AI in Marketing

AI Doesn’t Make Social Media Audits Outdated. It Makes Human Listening More Important.

Pictures of Social Media Marketing by Keith Quesenberry first through fourth editions.
~ K. Quesenberry ~ Leave a comment

Years before AI became a part of everyday talk about work and education, I developed a social media audit template built around a simple idea: before brands talk, they need to listen.

I developed it after years teaching one of the first social media strategy courses, made it a core part of my Social Media Strategy text, and outlined the framework in a 2015 Harvard Business Review article. Rooted in the 5 Ws I learned in journalism school, it’s designed to be a systematic analysis of brand, competitor, and consumer conversation to shift marketing mindset from top-down control toward authentic, consumer-centric engagement.

Pictures of Social Media Marketing by Keith Quesenberry first through fourth editions.
I’ve revised my social media text many times but the Social Media Audit remains a core component.

Over the years, audits in my consulting work and student projects always surface significant insights busy professionals overlook and students would otherwise never see. In the HBR article and my book, I recommend conducting a social media audit at the beginning of a project and at least every 12 months after. I also emphasize doing the work yourself. Visit each platform, scroll the feed, make your own observations.

That includes social media professionals who manage brand accounts every day. Being in the accounts isn’t the same as stepping back to analyze them. When you’re busy posting, responding, monitoring, reporting, and keeping up with the daily demands of content, it is easy to become buried in the weeds.

A social media audit creates the discipline to pause, zoom out, and look across the company, consumer, and competitor conversation as a whole. It turns everyday activity and a surface level view into deeper strategic perspective.

That first hand listening advice still holds. It matters more now.

What AI Changes and What It Doesn’t

AI has dramatically changed how quickly we can collect, sort, summarize, and compare social media information. In seconds, AI can identify themes in comments, classify content types, summarize sentiment, spot recurring hashtags, and compare competitor activity across platforms. That’s powerful.

AI doesn’t make social media audits outdated. It makes them more necessary.

The goal of a social media audit was never to fill out a spreadsheet. The goal was to understand the conversation around a brand: What is the company saying? What are consumers saying? What are competitors doing? Where is the conversation happening? What earns attention and engagement and what does all of this suggest about strategy?

Those questions haven’t changed. And they require uniquely human skills: empathy, emotional intelligence, nuanced judgment, intuition, and ethical reasoning. What has changed is the process we use to answer them.

The Template Still Works Because the Thinking Still Works

I haven’t changed the core audit template through multiple editions of my book because the structure still teaches the right way to think. It asks students and social pros to examine three areas:

  • Company: What is the brand saying and doing on social media?
  • Consumer: What are people saying about the brand, category, problem, experience?
  • Competitor: What are direct and indirect competitors doing in the same space?

It then organizes that listening through the basics: who, where, what, when, and why.

That framework prevents one of the most common mistakes in social media strategy: mistaking activity for understanding. For students, AI accelerates that mistake. Ask for “social media recommendations for Brand X” and a polished list appears in seconds with no listening required. Polished, but not grounded.

For professionals, the trap is different. Daily management of posting, responding, and reporting can create the illusion of strategic awareness. But being in the accounts every day is not the same as auditing them. A social media manager focused on owned channels may miss revealing conversation happening in a Reddit thread, competitor community, or review site. The audit forces that discovery.

Human Listening Comes First

Before students or pros use AI to analyze social media activity, I encourage them to look at the accounts themselves. This isn’t old-fashioned. Like any relationship, real understanding comes from first-hand experience, not data.

We know this instinctively. A reason people react negatively to AI-generated comments is they feel manufactured. The reply may be fluent, but doesn’t feel real.

Social media is supposed to be social. If people are frustrated when brands or individuals use AI to fake conversation, why would we teach students or professionals to understand those conversations only through AI summaries?

The point of an audit is to listen to real people in real contexts before deciding what a brand should say or do.

