266 | From Spreadsheets to Strategy: How AI Turns Business Data Into Decisions with Keith Moehring

0:00Hello and welcome to the Leveraging AI podcast, the podcast that shares practical ethical ways to leverage AI to improve efficiency, grow your business and advance your career. This is Isar Maitis, your host, and we have a really awesome episode for you today. And me, as a data geek, I'm really excited about it personally. But every business has multiple data points. And being able to pull the data from all these different points and actually identify deviations from what expected and finding reasons for these deviations and diving into specific unique events and analyzing what's happening in them is one of the most critical aspects of driving a successful business.

0:49Now, that being said, doing that, especially doing that consistency and at scale is not easy. You either need somebody in your company who is a data analyst or at least somebody who is an Excel with in order to do this. And even then doing it consistently and at scale is not easy. So that's why bigger companies have departments or teams that doing business intelligence and they invest a lot of money in really sophisticated and advanced BI platforms. And that enables them to do some magic, but most companies do not have the budget or the right people or resources to do that. The good news, AI enables to do it amazingly well.

1:32AI is really, really good at analyzing data and finding needles in haystacks in really big chunks of data. And you can do it with almost no technical skills and almost for free, which is a huge benefit. So in today's episode, we're going to teach you exactly how you can set up something like this in your business, how you can go to data sources that you have access to and how you can build the right processes around them. So AI can help you analyze the data, find what's good, bad, and ugly. So you can make informed business decisions. So you can grow your business in the most effective way and how you can do this while at the end of the process, it just sends you an email with the results on whatever cadence you want without you having to go and doing the query on your own every single time.

2:26Now, as I mentioned, I think that being able to make data-driven decisions as the way to drive a business is maybe the most critical aspect of making the right decisions. Executing them is a whole other problem, but making the right decisions to drive your business in the right direction. And so as a data geek myself, I'm very excited about this. And to guide us through this process today, we have Keith Mooring. And Keith has been the CEO of L2 Digital, which is a marketing and sales agency in the B2B space. He's been running it for six years. But before that, he was the VP of Growth at PR2020, which is another agency he's been at driving growth, both of them in the Cleveland area.

3:08By the way, PR2020 has some other AI ninjas like Paul Reutzer and Michael Putt, which you may know if you listen to podcasts or in the AI space. But Keith has been using AI combined with automation tools for a while. And every time I see what he does, it blows my mind on how effective the stuff that he builds is. And so he brings to the table 20 years of experience of running businesses. And he uses his AI and automation knowledge to grow the business, which is what makes it more interesting. It's just such a geeky exercise in building automation. It actually drives business value. And as somebody who's A, an AI geek, B, a data geek, and C, somebody who started and grew companies, this is really the core of what I love doing.

3:59And so I'm personally very, very excited to welcome Keith to the show. Keith, welcome to Leveraging AI.

4:07In the next few years, AI technology will change our world dramatically. Whether you are a business executive trying to catapult your business forward, or just somebody who refuses to be left behind and want to advance your career, this is the show for you. I'm your host, Isar Maitis, a serial entrepreneur and an AI enthusiast. You'll hear invaluable practical tips from innovative business leaders, AI practitioners, and some of the brightest AI minds in our world today on how you can leverage AI in ethical ways to advance your career and grow your business.

4:48Thank you very much for having me and for the intro and for calling me a geek several times. I actually, I love it. So that's awesome. Again, I was very genuine. I think this is going to be really fun. And I'm looking forward to see what you prepared for us. So stage is yours. Yeah, no, I appreciate it. Yeah. And I think the we're going to kind of talk through is how to approach building these AI automations in a way that can surface usable insights out of data. So I'll give you an example. So every week, every Monday, I get an email in my inbox. In that email, there's kind of the main website KPIs, sessions, users, that kind of thing.

5:30It breaks down channel performance, how each channel did, did it go up or did it go down? And then overall, is the site up or down? But then it goes a level deeper. So it identifies the top one to two to three channels that were the biggest reason that we're up or that we're down. And then in each of those channels, it goes in and dives deeper. So channel, okay, then what source details do we get? And then for each of those sources that had the biggest change, landing pages. And then the email summarizes all of that. It says, okay, here are some of the insights that we found based on the data of what we're seeing.

6:09and then in that same email for that specific channel it'll also recommend next steps. So it's like organic search you're going to want to write these three pieces of content update this other piece of content. Social media here's some posts to share based on what seems to be getting the most engagement. This thing is awesome. Now I've done these types of reporting for years and when I would do this manually the process of building these reports we're talking about two hours to pull the data, process data, check it, balance it, do the calculations. Then another two hours to sit there and like write up the whole thing, edit it, polish it up.

