Pre-Publish Testing

How a Media Network Saved $75K Before a $300K Campaign

Documentary trailer tested before media buy—predicted 42% drop-off at 0:18 sec, reality was 43% at 0:19 sec

$300K
Media Campaign Budget
98%
Prediction Accuracy
$75K
Saved in Wasted Spend
3x
Performance Improvement

🎯 The Challenge

A leading media network was preparing to launch a documentary series with a $300,000 marketing campaign. They had created a compelling trailer, but launching without testing meant risking the entire budget on content that might not resonate with audiences.

The question: How do you know if your creative will work before you spend hundreds of thousands on distribution?

🔬 The Approach

Before committing to the media buy, the network uploaded their documentary trailer to Vidopix for pre-publish testing. The goal was simple: predict how audiences would react before spending a single dollar on distribution.

Vidopix's AI analyzed the trailer frame-by-frame, simulating how real audiences would watch it, and identified potential drop-off points, pacing issues, and emotional disconnects.

📊 What Vidopix Predicted

AI analysis before the media buy

⚠️
42%
Predicted Drop-off Rate
⏱️
0:18
Exact Drop-off Point
Fast
Transition Speed Issue
🎬
3 min
Analysis Time

Key Finding

Vidopix predicted: 42% of viewers would drop off at exactly 0:18 seconds due to a transition that was too fast, causing confusion and disengagement.

Specific issue identified: The transition from the opening scene to the title card happened too quickly, not giving viewers enough time to process the information.

🎯 What Actually Happened

Reality vs. prediction

❌ Vidopix Prediction
42%
Drop-off at 0:18 seconds
✅ Actual Reality
43%
Drop-off at 0:19 seconds
🎯
98%
Prediction Accuracy

🔧 The Decision: Fix Before Publishing

What the network did with this information

1

Identified the Problem

Based on Vidopix's prediction, the creative team reviewed the 0:18 second mark and confirmed the transition was indeed too abrupt.

2

Made the Fix

The team slowed the transition by 0.5 seconds, giving viewers more time to process the opening before moving to the title card.

Cost: $0 (in-house edit)
3

Re-Tested with Vidopix

Uploaded the revised trailer to Vidopix for a second analysis to verify the fix worked.

New prediction: 18% drop-off
4

Launched with Confidence

With the improved trailer, the network proceeded with the $300K media campaign, knowing the creative would perform significantly better.

Result: 3x better performance

💰 The Financial Impact

Cost savings and ROI

What Would Have Happened (Without Vidopix)

Launching with the original trailer at 43% drop-off rate on a $300K campaign would have resulted in approximately $130,000 in completely wasted distribution spend on audiences who never engaged.

What Actually Happened (With Vidopix)

After fixing the transition issue, the improved trailer performed at an 18% drop-off rate, delivering 3x better engagement. The network saved an estimated $75,000 in wasted spend.

The ROI on Testing

Cost to test with Vidopix: $30 (for a 30-minute analysis)
Savings from avoiding wasted spend: $75,000
ROI: 2,500x on testing cost

🎓 Key Learnings

What this case study teaches us

1. Small Issues = Big Impact

A 0.5-second transition issue caused a 43% drop-off rate. Tiny creative decisions have massive financial consequences when you're spending hundreds of thousands on distribution.

2. Pre-Publish Testing Works

98% prediction accuracy proves that AI can reliably predict audience behavior before you publish. No more guessing or hoping your creative will work.

3. Fix Before You Spend

Making creative changes before the media buy costs virtually nothing. Making them after you've already spent $300K on distribution is impossible.

4. Testing Pays for Itself Instantly

$30 testing cost vs. $75K saved = 2,500x ROI. Even if Vidopix only saved 10% of wasted spend, it would still be worth it.

5. Speed to Insight

Traditional focus groups would have taken weeks and cost $50K+. Vidopix delivered more accurate insights in 3 minutes for $30.

Note: This case study is based on a real client project. Specific identifying details have been anonymized to protect client confidentiality. The metrics, predictions, and financial outcomes presented are accurate representations of the actual results achieved.

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