. CVR, Avg, CPA, Spend Share<|im_end|>
<|im_start|>user
Can you share a line chart tracking CTR, CVR and CPA over the last 8 weeks. Plot each week as the X axis. Use these for the weeks (18-May'25 - 24-May'25), (25-May'25 - 31-May'25), (01-Jun'25 - 07-Jun'25), (08-Jun'25 - 14-Jun'25), (15-Jun'25 - 21-Jun'25), (22-Jun'25 - 28-Jun'25), (29-Jun'25 - 05-Jul'25), (06-Jul'25 - 12-Jul'25). Based on this data, would you think there is ad fatigue in the account?<|im_end|>
<|im_start|>user
continue<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Help me with analysis of ad account id: 1079708247256751<|im_end|>
<|im_start|>user
act_1079708247256751<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 675359574092211<|im_end|>
<|im_start|>user
can you provide an audit on our creatives over the last 30 days<|im_end|>
<|im_start|>user
can you do an audit on the creatives in the ad account based on their level of fatigue, which ones should be switched off?<|im_end|>
<|im_start|>user
can you provide a breakdown on the best performing ad types over the last 30 days as well as the messaging/angle/hooks that are working the best, over the last 30 days?<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 1435134779851841<|im_end|>
<|im_start|>user
https://docs.google.com/spreadsheets/d/1dJJ0H2QBxRLwBuPscrlbDb_f034XAdyIoH4RU2dnxrg/edit?gid=528211408#gid=528211408<|im_end|>
<|im_start|>user
Analyze Activations(adset_level)<|im_end|>
<|im_start|>user
Compare for 1 to 7 June vs 1 to 7 July performance for adsets and the change in %
include metrics - adset name, amount spent, activations, cost per activations, registrations, cost per registrations, activation conversion rate, cpm
give insights for the drop<|im_end|>
<|im_start|>user
Compare for 1 to 7 June vs 1 to 7 July performance for adsets and the change in %
include metrics - adset name, amount spent, activations, cost per activations, registrations, cost per registrations, activation conversion rate, cpm
take activations data from google sheet
give insights for the drop<|im_end|>
<|im_start|>user
act_1435134779851841
sheet url - https://docs.google.com/spreadsheets/d/1dJJ0H2QBxRLwBuPscrlbDb_f034XAdyIoH4RU2dnxrg/edit?gid=528211408#gid=528211408
Compare for 1 to 7 June vs 1 to 7 July performance for adsets and the change in %
include metrics - adset name, amount spent, activations, cost per activations, registrations, cost per registrations, activation conversion rate, cpm
take activations data from google sheet
give insights for the drop<|im_end|>
<|im_start|>user
What is the drop in activation conversion rate for above data<|im_end|>
<|im_start|>user
take activation conversion rate as registrations/activations and reshare above data<|im_end|>
<|im_start|>user
take activation conversion rate as activations/registrations and reshare above data<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 1435134779851841<|im_end|>
<|im_start|>user
analyze the tab Snapshot-IMP in this Source of Truth; https://docs.google.com/spreadsheets/d/1ea3SKRWJA95wC9Sn-JhjmT67MvekG5dKN5n1Uz1A2uQ/edit?gid=1112177208#gid=1112177208 and return; Month to date performance, yesterday performance, last 3 days performance and last 7 days performance in table format.<|im_end|>
<|im_start|>user
analyze the tab Snapshot-IMP in this Source of Truth; https://docs.google.com/spreadsheets/d/1ea3SKRWJA95wC9Sn-JhjmT67MvekG5dKN5n1Uz1A2uQ/edit?gid=1112177208#gid=1112177208 and return; Month to date performance, yesterday performance, last 3 days performance and last 7 days performance in table format.<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Solve this marketing analytics problem<|im_end|>
<|im_start|>assistant
<think>
Breaking down the problem into steps...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Solve this marketing analytics problem<|im_end|>
<|im_start|>assistant
<think>
Breaking down the problem into steps...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
# Facebook Ad Creative Analysis & Performance Report
Create a comprehensive Facebook Ad Creative Analysis & Performance Report that identifies creative patterns, analyzes success factors, and recommends new creative approaches based on data.
## Data Collection Workflow
Follow this sequential workflow to collect comprehensive data:
### 1. Ad Performance Data Collection
- Work with the specific ad_ids provided by the user
- For each provided ad_id, collect detailed performance metrics including:
* Conversion data (conversion rate, cost per purchase, total purchases)
* Demographic breakdowns (age, gender)
* Platform performance (Facebook vs. Instagram)
* Device performance (mobile vs. desktop)
* Placement performance (feed, stories, etc.)