6:46And then that probably involves at least two to three people, someone else to check it, sanity check, kind of all that fun stuff. And all for what? I mean, we're talking about doing this weekly. You're not unearthing mind blowing revelations every time you do this. It's maybe one or two small insights that can help incrementally improve overall performance. But with all the data we have available, with all the tools that can connect back and forth, we're expected to make these decisions using this data. And so the challenge then comes, how much time is worth your time? And then what can we do to automate some of this process?

7:27And thanks to AI and automation, that report I get every Monday, I don't do anything. All I do is read it Monday morning. It pulls the data, it processes it the exact way I want to process. It uses AI to understand what's going on, generates the report and sends it off into my inbox. Amazing. I'll add one thing, again, just to generalize it for those of you who are not in the marketing space. I'm doing a workshop tomorrow for a pretty large group of financial analysts from a large international corporation. They do financial analysis of large international corporations. So they have these really ugly big Excels that have 40 columns and with thousands or tens of thousands of rows of cost analysis per their projects, because they have people in multiple locations with multiple price points in multiple roles working on multiple components of the project.

8:29So every time a person somewhere does a work, it adds a line to that Excel that says, he is from this location. He has these roles, these capabilities, he's working under that project on these two products under et cetera, et cetera, et cetera. Like, so there's again, multiple columns for every time a work gets done. And then somehow you got to figure out, are we ahead of budget or behind budget? And where are we behind budget and why? And stuff like that. And again, it's just, it's impossible for, it's not impossible, but you have a team of people sitting there doing pivot tables and different macros and different analysis to try to figure out, first of all, where is the deviation?

9:16And second, like Keith said, so what are the recommendations? Okay, what should I do next? and now AI knows how to just do these things. And does it replace the human? Probably not because you still want to read it and decide what you want to do as far as the taking action on the business. But analyzing the data and getting the report, you can apply this to any aspect of your business from finance, marketing, sales, customer service, HR, et cetera, wherever you have data, you can do the same process. And that's why I love where Keith is taking this, because we're going to show you how to think about this and how to build this and not just, OK, here's an automation that does marketing data analysis.

10:02Yeah, and I totally agree. And the fun part about, so AI, these tools are mind-blowing amazing, but they do have their limitations, especially when it comes to what they connect to, what they know to go get, contacts and all that, which is why I'm so bullish on AI plus automation. So with the automation tools, now you have the ability to programmatically determine exactly what you want to go get and bring back. And then you layer that in with some additional context to the AI to then do what it does best. And so what we're going to walk through today is kind of like the four-step process that when we attack these different projects, like what we think about and how we go step by step through them to make sure that we're covering all the bases and everything's thought through and we're using AI to its max potential.

10:52Okay, sounds awesome. Yeah, so I guess the kind of real quick high level, four main steps when you're approaching any of these AI automation projects. The first one is to define approach. So define your approach. So think about it in terms of there's the way you go about doing something or your thought process behind why you approach generating content this way or how you go about pulling data specifically and looking at the metrics. So it's your overarching set of principles and how something should be done. And then kind of the next step, which we'll get into, is the detailed process. This is the step-by-step walkthrough on exactly how this stuff works.

11:33And then from there, once you have that process kind of mapped out, how do we layer AI on top of it to do what it does best? And then you get into, okay, now that we know what the process is, We know where AI is. How do we build the actual automation itself? And I'll kind of walk you through an example of how this analytics assistant that I've built, how this whole thing works. So awesome. Let's let's dive in. Perfect. OK, so in terms of let's start with the approach. So when I'm thinking about an approach for the analytics assistant, there are really four main things that we look at. And I've done I get done these reports for 20 years now.

12:13and the process to surface the most tangible insights I have fairly well or the approach I have pretty well defined. So there's really four main steps here. The first one, just to kind of give you an overall example of how to think about this. So with analytics, the first one is to look at how much the change actually was. So we don't want to look at the top level metric. We don't want to and just look at that percentage of how much it went up or down. We want the actual number of how much it increased or decreased. So it's the diff. A perfect example of this is, so if you go to a Cleveland Cavaliers game, I'm from Cleveland, up on the scoreboard, they have one team score, other team score, how much time is left, what quarter it is.

