Super Bowl LX: The Complete Ad Effectiveness Report:
10 segments simulate real purchase intent for every major Super Bowl LX ad.
"Likability was cheap at Super Bowl LX—only 3 of the top 8 “most liked” ads were also top-5 on modeled purchase intent lift."
The research suggests a fundamental decoupling between trust and transaction. While Gen Z consumers report record-low levels of institutional brand trust, their purchase behavior remains robust, driven by a new architecture of peer-to-peer verification.
"The funniest ads bought attention; the clearest ads bought intent—only 3 of the top 8 most-liked spots were also top-5 in modeled purchase lift."
"Toyota’s “Second Chance Test Drive” led intent lift at +18.6pp, not because it was the most loved, but because it reduced friction and showed the product working."
"59% did nothing after the ads; among the 41% who acted, Google Search (35.8%) beat sharing and rewatch behavior."
"Demo-led creative delivered a 2.4× intent multiplier versus celebrity-led creative in this simulation—proof beat fame."
"Reddit’s reach was low (22% usage) but its trust was relatively high (58), making it disproportionately influential for Skeptical Researchers."
"Most memory evaporates fast: 66.7% forgot most ads within 24 hours—your conversion stack has to activate the same night."
"AI/finance hype is the tax: 12% saw trust penalties from ‘too-good-to-be-true’ claims, rising to 17% among privacy-conscious critics."
Analytical Exhibits
10 data-driven deep dives into signal architecture.
Which Super Bowl LX ads actually moved purchase intent
Net purchase-intent lift among exposed viewers (segment-weighted).
"Auto + retail utility ads produced the largest intent lift; pure entertainment rarely cracked +10pp."
Net Purchase-Intent Lift by Ad (pp)
Raw Data Matrix
| Ad | Intent lift (pp) | High-intent segment contribution | Modeled CPA index (lower is better) |
|---|---|---|---|
| Toyota — “Second Chance Test Drive” | +18.6 | 61% | 0.86 |
| Amazon — “One-Click Local” | +16.2 | 58% | 0.89 |
| Doritos — “Crash the Chat” | +14.1 | 49% | 0.95 |
| T-Mobile — “5G Home Swap” | +13.4 | 52% | 0.93 |
Modeled intent lift reflects net change in “likely to purchase/try in next 30 days,” controlling for baseline category buyers. Values are segment-weighted to the U.S. viewing composition in this simulation.
Likability is not effectiveness
Likeability vs. purchase intent for the same ad set.
"Several “funniest” spots posted strong likeability but weak purchase movement; the best performers were clearer on product and payoff."
Likeability (0–100) vs. Net Purchase-Intent Lift (pp)
Raw Data Matrix
| Ad | Likeability | Intent lift (pp) | Gap indicator |
|---|---|---|---|
| Mountain Dew — “Lizard Lounge” | 82 | +5.9 | High like / low buy |
| Apple — “Vision Air” | 74 | +8.7 | Liked, but pricey friction |
| Toyota — “Second Chance Test Drive” | 71 | +18.6 | Balanced |
| CryptoX — “The Future Is Now” | 57 | +4.1 | Low trust ceiling |
Likeability is modeled from entertainment value + emotional resonance. Intent lift requires comprehension + trust + perceived personal relevance; these do not move in lockstep.
Which ads won the high-intent segments (where money moves)
Lift concentration among the 46% of viewers most likely to buy within 30 days.
"Toyota and Amazon converted pragmatic and deal-driven segments; betting and telecom ads were efficient but narrower."
Lift Contribution from High-Intent Segments (%)
Raw Data Matrix
| Ad | High-intent share of lift | Primary converting segments | Largest friction |
|---|---|---|---|
| Toyota | 61% | Deal-First, Pragmatists, Researchers | Financing clarity |
| Amazon | 58% | Pragmatists, Loyalists, Social Proof | Local delivery skepticism |
| DraftKings | 51% | Trend-Driven, Sports-Core | Responsible play concerns |
| Google Pixel | 47% | Researchers, Affluent Experience | AI claim trust |
High-intent pool defined as viewers with baseline category purchase probability above the 60th percentile plus low switching resistance.
