The Challenge: A major Super Bowl advertiser was struggling with TV ad performance and needed to improve acquisition efficiency without increasing media spend.

The Solution: Videoquant was used to test multiple TV ad variants before airing. The creative with the highest Videoquant score was selected and tested against the control in a controlled environment.

The Result: The Videoquant-informed creative drove 3.6x more acquisitions using the same media spend and flighting schedule, cutting TV CPA by over 70%. On a single $300K+ test flight, the brand received the equivalent of $1.08M+ in acquisition value — a $780K+ efficiency gain from selecting the right creative before spend.

This success led to a multi-year renewal.

The Challenge: A Fortune 500 company with $2B in annual marketing spend needed to optimize their TV advertising strategy across 22 different ad creatives.

The Solution: The client conducted extensive blind testing of 22 TV ads using Videoquant predictions. Ads were categorized as "high prediction" or "low prediction" based on Videoquant scores.

The Result: Videoquant high predictions delivered 4.4x more brand lift than low predictions on the same media spend, with statistically significant results. This translated to an estimated $240K in savings per $1M in media spend.

The Problem: This national home services advertiser runs continuous direct mail A/B tests to optimize acquisition creative. Each test takes six weeks to produce results and days of manual setup: print production, list splits, holdout groups, postage, and response tracking. By the time one test reads, the next campaign may already be in market. Poor creative doesn't only underperform — it burns budget at scale with no mid-flight kill switch. The team needed a way to know which creative would win before committing the time and spend.

The Test: Videoquant scored 20 head-to-head direct mail A/B matchups using its behavioral prediction engine — powered by 18T+ real-world interactions across 200M+ U.S. adults. No client data was used for training. The model and methodology are disclosed in U.S. Patent No. 12,020,279. Each prediction was delivered in approximately 2 seconds.

The Results: Across 20 head-to-head mail matchups, Videoquant called the winner correctly 18 times (90%). Treating each matchup as independent with a guessing benchmark (p = ½ per correct call), binomial probability of X ≥ 18 successes is roughly p ≈ 0.000201 — far beyond conventional significance thresholds. Predicted gaps tracked magnitude in market — larger modeled differences tended to correspond to larger observed lifts or losses. For this advertiser, what used to take six weeks and days of manual workflow can be surfaced in roughly two seconds per read.

Why It Matters: Direct mail is one of the cleanest channels to validate creative prediction. Unlike digital, there's no algorithmic optimization layer masking creative quality. What you mail is what you get — an ideal proving ground for the thesis that creative is one of the largest controllable drivers of mail performance. That lets teams bias dollars toward creative most likely to convert before postage and production are locked.

What This Means for Your Business: If you run direct mail A/B tests, you're already investing in learning which creative works. Videoquant shortens that learning cycle from weeks to seconds and lets you enter each test with a strong signal on the likely winner. Conservative expected impact: 20–25% CAC reduction on an ongoing basis.

Videoquant predictions use behavioral data from 200M+ U.S. adults and 18T+ interactions. No client data is used for model training. Protected by U.S. Patent No. 12,020,279, valid through 2042.

The Problem: This growth team chases aggressive targets under tight CAC. There’s no separate “testing budget” — every experimental dollar eats the same constrained acquisition cost metric. Strategically they want 6–10 variations per concept, but can't run all ten in-market: historically most ads fail; testing everything burns budget while waiting weeks for statistically significant reads on downfunnel qualified leads, not cheap clicks — pricier impressions, slower signal, less margin for experimentation. They needed to cut 10 creatives down to the ~3 worth testing before spending.

How They Use Videoquant:

• Pre-screening: Batches of 6–10 finished assets scored in minutes; top 3–5 go to market — bottom tier cut before spend.
• Iterative testing: Winners feed live campaigns; middle tier gets a structured second round; losers revisited only if needed — replacing 3–4 rounds of brute-force testing with a score-guided sequence.
• Fatigue management: When a champion fades overnight, the team already knows the next-best rotations by Videoquant rank — minimal scramble.
• Ideation: Messaging, CTAs, imagery, captions, typography scored before heavy production — test at the speed of thinking, not the speed of media spend.

The Results: On UGC, four of Videoquant's top-five picks matched true top-market performers, including the #1 asset across short and long edits. On six polished testimonials run on Meta, ranking matched on four to five creatives; aligning the audience definition inside Videoquant with live targeting brought agreement to five of six. The champion beat the next-best option on CPA by roughly 45% — a gap that compounds when algorithms would otherwise give early impressions to eventual losers.