You can’t fully grasp a brand’s presence from a summary. You need to see the posts, feel the tone, notice visual rhythm, quality of comments, the way a brand responds or doesn’t. The brand may show “positive engagement” in surface analysis, but a closer look might reveal shallow or sarcastic comments not connected to the analysis.

Another brand may have fewer likes but a far more committed community. A TikTok comment section carries a different meaning than a LinkedIn thread. A Reddit discussion may surface frustrations that never appear in the brand’s owned channels.

Those are human insights, and students need to develop the ability to notice them. So do experienced practitioners. Staying in the feeds keeps you sharp, but managing owned brand channels every day can also keep you buried in posting, responding, and reporting.

A social media manager may be so focused on the brand’s Instagram, TikTok, or LinkedIn activity they miss a more revealing conversation happening in a Reddit thread, competitor community, or review site. A social media audit forces that discovery. It creates the structure to step back from daily doing and see a larger strategic pattern across company, consumer, and competitor activity.

This is especially important in education because students aren’t just learning to collect information. They’re learning to interpret markets, audiences, and behavior. That judgment comes from looking closely, comparing examples, asking better questions, and sitting with ambiguity. AI can support this, but it shouldn’t remove students from it.

Where AI Genuinely Helps

After that first-hand review, AI becomes a valuable tool. It can help:

  • Summarize recurring themes across comments or reviews
  • Identify sentiment patterns across a large volume of content
  • Group common hashtags or keywords
  • Classify posts by content category
  • Surface repeated customer questions or common complaints
  • Compare the content mix of a brand and its main competitors
  • Identify gaps in the conversation
  • Organize messy notes into a cleaner audit observation and presentation

This is especially useful when students and even pros are dealing with more content than they can manually process. They might review a representative sample themselves, then use AI to help scale and organize the broader set.

But there’s a critical distinction: AI can identify patterns. The student and professional still needs to decide what those patterns mean.

AI might report a brand’s comments are mostly positive. But positive about what? The product, price, or humor? The packaging, nostalgia, or customer service? Sentiment is an input. Strategic interpretation is the job that still belongs to humans, who can pick up on nuance AI misses.

Social Media Audit Template To Improve Social Media Marketing Strategy.

(Click image for a downloadable PDF of the social media audit template.)

An Updated AI-Assisted Workflow

The core social media audit template has not changed. What I’ve changed is how I recommend students and social pros use it.

  1. Start with human observation. Look at the brand’s accounts directly including recent posts, captions, comments, replies, visuals, hashtags, engagement. Do the same for key competitors. Then search further looking for consumer conversations beyond the brand’s owned channels. Don’t start with AI. Start by looking.
  2. Capture evidence in the template. Record specific examples: posts, comments, themes, content types, timing, engagement, strategic choices. The goal isn’t to collect everything. It’s to build evidence. What does the brand emphasize? What does the audience respond to? What do competitors do differently?
  3. Use AI to organize and scale. After forming your own observations, use AI to help summarize larger content sets, group themes, compare post types, or clean up your notes. This is where AI saves time. Saving time, however, is not the same as outsourcing thought.
  4. Verify AI output. Don’t assume AI is right. Check its claims against the actual posts and examples you reviewed. AI can miss context, flatten nuance, misread irony, and make a weak pattern sound stronger than it is. If AI says customers are frustrated, you should be able to point to real evidence. Evidence still matters.
  5. Interpret strategically. This is the most important step. The real value of an audit isn’t the list of observations. It’s the interpretation. What should the brand keep doing? Stop doing? Improve? Test? What audience insight should guide future content? What competitor opportunity exists? AI can organize the inputs. You make the strategic argument.
  6. Disclose how AI was used. A simple note is enough: what tool you used, what you asked it to do, what you provided, and how you checked the output. For example:

I used AI to summarize recurring themes from a sample of Instagram and TikTok comments. I compared the AI summary to my own manual review and included only themes I could verify with examples from the accounts.

That transparency teaches responsible AI use and it’s a reminder that AI support doesn’t remove responsibility for the final analysis.

My new Social Media Audit GPT. Available as an AI assited social media strategy tool.

