Semrush Study: Super Bowl Ads Are No Longer a One-Night Event

Super Bowl advertising brings the same amount of hype and reach for marketers as the game does for sports fans. It’s unmatched. This year, we saw that reach alone no longer defines success. The real competition now plays out across days and platforms, with audiences revisiting ads on social media, searching for explanations and rankings, and increasingly turning to AI tools to summarize what mattered.

To understand which brands actually captured attention on Super Bowl the the days after, Semrush analyzed multiple visibility signals at once: social media engagement, search and traffic behavior, and how often brands appear in large language model (LLM) responses to prompts related to “Super Bowl ads.” Together, these signals show how brand visibility is created and sustained well beyond the broadcast itself.

1. Engagement separated impact from exposure.
Michelob Ultra drove 58M YouTube views but only ~150 engagements, while Pepsi paired 51.1M views with 10.5K engagements and 443K total social interactions. Reach was common. Resonance wasn’t.

2. Traffic didn’t automatically follow airtime – even on game day.
State Farm and Toyota saw traffic drop more than 30% on February 8 versus their pre-game daily averages, while Poppi surged 249% and Michelob Ultra rose 81%. Only brands tied to real-time consumption reliably converted awareness into visits.

3. AI visibility favored brands that stayed in the narrative, not just advertisers.
Across 637 LLM mentions, the top five brands captured 18.5% of visibility, led by YouTube (33 mentions) and Coffee Mate (22). In AI-generated answers, being “top of mind” increasingly means being part of the stories people keep asking about – rankings, debates, and media coverage – not just buying airtime.

Historically, Super Bowl ads were judged through recall surveys and post-game buzz. That approach assumed attention was short-lived and linear. Today, attention is fragmented and recursive. Viewers don’t just watch ads; they look them up, share them, criticize them, and ask AI assistants to explain or rank them.As a result, Super Bowl advertising has become a visibility test rather than a visibility moment. Brands that fail to generate follow-on activity often disappear from the conversation quickly, regardless of production quality or media spend.

Social media data from Super Bowl week shows how uneven attention really is once the game ends. While many brands saw short-term spikes, only a small group translated exposure into meaningful follower growth and engagement across platforms including Facebook, Instagram, X, TikTok, and YouTube.

Between February 2–8, a handful of brands stood out for net new follower growth: Pepsi with +171K followers, Budweiser with +38K, OpenAI with +36K and Dove with +18K.

The spread is telling. Pepsi and Budweiser came in with their  familiar Super Bowl advertising approaches, while tech like OpenAI and Gemini appearing underscore the everyday relevance of tech alongside traditional consumer goods. 

YouTube significantly extends the lifespan of Super Bowl ads, but views alone proved misleading. The most-viewed commercial, Michelob Ultra, reached 58M views yet generated only ~150 engagements, indicating heavy paid distribution with limited organic interaction.

By comparison, Pepsi’s ad reached 51.1M views and drove 10.5K engagements, signaling far stronger audience response relative to reach. The gap underscores a clear shift: replay volume no longer equals impact. Engagement is now the more reliable signal of resonance.

When engagement across Facebook, Instagram, X, TikTok, and YouTube is combined, a different leaderboard emerges, one shaped by sustained interaction rather than exposure alone.

Top brands by total social engagement (Feb 2–8):

This group includes both Super Bowl mainstays and newer, digitally native brands. What connects them isn’t production scale, but post-game momentum: content that continued to be shared, discussed, and rediscovered across platforms. In 2026, winning attention requires cross-platform, multi-day visibility, not a single viral moment.

Contrary to expectations, Super Bowl Sunday did not produce universal traffic surges across advertisers. In fact, many large brands saw declines compared to the week prior. Insurance, automotive, fintech, and real estate advertisers recorded double-digit drops in site traffic on February 8.

For example, State Farm and Toyota both experienced declines of more than 30% compared to their pre-game daily averages. Even high-traffic platforms such as ChatGPT saw a 24% dip on game day.

The exceptions were brands closely tied to real-time consumption. Poppi saw a 249% surge versus its prior daily average, while Michelob Ultra rose 81%. Uber Eats increased 35%, reflecting clear alignment with game-day behavior.

The data suggests a structural pattern: Super Bowl advertising does not automatically translate into immediate website visits. Instead, traffic spikes are most pronounced when the product category aligns directly with live consumption or transactional intent.

As discovery increasingly happens inside AI-generated answers, brand visibility now depends on whether companies appear in those responses at all. When users ask LLMs about “Super Bowl ads,” the systems do not simply list advertisers. Instead, they synthesize information from media coverage, search behavior, and social discussion.

Analysis of LLM responses shows that a relatively small group of brands surfaced most frequently. Coffee Mate, YouTube, Michelob Ultra, OpenAI, and T-Mobile ranked among the top brands by mentions, benefiting from both advertising exposure and extensive media coverage. 

At the same time, AI visibility followed a clear long-tail pattern. Out of 637 total brand mentions in the analyzed sample, the top five brands accounted for just 18.5% of mentions, while nearly half of all visibility was spread across dozens of smaller brands and entities. In practice, this means AI-generated answers reflect the broader conversation, not just the biggest advertisers.

Category-level analysis explains why. LLMs frequently associated “Super Bowl ads” with platforms, publishers, health and telehealth brands, and even regulators such as the FDA and FTC. Super Bowl advertising, from an AI perspective, is a media and cultural event as much as a marketing one.

One of the clearest signals from the AI data is that visibility depends heavily on how people ask about ads. Celebrity-focused prompts (“Who’s in this ad?”), controversy-driven questions (“worst Super Bowl ads”), and regulated topics consistently pulled brands into AI responses.

Comparison and ranking prompts expanded visibility further, surfacing multiple brands at once through list-style answers. Platform-focused questions tied Super Bowl ads to YouTube, Google, and AI tools themselves, elevating platforms and publishers alongside advertisers. In AI-driven discovery, brands are no longer competing only for airtime. They are competing to become part of the narratives that users, and AI systems, repeatedly reference.

This analysis combines social engagement data, website traffic behavior, and AI visibility signals to evaluate how brands performed during Super Bowl week (February 2–8, 2026).

Social Media Engagement: Engagement and follower growth data were collected using the Semrush Social Media Toolkit across Facebook, Instagram, X, TikTok, and YouTube. Total engagement reflects aggregated interactions (likes, comments, shares, and platform-equivalent actions) between February 2–8, 2026. YouTube view and engagement metrics reflect publicly available video performance during the same window.

Website Traffic Analysis: Traffic data was sourced from Semrush Traffic Analytics. Game-day performance (February 8, 2026) was compared against each brand’s pre-game daily average during the prior week to measure relative change. Percentage increases or declines reflect estimated total website visits across devices and geographies, unless otherwise specified.

AI Visibility (LLM Analysis): AI visibility data reflects how often brands appeared in large language model (LLM) responses to prompts containing the phrase “Super Bowl ads.” Analysis includes the top 100 brands by mentions as displayed in the Semrush AI Visibility Toolkit interface. Dataset scope: all supported LLMs, United States. Total analyzed sample: 637 brand mentions.

Together, these datasets provide a multi-signal view of brand visibility, spanning engagement, behavioral intent, and inclusion in AI-generated discovery environments.