Almost Timely News: 🗞️ Using AI for Analytics (2025-09-21)

Slightly more technical this week, but I believe in you

Christopher S. Penn

Sep 21, 2025

Almost Timely News: 🗞️ Using AI for Analytics (2025-09-21) :: View in Browser

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What's On My Mind: Using AI for Analytics

In this week’s issue, let’s talk about one of my favorite topics, a topic I was known for 20 years - analytics. Analytics remains a great love of mine, the ability to unlock data’s secrets and tell us useful things.

In all the hubbub about generative AI, we’ve lost sight of some of our analytics, and in other cases, we’ve lost the data. Data privacy changes have reduced the amount of data we have access to; some browsers and devices send no data at all.

But even with these changes, we still have enough data to work with in some capacity, especially if we’re doing a good job in asking people to help provide us with data - and exchanging value in kind.

This week, let’s look at how we can use the power of generative AI for analytics.

I also want to add a caution that this week's issue is a little more technical because we can't trust generative AI to do math on its own. Generative AI models just can't do math. It's literally not part of their architecture. But they can write code, which is a language, to do the math for us - and that's what we're going to do.

Part 1: Mise En Place

Before we do anything else, we need to establish what it is we're doing. Take some time to sit down and think through - and explain - what it is you want to know. What are your goals? Why are you even looking at your data? What do you hope to gain?

For example, with this newsletter, I might say something like:

I deeply want to understand the Almost Timely newsletter, which is the Substack newsletter I run, and how to go from the nearly 300,000 subscribers I have today to 400,000 subscribers in the next six months. My goal is to get to 400,000 subscribers by March of 2026. What do I need to do more of or less of or do differently to reach that goal?

The second thing you’ll need is analytics data. Because we’re going to be working with code indirectly, this should be in clean, machine-ready formats. CSV files work fine for this, as do JSON files, and pretty much any machine file. Most software platforms can export in one of those two formats, so be sure to export all the data you can.

For this newsletter, I might export my Substack analytics, my social media analytics, my Google Search Console analytics, my Google Analytics 4 data - basically, anything I can get my hands on that will help me answer the questions about my goals. There's a good chance you'll run into things you can only take screenshots of, so you'll want to use your favorite AI tool to transcribe those screenshots into text files as well.

The third thing you’ll need is a collection of what you did in a given timeframe that matches your analytics data. If you’re looking at 30 days of data, what did you do in your activities during that period? For example, if we’re talking marketing, how many emails and what emails went out, what social posts went up, what videos did you post, etc.

If you’re looking at physical fitness, you’d have your health data like number of steps, heart rate, etc., as well as what you did during those 30 days. What did you eat, when and how did you work out, etc., in as great a level of detail as you can provide.

Often, the easiest way to gather this data is to grab the beverage of your choice, a voice memos app on your phone, a copy of your calendar and maybe your inbox, and talk through what you did during that time. Depending on the system, you might also be able to export your data. Social networks like LinkedIn allow you to export all your data.

For example, with this newsletter, I might say something like:

Each week I send out the Almost Timely newsletter at 6 30 AM Eastern Time on Sunday mornings. I send this out on Substack, and I also post it to my website, ChristophersPenn.com, and I also post it to LinkedIn using the LinkedIn newsletter feature and the accompanying social post. Throughout the week. I post once or twice a day on LinkedIn during weekdays and I also post a note to my Substack every weekday. I post videos every day on YouTube and those videos have links to my various properties. I post a regular blog post every weekday on my personal blog, ChristophersPenn.com.

The fourth thing you'll need is acces to coding tools. There’s no way of getting around that; some of the best analytics come from mathematical and statistical methods that generative AI simply can’t do.

The good news is that there are great options now for this. Google’s Colab is excellent, and allows you to use generative AI prompting with Gemini right inside the experience. Nothing to install - it’s super useful. If you’re working with really sensitive data, you can use open models like Qwen 3 Coder with Ollama privately and safely. It takes a lot more work to set up, but it’s guaranteed private.

Part 2: What’s in Kelsey’s Refrigerator?

My friend and coworker Kelsey often says her favorite use case for generative AI is to take a photo of her refrigerator's contents and ask AI what dishes she could make from it. While that seems like a fairly straightforward use case, it’s the foundation of one of the most profound analytics use cases of generative AI there is, something I detail in Principle X of my book, Almost Timeless: ask AI.

Ask AI what you COULD do with your data, based on your goals and what you have.

Here’s an example prompt:

You’re an award-winning marketing analytics and statistics expert with a deep understanding of marketing data analysis, data architecture, data engineering. I want to understand what insights I can extract from the marketing data I have to better understand what’s working in my marketing. My marketing goals are {detailed list of goals}. In the last X days, I did {comprehensive summary of your marketing activities}. The data I have available includes {list of your data} and I can export data from {list of your Martech}. I’ve included some samples. {be sure to actually include them}. Let’s explore what I could do with this data. Tell me what statistical and machine learning methods, supervised and unsupervised, I might use in Python to do root cause analysis, attribution, uplift, propensity, or other ways of deriving useful, actionable insights - use your best judgement based on my data and my goals, with the understanding that this is all the data I have. Think about how the data could be cross-referenced, linked together, normalized or denormalized, and other data engineering techniques that you know. Think of this like a recipe challenge where I provide ingredients and we figure out what I can cook. Based on the data, what insights could I cook, and how? Expand your thinking beyond just common marketing analysis. What techniques might we borrow from other disciplines and fields? Return your results in Markdown format explaining the specific Python libraries, methods, tactics, and techniques that would help me make the most of my data. Remember, we are planning, brainstorming, and thinking, not coding. Do not code. Do not write code.

