Almost Timely News: 🗞️ How To Optimize Your Marketing for AI (2025-03-02)
This issue probably should have been a course. Oh well.
Almost Timely News: 🗞️ How To Optimize Your Marketing for AI (2025-03-02) :: View in Browser
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What's On My Mind: How To Optimize Your Marketing for AI
In this week’s issue, let’s clear the air and tackle a topic that’s on everyone’s mind: how do we get AI systems to recommend us? How do we optimize for tools like ChatGPT Search, Gemini Deep Research, and the gazillion other AI tools out there?
A friend of mine told me I was nuts for not charging for this newsletter or gatekeeping it somehow. I hate gatekeeping when it's done to me, though. If you feel compelled to exchange value somehow, I always happily accept referrals for consulting or speaking. And if that's not possible, a donation to my favorite animal shelter, Baypath Humane Society, is always welcome.
Part 1: What Not To Do
Before we begin, let’s get to some mythbusting. First and foremost, there is absolutely no way whatsoever to determine “brand placement” or “brand awareness” in an AI model. None, zero, zilch. Anyone claiming otherwise is either unaware of how the technology works or is lying. If they’re asking for your money, they’re definitely lying.
Here’s why: generative AI tools aren’t search engines. People don’t use them like search engines. No one goes to ChatGPT and types “best AI agency Boston” in the same way we did in Google a decade ago. What do we do instead? We have conversations. We discuss things like what our goals are, or ask AI to help us make a decision or a shortlist or… you get the idea.
And with every word in a conversation, the complexity of determining how an AI tool even decides to make recommendations goes up quadratically.
Here’s an easy test to prove this. Start by typing in a prompt like this:
Recommend a [your company/brand/product/service] that fits the needs of a company like [whatever your ideal customer is] in the [your industry] industry.
Just with those little mad libs, how many ways could you write that?
Recommend a management consulting firm that fits the needs of a midsize business in the manufacturing industry.
Recommend an AI consulting firm that fits the needs of a 50-500M revenue midsize business in the manufacturing industry.
Recommend an AI consulting firm in the management consulting space that fits the needs of a 50-500M revenue midsize business in the nail clipper manufacturing industry.
And what will happen? Each prompt will return different results - sometimes wildly different. A few months ago, Olga Andrienko and Tim Soulo proved this nicely. They each typed a leading question into ChatGPT about who the best SEO software was, but their prompts differed by one punctuation mark and one word. The result? They got different recommendations.
AI models are inherently probabilistic. That means there’s randomness involved, there’s chance involved, there’s all sorts of things that can change how a model responds. Any service claiming to measure the strength of a brand in a generative AI model would have to run millions of dollars of different queries PER BRAND to get even a halfway decent approximation of a model’s knowledge from the most naive, simple prompts.
And if you’re using frameworks like the Trust Insights RAPPEL framework to prime a model before undertaking an important task (like, oh, vendor selection)? You’re never going to even guesstimate brand presence in a prompt chain that long.
Okay, so what can we know?
Part 2: What’s Measurable
As the old adage goes, if you can’t measure it, you can’t manage it. Even in AI, that’s largely still true. What can we measure? Well, for one thing, we can measure referral traffic from generative AI tools to our websites. There’s a step by step tutorial on the Trust Insights website for how to set this up in Google Analytics. To be clear, you can never, ever measure what the conversation was - but you can measure the pages that people land on.
Second, we can at least roughly measure what sources generative AI tools are using, because more and more tools are using search as a grounding function for AI. Grounding is fancy for “reduce lying” - when an AI model responds in a grounded system, the system checks the answer AI produces against search results (Gemini), or even fetches search results in advance to inform the answer (Perplexity).
And that means we have a rubric, an understanding of what’s helping condition AI models: search results.
SEO is dead.
Long live SEO.