- Example API call: `get_ad_insights` for each provided ad_id with relevant breakdowns
### 2. Creative Asset Collection
- For each provided ad, get associated creative_ids using `get_ad_creatives_by_ad_id`
- For each creative_id, get full creative details using `get_ad_creative_by_id` with comprehensive fields:
```
fields: [
"id", "name", "title", "body", "link_url", "call_to_action_type",
"object_story_spec", "image_url", "video_id", "thumbnail_url",
"asset_feed_spec", "template_url_spec", "object_type"
]
```
- For dynamic ads (identified by templated names like ('product.name'), parse the `asset_feed_spec` to extract:
* All headline/title variants in titles array
* All ad copy variants in bodies array
* All CTA types in call_to_action_types array
* All description variants in descriptions array
* Link information in link_urls array
- Retrieve thumbnail_urls for visual analysis using thumbnail width and length as 1200
- Example API call: `get_ad_creative_by_id` with thumbnail_width=1200 and thumbnail_height=1200
### 3. Creative Visual Analysis
- For each thumbnail_url, use image description tool to analyze visual elements
## Creative Analysis Requirements
For each provided creative, analyze:
- Creative ID and associated campaign objective
- Ad Copy Elements:
* Headlines/Titles (all variants in dynamic ads)
* Body Text (all variants in dynamic ads)
* Descriptions (all variants in dynamic ads)
* CTA Type and exact wording
* For dynamic ads, note the customization rules for different placements
- Performance metrics:
* Total spend and impression volume
* Click performance (total clicks, CTR, CPC)
* Conversion metrics (conversion rate, cost per purchase, total purchases)
- Visual elements:
* Style, layout, color scheme, product presentation
- Copy elements:
* Length, tone, messaging focus, value proposition
- CTA type and placement
- Ad format (single image, carousel, video, collection)
## Report Structure Requirements
### Section 1: Detailed Creative Performance Breakdown
For each of the provided creative concepts:
**Performance Metrics:**
- Top Ad ID: [ID NUMBER]
- Creative ID: [CREATIVE ID]
- Total Spend: $[AMOUNT]
- Impressions: [NUMBER]
- Clicks: [NUMBER]
- CTR: [PERCENTAGE]
- CPC: $[AMOUNT]
- Conversion Rate: [PERCENTAGE] (from click to purchase)
- Cost Per Purchase: $[AMOUNT]
- Total Purchases: [NUMBER]
**Creative Elements:**
- Thumbnail URL: [URL]
- Visual Style: [DESCRIPTION]
- Product Visualization: [DESCRIPTION]
- Messaging Focus: [DESCRIPTION]
- Color Scheme: [DESCRIPTION]
- Call to Action: [DESCRIPTION]
- Visual Hierarchy: [DESCRIPTION]
- Ad Copy Length: [DESCRIPTION]
**Key Performance Insights:** [4-6 BULLET POINTS highlighting demographic, placement, timing, or creative element insights]
### Section 2: Critical Success Pattern Analysis
Identify 4-5 key success patterns across creatives:
**[PATTERN NAME] Psychology**
- Evidence-Based Finding: [QUANTITATIVE FINDING WITH PERCENTAGES]
- Psychological Mechanism: [EXPLANATION]
- Performance Data: [SPECIFIC METRICS COMPARISON]
- Implementation Effectiveness: [ANALYSIS]
[REPEAT FOR EACH PATTERN]
### Section 3: Strategic Creative Recommendations
Provide 5 detailed creative concepts based on data:
**[CONCEPT NAME] Concept Details:**
- Primary Visual: [DESCRIPTION]
- Product Visual: [DESCRIPTION]
- Copy Focus: [DESCRIPTION]
- Supporting Elements: [DESCRIPTION]
- Creative Execution: [DESCRIPTION]
**Evidence-Based Rationale:** [4 BULLET POINTS connecting recommendation to performance data]
**Implementation Guidance:** [4 BULLET POINTS with specific testing approaches]
[REPEAT FOR EACH RECOMMENDATION]
## Analysis Guidelines
- Base all findings on actual performance data, not assumptions
- Include specific percentages and metrics for all comparisons
- Connect recommendations directly to performance patterns
- Identify audience segment-specific insights where relevant
- Focus on creative elements that drove measurable differences in performance
- Prioritize recommendations based on scale potential and proven patterns
- Include both copy and visual recommendations
- Consider platform-specific and device-specific optimizations<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 10154364921761054<|im_end|>
<|im_start|>user
I need new creatives. based on how the current creatives are performing, do you have any recommendations for me to make some more high performing creatives?<|im_end|>
<|im_start|>user
why are the current ads running so well creatively?<|im_end|>
<|im_start|>user
How can I improve them?<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 429169742480600<|im_end|>
<|im_start|>user
Assuming that we maintain a 3.5x return on ad spend using meta ads, what do we need to spend each day to be cash positive. Our fixed daily cost is $410.87 and variable costs include 30% product costs, 6.1% merchant fees, $0.95 3PL costs, $0.55 packaging costs and $0.55 transaction fees. We also spend $30 per day on google. Assume a 1.7x return from this<|im_end|>
<|im_start|>user
Redo and factor in an AOC of $99<|im_end|>
<|im_start|>user
Now with the same input, how much do we need to spend to generate a $10k profit<|im_end|>
<|im_start|>user
Let me rephrase, how much do I need to spend each day on meta ads to generate $10k profit over a 30 day period<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Solve this marketing analytics problem<|im_end|>
<|im_start|>assistant
<think>
Breaking down the problem into steps...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Solve this marketing analytics problem<|im_end|>
<|im_start|>assistant
<think>
Breaking down the problem into steps...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 1275304190557791<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Here is a list of keywords.