Why Platforms Aren't Enough: The team's view: without a roster of validated winners, you can't blindly trust algorithms — they chase platform objectives (often spend throughput), not the advertiser's downfunnel goal. Videoquant stacks pre-spend, contextual signal (audience, intent, outcomes) upstream of auction dynamics.

Compared to Alternatives: The team had evaluated attention-based tools: those show where eyes go, not how deeply creative persuades. Rivals struggled to stress-test scripts and NLP-level copy before prod; competitors underperformed on text-heavy assets — a gap this team cared about for word-order and semantic precision.

The Creative–Performance Bridge: Videoquant became a shared language between brand and performance — fewer opinion standoffs because weak ideas face data before fragile relationships fracture.

Four Practical Wins: (1) Pre-screen → fund only likely winners; (2) Speed — minutes not weeks plus ~10hrs/mo reclaimed from grunt work; (3) Strategy-grade testing pre-production; (4) A neutral arbitrator both sides trust.

Why This Matters: Most ad testing claims are about picking a winner from two options. This is different — Videoquant predicted the rank order of 20 ads against the client's own internal expectations. A Spearman correlation of 0.64 at p = 0.002 means the scoring model reliably distinguishes not just which ad is best, but the full gradient from strongest to weakest.

For Statistical Thinkers: A 0.64 Spearman ρ across 20 items means the model captures meaningful signal about relative creative quality. This isn't a coin flip on binary outcomes — it's a rank-order prediction across a real portfolio of TV creative, validated against the client's own ground truth.

The Challenge: One of the UK's most prestigious performance TV media agencies needed to predict which ad creatives would drive stronger visit response rates before committing client media budgets.

The Solution: The agency used Videoquant to score TV creatives across multiple client campaigns spanning 10-second and 30-second ad formats. Predictions were then compared head-to-head against actual visit response rates from live media.

The Result: Videoquant correctly predicted the higher-performing creative in 71.4% of head-to-head matchups (p = 0.039), with a positive linear relationship between VQ scores and visit response rates after controlling for duration. This is the first validation of Videoquant's predictive accuracy on UK campaigns — and the results match the 60–70% win rate pattern seen consistently across US advertisers, confirming that the model generalizes across markets.

Note: This is an underestimate since it only uses existing ads. Results show Videoquant scores don't just correlate — they materially improve ROI when used for optimization.

The Challenge: A B2C service company needed to optimize their paid SEM performance across hundreds of ad creatives to improve ROI.

The Solution: The company used Videoquant to score their SEM ads and identified the top 20% and bottom 20% performers. They shifted spend to high-scoring ads and reduced spend on low-scoring ones.

The Result: Focusing on top 20% ads generated $2.5–3M in incremental value, while cutting bottom 20% saved $350–400K. Combined impact of $3–4M with conservative assumptions — demonstrating that Videoquant scores materially improve ROI when used for optimization.

The Challenge: A YouTube creator with 1.4 million subscribers needed to identify which video concept would resonate most with their audience before investing time and resources into production.

The Solution: The creator used Videoquant to evaluate video concepts. Videoquant identified a high-performing concept, which the channel then produced and published.

The Result: The video based on Videoquant's concept became the #1 most-viewed video on the channel for over a year, outperforming all 360 other videos. This demonstrates Videoquant's ability to predict winning concepts before production, helping creators maximize their content investment.

Why This Matters: Individual channel results tell part of the story. Combined, they show the model generalizes: 6 for 6 on static App Store creatives and 7 for 10 on paid social ads — across different formats, audiences, and success metrics. That's 13 of 16 predictions correct (81%) without any channel-specific tuning.

App Store: A brand tested 6 Apple App Store carousel ad options using Videoquant. The model correctly predicted the directional winner in all 6 controlled tests, allowing the brand to confidently launch with the highest-scoring creatives.

Paid Social: A separate team used Videoquant to compare ad copy variants head-to-head, rate video and static assets, and benchmark against competitors. Across 10 paid social matchups, Videoquant correctly predicted 7 winners. The client independently confirmed that ratings consistently align with which assets perform strongest in market.

Videoquant's founder has one of the most viral posts ever on LinkedIn — over 5 million views. He used Videoquant to make it, with no LinkedIn data in the model.

The same prediction engine that optimizes Super Bowl ads works on any content format — including long-form organic social.