Creative signals that actually drove purchase intent
Modeled drivers ranked by marginal impact on intent (not recall).
"Proof beats vibe: product demo, price clarity, and friction removal outperformed celebrity presence and plot twists."
Marginal Contribution to Intent Lift (share of total lift)
Raw Data Matrix
| Driver | Intent impact | Recall impact | Risk if overused |
|---|---|---|---|
| Clear product demo | High | Medium | Can feel boring without story |
| Offer/price anchor | High | Low | Margin pressure / promo training |
| Humor | Low-Med | High | Creates “liked not buying” gap |
| Celebrity (standalone) | Low | High | Reactance / authenticity doubt |
Marginal contributions are derived from simulated counterfactuals (removing a signal while holding other creative elements constant). Totals sum to 100% across the modeled driver set.
Clarity vs. entertainment: the intent trade-off is real
How comprehension and entertainment jointly shaped downstream action.
"Entertainment without clarity boosted shares, not sales; clarity without entertainment boosted sales, not shares. The winners did both above-average."
Average Outcomes by Creative Mode
Raw Data Matrix
| Creative mode | Any action within 24h | Search rate | Share rate |
|---|---|---|---|
| Entertainment-heavy / low-clarity | 31% | 14% | 19% |
| Balanced | 46% | 22% | 15% |
| Proof-first | 49% | 26% | 12% |
| Celebrity-first | 33% | 15% | 18% |
Creative modes are assigned from cognitive-load scoring: message density, claim burden, and product-link strength. Outcomes are simulated with segment-specific thresholds for action.
Where people went to verify claims (and which channels they trust)
Usage and trust for post-Super Bowl research and verification.
"YouTube and Google are the workhorses for verification; Reddit is trusted by fewer people but disproportionately influences Skeptical Researchers."
Platform Usage vs. Trust (0–100)
Raw Data Matrix
| Platform | Used within 24h | Primary motivation | Highest-usage segment |
|---|---|---|---|
| Google Search | 64% | Confirm price/terms | Deal-First Switchers |
| YouTube | 58% | See real-world proof | Skeptical Researchers |
| 22% | Find the catch | Skeptical Researchers | |
| TikTok | 36% | See if it’s trending | Trend-Driven Gen Z |
Trust scores reflect perceived credibility for purchase decisions (not entertainment). Usage reflects modeled behavior within 24 hours of exposure.
Price and offer clarity: the fastest lever for real intent
How explicit economics shifted intent and action.
"When an ad anchored cost or savings, it produced +6pp higher intent on average and reduced “looks cool, but…” drop-off by 10 points."
Incremental Intent Lift from Offer Elements (pp)
Raw Data Matrix
| Offer element | Best-fit categories | Most responsive segment | Main risk |
|---|---|---|---|
| Trade-in guarantee | Auto, Tech | Deal-First Switchers | Margin exposure |
| $/mo price clarity | Telecom, Auto | Quality Pragmatists | Anchor too high |
| Free trial / easy cancel | Subscriptions | Skeptical Researchers | Churn if onboarding weak |
| Bundle clarity | Retail, Tech | Brand Loyalists | Complexity if overexplained |
Incremental lift values are derived from simulated creative variants, holding category and brand baseline constant.
Trust winners and losers by ad (not by brand fame)
Net trust delta after exposure (0–100 scale).
"Trust gains came from proof and restraint; trust losses came from inflated AI/finance claims and unclear terms—even when the spot was entertaining."
Trust Change vs. Claim Skepticism (0–100)
Raw Data Matrix
| Ad | Net trust delta | Top trust driver | Top trust breaker |
|---|---|---|---|
| Toyota | +12 | Demo + safety proof | Financing questions |
| Amazon | +9 | Utility + familiar process | Skepticism about “local” |
| Google Pixel | +6 | Practical use cases | AI exaggeration concern |
| CryptoX | -6 | None dominant | Too-good-to-be-true |
Net trust delta is a post-exposure shift in “I believe this brand’s claims” and “I’d feel safe buying.” Skepticism reflects perceived claim burden and mismatch with known category realities.