The objective is to reduce the number of keywords, but capture the same amount of volume.
This can be done by identifying common terms across these keywords, and replacing those common terms with keywords
Example: Multiple keywords have "Meal Delivery Services" with pre-fixes and suffixes, your job would be to write the same against each keyword.
Context (Good to know): This is for CookUnity, a prepared meal delivery aggregator in the united states.
Terms like high protein and keto should be give more priority if a search terms consists of two common phrases.
Any questions before I share the keywords?<|im_end|>
<|im_start|>user
Answers
1. No metrics, I am sharing just keywords. Do not consider any metrics, do this solely from the perspective of reducing the keyword length / count - while staying relevant
2. Keywords I will be sharing are exact match. Keywords you will be sharing as suggestion are to not be broad
3. Consider this as a no.
4. No. it could be down to 10% or even 90% Idea is to reduce keywords (as they are very specific in nature) and replace them with keywords that capture multiple of these specific keywords.
5. Competitiveness is not need, but consider that these keywords are high intent, and the replacement should also be high intent. For example if keyword is "Meal Delivery Services in LA" - an Ideal response from you would be "Meal Delivery Services" Rather than just "LA". Why? Because "LA" would be extremely broad and not sensible. Hence avoid sharing too generic replacements.<|im_end|>
<|im_start|>user
One slight change in the rules. Keywords I am giving are in exact, keywords you will be giving as replacements are in Phrase.<|im_end|>
<|im_start|>user
Do you have enough context on CookUnity before we proceed?
This is important to understand which terms to consider as relevant and which to not!<|im_end|>
<|im_start|>user
For these, do you have the capability to read a website??<|im_end|>
<|im_start|>user
Okay I will share the same
1. They try to cover all dietary options (Low Carb, Pescatarian, Keto, High Protein etc.)
2. This is irrelevant, since we are working on pull marketing (i.e Search) but ideally anyone that doesnt have time to prepare meals on a daily basis
3. Not really
4. USA<|im_end|>
<|im_start|>user
Great sharing the list of keywords
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[healthiest prepared meal delivery service]
[home cooked meals delivered to your door]
[best and cheapest food delivery service]
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Thistle GLP1 meals<|im_end|>
<|im_start|>user
are all 900+ keywords considered for this?<|im_end|>
<|im_start|>user
I would like to have an approach that covers every keyword. It is alright to have 100 keywords or even 200 after consolidation. Just FYI. Not required or necessary to have that many or more, but absolutely okay<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 47330139<|im_end|>
<|im_start|>user
What is the average CPC of consideration campaigns?<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 70951802<|im_end|>
<|im_start|>user
Can you analyse the awareness based metrics such as reach and impressions and cross reference this with the number of purchases in the account as well as the cost per purchase, over the last 6 months<|im_end|>
<|im_start|>user
this ad account uses US dollars. We have a monthly budget of $10,000 Australian dollars. Can you analyse the ad account over the last 12 months to provide how the fluctuations in exchange rate have affected our purchasing power?<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 590979357620248
What Youtube influencers should I work with?<|im_end|>
<|im_start|>user
Can you show me ROAS by day for the last 14 days?<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 5662637227171756<|im_end|>
<|im_start|>user
what are the best performing creatives PL-10899-001_DXBUnheard_S1Interim_DubaiOnly_Boosted campaign in the phase two adsets<|im_end|>
<|im_start|>user
which ads should i turn off to optimise the campaign<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Solve this marketing analytics problem<|im_end|>
<|im_start|>assistant
<think>
Breaking down the problem into steps...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Solve this marketing analytics problem<|im_end|>
<|im_start|>assistant
<think>
Breaking down the problem into steps...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 1228293999<|im_end|>
<|im_start|>user
Pull all active Responsive Search Ads (RSAs) for the following campaigns:
- "UberSuggest"
For each RSA, return the following data structured by:
1. Campaign Name
2. Ad Group Name
3. RSA Headlines
4. RSA Descriptions
5. Sitelinks (Headline + Final URL)
6. Structured Snippets (Header + Values)
7. Callouts
Also, for sitelinks, callouts, and structured snippets:
- Indicate whether each asset is applied at the account level, campaign level, or ad group level.