Why I Built a Social Media Audit GPT

To support this process, I created a custom Social Media Audit GPT. Its purpose isn’t to complete the audit for you. It’s to guide you through it with firsthand listening.

A good educational AI tool doesn’t hand you an answer. It helps you ask better questions and move through the work with more confidence. The GPT prompts students to think about company, consumer, competitor, platforms, content, engagement, and strategy. It scaffolds the process and doesn’t replace the learning. It’s also place for students to turn for help when they’re up late and I’m probably already asleep.

A social media audit is valuable not because students manually count things that software could count faster. It’s valuable because it teaches them to listen before they recommend, to compare brand, consumer, and competitor activity, to move from observation to insight, and to ground strategy in evidence.

The future of social media education shouldn’t be students staring at feeds for hours with no assistance from modern tools. But it also shouldn’t be students handing the thinking to AI and accepting the first polished answer.

The better path is in the middle: look with your own eyes first, use AI to organize and scale what you find, then return to human judgment to decide what it means.

Social media audits aren’t outdated. They’re one of the best ways to teach one of the most important skills: listening before you speak.

This post was created with the assistance of ChatGPT and Claude. The ideas, experiences, and opinions are my own.

The Token Trap: Why “Tokenmaxing” in AI is the New Klout Score

MIDE Studio Brainstorming Wiith Post Its
~ K. Quesenberry ~ Leave a comment

Around 2012, an FSU professor made headlines for including Klout scores as part of his students’ course grades (10% of the total). It wasn’t an unreasonable experiment. If the market cares about this number, students should understand how it works. Klout scores shaped influencer partnerships, opened doors to brand deals, and establish who counted as a thought leader. The number had real weight.

I remember first encountering Klout at an IMC conference in Mark Schaefer’s keynote. He recently published Return on Influence. Right after that talk I checked my score and started thinking more intentionally about my online presence. That led to this blog, job opportunities, contributing to respected publications and eventually my two books.

The number was motivating, but I never got obsessed to the point of doing things just to watch it climb.

What’s interesting in retrospect isn’t that the professor was wrong to take it seriously. It’s how quickly the controversy revealed the problem with legitimizing a metric that could be gamed. When a score carries institutional weight, a market grows around engineering it. Soon you could buy fake followers and engagement. The score became more important than what it was supposed to represent until it stopped representing much at all. Klout shut down in 2018.

I find myself thinking about that moment again as I watch a new metric start to carry similar weight in AI-driven circles. My prediction: it won’t be long before we hear about a professor basing an AI course grade on token usage. Or a manager rewarding the employee who ran the most prompts last quarter. We may already be there.

“When a measure becomes a target, it ceases to be a good measure.” — Marilyn Strathern (popularizing Goodhart’s Law)

Amazon mandated 80% of its engineers use AI coding. Within months several major incidents happened, including a 6-hour outage with 6.3 million lost orders. Amazon attributed the failures to user error. That may be true, but when usage is the mandate and metric, conditions for error become easier. The 80% target can become more important than the safeguards surrounding it.

We may be entering the era of what’s called Tokenmaxing. It’s like Klout in a more expensive suit. You’ve probably already seen a version of it on LinkedIn. People sharing screenshots of a dozen AI agent tabs open on their monitor as a signal of how AI-forward they are. It’s the AI equivalent of watching that little orange Klout score climb.

MIDE Studio Brainstorming Wiith Post Its
Should we feel guilty for thinking with our hands and allowing time for inspiration and the slow hunch? This was from a brainstorming session on PostIt notes that my students had in the Markets, Innovation & Design studio.

What is Tokenmaxing?

In AI, a token is a unit of compute or 0.75 words. For developers, token usage is a meaningful metric. It affects cost, context window management, and efficiency. Paying attention to it makes sense in that context.

The problem arises when that same logic migrates into other fields like marketing as a proxy for effort or value. Tokenmaxing is what happens when the volume of AI interactions becomes the goal in itself. People burning through a monthly subscription budget, climbing a usage leaderboard, generating thousands of prompt variations to prove they’re being “AI-forward.”