Recall doesn’t equal intent: the 24-hour decay pattern
Memory persistence vs. purchase movement by ad type.
"High-recall comedy decayed slower in memory but didn’t convert; proof-first ads converted even when recall was only mid-tier."
24h Aided Recall vs. Purchase-Intent Lift
Raw Data Matrix
| Ad type | Immediate recall | 24h recall | Any action within 24h |
|---|---|---|---|
| Comedy sketch | 71% | 62% | 28% |
| Product demo | 57% | 48% | 49% |
| Offer-forward utility | 52% | 44% | 46% |
| Emotional story | 63% | 55% | 34% |
Recall is modeled via attention retention + distinctiveness. Action is modeled via comprehension + trust + situational fit. High distinctiveness can inflate recall without improving intent.
The post-game conversion stack: where to spend to capture intent
Best-performing retargeting channels by simulated incremental conversion efficiency.
"Search and YouTube close demand; TikTok sparks discovery but needs proof assets; brand sites underperform unless they land on an offer page with minimal friction."
Incremental Conversion Efficiency vs. Usage (0–100)
Raw Data Matrix
| Channel | Recommended share of retargeting budget | Primary KPI | Expected lift driver |
|---|---|---|---|
| Paid Search | 30% | CPA / ROAS | Capture intent spikes |
| YouTube | 25% | View-through conversions | Proof + explanation |
| Retail media | 18% | Add-to-cart rate | Close near purchase |
| Social (IG/TikTok) | 17% | CTR + assisted conv. | Social validation |
Efficiency scores incorporate trust, usage, and modeled propensity to convert given ad-type follow-up assets (offer pages, demo cut-downs, creator proof).
Cross-Tabulation Intelligence
Segment response matrix (0–100 indices)
| Intent Lift Index | Likeability Index | Trust Gain Index | Research Likelihood | Promo Elasticity | |
|---|---|---|---|---|---|
| Deal-First Switchers (14%%) | 78 | 64 | 55 | 72 | 84 |
| Brand Loyalists (13%%) | 62 | 66 | 61 | 48 | 44 |
| Skeptical Researchers (11%%) | 58 | 60 | 52 | 81 | 50 |
| Social Proof Seekers (10%%) | 60 | 72 | 58 | 54 | 63 |
| Quality Pragmatists (12%%) | 74 | 63 | 62 | 69 | 58 |
| Trend-Driven Gen Z (10%%) | 55 | 79 | 49 | 46 | 67 |
| Affluent Experience Seekers (9%%) | 57 | 68 | 57 | 62 | 39 |
| Sports-Core Traditionalists (8%%) | 66 | 61 | 56 | 44 | 52 |
| Entertainment-Only Watchers (7%%) | 32 | 81 | 42 | 28 | 46 |
| Privacy-Conscious Tech Critics (6%%) | 41 | 54 | 38 | 76 | 33 |
Trust Architecture Funnel
Trust-to-purchase funnel (Super Bowl LX ad exposure → conversion readiness)
Demographic Variance Analysis
Variance Explorer: Demographic Stress Test
"Brand Distrust 73% → 78% ▲ (High reliance on peer verification in lower income brackets)"
$50K HHI: entertainment-only likeability rarely converts; price/offer clarity is the unlock. $150K: more tolerance for brand-led storytelling, but still needs a concrete reason-to-believe to switch. $300K+: lowest sensitivity to price; highest sensitivity to brand status/values, but also the most cynical about ‘hype.’ This demographic slice exhibits high sensitivity to Category involvement / purchase cycle length (a contextual variable that usually beats demographics). If you’re not in-market, likeability is cheap and intent lift is capped.. The peer multiplier effect is most pronounced here, suggesting a tactical shift toward community-led verification rather than broad brand messaging.
Segment Profiles
Deal-First Switchers
Quality Pragmatists
Skeptical Researchers
Trend-Driven Gen Z
Sports-Core Traditionalists
Entertainment-Only Watchers
Persona Theater
MAYA, 29
"Wants the best deal with low hassle; will switch brands if savings are explicit and credible."