Only include:
- Active campaigns
- Active ad groups
- Active RSAs
Do **not** include asset performance ratings (Best, Good, Low) in this response.
Format the response cleanly for export or copying into Google Sheets.<|im_end|>
<|im_start|>user
Change the output to this format: {
"campaign_name": "[UberSuggest | Screaming Frog | Hike SEO]",
"ad_group_name": "[Ad Group Name]",
"rsa_headlines": [
"Headline 1",
"Headline 2",
...
],
"rsa_descriptions": [
"Description 1",
"Description 2",
...
],
"sitelinks": [
{
"headline": "Sitelink Text",
"final_url": "https://example.com/page"
},
...
],
"structured_snippets": [
{
"header": "Header Label",
"values": ["Value 1", "Value 2"]
},
...
],
"callouts": [
"Callout 1",
"Callout 2",
...
],
"notes": {
"shared_sitelinks_source": "Account-level / Campaign-level / Ad group-level",
"shared_callouts_source": "Account-level / Campaign-level / Ad group-level",
"shared_snippets_source": "Account-level / Campaign-level / Ad group-level"
}
}<|im_end|>
<|im_start|>user
You need to split it up by ad<|im_end|>
<|im_start|>user
Redo it and include ad ids<|im_end|>
<|im_start|>user
Do the same thing for Screaming Frog Campaign<|im_end|>
<|im_start|>user
Now do the same things for Hike SEO campaign<|im_end|>
<|im_start|>user
Now do the same for the branded camapign<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Solve this marketing analytics problem<|im_end|>
<|im_start|>assistant
<think>
Breaking down the problem into steps...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
what are you built over?<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 1666060017<|im_end|>
<|im_start|>user
You’re my Google Ads creative strategist.
1️⃣ Find the Performance Max asset-group with the highest conversion value (ROAS as tiebreaker).
• Give me a short summary of that group’s name, campaign, key stats, and why it wins.
• Ask:
- Product / offer?
- Target market / geo?
- Primary objective (sales, leads, store visits, etc.)
→ Pause for my answers.
2️⃣ After I reply, deep-dive into that winning group:
• Pull its best-/good/low-rated assets, auto-describe the images, and spot the copy & visual themes that drive success.
• Map those insights to my campaign focus and draft an **asset-composition blueprint** that includes:
• Executive insight (1 para) linking top-group learnings to my goals.
• Copy table (headline / description / CTA + rationale).
• Image/video guidance table (visual theme, key features, why it fits).
• Structuring tips (naming, pinning, budget split, test plan).
Finish by asking if I’d like more angles, extra image concepts, or a deeper dive into other groups.<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Solve this marketing analytics problem<|im_end|>
<|im_start|>assistant
<think>
Breaking down the problem into steps...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 2594896676<|im_end|>
<|im_start|>user
Can you analyse the brand called "lattafa" and assess the impact this brand has on our revenue comparing the last 30 days to the previous 30 days?<|im_end|>
<|im_start|>user
1. yes
2. it is a product
3. revenue is fine for now<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 84837325 what is the roas maintatined if we have high performing<|im_end|>
<|im_start|>user
give which campaigns have will give highest roas or perrformance, write 2 lines<|im_end|>
<|im_start|>user
Current 25-34 age targeting consumes 57% of budget (AUD $3,703) but shows weaker performance metrics. indicating misaligned budget allocation. this is a casy with naked vice, what can we identify like which camoaign or it can be improved<|im_end|>
<|im_start|>user
write just one pointer to define<|im_end|>
<|im_start|>user
Can we specify that the overspending is occurring in underperforming campaigns/ad sets? give ver-Spending Issues: 3 of 8 ad sets consistently exceed daily budgets (104-106%), indicating poor budget controls and potential wasted spend.<|im_end|>
<|im_start|>user
is this same for past 30 days<|im_end|>
<|im_start|>user
is this righr ?<|im_end|>
<|im_start|>user
Audience Overlap Competition: "Cold - [New Arrivals]" vs "Cold - New Arrivals Q1" targeting similar audiences, creating internal competition and driving up costs. please suggest how to take screenshot of this from ads manager<|im_end|>
<|im_start|>user
change first point and give me what can be other ad set structural ineffiecience<|im_end|>
<|im_start|>user
how will io get screenshot<|im_end|>
<|im_start|>user
give some other point<|im_end|>
<|im_start|>user
give something elkse<|im_end|>
<|im_start|>user
can we put this is this right<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
#ADROOM_TILE:Generate heatmap for a creative## Creative-Score Prompt – Evaluate One Draft Image
## SYSTEM
You are a Meta-Ads Creative-Scoring Assistant.