There’s a slight twist here. Humans are getting to our sites less and less. Machines are getting to our sites more and more. What you can measure - and you’ll need the help of your website’s software and perhaps even DNS software like Cloudlare or Akamai - is how often AI crawlers themselves are devouring your content. You can measure that and see what they consumed and how often.
Great. Now we know how to measure. Let’s move onto what we should do. As with traditional legacy SEO, there’s three branches: technical, content, and off-site.
Part 3: Technical AI Optimization
I have no idea what to call it, either. Some folks are pimping Generative Engine Optimization (GEO), other people call it AI Optimization (AIO), other people call it weird contorted phrases that sound like a cross between management consulting speak, IKEA furniture names, and BDSM practices. AI Optimization sounds the least tortured, so let's roll with that.
What should you do on your digital properties that you own to optimize for AI? First, realize that digital properties means more than just a website. It's ANYTHING you own that's a digital asset.
Like what? Like your YouTube content. Your social media channels where you post content. Your website. Your podcast. Your email newsletter. Any place that's visible to the general public where you have the ability to post your own content in part or in whole is your digital asset landscape.
Screen Reader Checks
First, your website. The number one thing you can do with your website to make sure it's well optimized for AI is to make sure it's well optimized for anyone using a screen reader or other visual assistance tool. By that I mean easy to navigate, easy to read, and gets to the point quickly. If I have to scroll through 23 pages of navigation and crap just to get to the content, your website sucks in a visual assistance tool. And that means it also sucks to AI, and to traditional search engines.
Install any text-only browser like w3m or lynx on your computer and browse your website. What do you see? If it’s a hot mess, if it takes 23 pages of scrolling to get to your content, then you’ve got a problem. Remember that all crawlers, old and new, have a crawl budget, a limit of how much they’ll crawl before they move onto the next site. You don’t want to burn that budget on endless pages of navigation.
Bonus: you’ll also help the 10% or so of any given population with vision impairments do business with you as well.
llms.txt
For technical optimization of your site, you'll want to implement llms.txt, which is Anthropic's LLM summary of your site. The easiest approach? Take your existing site, archive the entire thing as one large text file, and ask the generative AI tool of your choice to summarize it all, building a sparse priming representation. It's the easiest way to encapsulate what you do. This goes at the root level of your site next to your robots.txt file.
You may also want to put this information on your regular about page as well - and consider using IPA notation for critical brand names in both, so that multimodal AI knows what to say and what to listen for. For example, we'd render Trust Insights as trʌst ˈɪnˌsaɪts in IPA (international phonetic alphabet). My CEO and partner, Katie Robbert, pronounces her last name differently than written. In English, it's written Robbert, but in IPA, it would be noted roʊbɛr.
Most people and almost all machines trying to pronounce it will do it wrong.
Permitting AI
Make sure you go into your YouTube channel settings and enable third-party AI scraping for any company making search engines. A company like Anthropic, Amazon, IBM, or Meta will use that data both for generation models and search. Those are the models to prioritize.
The same goes for any platform where AI scraping is allowed - enable it unless you have a specific reason not to. In Substack, there's a switch in settings allowing third-party AI scrapers. The same applies to the robots.txt file on your site - permit every agent unless there are specific reasons not to.
On-Site Knowledge Blocks
You'll also want to create knowledge blocks that appear on every page, preferably within the main content of your site template. This is crucial - it should be invoked in the main template itself, not in navigation or other parts of the page that are easily detected. Most AI tools (and most web crawlers) will specifically exclude navigation, ad units, and other non-main text parts of the page if they can detect it (and Python libraries like Trafilatura are excellent at detecting it). Think of it as a footer within individual posts.
These knowledge blocks should contain the most important facets of your organization and/or your personal biography. When you're posting transcripts, it's perfectly fine if the knowledge block appears both in the transcript itself and in the post - you're just reinforcing the number of relevant tokens. For on-site content - meaning any channel you have control over - make sure you have those knowledge blocks in place.