If you aren’t maxing out your tokens, you aren’t really using the tool.

It’s a Use It or Lose It fallacy. Like Klout, the number starts to feel meaningful. Even if it might not be measuring anything that matters.

What Gets Lost Upstream

The concern isn’t just wasted budget. It’s what happens to the thinking process when output volume becomes the metric. Real creative work has an upstream phase. The slow, often uncomfortable part where you sit with a problem before you know what to do about it. Where you ask why before you ask what’s next.

That phase doesn’t generate tokens. It doesn’t show up in a usage dashboard. But it’s often where the most useful thinking happens, and it’s the phase that gets compressed when we start measuring activity instead of impact.

Not all important thinking happens in the ones and zeros of the digital world.

Mark Schaefer has written about AI turning marketing into “a pandemic of dull.” Everyone converging toward the same outputs, faster. Tokenmaxing feeds that pattern. When we optimize for volume, we tend to get incrementally better at doing the same thing as everyone else, until the work becomes difficult to distinguish from anyone else’s.

Reclaiming the Tiny Experiment

I’ve been spending time with Anne-Laure Le Cunff’s book Tiny Experiments. It’s been a useful counterweight to this kind of thinking. In a design-thinking context, a tiny experiment isn’t a step toward a usage milestone. It’s more like a probe into uncertain territory. A way to follow a question you can’t fully answer yet, and learn something from the attempt.

The difference in practice is meaningful. A tokenmaxing mindset measures success by what it produces: content, variations, volume. A design-thinking mindset measures success by what it discovers and the difference it makes.

Sitting in a coffee shop and overhearing how someone actually describes a problem you thought you understood, or visiting a store and watching how a customer navigates a decision in real time, and then designing a small test around what you observed, that’s a tiny experiment in the spirit Le Cunff intends.

The output isn’t content. It’s a new understanding of a real human that no prompt, and no amount of online data, could have surfaced on its own.

When we set goals around usage, we quietly change what we’re optimizing for and it stops being an experiment. A tool built for exploration becomes a treadmill for output. More than a decade ago Lisa Earle McLeod found salespeople who focused on quotas were out sold by ones who focused instead on helping people with a “noble purpose.”

Protecting the Thinking That Happens Before the Prompt

The answer to this isn’t less AI. It’s being more intentional about where the human thinking lives in the process. Klout scores didn’t make anyone a better marketer, but they did shape how influence was perceived and rewarded, until the market for gaming them undermined the whole thing.

Token counts carry a similar risk. They can start to feel like a proxy for strategic thinking without doing any of the work that strategic thinking actually requires.

The most valuable part of the design process is the part that doesn’t cost a cent in API fees. It’s the conjecture, the what-if, the question you sit with before you ever write a prompt. Le Cunff’s framing is most useful when it’s pointed at expanding your thinking rather than refining the path you’re already on.

Experiment to learn, not just to optimize. We didn’t fully absorb that lesson from Klout. It’s worth keeping in mind as token usage starts to feel like a measure of something meaningful.

I feel it. There’s a guilt that creeps in when I’m not reading AI articles, listening to podcasts, or running a deep research report to stay current. AI is moving quickly, and falling behind feels like a real risk. Then I open LinkedIn and see someone’s screenshot of a dozen AI agent tabs and the pressure compounds.

It’s just one more reason to feel like offline, in-person time is something I need to justify rather than protect.

But if I’m sitting in that coffee shop with my laptop open, catching up on AI news, or counting other people’s agent tabs, I’m missing the thing that makes the work better. The overheard conversation, the moment of watching someone struggle with a decision, the small human observation that no LLM model thought to or could collect.

That upstream time isn’t wasted time. It may be the most innovative thing a marketer can do. It’s just the hardest to justify in a dashboard.

About This Post’s Creation

This post was developed in partnership with Claude. I had initial ideas from observation, articles, podcasts, and reading Le Cunff. Claude helped organize, research further and refine.