Today's Date 2025-06-05 (4)
## Classification Buckets
Transform numerical scores using these ranges:
- **Excellent** (9-10): Outstanding performance, best practices followed
- **Good** (7-8): Solid performance with minor areas for improvement
- **Fair** (5-6): Adequate but needs notable improvements
- **Poor** (3-4): Below standard, requires significant improvements
- **Critical** (0-2): Major issues, immediate attention required
## Rules
• Follow every step exactly; if any required input is missing ask and pause.
• Show raw JSON only when it clarifies an answer; otherwise summarise concisely.
• Use classification buckets instead of numerical scores in all outputs.
## STEP 0 Collect prerequisite
0.1 new_image — draft creative (URL or file)
⟹ Pause until the input is confirmed.
## STEP 1 Score the draft creative
• Run analyze_creative_insight_mcp_analyze-image on new_image.
• Run discribe_image_from_thumbnail tool to get description
• Transform the full hierarchy of classes and sub-classes returned by the tool into classification format using the buckets above.
## OUTPUT — Class-wise Classifications
**Overall Classification:** [Excellent/Good/Fair/Poor/Critical]
**Reasoning for overall classification:**
• [2-3 bullet points explaining the classification]
**Description of image:**
• [2-3 bullet points using the discribe_image_from_thumbnail tool]
**Render heat map**
**Focus distribution analysis:**
• [2-3 bullet points]
| Class | Sub-class | Classification | Notes |
|-------|-----------|----------------|-------|
| Ad Clarity | - | [Classification] | [Specific feedback] |
| Text Analysis | Overall | [Classification] | [Specific feedback] |
| | Readability | [Classification] | [Specific feedback] |
| | Font Style | [Classification] | [Specific feedback] |
| | Hierarchy | [Classification] | [Specific feedback] |
| | Spelling/Grammar | [Classification] | [Specific feedback] |
| | Copy Engagement | [Classification] | [Specific feedback] |
| Visual Cohesion | Overall | [Classification] | [Specific feedback] |
| | Background Relevance | [Classification] | [Specific feedback] |
| | Product Visibility | [Classification] | [Specific feedback] |
| | Lighting Effectiveness | [Classification] | [Specific feedback] |
| | Element Alignment | [Classification] | [Specific feedback] |
| | Visual Hooks | [Classification] | [Specific feedback] |
| | Simplicity/Minimalism | [Classification] | [Specific feedback] |
| | Adaptability | [Classification] | [Specific feedback] |
| Color Analysis | Overall | [Classification] | [Specific feedback] |
| | Contrast Ratio | [Classification] | [Specific feedback] |
| | Emotional Impact | [Classification] | [Specific feedback] |
| | Brand Consistency | [Classification] | [Specific feedback] |
| | Color Temperature | [Classification] | [Specific feedback] |
| Branding | Overall | [Classification] | [Specific feedback] |
| | Logo Visibility | [Classification] | [Specific feedback] |
| | Placement Impact | [Classification] | [Specific feedback] |
| | Size Proportion | [Classification] | [Specific feedback] |
| CTA Analysis | Overall | [Classification] | [Specific feedback] |
| | Visibility | [Classification] | [Specific feedback] |
| | Urgency Factor | [Classification] | [Specific feedback] |
| | Action Clarity | [Classification] | [Specific feedback] |
| Psychology | Overall | [Classification] | [Specific feedback] |
| | Emotional Resonance | [Classification] | [Specific feedback] |
| | Psychological Triggers | [Classification] | [Specific feedback] |
| | Cultural Sensitivity | [Classification] | [Specific feedback] |
**End of output report.**
#END_ADROOM_TILE<|im_end|>
<|im_start|>user
https://i.postimg.cc/nLwhP2f9/Aeromexico-Creative-3.png<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Solve this marketing analytics problem<|im_end|>
<|im_start|>assistant
<think>
Breaking down the problem into steps...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 8352488847<|im_end|>
<|im_start|>user
Can you run an account audit for campaign names that contain PSS and CMP<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
create a deep dive analysis of Subway, Burger King, Mcdonald's & KFC. Show what tactics each brand is deploying, bidding strategy, keyword strategy, search volumes, differentiators, targeting show all the findings & output in one table. And give recommendations what KFC should do to gain market share & incremental revenue without any addtional cost<|im_end|>
<|im_start|>user
yes, please<|im_end|>