12:55But right in the middle, they have a number. It's called the diff. And it's either like plus 10, plus two, minus three. It's the difference between the calf score and the opponent score. At the end of the day, if that number has a plus in front of it, everything's good but as a minus it's bad that one number is key is critical to the whole thing and really what we're trying to do to uncover the most valuable insights with analytics is to uncover why that number is up or down the way it is and so that's why and we'll get into when i show kind of the automation how that works that's really a lot of what the automation is designed to do is to pull out that type of information.

13:38Another one, another kind of approach principle that we have is five whys. So if you've ever read like the Toyota way, I think is where I saw it. They have this five whys philosophy where you continue to ask why five times to ultimately get like the root cause of the problem. So the, if you, and if you do that, if you look at it from analytics perspective traffic's up okay why organic search is up okay why is organic search up well Google specifically is up okay why is Google up well traffic to the blog is up and then you kind of work your way down to get something very tangible and very specific that's where the real insights lie is buried in the why why why why why so having a process to unearth that that level of detail is very important to this whole process thing.

14:30The other side of it too, I have two children, uh, and they're now nine and 11. And even when they were kids, like you'd walk into a room, one would be on the floor crying. And then you'd say, okay, why, why are you on the floor crying? And then you would get some sort of explanation of how it was the other one's fault. and she did this, she did this, she did this. And then as the unbiased judge in this situation, I have to go talk to the other one. And turns out her story, while there are some parallels, is wildly different. They're both describing the same event. So the third approach, kind of the principle we have is like, there are multiple sources of truth for different channels.

15:18Perfect example with analytics is Google Analytics 4. I'm sorry, with analytics. Google Analytics 4 can give you organic search performance. It can show you how people, organic search traffic got to the site, what they did on it, what pages they viewed. But there is also Google Search Console, which can show you the other side of that same story, where how many, what rankings are, average position, impressions, clicks, all that kind of stuff. You marry those two together, and now you've got something very, very tangible that you can use with the reporting. So when there are opportunities to layer different technologies on top of each other to combine the data, that becomes very, very powerful.

16:00And then the fourth and kind of the other one that's really very useful when it comes to analytics reporting is context. Context matters a ton. uh the example um for one of uh one of the sites that i manage it's a golf uh live scoring system and they get a lot of traffic golf related traffic well on i think it was april 13th of 2025 there was this enormous spike in uh in traffic on the site you're like what what was going on like why why did this happen now we're trying to analyze the data see what was going on and nothing was lining up. Well, that was also the same day that Rory McIlroy won the Masters.

16:44So there was this huge, enormous spike in interest in golf that day that we couldn't do anything to replicate with marketing. But it's context. You need it to kind of level out the story. Another good example is a lot of the sites that I manage, the last week of December, traffic disappears. In most cases, they're B2B service companies because everyone's on vacation. That context matters. Otherwise, like you give that to the ITIL, it's going to go deep on what happened and may come up with insights that aren't really relevant because there's no traffic that week. So it's like looking at it from a holistic view of how do you approach this problem?

17:23How do you want, how do you need to go about it to get the outcome you're looking for? That's really the goal of the approach process and taking time to really think it all through is a very, very valuable first step. Yeah, I love what you're saying. I want to add a few small nuggets. One, I think is the most critical thing is at the end of the day, you don't need data for the data. You need data that will support business decisions, right? So having more data doesn't necessarily help. Having the right data and the right outcome and the right analysis is what helps. So you need to figure out what business decisions you want to make and what data will help you or what analysis will help you make those decisions.

18:06The other thing, and you touched about it a lot, knowing what happened doesn't help. You need to know why it happened. Because knowing what happened is great. Like, okay, traffic is up, traffic is down. Sales are up. Sales are down. Customers are happy. Customers are not. Okay. But in order to actually change something in your business, you need to know why. And to need to know why, there's usually two ways to do this. One is to add more layers of quantitative data, which is what Keith is talking about, in order to get additional layers of views. and then you're speculating less and basing your results on real data more, the other option, which is maybe less relevant in this example, but is extremely relevant in many examples, is getting qualitative data.

18:58Qualitative data, like actual customer reviews, like actual summaries of meetings with clients, which is stuff that before AI we couldn't analyze. It was literally impossible because you couldn't go through 3 ,700 reviews that make sense in them. Now you can. And so qualitative data is an extremely valuable tool to know why that you couldn't analyze previously and you can analyze right now. And then the last thing that Keith said as an afterthought, but is mind-blowing when it comes to capabilities that we didn't have before, is the ability to cross-pollinate siloed data from three or four different sources with one AI tool to make sense in them.

