What Recent Marketing Data Reveals About Attention and Performance
Key Highlights
- What the data suggests marketers should rethink about channel mix and creative approach.
- How trust and credibility are becoming measurable performance factors, not just brand ideals.
- Why discovery is fragmenting beyond traditional search, changing how visibility works in practice.
One of my favorite parts of any conference is the promise of new industry data points. If a session description includes words like study, research or survey results, I’m already planning to attend. Not because statistics are exciting for their own sake but because good data has a way of cutting through opinions, assumptions and hot takes to show what’s actually changing.
At Digital Summit Chicago, the most compelling stats weren’t just interesting; they were clarifying. They challenged long‑held beliefs about attention, channel priority, discovery and performance. Below are the data points that stuck with me the most, paired with why each one matters for marketers trying to make smarter, more relevant decisions in a rapidly shifting landscape.
Stat: 86% of Spotify users silence video on other platforms so they can stream podcast audio and audiobooks.
Why it Matters: Your audience is actively choosing audio over video in their daily lives, which means the channel you’re probably underinvesting in is the one they’re most engaged with. If your brand has no audio presence, you’re absent from the environment your customers are deliberately seeking out.
Stat: 80% of marketers agree audio ads benefit from greater trust than other digital media; 72% agree the intimate nature of audio allows brands to build trust over time.
Why it Matters: Trust is the scarcest resource in marketing right now, and audio has a structural advantage in building it that most other formats don’t. For B2B marketers whose entire sales cycle depends on credibility, this is a channel argument worth taking seriously — especially as AI erodes trust in written content.
Stat: 82% of marketers agree that dynamic audio ad personalization is going to increase campaign performance.
Why it Matters: Personalization has driven performance gains in email, display and social for years, and the same dynamic is coming to audio. Marketers who learn how to build personalized audio experiences now are positioning themselves ahead of a performance curve that the majority of their peers already see coming.
Stat: 79% of video on LinkedIn is watched with sound off; design for captions, text, movement and bold colors.
Why it Matters: If your video strategy depends on a voiceover or dialogue to land the message, nearly 80% of your LinkedIn audience is missing it entirely. Sound-off design isn’t a nice-to-have accessibility feature; it’s the default viewing condition for the majority of your audience, and your creative brief should treat it that way.
Stat: 81% of B2B ads fail to gain attention or drive any kind of recall.
Why it Matters: This stat reframes the ROI conversation. The problem isn’t that your ads aren’t converting — it’s that most of them aren’t being seen or remembered at all. Before optimizing for click-through rate or cost per lead, the more urgent question is whether your creative is actually breaking through, and for most B2B brands, the data suggests it isn’t.
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How Marketers Are Rethinking Trust, AI, and Long‑Term StrategyStat: Using only one type of creative format results in a 50% decline in the probability of leads compared to format diversity.
Why it Matters: Defaulting to a single format — whether that’s static images, video or a single ad unit — isn’t just a creative limitation, it’s a lead generation penalty. Different audience members consume content differently depending on context, device and mindset, and a format mix is what allows the same message to reach the same person in multiple modes without feeling repetitive.
Stat: People are 20% more likely to generate leads when employees post regularly, but only 3% of employees post regularly.
Why it Matters: Employee advocacy is one of the most underutilized lead generation levers available to B2B marketers, and the gap between its proven impact and current adoption is enormous. If you can move even a small percentage of your team from zero posts to consistent posting, the compounding effect on reach, trust and pipeline is significant without adding a dollar to your media budget.
Stat: Large language model traffic converts at 15–20% while Google traffic converts at 3% or lower.
Why it Matters: Traffic from large language models isn’t just a new source of visitors — it’s a fundamentally different quality of visitor who arrives pre-qualified and ready to act. A 15–20% conversion rate versus 3% means that a relatively small volume of LLM referrals can outperform a much larger volume of organic search traffic, which changes the math on where it’s worth investing to be visible.
Stat: There is only a 21% overlap between ChatGPT and Google page-one results (4% for Gemini).
Why it Matters: Ranking well on Google is no longer a proxy for being visible where your buyers are actually searching — and for Gemini, the gap is almost total. If your entire search visibility strategy is built around Google rankings, you have a blind spot that covers the majority of AI-driven discovery, which is exactly where buyer behavior is shifting.
Stat: Large language models most frequently cite the first half of content; content in the last 10% of pages is almost never cited.
Why it Matters: The way most marketers structure long-form content — building to a conclusion, burying the key insight, saving the proof for the end — is the opposite of how LLMs read and cite pages. If you want your content to show up in AI-generated answers, the most important edit you can make right now is moving your strongest, most specific claims to the top of the page.
About the Author
Alexis GajewskiAlexis Gajewski
Contributor/AI Expert
Alexis Gajewski is a media strategist with nearly 20 years of experience in editorial leadership, content strategy, digital content creation, and search optimization. She is known for building the systems, training, and standards that help writers, editors, digital creators, and marketers produce better work more efficiently, without sacrificing accuracy or voice.
Alexis specializes in turning complex, emerging concepts into clear, actionable frameworks. Whether the subject is AI, search engine optimization (SEO), or editorial standards, she develops approaches teams can adopt quickly and apply with confidence. Her training serves every level, from foundational AI literacy to advanced workflows.
She is recognized for her work at the intersection of journalism, AI, and search. As audiences increasingly find content through AI assistants and answer engines, Alexis helps organizations adapt through answer engine optimization (AEO) and generative engine optimization (GEO), ensuring their content is well structured, authoritative, and readily surfaced and cited. She believes AI should strengthen sound writing and editorial judgment, not replace them.
An experienced educator and collaborator, Alexis has designed and led professional development programs for more than 150 editors across dozens of B2B brands and has aligned editorial, marketing, and audience development teams around shared goals. Her years covering the manufacturing industry instilled a continuous-improvement mindset that shapes her approach: identify inefficiencies, implement solutions, measure results, and refine.
Alexis also works as a freelance writer, editor, and consultant for publishers, agencies, and brands, specializing in content optimization and discoverability, AI-assisted workflows, content strategy, audience development and analytics, and editorial quality.
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