<|im_start|>user
yes, please<|im_end|>
<|im_start|>user
yes, please<|im_end|>
<|im_start|>user
yes, please<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 3075891307<|im_end|>
<|im_start|>user
Audit my Google Ads campaigns from the last 14 days for [specify platform: Search Network, Display Network, YouTube, Shopping, Discovery, or Gmail Promotions] and flag creative fatigue wherever an ad's impression share has dropped more than 15%, CTR has fallen more than 25%, CPC has climbed over 30%, quality score has decreased by 2+ points, or the ad has run longer than 21 days without refresh. For each campaign, give me a brief overview, then spotlight the three to five ads showing the worst fatigue—rate their severity as Critical, High, or Moderate and include the key metrics that triggered those ratings. Wrap up with a short root-cause summary covering creative elements, audience saturation, and bid competition factors, and finish with a handful of quick-action fixes I can apply within the next 48 hours.<|im_end|>
<|im_start|>user
Examine how your creative assets perform across Google's entire ecosystem within Performance Max campaigns. Identify which image formats drive conversions on YouTube versus Display Network, and which headlines work best on Shopping versus Search placements. Analyze how audience signals influence creative performance across different Google properties to optimize your creative mix for cross-platform success.<|im_end|>
<|im_start|>user
Examine how your creative assets perform across Google's entire ecosystem within Performance Max campaigns. Identify which image formats drive conversions on YouTube versus Display Network, and which headlines work best on Shopping versus Search placements. Analyze how audience signals influence creative performance across different Google properties to optimize your creative mix for cross-platform success.<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 1015714995657899<|im_end|>
<|im_start|>user
in the last 14 days, check for the best performing and worse performing ads. Tell me if there are any common ads across all ads that are performing good or bad.<|im_end|>
<|im_start|>user
in the last 14 days, check for the best performing and worse performing ads. Tell me if there are any common ads across all ads that are performing good or bad.<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 5785703746<|im_end|>
<|im_start|>user
Prestige Towing Sydney are ultra-low ground clearance specialists. Professional transport for supercars, race cars & prestige vehicles with clearance as low as 50mm. Operate in NSW, Australia.
With $50/day budget, provide a search campaign strategy, crafting campaign ad group, keyword and ad copy structure. Also provide keyword volumes. Radius targeting would be 30km around sydney<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 1220381671347506<|im_end|>
<|im_start|>user
can you analyse how lookalike campaigns/adsets have performed on this account previously?<|im_end|>
<|im_start|>user
can you analyse this for the entire period the Lookalike audiences were live rather than L90 days<|im_end|>
<|im_start|>user
which LAL worked best in the 2025 campaign from a ROAS standpoint (7DC)<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 10156091976900803 also run get attribution configuration for this<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 1832497760433080
Show ad sets that spent more than 0 but recorded 0 conversions in the past week; add CPA if just one sale had occurred<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Solve this marketing analytics problem<|im_end|>
<|im_start|>assistant
<think>
Breaking down the problem into steps...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Solve this marketing analytics problem<|im_end|>
<|im_start|>assistant
<think>
Breaking down the problem into steps...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Solve this marketing analytics problem<|im_end|>
<|im_start|>assistant
<think>
Breaking down the problem into steps...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 2350955862<|im_end|>
<|im_start|>user
can you pull restuls for july vs june for spend and conversion volume. then give me insights for the account and camapigns for july vs june and insights. and next steps<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
#ADROOM_TILE:Generate heatmap for a creative## Creative-Score Prompt – Evaluate One Draft Image
## SYSTEM
You are a Meta-Ads Creative-Scoring Assistant.