Do you sound like a raging narcissist? Yes. But it's not for you or me. It's for the machines.
Basic Good SEO Practices
Everything that you learned for traditional SEO, like schema.org markup, JSON-LD, clean markup, etc. also still applies to the AI era.
Part 4: Content Optimization
Infinite Content in Infinite Forms
Today's content can't just be in one format. Multimodal AI models are training on everything they can get their hands on - video, audio, images, and text. If you're not creating in all these formats, you should be. A long time ago, I created the Video-First Transmedia Framework, which is a mouthful.
The general idea is this: make video first, and then you can make other forms of content from it.
Record a video, rip out the audio, and you've got a podcast.
Transcribe it with generative AI and rewrite it, and you've got a blog post or an article.
Summarize the article into a checklist, and now you've got a nice PDF download.
Translate it into the top 10 different languages your audience speaks, and you have 10 times the text content on your channels.
Condense it with generative AI to an image prompt, and now you've got content for your Instagram.
Rephrase it with generative AI and feed it to Sora, Veo, or Kling, and now you've got short form video for TikTok.
Rephrase it again with generative AI and convert it into song lyrics, feed it into Suno, and now you have music for Spotify, YouTube, and wherever else you can put it.
Yes, this newsletter issue is available as a song. It's not horrible.
That's the modern, AI-first transmedia framework. One piece of content can become an infinite number of pieces, just by having AI rewrite it for different formats. And every piece of content you publish adds to the overall training corpus about you.
Answer the Questions
When you create content, put it through the generative AI tool of your choice with this relatively straightforward prompt to ask questions of the content. The goal is to determine what else SHOULD be in your content that a user is likely to ask a followup question in ChatGPT/Gemini/Claude:
You're an expert in {topic}. Today, we're going to review a piece of content to determine how well it fulfills the needs of our audience.
Determine the overall intent of the article. What is it about?
Then determine who the audience of the article is. What are their needs and pain points, goals and motivations for reading an article like this?
Evaluate how comprehensively the article fulfills the intent of the author and how well the article satisfies the inferred needs of the audience. What questions is the audience likely to have after reading this article?
Determine based on your knowledge of the intent, the audience, and the current state of the article what, if anything, is missing from the article that would fulfill the needs of the audience more and is aligned with the intent of the article. If nothing is missing, state this.
If nothing is missing, or nothing can be substantially improved, state so. If things are missing or can be substantially improved, then produce a concrete, specific set of recommendations for filling any gaps that exist.
Produce your analysis in outline format in five parts:
The intent of the article
The audience of the article and their needs
How well the article fulfills the intent and the audience
The questions the audience would have as follow ups
What's missing, if anything
Concrete next steps, if any
For example, if your content is about baking bread, what are the expected questions someone might have after reading your content? Ask an AI to give you those questions, and then you incorporate those questions into your content.
And remember to keep your FAQ pages relevant, fresh, and beefy. The bigger they are, the more training data they provide to AI models. Make sure they're loaded up with appropriate brand references so that each question has an answer pair that contains your brand.
Structural Elements
One common mistake many sites make? They use styling to denote structure instead of having structure and then applying styles to the structure. Simplify your styling while still adhering to your brand guidelines.
Here’s what I mean. In HTML in particular, you can set styles like font size, bold and italics, etc. with CSS, with styling. A lot of folks who are design-oriented but not information architecture oriented tend to do this. It makes your site look nice, but if you look at the code, it’s basically just a wall of text.
HTML and other markup languages have discrete forms of structural elements like title tags, heading tags, etc. that denote the actual structure of the information. For those versed in SEO, these are all the elements like H1, H2 tags, etc.
What makes these important is that they define structure to our content, and structure is something AI models can both consume and understand. When a section has an H2 and an H3 tag, it’s implicit that the content in the H3 section is subordinate to the content in the H2. You can see that in this newsletter, with the subheadings. That conveys structure and document layout to AI engines, to help them understand what they’re reading, so to the best of your ability, use structural tagging in your content, not just CSS styling. You want actual H1 tags, H2 tags, etc. - structural items in the content itself.
