Manage LinkedIn and Reddit Ad Campaigns

You’re reviewing your LinkedIn Ads performance. The platform tells you Campaign A outperformed Campaign B by 23%. But when someone asks “what specifically drove that performance - was it the headline, the visual treatment, or the audience segment?”, you’re left guessing. LinkedIn Campaign Manager and Reddit Ads Manager excel at showing you which campaigns won. They fail at telling you why. Campaign metadata - ad copy variants, visual treatments, banner colors, specific messaging themes - lives scattered across planning documents, creative briefs, and the memories of your team members. The platforms don’t store it. They can’t analyze it. This tutorial shows you how to bridge that gap by connecting your campaign planning documents directly to platform performance data, enabling the kind of strategic analysis that justifies enterprise ad spend.

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What the platforms miss

LinkedIn’s Campaign Manager shows you performance metrics: impressions, clicks, conversions, cost per result. It stores ad copy if you type it into the title field. But it won’t tell you:

• Which banner background color performed best with 501-1000 employee companies
• Whether data-focused messaging outperforms feature-focused messaging for your target persona
• If creative variant A consistently beats variant B across different audience segments

Reddit Ads has the same limitation. You see campaign performance, but the connection between creative decisions and results requires manual detective work. For teams managing sophisticated multi-variant campaigns, this gap is expensive. You’re running systematic experiments - different ad copy, visual treatments, and audience combinations - but the platforms can’t systematically tell you what works.

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The planning document

Experienced marketing teams solve this with campaign planning spreadsheets. Here’s what one looks like for AstroBee’s actual LinkedIn and Reddit campaigns: This isn’t random data. It’s a meticulously crafted UTM tracking matrix - the kind of operational documentation that turns campaign codes into business insights. You can download a copy of this data to use as a template for your own campaigns: sheets.csv In this example, it’s a 25-record planning spreadsheet mapping one major campaign: “ntbu-M-ml” targeting US Non-Technical Business Users in Marketing roles at mid-level seniority. Each record represents a specific combination of:

• Company size segment: 2-10, 11-50, 51-200, 201-500, 501-1000 employees
• Ad variant: ntbu_ad1 through ntbu_ad6, each with different value propositions
• Ad copy: Documented messaging for each variant
  • “From messy spreadsheets to clear answers in minutes”
  • “Connect, map, and visualize, with one AI Assistant”
  • “Map every moving part of your business in one visual layer”
  • “Stop cleaning data and start finding insights”
  • “Turn scattered sources into one trusted truth”
• UTM parameters: Pre-generated tracking links for every combination

This planning approach prevents the chaos that happens when teams try to decode campaign performance without documentation. It transforms cryptic campaign codes like “ntbu-M-ml” into meaningful business context: “US Non-Technical Business Users - Marketing - Mid Level” targeting specific company sizes with tailored messaging. The spreadsheet serves as the campaign’s “source code” - documenting the exact audience targeting strategy, recording specific ad copy for each variant, and creating a bridge between platform campaign names and actual business segments.

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Connecting your data to AstroBee

Let’s connect all three sources: the planning document (Google Sheets), LinkedIn Ads performance data, and Reddit Ads metrics. Once your data layer is generated, you can explore the tables AstroBee created by clicking on the Tables tab. Each table represents a key entity from your connected data sources like Campaign_Tracking, LinkedIn_Campaign, LinkedIn_Creative, Reddit_Ad_Group, and Reddit_Campaign. Click any table to examine four aspects: Data shows raw data in table format, Description provides generated explanations of what the table represents and how it connects to your business domain, Properties & Relationships reveals all table properties (columns with data types) and relationships to other tables (showing how tables connect via foreign keys), and the SQL tab displays the underlying queries used to generate the table.

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Strategic questions

Now you can ask questions that connect creative decisions to performance outcomes - the kind of analysis that platforms can’t provide on their own.

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Which ads drive the cheapest clicks with high engagement?

Question: “Among ads with CTR above 0.8%, which had the lowest CPC on Reddit?” Reddit’s most efficient high-engagement ad: ntl_ad6 pulls a 0.88% click-through rate while costing only $1.02 per click—the lowest CPC among every Reddit ad that beats the 0.8% CTR bar. Top five bargain ads (all above 0.8% CTR):

• ntl_ad6 – $1.02/click, 0.88% CTR
• nt3_ad5 – $1.26/click, 0.82% CTR
• nt3_ad6 – $1.37/click, 1.05% CTR
• tbu1_ad5 – $1.37/click, 0.82% CTR
• tbu3_ad6 – $1.43/click, 1.35% CTR

At roughly $1-2 per click, these Reddit placements sit in the same cost band as LinkedIn’s high-CTR campaigns, so you can chase strong engagement on either platform without a major price swing. AstroBee proactively suggests a comparison: After answering the Reddit question, AstroBee asks “What LinkedIn campaign exceeds this threshold, if any?”—demonstrating how it can guide your analysis by connecting insights across platforms. This cross-platform comparison is the kind of strategic insight that individual ad platforms can’t surface. By connecting Reddit and LinkedIn data through AstroBee, you can identify where to allocate budget for maximum cost efficiency.

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How do creative variants perform across audience segments?