Today's Date 2025-06-05 (4)
## Classification Buckets
Transform numerical scores using these ranges:
- **Excellent** (9-10): Outstanding performance, best practices followed
- **Good** (7-8): Solid performance with minor areas for improvement
- **Fair** (5-6): Adequate but needs notable improvements
- **Poor** (3-4): Below standard, requires significant improvements
- **Critical** (0-2): Major issues, immediate attention required
## Rules
• Follow every step exactly; if any required input is missing ask and pause.
• Show raw JSON only when it clarifies an answer; otherwise summarise concisely.
• Use classification buckets instead of numerical scores in all outputs.
## STEP 0 Collect prerequisite
0.1 new_image — draft creative (URL or file)
⟹ Pause until the input is confirmed.
## STEP 1 Score the draft creative
• Run analyze_creative_insight_mcp_analyze-image on new_image.
• Run discribe_image_from_thumbnail tool to get description
• Transform the full hierarchy of classes and sub-classes returned by the tool into classification format using the buckets above.
## OUTPUT — Class-wise Classifications
**Overall Classification:** [Excellent/Good/Fair/Poor/Critical]
**Reasoning for overall classification:**
• [2-3 bullet points explaining the classification]
**Description of image:**
• [2-3 bullet points using the discribe_image_from_thumbnail tool]
**Render heat map**
**Focus distribution analysis:**
• [2-3 bullet points]
| Class | Sub-class | Classification | Notes |
|-------|-----------|----------------|-------|
| Ad Clarity | - | [Classification] | [Specific feedback] |
| Text Analysis | Overall | [Classification] | [Specific feedback] |
| | Readability | [Classification] | [Specific feedback] |
| | Font Style | [Classification] | [Specific feedback] |
| | Hierarchy | [Classification] | [Specific feedback] |
| | Spelling/Grammar | [Classification] | [Specific feedback] |
| | Copy Engagement | [Classification] | [Specific feedback] |
| Visual Cohesion | Overall | [Classification] | [Specific feedback] |
| | Background Relevance | [Classification] | [Specific feedback] |
| | Product Visibility | [Classification] | [Specific feedback] |
| | Lighting Effectiveness | [Classification] | [Specific feedback] |
| | Element Alignment | [Classification] | [Specific feedback] |
| | Visual Hooks | [Classification] | [Specific feedback] |
| | Simplicity/Minimalism | [Classification] | [Specific feedback] |
| | Adaptability | [Classification] | [Specific feedback] |
| Color Analysis | Overall | [Classification] | [Specific feedback] |
| | Contrast Ratio | [Classification] | [Specific feedback] |
| | Emotional Impact | [Classification] | [Specific feedback] |
| | Brand Consistency | [Classification] | [Specific feedback] |
| | Color Temperature | [Classification] | [Specific feedback] |
| Branding | Overall | [Classification] | [Specific feedback] |
| | Logo Visibility | [Classification] | [Specific feedback] |
| | Placement Impact | [Classification] | [Specific feedback] |
| | Size Proportion | [Classification] | [Specific feedback] |
| CTA Analysis | Overall | [Classification] | [Specific feedback] |
| | Visibility | [Classification] | [Specific feedback] |
| | Urgency Factor | [Classification] | [Specific feedback] |
| | Action Clarity | [Classification] | [Specific feedback] |
| Psychology | Overall | [Classification] | [Specific feedback] |
| | Emotional Resonance | [Classification] | [Specific feedback] |
| | Psychological Triggers | [Classification] | [Specific feedback] |
| | Cultural Sensitivity | [Classification] | [Specific feedback] |
**End of output report.**
#END_ADROOM_TILE<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 7816793630<|im_end|>
<|im_start|>user
please give me insights over the last 7 day period of account performance<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
2012840205522942<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
give me heat map for this image<|im_end|>
<|im_start|>user
# Creative-Score Prompt – Evaluate One Draft Image
## SYSTEM
You are a Meta-Ads Creative-Scoring Assistant.
Today's Date 2025-06-05 (4)
## Classification Buckets
Transform numerical scores using these ranges:
- **Excellent** (9-10): Outstanding performance, best practices followed
- **Good** (7-8): Solid performance with minor areas for improvement
- **Fair** (5-6): Adequate but needs notable improvements
- **Poor** (3-4): Below standard, requires significant improvements
- **Critical** (0-2): Major issues, immediate attention required
## Rules
• Follow every step exactly; if any required input is missing ask and pause.
• Show raw JSON only when it clarifies an answer; otherwise summarise concisely.
• Use classification buckets instead of numerical scores in all outputs.
## STEP 0 Collect prerequisite
0.1 new_image — draft creative (URL or file)
⟹ Pause until the input is confirmed.
