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I recently published academic research in the Quarterly Review of Business Disciplines with Michael Coolsen titled, “Engagement on Twitter: Connecting Consumer Social Media Gratifications and Forms of Interactivity to Brand Goals as Model for Social Media Engagement.” Exciting right?

If you’re a research geek or academic maybe. A social media manager? No way. Yet, I know the findings, specifically our Brand Consumer Goal Model for Social Media Engagement is very exciting for social media pros! So I wanted to write this blog post.

But, as you can tell by the title, an academic audience, and a professional audience are very different. Taking a complicated 25-page academic research article and translating it into a practical and concise professional blog post could take me hours.

I’ve been meaning to experiment with Google’s new AI generator tool NotebookLM so I thought I would try it. Thus, this blog post is about our research on social media engagement framework and how I used AI to streamline my process to create it. As a bonus, I got a podcast out of it!

My co-author and I did the hard work of the research. I was okay with an AI assistant helping translate it into different media for different audiences. Click for my AI Take Framework.

Using NotebookLM.

Our study was on types of content that generate engagement on Twitter, but the real value of the study was our proposed model for engagement. So before uploading anything into the AI tool, I condensed it to just the theoretical and managerial implications sections. Then I added a title, and journal citation, and saved it as a PDF.

NotebookLM uses Gemini 1.5 Pro. Google describes it as a virtual research assistant. Think of it as an AI assistant helping you explore and take notes about a source or sources that you upload. Each project you work on is saved in a Notebook that you title. I titled mine “Brand Consumer Goal Model for Social Media Engagement” because that is what I wanted to focus on.

Whatever you upload NotebookLM becomes an expert on your information. It uses your sources to answer your questions or complete your requests. It responds with citations, showing you original quotes from your sources. Google says that your data is not used to train NotebookLM, so sensitive information stays private (I would still double-check before uploading).

Source files accepted include Google Docs, Google Slides, PDF, Text and Markdown files, Web URLs, Copy-pasted text, YouTube URLs of public videos, and Audio files. Each source can contain up to 500,000 words, or up to 200MB for uploaded files. Each notebook can contain up to 50 sources. If you add that up NotebookLM’s context window is huge compared to other models. ChatGPT 4o’s context window is roughly 96,000 words.

When you upload a source to NotebookLM, it instantly creates an overview that summarizes all sources and offers key topics and questions to ask. It gives set documents to create such as a FAQ, Study Guide, Table of Contents, Timeline, and Briefing Doc.

You can also ask it to create something else. I asked it to write a blog post about the findings of our research. Which can be seen below. Yet, by far, the most impressive feature is the Audio Overview. This generates an audio file of two podcast hosts explaining your source or sources in the Notebook.

The NotebookLM dashboard gives you a variety of options to interact with your sources.

Using Audio Overviews.

There are no options for the Audio Overview so for now you get what it creates. But what it creates is amazing! My jaw literally dropped when I heard it. And it will give you slightly different results each time you run it.

I noticed things missing in the podcast such as the journal and article title and the authors’ names. I did figure out how to make adjustments by modifying my source document. Through five rounds of modifying my source document, I was able to get that information in and more.

Sometimes overviews aren’t 100% accurate. It says, “NotebookLM may still sometimes give inaccurate responses, so you may want to confirm any facts independently.” The running shoe brand following our model in the article was hypothetical. But in a version of Audio Overviews, the podcast hosts talk as if the company did what we said and got real results that we measured.

I was impressed that in other versions it didn’t use my example and applied the model to new ones. One time it used an organic tea company and another a sustainable clothing brand. On the fifth attempt it even built in a commercial break! This last version gave my running shoe example and added its own about a sustainable activewear brand.

What’s really interesting about the last version is that it pulled in other knowledge and applied it to this new information. At the end, the hosts bring up how our model will help you know what to say but that you still need to customize the content appropriate for each social platform. That is a social media best practice but not something we specifically mention in the article.

The Audio Overview Podcast NotebookLM Created.

It’s amazing these podcast hosts discussed our research and explained it so well for social pros. What’s more amazing is that they are not real people! Yet NotebookLM did more. Below is the blog post it wrote. It included our diagram of the model, but had trouble getting it right. So, I replaced the image with one I created from our article.

Brand Consumer Goal Model for Social Media Engagement.

This post examines a model for social media engagement based on an October 2024 study in the Quarterly Review of Business Disciplines. “Engagement on Twitter: Connecting Consumer Social Media Gratifications and Forms of Interactivity to Brand Goals as Model for Social Media Engagement,” published by Keith Quesenberry and Mike Coolsen.

The Brand Consumer Goal Model for Social Media Engagement is a framework to help social pros create more effective plans by aligning brand goals with consumer goals. It emphasizes understanding the motivations behind consumer engagement and tailoring content accordingly.