Other structural elements like lists and such are also good. You’ve probably noticed how much AI systems like ChatGPT and Claude use bulleted lists in their writing. There’s a reason for that - it’s easy to parse. Use them in your content too.
Subtitles and Captions
For all image content, be sure you’re providing alt text, the text displayed for when content is being read aloud in screen readers. If your images are relevant to your company, be especially sure to include your company name and a beefy description in the alt text. For example, if you’re showing an image of your proprietary framework (like the Trust Insights 5P Framework, this would be an inadequate alternative text:
5P Framework image
This would be a much better alternative text - and this is what AI models train on, especially diffusion and image analysis models (VLMs, or visual language models):
TrustInsights.ai 5P Framework for management consulting by Trust Insights : purpose people process platform performance
You can pretty clearly see we’re declaring not only that it’s an image of the 5P framework, but it’s loaded up with the relevant components and our brand. You don’t need to do this for every single image, but you should for important or branded images.
For all audio and video content, always use captions. Always use subtitles. Provide them in industry standard formats like SRT or VTT files. Some services like YouTube automatically generate these, but their transcriptions may not be reliable for certain types of jargon or certain kinds of accents, so use the best converters you have access to. Upload them with your media; many services provide the ability to do this, even audio podcasting services like Libsyn.
Almost every AI transcription service has the ability to export captions, services like Fireflies, Otter, etc. And there are free, open source options like Whisper.cpp that can run on your computer and generate transcripts and captions files as well.
When using captioning software, make sure it supports a custom dictionary - especially crucial if you're talking about anything with jargon where built-in captions simply won't understand the unique language of your business and industry.
Speaking of jargon - it's your friend! Use it within your copy and text to the extent possible without interfering with human readability. You want invocations within the language models themselves. You could even add prompts inside your emails - consider adding them to your signature in light-colored text at the end so that when a tool reads it, the prompt becomes part of the summarization.
Credit Where It's Due
Marketers have a very bad habit (especially on social networks) of claiming and repeating ideas without giving credit for them. In the old days, this was obnoxious and unnethical. In the AI-first era, it's also deeply stupid.
Why? Because, like jargon, citations and credit add associations that AI models can build to understand the world better. If I write an article about SEO and I'm not citing people like Wil Reynolds, Aleyda Solis, Andy Crestodina, Lily Ray, and others, then what am I not doing? That's right - I'm not building associations within my own text to those people. If my name (from my own article) is in the training data alongside those folks, then when AI model makers scrape that data, they'll see those names in proximity to my own, repeatedly in the text.
If I'm writing about AI in Marketing and I'm not talking about Katie Robbert, Cathy McPhilips, Paul Roetzer, Mike Kaput, Liza Adams, Nicole Leffer, and others, then again, I'm not creating the statistical associations in text that I should be. Who are you citing in your works? Which names do you want to be associated with? Start creating content that has those associations by giving credit where it's due.
Housekeeping
As with traditional SEO, housekeeping is important - probably even more important in the modern AI era than before. By this I mean keeping content fresh, factually correct, and up to date. Critically, this also means pruning and retiring old content, contnet that you don't want to be associated with any more.
In the old days, having irrelevant content wasn't necessarily bad in traditional SEO. Any traffic you could get was a good thing because there was a chance that a small part of the audience that made it to your blog post about My Little Pony would also need your B2B marketing services - that's a very human approach.
In the modern, AI-first era, when someone invokes your name or your brand in AI, the associations that come back are going to be a composite of all the knowledge it has about you, and if there's a lot of irrelevant fluff, you will not have as strong a set of associations with the things you do want to be found for. Take a look in any AI model that allows you to see token generation and you'll see the probabilities next to each word as the model tries to guess what to say next about you.