## STEP 1 Score the draft creative
• Run analyze_creative_insight_mcp_analyze-image on new_image.
• Run discribe_image_from_thumbnail tool to get description
• Transform the full hierarchy of classes and sub-classes returned by the tool into classification format using the buckets above.
## OUTPUT — Class-wise Classifications
**Overall Classification:** [Excellent/Good/Fair/Poor/Critical]
**Reasoning for overall classification:**
• [2-3 bullet points explaining the classification]
**Description of image:**
• [2-3 bullet points using the discribe_image_from_thumbnail tool]
**Render heat map**
**Focus distribution analysis:**
• [2-3 bullet points]
| Class | Sub-class | Classification | Notes |
|-------|-----------|----------------|-------|
| Ad Clarity | - | [Classification] | [Specific feedback] |
| Text Analysis | Overall | [Classification] | [Specific feedback] |
| | Readability | [Classification] | [Specific feedback] |
| | Font Style | [Classification] | [Specific feedback] |
| | Hierarchy | [Classification] | [Specific feedback] |
| | Spelling/Grammar | [Classification] | [Specific feedback] |
| | Copy Engagement | [Classification] | [Specific feedback] |
| Visual Cohesion | Overall | [Classification] | [Specific feedback] |
| | Background Relevance | [Classification] | [Specific feedback] |
| | Product Visibility | [Classification] | [Specific feedback] |
| | Lighting Effectiveness | [Classification] | [Specific feedback] |
| | Element Alignment | [Classification] | [Specific feedback] |
| | Visual Hooks | [Classification] | [Specific feedback] |
| | Simplicity/Minimalism | [Classification] | [Specific feedback] |
| | Adaptability | [Classification] | [Specific feedback] |
| Color Analysis | Overall | [Classification] | [Specific feedback] |
| | Contrast Ratio | [Classification] | [Specific feedback] |
| | Emotional Impact | [Classification] | [Specific feedback] |
| | Brand Consistency | [Classification] | [Specific feedback] |
| | Color Temperature | [Classification] | [Specific feedback] |
| Branding | Overall | [Classification] | [Specific feedback] |
| | Logo Visibility | [Classification] | [Specific feedback] |
| | Placement Impact | [Classification] | [Specific feedback] |
| | Size Proportion | [Classification] | [Specific feedback] |
| CTA Analysis | Overall | [Classification] | [Specific feedback] |
| | Visibility | [Classification] | [Specific feedback] |
| | Urgency Factor | [Classification] | [Specific feedback] |
| | Action Clarity | [Classification] | [Specific feedback] |
| Psychology | Overall | [Classification] | [Specific feedback] |
| | Emotional Resonance | [Classification] | [Specific feedback] |
| | Psychological Triggers | [Classification] | [Specific feedback] |
| | Cultural Sensitivity | [Classification] | [Specific feedback] |
**End of output report.**<|im_end|>
<|im_start|>user
can you not use this image directly?<|im_end|>
<|im_start|>user
https://d3va8zkkzxpydd.cloudfront.net/images/681a33ce909ba7483db43121/c8704df1-ef1e-443c-af6a-c3d95dad3515__0e22c8b7-73c1-46da-b04a-47c6e33fb83a__1_.png<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Get details for ad account: 2550727528<|im_end|>
<|im_start|>user
You’re my Google Ads insights analyst.
1️⃣ First, scan the account’s Performance Max asset groups.
• Show a markdown table (highest → lowest conversions) with an Index column.
• Ask me to pick an index or exact group name for deeper analysis.
2️⃣ After I choose, deliver a full deep dive that includes:
• Key performance stats & Google labels for every asset.
• Image-content audit (describe what each image shows).
• 30-day vs 90-day trend comparison for context/fatigue.
• Actionable recommendations—what to replace, refresh, or A/B-test—each with a data-backed rationale and priority.
Finish with a quick prompt asking if I’d like another group or extra breakdowns.<|im_end|>
<|im_start|>user
1<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Execute this marketing API function<|im_end|>
<|im_start|>assistant
<think>
Analyzing the API call parameters and expected results...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Solve this marketing analytics problem<|im_end|>
<|im_start|>assistant
<think>
Breaking down the problem into steps...
</think>
<|im_end|>
|
<|im_start|>system
You are Stellar, Pixis AI's advanced marketing copilot.<|im_end|>
<|im_start|>user
Solve this marketing analytics problem<|im_end|>
<|im_start|>assistant
<think>
Breaking down the problem into steps...
</think>
<|im_end|>
|
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