How the Model Works

The model outlines three key brand goals:

Building brand community (Reach): This goal focuses on expanding the brand’s audience and increasing awareness.
Building brand-consumer relationships (Response): This goal aims to foster brand interaction and engagement.
Building brand-consumer bonds (Recognition): This goal seeks to create brand emotional connections and loyalty.

Each brand goal is associated with a corresponding consumer goal and form of social media engagement as seen in the graphic below. Consumers can manage people’s impressions and persuade others by sharing certain brand posts. They can gain information about a brand with other types of brand content for deliberation. They can bond with others regulating their emotions in evaluating brand posts with other fans.

Click on the graphic to download this model for social media engagement.

Here is an example

A sneaker brand launches a running shoe and aims to build brand community (reach) creating content that appeals to runners’ desire for impression management and persuasion. This could be sharing inspirational stories about runners breaking records encouraging retweets and brand visibility.
Once awareness is established, the brand could shift its focus to building relationships (response) by providing information about the shoe’s features and benefits, appealing to consumers’ information acquisition goals, and prompting replies and comments seeking further details.
Finally, the brand can foster brand bonds (recognition) by sharing content that resonates with runners’ social bonding and emotion regulation needs, such as posts about the challenges and rewards of training, which encourage likes and emotional connections.

Key Insights from the Study

The study found that simply using popular content types like videos or photos is not enough to guarantee success on social media. The message accompanying the content is crucial.

Marketers need to consider:

The target audience’s motivations for using social media
Buying cycle stage (awareness, consideration, purchase, loyalty)
Desired word-of-mouth function (sharing, deliberation, evaluation)

The Brand Consumer Goal Model for Social Media Engagement offers a strategic framework for developing effective social media campaigns. By understanding the motivations behind consumer behavior and aligning content with both brand and consumer goals, marketers can achieve better results and build stronger relationships with their target audience.

I hope you found this look at NotebookLM and the insights from our social media research helpful. In what ways do you think NotebookLM can help in your job? How will the insights from our Brand Consumer Engagement Model change your social media content strategy?

Could NotebookLM be a good tool for my students? I believe as long as they use it as a study guide, reinforcement, or tutor and not simply replace the reading and listening in the first place as seen in the graphic below from my previous post on subject matter expertise. Personally was okay with using this tool I co-author and I did the hard work of the research. I was okay with an AI assistant helping translate it into different media for different audiences. The same would apply to my students.

This graphic shows that in stages of learning you go through attention, encoding, storage, and retrieval. You need your brain to learn this process not just use AI for the process. — Click the image for a downloadable PDF of this graphic.

Half of This Content Was Human Created!

This is the first post in a series of five on AI. With any new technology, there are benefits and unintended consequences. Often the negative outcomes happen without thought or planning. We get caught up in the “new shiny object” mesmerized by its “magical capabilities.” That happened with social media. We can’t go back on that technology, but we are in the early stages of AI. In WIRED Rachel Botsman called for frameworks to do more to avoid the negative of tech developments.

Before jumping all in, ask, “What role should AI play in our tasks?”

Just because AI can do something doesn’t mean it is good or it should. AI’s capabilities are both exciting and frightening causing some to be all in and others to be all out. Being strategic takes more nuance. Be intentional about planning the role AI could and should play in your job or business with the AI Use Template below.

Click the image to download a PDF template.

First, make a list of common tasks and the goal of each.

List tasks you perform in your job, on client projects, or in daily business operations. Then describe the goal of the task. Understanding the goal can help determine the human versus AI value in it. If the goal is to build a personal relationship with a customer or client, AI outsourcing may save time but undermine the task objective.

Recently a university outsourced their commencement speaker to an AI robot. Students started an unsuccessful petition for a speaker who could offer a “human connection.” The AI robot’s speech was described as weird and unmoving. Without any personal anecdotes, The Chronicle of Higher Education reports, “Sophia … delivered an amalgamation of lessons taken from other commencement speakers.”

Second, determine which type of AI Function each task requires.

The six AI functions (Generate, Extract, Summarize, Rewrite, Classify, Answer Questions) are modified from Christopher S. Penn’s AI Use Case Categories. Can the task be performed by one or multiple of these AI functions? If yes, you still want to consider how well AI can perform the function compared to a human and consider benefits that may be lost outsourcing to AI.

In my ad career clients often asked why a certain phrase or benefit was in the ad copy or ad script. Because I wrote it, I could explain it. It could be human insight from research (which AI can summarize), truths from lived experience, or talking with customers. If AI wrote the copy or script it may be missing and I wouldn’t know why AI wrote what it did. If you ask AI it often doesn’t know. Scientists call this the “unknowability” of how AI works.