Part 5: Going Off-Site
Off-site specifically means channels you don't own. YouTube, for example, can be both on-site (your channel) and off-site (other people's channels).
The memo here is dead simple: be in as many places as you can be.
Press Releases & Distribution
Consider issuing press releases on reputable wire services that can achieve large-scale distribution. You don't care about the quality of publications beyond a certain minimum amount. What you do care about is breadth of distribution.
Why? Because every time you issue a press release, multiple copies are made throughout the distribution network. You'll see them on TV affiliate sites, news affiliate sites, even the backwater pages of classified sites. Any place picking up wire services should have your press release.
Unlike traditional SEO, which looks at inbound links for credibility, language models work on a token basis. The more times text is repeated within the model's training data set, the more it reinforces the probability of those tokens. If you're putting out news about your product, services, company, or personal brand, the more copies that exist on the internet, the better it's going to perform.
Your machine-focused press releases are going to read differently than human-focused press releases. They won’t read well for people, and that’s okay. They’re not made for people. They’re made to help machines associate concepts and topics together.
Guest Appearances & Rich Media
This overlooked fact is crucial: You want to be a guest on as many other people's channels as possible. Say yes to pretty much any podcast that will take you. Say yes to any YouTube or Twitch streamer. Anyone who can get audio and video distributed around the internet is a place you want to be, as much as time permits.
When it comes to distribution, prioritize rich media - podcasts, YouTube channels, streamers - anything with video. Video is the most information-dense data format. Companies training AI models will take the video, the audio, and the caption files. Rather than creating content for all those different modalities, you're better off just having videos out there.
That's why being a guest on podcasts is so valuable - most podcasters with any sense put episodes on YouTube as well as on their RSS feeds.
In podcast interviews, make sure you're name-checking yourself, your company, your products, your services, and all relevant things. Enunciate clearly and ideally alternate between mentioning your company name and domain. For example, talk about Trust Insights, but also reference trustinsights.ai to create associations with both. Does it sound weirdly egomaniacal? Yes. Is it effective for getting your brand in the relevant text? Also yes.
For traditional PR, go for every publication that will take you, even if it's the East Peoria Evening News. We don't actually care if humans read it - we care if machines read it. The more placements you can get all over the web, the better. Avoid truly junk sites like BlogSpot, but otherwise, be everywhere you can be.
For newsletters, particularly those on Substacks or Beehiives or anything with a web presence as well as email delivery, try to appear in those too, since that data will be crawled and ingested into models.
If you're on a podcast or blog, get permission from the producer to embed the video on your own site, and include your own version of the transcript. You want that text repeated in as many places as possible. Call it a special guest appearance, whatever - just get that data replicated widely, especially if you can create a summary alongside the main content.
Consider running it through a language model to clean up disfluencies and speech anomalies, making the text higher quality. As language models evolve, they'll likely give preferential treatment to higher quality text.
The kids all call this collaborations, or collabs. Whatever you want to call it, do it. Co-create content as much as possible, and get yourself everywhere you can be.
Social Networks & Platforms
Social networks matter too. Know which ones are ingesting training data from users and create content there. For the Meta family, post content on Facebook, Instagram, and Threads - even if nobody reads it, who cares? You just want it in the training data library. (Finally, a use for that Facebook page no one reads!)
For Microsoft's models, publish rich content on LinkedIn, both in post format and article format - there are no privacy settings that disallow AI use on LinkedIn articles, so that content is definitely being ingested.
Want to appear in Grok 3? You'll need to post on X (formerly Twitter). Even if you don't like the site, you don't need to pay - just post content with frequent links to your stuff so citations can be linked up and the Grok crawler understands you're providing those links. Fire up a free or very low cost social media scheduler and just spam it with links to your content and topic-rich posts to help guide the model when it's searching for relevant posts to build results and summaries.