Question: “Compare cost per click for each ad variant with the same audience segment” Key findings: Best performing ad variants by company size:

• Small companies (2-10 employees): Ad Copy 1 & 3 both deliver excellent results at $0.86 per click
• Small-medium (11-50): Ad Copy 2 performs best at $0.10 per click
• Medium companies (51-200): Ad Copy 2 maintains strong performance
• Medium-large (201-500): Ad Copy 2 leads at $0.03 per click
• Large companies (501-1000): Ad Copy 1 performs best at $0.94 per click

Performance consistency:

• Smaller companies (2-10 employees) show more predictable costs across all variants
• Medium companies (51-500) have the most variation, with costs ranging from $0.03 to $1.36 per click
• Larger companies (501-1000) have the most consistent performance across creative variants

Overall winner: Ad Copy 1 emerges as the most cost-effective choice, balancing strong performance across 4 out of 5 company size segments. This analysis shows you which creative resonates specifically with different organization sizes - insight you can’t get from campaign-level dashboards.

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Do headlines perform differently on LinkedIn vs Reddit?

Question: “Which creative headlines perform best on LinkedIn vs Reddit?” Platform-specific performance gaps: LinkedIn strong performers - 1.7% CTR:

• “Stop cleaning data and start finding insights”
• “Transform Your Data Effortlessly” (shown in Ad Assistant creative)
• “Map every moving part of your business in one visual layer”

These three headlines generated 691,961 impressions with 6,965 clicks each, making them your top performers on LinkedIn. LinkedIn users responding to data pain points consistently outperform feature-focused messaging. Reddit top performers - 0.52-0.82% CTR:

• Ad2_B leads at 0.82% CTR (3,547 impressions, 29 clicks)
• ntl_ad7 at 0.82% CTR (6,334 impressions with highest click volume: 244 clicks)
• Ad1_D at 0.69% CTR (6,530 impressions, 37 clicks)

Key insights: LinkedIn significantly outperforms Reddit CTRs (1.7% vs 0.52-0.82%). Your problem-solving headlines focusing on data pain points resonate strongly with LinkedIn’s professional audience, suggesting they effectively communicate the core value proposition of eliminating data frustration. Reddit’s lower CTRs suggest creative treatment matters more on that platform - visual variants A-E drive performance gaps across both platforms. Ad targeting conventions (like “Ad_B”, “ntl_ad7”) use systematic AB testing conventions, Ad naming conventions are not directly stored in the database and require external access to URL tracking parameters for complete mapping.

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What’s the optimal campaign configuration?

Question: “Which combination of audience type, company size, and ad creative delivers optimal engagement rates?” Your LinkedIn campaigns targeting US Non-Technical Business Users (Marketing, Mid Level) show clear winners: Best overall combination: 201-500 employee companies with ntbu_ad2 achieving 2.81% engagement Top 3 contributors by engagement rate:

1. 201-500 employees + ntbu_ad2: 2.81% engagement
2. 501-1000 employees + ntbu_ad2: 2.38% engagement
3. 11-50 employees + ntbu_ad3: 2.24% engagement

Key findings: Mid-sized companies (201-500 employees) deliver peak engagement - this sweet spot combines organization scale with decision-making agility, increasing the likelihood of teams acting on data insights. ntbu_ad2 (“Connect, map, and visualize, with one AI Assistant”) significantly outperforms other creative across mid to large company segments - creative focusing on visual organization resonates strongest. Scale campaigns targeting the top 3 combinations - increase budget allocation to 201-500 employee segments with ntbu_ad2, while continuing to test variations for refinement. The complete performance data shows your unique combinations generate 2,275 impressions and 55 engagements across all tested segments.

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What you can discover

This approach reveals patterns that LinkedIn and Reddit Ads managers physically cannot show you: Creative element performance: Connect specific ad copy and visual treatments to audience segments. Instead of guessing which messaging works, you’ll know exactly which creative variants drive engagement with different company sizes, industries, or job roles. Platform-specific optimization: Understand how your creative performs differently across LinkedIn versus Reddit. You might discover that problem-solving headlines drive higher CTRs on LinkedIn, while visual-focused treatments perform better on Reddit - insights that tell you where to invest creative resources for each platform. Audience segment behavior: Identify which audience segments deliver the best performance and which show the most cost variability. You’ll see if smaller companies offer more predictable costs, or if mid-sized organizations deliver higher engagement despite wider cost variance. Cross-platform creative patterns: Analyze whether visual design iteration or copy variation drives better results across platforms. Your systematic AB testing can reveal if visual treatments matter more than headline changes, guiding your creative strategy. Cost efficiency by segment: Discover your cheapest high-engagement clicks by connecting creative variants to specific audience combinations - insights platform dashboards can’t provide because they can’t link your planning spreadsheet to performance data.

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Why this matters

If you’re a VP of Marketing at a company spending $1M+ annually on LinkedIn and Reddit Ads, these insights directly impact board-level reporting. Instead of “Campaign A performed 23% better than Campaign B,” you can report “mid-sized companies respond best to visual organization messaging, driving 2.81% engagement at $0.03 per click, while our data pain point headlines outperform on LinkedIn with 1.7% CTR.” This level of analysis justifies multi-million dollar budgets. It shows you’re not just running ads - you’re systematically testing, learning, and optimizing based on granular creative and audience performance. For smaller companies, the same principles apply. Even at lower budgets, understanding which creative elements drive performance lets you optimize faster and waste less on underperforming variants. The ROI impact compounds over time. When you can definitively answer “which banner color works best with 501-1000 employee companies?” or “does data-focused messaging outperform feature messaging for our persona?”, you make optimization decisions based on evidence instead of intuition.