Third, categorize the level of thinking each task entails.

The six levels of thinking (Remember, Understand, Apply, Analyze, Evaluate, Create) are modified from Oregon State’s Bloom’s Taxonomy Revisited. Bloom’s Taxonomy categorizes levels of thinking in the learning process. It was revisited to consider AI’s role. In each level determine the level of the task and discern AI’s capabilities versus distinctive human skills.

I had a student create a situation analysis of Spotify with ChatGPT. It was good at extracting information, summarizing, and suggesting alternatives (AI Capabilities of the Create Level). It wasn’t good at “Formulating original solutions, incorporating human judgment, and collaborating spontaneously” (Create Level Distinctive Human Skills). GPT’s recommendations lacked the nuanced understanding I’d expect from professionals or students.

Fourth, review the legal and ethical issues of outsourcing to AI.

Does the task require uploading copyrighted material? Are you able to copyright the output (copy/images) to sell to a client or protect it from competitor use? Does your employer or client permit using AI in this way? Are you sharing private or proprietary data (IP)? What’s the human impact? For some AI will take some tasks. For others, it could take their entire job.

Many companies are adding AI restrictions to contracts for agency partners. Samsung and others are restricting certain AI use by employees. There’s concern about performance or customer data uploaded into AI systems training a model competitors could use. Some agencies and companies are developing Closed AI versus Open AI to run local AI storing data on local versus cloud servers. For a summary of main AI legal concerns see “The real costs of ChatGPT” by Mintz.

Fifth, employ human agency to produce desirable results.

We shouldn’t be resigned to undesirable outcomes because AI change is complex and happening quickly. Penn’s TRIPS Framework for AI Outsourcing includes “pleasantness.” The more Time consuming, Repetitive, less Important, less Pleasant tasks that have Sufficient data are better candidates for AI. Don’t give away your human agency. Decide on your own or influence others to save the good stuff for yourself.

A post on X (Twitter) by author Joanna Maciejewska struck a nerve going viral “You know what the biggest problem with pushing all-things-AI is? Wrong Direction. I want AI to do my laundry and dishes so I can do art and writing, not for AI to do my art and writing so that I can do my laundry and dishes.” She later clarified it wasn’t about actual laundry robots, “it’s about wishing that AI focused on taking away those tasks we hate and don’t enjoy instead of trying to take away what we love to do and what makes us human.”

Marketers are getting this message. In a survey of CMOs most are using AI for draft copy and images that are refined by humans. And over 70% are concerned about AI’s impact on creativity and brand voice.

It’s easy to get overwhelmed and afraid of the AI future.

As Tech leaders sprint forward in an AI arms race and regulators woefully lag behind, the rest of us shouldn’t sit back and wait for our world to change. Unlike the Internet and social media, let’s be more intentional. Don’t fall prey to The Tradeoff Fallacy believing that to gain the benefits of AI we must give everything away.

In Co-Intelligence, Ethan Mollick says it’s important to keep the human in the loop. It’s not all-or-nothing. Some warn of a future when we don’t have choices in what role AI plays in our lives. It’s not the future. Today we can choose how to use AI in our professional, educational, and personal lives.

You know your job best, but if you want some help brainstorming tasks to outsource to AI, Paul Roetzer and SmarterX have created a custom GPT. Visit JobsGPT and enter a job title or job description. It uses AI to break down the job into tasks, estimate AI impact, time saved, and provides rationale.

Advocate for a pilot program if your employer is AI hesitant.

Some companies are holding employees back from AI use due to fears and some early adopters are failing to see the value of AI. The CIO of Chevron recently said, “the jury is still out on whether it’s helpful enough to justify the cost.” If you find yourself in a company or organization that is either not allowing AI or skeptical of paying the cost of a CoPilot or ChatGPT license ($20 or $30 per user per month), Paul Roetzer of the Marketing AI Institute suggests a 90-day pilot program.

Advocate to be part of a pilot program of small groups of employees to test use cases of AI for three months. Use this AI task framework to discover 3-5 of the most valuable. Keep track of the time you spend on each task before and after GPT use. Add up the hours saved each month and multiply by your actual or estimated hourly rate. If it’s more than $30 you have justified the costs. You’ve also become more valuable as you can train other employees in these tasks. Christopher Penn offers a more detailed method to calculate the ROI of AI.

What keeps me hopeful is breaking my job down into tasks and making intentional decisions on what to outsource to AI. Then I can see the time savings for me to focus on higher value aspects of my job. Using this framework allows me to get excited about the possibilities of AI taking over my least favorite or most time consuming tasks. In my next post, I’ll give some specific examples using this framework.

This Was Human Created Content!