"A $/mo anchor increased her modeled purchase intent by +9.1pp versus “value” language without numbers."
"Run a 14-day post-game Search + landing page test with two price anchors; target a -12% CPA vs. baseline."
CHRIS, 41
"Buys when he sees the product solve a real problem; wants proof more than personality."
"Demo-first cutdowns generated 1.8× higher modeled site-visit propensity than the same ad’s cinematic edit."
"Deploy a proof-first YouTube sequence (15s → 30s demo → review montage) and optimize for +20% search lift."
ELENA, 36
"Assumes marketing exaggerates; verifies with reviews and forums before acting."
"Adding a single third-party validation cue reduced her claim skepticism by 14 points and raised intent by +4.6pp."
"Build a “proof hub” landing page (ratings, comparisons, limitations) and measure +8pp trust gain among researchers."
JORDAN, 23
"Responds to what’s socially validated; shares faster than they purchase."
"Creator-backed proof increased trust by +7 points versus brand-only messaging, but only when paired with a clear next step."
"Spark Ads with creator demos + pinned offer; target 1.3× higher assisted conversions versus standard UGC."
DEREK, 52
"Wants straight talk and reliability; low patience for abstract brand films."
"Overly clever humor reduced comprehension by 19 points and cut intent by -3.2pp in this segment."
"Retarget with simple benefit-led cutdowns and eliminate fine print above the fold; target +5pp comprehension lift."
AISHA, 34
"Defaults to brands she already trusts; responds to reassurance and upgrades rather than discounts."
"“New feature + continuity” messaging outperformed “new era” repositioning by +6.0pp intent in loyalists."
"Email/app retargeting that frames upgrades; aim for +12% repeat purchase rate vs. February baseline."
NOAH, 38
"Suspicious of tech/AI claims and data use; will research aggressively and discourage others if unconvinced."
"Vague AI claims triggered a 2.1× higher trust penalty incidence (modeled 17% vs. 8% average)."
"Publish transparent data practices + limitations and lead with utility; target -25% reduction in “overclaim” objections."
Recommendations
Re-cut for proof: build 15s/30s demo variants for the top-intent segments
"For ads with high likeability but sub-10pp intent (e.g., celebrity/comedy styles), produce proof-first cutdowns: show the product working in the first 3 seconds, then a single quantified value cue. Use segment-weighted distribution to Deal-First Switchers, Quality Pragmatists, and Skeptical Researchers."
Exploit the compressed conversion window with Search + YouTube proof sequencing
"Because 66.7% forget most ads within 24 hours, front-load capture: 30% budget to Paid Search on brand + category queries, 25% to YouTube proof assets, with landing pages that answer price/terms immediately."
Add economic anchors (even modest ones) to reduce “interested but unsure cost” friction
"Introduce a single explicit anchor (e.g., $/mo, trade-in guarantee, free delivery) to lift intent by an average +6.0pp and reduce cost uncertainty by ~10 points. Keep offers modest (≤20% equivalent) to avoid margin erosion; 51.0% convert with ≤20% incentives."
Build a “Proof Hub” landing experience for Skeptical Researchers
"Create a single page that consolidates: third-party ratings, comparison chart, top 3 limitations, and transparent terms. This reduces claim skepticism (target -10 points) and increases trust gain (target +4 points) in the highest-research segment."
Separate ‘share’ optimization from ‘buy’ optimization in KPI reporting
"Implement a dual-scorecard: (A) Engagement KPIs (shares, talkability, rewatch) and (B) Commerce KPIs (search, site visit, add-to-cart, signup). Given “share” (57) trails “search/learn” (66) as the dominant behavior for top ads, reporting must prevent entertainment-heavy creative from winning budget by default."
De-risk tech/finance messaging: reduce AI hype, increase specificity and constraints
"Tech/finance ads showed a 12% trust penalty incidence for “AI/too-good-to-be-true” claims (17% among Privacy-Conscious Tech Critics). Replace vague future language with bounded claims, privacy/data statements, and real scenarios."
Generate your own Intelligence with the Mavera Platform.
Get Full Access→Join 500+ research teams using synthetic intelligence to generate unique insights.