For other platforms like Pinterest, there's no harm in having extra copies of your information online. We're not necessarily making this for humans - we're making it for machines.
Engagement doesn’t matter. It’s all about getting information into the corpus.
Reviews and Discussions
If you don't solicit reviews of your company, products, or services, today is the day to start. User generated content on as many different platforms as possible is important - again, this is all about getting text about you in as many places as possible.
Look at sites like Reddit, Ask.com, JustAnswer.com, Quora, and many others - all of those sites are harvested by AI crawlers because they contain ideal question / answer pairings, pre-formatted as training data to teach AI models how to answer questions.
Checking Sources
If time is scarce, how do you know where to invest your time? Here’s an easy method: go into the deep research tools of every platform you care about, such as Gemini Deep Research, Perplexity Deep Research, OpenAI Deep Research, Grok Deep Research… you get the idea. Build a research project from the perspective of your ideal customer profile (using generative AI). Ask your favorite AI to construct the parameters of a deep research inquiry from your ideal customer that would search for the products and services you provide at an industry or category level.
Then run those projects. Ignore the summaries, they’re not helpful. Instead, catalog all the sites, documents, and places that the Deep Research tools all find.
Then figure out how to get your content in those specific places first.
Multilingual Content Strategy
What about languages? If you have the ability and time, post in the languages that make sense for your target markets. For the US, use US English but consider adding Spanish. In Canada, use both English and French. For Germany, consider English, German, French, Arabic, and Chinese.
The more content you have in different languages, the better it will perform in both traditional search and generative models. You're creating token distributions and associations across multiple languages. As multilingual models like Mistral and Deepseek develop, this approach will pay dividends.
One language you should always consider is Chinese (standard Mandarin). Many models like Deepseek are fluent in both English and Chinese, and as the AI race continues, Chinese will become one of the flagship languages of generative AI. Use a model like Deepseek for translations since its language capabilities are strong.
Important: make these translations static content, not dynamically generated. No Google Translate widgets with dropdowns - you want the actual content available in those languages as static content on your site.
The same principle applies to video. If you can have content translated and spoken in target languages, models like Gemini or Deepseek can help with translation, and tools like Eleven Labs or Google TTS can speak the language in native translation. Make these available either as separate audio tracks or as separate videos entirely.
The golden rule throughout all of this? If machines can't see it, it doesn't exist. And if it exists in more places, it matters more.
Part 6: Wrapping Up
Here's the bad news. The window to significantly influence AI models is closing. Why? Because model makers have run out of content they can use. Humans only generate so much content, and more and more content channels have closed themselves off to AI (for perfectly good reasons).
What have model makers done in response? They're creating and feeding synthetic data - data made by AI - to train AI. Instead of a huge corpus of spam from Blogspot or random drunken shitposts from Reddit, model makers are using their own technology to feed newer models.
And guess what's not in that synthetic data? Us. We're not in there. We're not feeding our original content in. The more model makers use synthetic data (which is typically higher quality than random crap from the Internet), the less influence we have.
So the time to get our ducks in a row, get our marketing houses in order is now. Right now, right this very minute. Take this entire newsletter and compare it to your current marketing practices (feel free to use generative AI to do this). Then build yourself a punchlist of what you need to do next, to influence models while model makers are still consuming as much public content as they can.
And don't forget your traditional SEO. As you've seen throughout this, and in your own experiences with generative AI, many AI engines use search grounding - meaning they check their responses with traditional search. If you're not ranking and showing up in traditional search, you're not part of the grounding mechanism for AI either.
I hope you found this guide helpful. We'll be looking at some examples of this on the Trust Insights livestream on Thursday, March 6 at 1 PM Eastern Time on the Trust Insights YouTube channel, if you want to come hang out and ask questions specific of it. You're also welcome to just hit reply and ask me the questions in advance.
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If you'd like to see more, here are:
ICYMI: In Case You Missed It
This week, Katie and I did an incredibly important episode about AI agents and what you need to know to get started with them. Be sure to check it out.
