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

The Result: The AdSeer-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 travel brand running Meta static and video acquisition creative needed to know whether AdSeer scores predicted downfunnel efficiency, not just clicks. At the individual ad level, CAC looked noisy: sparse conversions per ad produced confidence bands too wide to interpret directly.

The Analysis: Rather than read underpowered ad-level CAC, the team pooled spend and conversions within each format. Active ads (excluding learning and no-spend) were split at that format's median AdSeer score; aggregate CAC was compared above vs. below the median. Static was also checked at the ad level for CTR, where sample size supported a clean read.

The Results: High-scoring static creatives delivered ~24% lower CAC; high-scoring video delivered ~25% lower CAC. That ~25% gap scales to ~$250K saved per $1M in acquisition spend (or ~$1.25M on a $5M program) without publishing brand-specific dollar CACs. Pooled significance sat around p ≈ 0.05–0.07. Separately, static score correlated strongly with CTR (Spearman ρ = 0.65, p = 0.004).

AdSeer 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. Brand, absolute CAC, and score thresholds withheld. Dollar savings are scale-invariant translations of the observed ~25% CAC gap.

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 AdSeer predictions. Ads were categorized as "high prediction" or "low prediction" based on AdSeer scores.

The Result: AdSeer 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 Question: Do AdSeer scores predict this national home services advertiser's in-market direct-mail performance, and which audience model fits the channel best?

How It Was Tested: Prediction exports were joined to performance matchback across a multi-month mail window. Ambiguous items were excluded. Outcomes covered response rate, the client's efficiency KPI, and cost-per-lead ranking. Absolute CAC, volume, and score thresholds are withheld. Win rate is pairwise concordance within the same mail month.

The Results: On a US Population audience, within-month win rates landed in the ~71–78% range (p ≤ 0.001). Translated conservatively into a ~20% pre-spend efficiency improvement, that is ~$200K saved per $1M mailed (~$2M on a $10M program). A Homeowners audience was a solid second; a category/competitor audience was not significant. High-volume mailings held the same ~2-in-3 band. Lead-to-close showed no signal, as expected when closing is sales- and list-driven rather than creative-driven.

AdSeer 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. Brand name, absolute CAC, and volumes withheld. Dollar figures are illustrative scale translations of modeled ~20% mail-cost efficiency, not the client's published P&L.

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: AdSeer 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, AdSeer 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. Conservative expected impact: 20–25% CAC reduction on an ongoing basis.

AdSeer 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 AdSeer:

• 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 AdSeer 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 AdSeer'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 AdSeer 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.

The Result: AdSeer predicted the rank order of 20 TV ads against the client's own internal performance expectations. Spearman correlation of 0.64 (p = 0.002): the model distinguishes not just which ad is best, but the full gradient from strongest to weakest.

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 AdSeer 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: AdSeer 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 AdSeer'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 AdSeer 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 AdSeer 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 AdSeer 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 AdSeer to evaluate video concepts. AdSeer identified a high-performing concept, which the channel then produced and published.

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

App Store: A brand tested 6 Apple App Store carousel ad options using AdSeer. 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 AdSeer to compare ad copy variants head-to-head, rate video and static assets, and benchmark against competitors. Across 10 paid social matchups, AdSeer correctly predicted 7 winners. The client independently confirmed that ratings consistently align with which assets perform strongest in market.

Combined: 13 of 16 predictions correct (81%) across two distinct channels and formats, without channel-specific tuning.

AdSeer's founder has one of the most viral posts ever on LinkedIn, over 5 million views. He used AdSeer 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.