Obviously adapt this to whatever domain you’re working in - marketing, sales, finance, health, whatever.

Why does this work? Because all generative AI models have consumed far more data on best practices and domains than any of us ever could as humans. We just don’t have brains that large. By asking AI, we are leveraging its knowledge not only of marketing, but also of every domain in analytics to come up with answers.

Once we’ve got an exhaustive list of possible options, ask AI some follow-ups. Here’s what should come next.

Based on my stated goals and your analysis so far, arrange your list of techniques in descending order by effectiveness for helping me achieve my goals. What specific technique, tactics, or technology should I use with my data first, and why? Remember, we’re still planning, so do not code. Do not write code.

Let the AI answer. Now, once we’ve really pushed it to explain itself, we’re ready to have it begin coding so we can actually perform our analysis. Give it this second followup:

Based on your answers, write a 2000 character prompt to guide a junior developer to write the Python code that will process my data. Be clear, specific, and concise, naming specific techniques and libraries for clarity. The developer will have no other context than what you write, so pack as much detail in as possible; proper grammar is unimportant, so you can cut out fluff, explanation, stop words, and other non-essential information to make the prompt as information-dense as possible. Generate the prompt now.

Grab the result. We’re going to take it to our coding environment now.

Part 3: Cooking the Dish

Depending on your particular coding environment, what you do next will vary wildly. If you’re technically savvy and skilled, then you probably already know what to do - take the generated prompt along with your data, fire up your coding tools, and have it write the software. In fact, if you're very technically skilled, you might even modify the prompt in the previous step specifically for your environment.

If you’re not as technically savvy, here’s what to do. Head over to Google Colab, and if you haven't got an account, sign up for one. Be sure to read the privacy policy - that may influence what data you put in! Open a new document. And in this document, drag and drop all your data files plus the 2000 character prompt from the last part.

Mine turned out to be a chain of five prompts, which you can watch in the video version.

Then you wait for Colab and Gemini to assemble the data, process it, and present you with results. At any point, you can prompt it for additional clarification or even have it write a report for you from the data.

If you find the analysis useful and it's something you want to repeat, you could simply add more data, or take the Colab contents, hand it to the generative AI tool of your choice, and have it turned into a full, bespoke piece of software for you and/or your company.

Once you get the analysis back, it's now time to make some decisions. What does the analysis tell you? What things are you doing that are working well? What things are you doing that are not working well? What things should you be doing that you're not? What things are you doing that you should stop?

This is always where the rubber meets the road. If you do analysis and you don't do anything with the products of the analysis, then what was the point? I've often said in keynotes past that analysis without action is distraction, that data without decisions is decor. Even in the age of generative AI, that still holds true.

Now if you're unsure what you should do, then you could take the products of the analysis, the reports, the screenshots, etc., and go back to your regular generative AI tool with all of your original goals and what you currently do and ask it based on the analysis what a strategy, tactics, and execution work plan might look like to achieve your goals based on the analysis.

It's up to me to either do the plan or not. But I no longer have the excuse that I don't know what the data said. I clearly know what the data says and I know what it's telling me to do if I want to achieve my goals. So I have to decide, are those goals something I'm willing to put in the work to achieve?

Part 4: Wrapping Up

What we did today is slightly more advanced than your average use case for generative AI, but it's still achievable for pretty much everybody who can copy and paste. The power of generative AI is that it knows so much about analysis across so many domains that we can leverage that power to work specifically with our data.

What you have to decide is what your goals are, what you're willing to do to achieve those goals, and what the data tells you to do in terms of priorities to get from here to where you want to go. Generative AI helps us get to those answers faster, with much greater statistical and mathematical rigor than maybe we're used to bringing to the party, and ultimately to get us one step closer to our goals.

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THERE ARE ONLY 4 SEATS LEFT

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I hope to see you in London. If you can't attend, or you're not the right person for this email, you have my thanks in advance for forwarding it to them.

ICYMI: In Case You Missed It

This week on the livestream, we looked at how to work with YouTube data and AI.

On The Tubes

Here's what debuted on my YouTube channel this week:

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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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.

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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:

Marketing Profs Working Webinar Series, September 2025
SMPS, Denver, October 2025
Marketing AI Conference, Cleveland, October 2025
Generative AI : The London Workshop, London, October 2025
MarketingProfs B2B Forum, Boston, November 2025
Social Media Marketing World, Anaheim, April 2026

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.

Advertisements in this newsletter have paid to be promoted, and as a result, I receive direct financial compensation for promoting them.

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