Mind Readings: Building Resilient Generative AI Capabilities
Mind Readings: AI Is An Appliance and Data Are The Ingredients
Almost Timely News: 🗞️ The Dark Side of Generative AI (2025-03-02)
Skill Up With Classes
These are just a few of the classes I have available over at the Trust Insights website that you can take.
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Powering Up Your LinkedIn Profile (For Job Hunters) 2023 Edition
Building the Data-Driven, AI-Powered Customer Journey for Retail and Ecommerce, 2024 Edition
The Marketing Singularity: How Generative AI Means the End of Marketing As We Knew It
Advertisement: New AI Course!
Mastering Prompt Engineering for Marketers is a 2 hour tour through prompt engineering. The first couple of modules walk through not just what prompting is, but what's happening INSIDE the AI model as it processes a prompt. I made the explanation non-technical (because who really enjoys softmax layers and attention matrices besides me) but the walkthrough really digs into what's going on inside the box.
Knowing that helps us understand WHY prompts do or don't work. You'll see why in the course, when you watch how a prompt is processed.
Then we walk through 3 prompt frameworks, plus "delve" 😏 into advanced prompting techniques, along with a downloadable guide of what each technique is, why you should care, when you should use it, and how to use it.
After that, we get into knowledge blocks and priming representations, then how to build and manage a prompt library.
What's In The Box? Here's a 5 Minute Tour
Here's a 5 minute video tour of the course so you can see what's inside.
Get Back to Work
Folks who post jobs in the free Analytics for Marketers Slack community may have those jobs shared here, too. If you're looking for work, check out these recent open positions, and check out the Slack group for the comprehensive list.
Senior Director, Local Media Forecasting at BIA Advisory Services
Vp, Market Research, B2b Consulting at Higher Research Recruiting
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How to Stay in Touch
Let's make sure we're connected in the places it suits you best. Here's where you can find different content:
My blog - daily videos, blog posts, and podcast episodes
My YouTube channel - daily videos, conference talks, and all things video
My company, Trust Insights - marketing analytics help
My podcast, Marketing over Coffee - weekly episodes of what's worth noting in marketing
My second podcast, In-Ear Insights - the Trust Insights weekly podcast focused on data and analytics
On Bluesky - random personal stuff and chaos
On LinkedIn - daily videos and news
On Instagram - personal photos and travels
My free Slack discussion forum, Analytics for Marketers - open conversations about marketing and analytics
Listen to my theme song as a new single:
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Events I'll Be At
Here are the public events where I'm speaking and attending. Say hi if you're at an event also:
Social Media Marketing World, San Diego, March 2025
Content Jam, Chicago, April 2025
TraceOne, Miami, April 205
SMPS, Washington DC, May 2025
SMPS, Los Angeles, Fall 2025
SMPS, Columbus, August 2025
There are also private events that aren't open to the public.
If you're an event organizer, let me help your event shine. Visit my speaking page for more details.
Can't be at an event? Stop by my private Slack group instead, Analytics for Marketers.
Required Disclosures
Events with links have purchased sponsorships in this newsletter and as a result, I receive direct financial compensation for promoting them.
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My company, Trust Insights, maintains business partnerships with companies including, but not limited to, IBM, Cisco Systems, Amazon, Talkwalker, MarketingProfs, MarketMuse, Agorapulse, Hubspot, Informa, Demandbase, The Marketing AI Institute, and others. While links shared from partners are not explicit endorsements, nor do they directly financially benefit Trust Insights, a commercial relationship exists for which Trust Insights may receive indirect financial benefit, and thus I may receive indirect financial benefit from them as well.
Thank You
Thanks for subscribing and reading this far. I appreciate it. As always, thank you for your support, your attention, and your kindness.
See you next week,
Christopher S. Penn
That song is pretty catchy!
Do you have a Spotify channel of all of your song/articles?