19:44So think about most of the tools you have, ERP, CRM, marketing platform, HR platform, have their own little analytics environment. They have reports, they have dashboards, but it tells you what happens in that siloed universe. If you want to know what happened to a specific channel based on the sales in that channel, combined with the customer reviews from that channel, combined with the inventory that goes to that channel, there's no way to do this. But AI, if you give it access to all the data sources, is very good at doing this and crossing the data and knowing that, which again is a huge benefit of doing some of the stuff that Keith is going to show us.

20:23For sure. Yeah. And that's kind of painting the full picture. And I think you outlined it perfectly. That was exactly what the goal with that was, is like, it's all about qualitative, quantitative, marrying the technology together, painting a full picture. The more context you give these AI tools, the stronger the outcome. And so combined with all of that, you're giving it some very specific system prompts. And this is where these reports become extremely valuable and require absolutely, like in my case, zero time, except for to read it and to implement whatever the recommendations are. So step two of this whole process where you're approaching the AI and automation projects is we've got the approach.

21:06now we need to translate that into a process. And the process is, if we're going to define it, is the step-by-step, the very minute kind of details, one after another after another, as if you were to teach this process, you're going to hand this entire process over to some brand new intern who's never had a job before. So think about it in those types of specificity terms. So I like to give you just kind of a very high level quick example of this, where if I was to do this for like the analytics reports, I would first have to log into Google Analytics, pull up a report and pull the session number for the current timeframe.

21:51Then I would have to, we have to calculate the diff on that. So I'm going to go in and pull that session data for the previous time frame. And then I'm going to calculate that diff. And then I've got the change, but now I need to understand why at a next level, kind of the first why. So now I'm going to go back into Google search or search Google Analytics. And then I'm going to pull source data. And then I'm going to do the same thing for the compare. I'm going to calculate my diff. Then the other next why you're going landing page data, step by step by step. And then at the end of that, we've got all this Google Analytics data.

22:26Well, let's use, we can use, now we have to go and kind of summarize exactly what's going on there. And so we're going to write down that summary. And then, okay, we've got Google Analytics 4 data. Now let's take Google Search Console. We're going to do the same exact process, step by step by step. And then once I've got that summary written down, okay, let's pull in some additional context. Who are we targeting? Let's pull in some persona details. Let's, if we have a log of campaign activities and content that we publish. Let's pull that context in. Any sort of additional website behavior context we need to layer in, like the drop-off at the end of December in traffic.

23:07Typically, traffic doesn't pick up, sales don't pick up till March because that's when the buying season is. That kind of context. Layer that all into a document. And so now I've got that paired with my data. Now I can start to really analyze and see what's going on. I've got all the information in front of me. Now I need to analyze it. And then once I've got the analysis, looking at the data, okay, what are the next three things that I need to do? And usually this is like the senior level person is going through and kind of like, okay, based on this, here's what the next three or four things need to be.

23:41You can build the AI tools when we get to that stage to be that level smart, that senior level smart. Giving that all the context and then having it write out, okay, here are the next three things that we're going to do based on what the data is telling us. So once we got that whole, and ideally that's a list. Like it's a bolded list. I'll start here and then this, this, this. So you can go all the way through. The third step of this whole process. So before you go there, one thing. This is basically a really detailed SOP, right? It's exactly what you said. This is an SOP for an intern that has never done this work.

24:21Down to what to click, where to go, what format to open, what to get, where to put it, how to copy, what to paste, what calculation to make. The easiest way to do that, by the way, is to either yourself or the people who do this, let them do the process, do a screen recording with whatever tool. You can do it in Zoom. You can do it in Loom. You can do it in any other tool that records the screen. Have them narrate what they're doing. Just explain in English. I'm doing this. I'm capturing that. I'm copying it here. I'm pasting it there. Do it three times just because you're not going to get it perfect the first time ever.

24:56and then take the three recordings, drop it into your favorite AI tool to transcribe and they will do, oh, here's the process. And then you ask it, do you think there's any open-ended things that you're not sure about how to do this process? Oh, what about this, this and that? It's going to ask you five or six questions, either you or whoever does it, let them answer the questions and you have your SOP. Now you're ready to go to the next step that Keith is going to describe. Yeah, no, that's perfect. And I do that, like the video recording of the process, I must have dozens of those recordings because it's the easiest way to like make sure that everything's covered and then you're right the AI tool just feed it into that and it becomes it's like I you don't no additional work you're just layering in some color where it needs to be so we've got our very detailed step-by-step SOP process doc the next part of this is to identify where in those steps AI can take over and do some of that work.

25:54And I do think it is worth kind of weighing out the pros and cons of what is AI good at versus what does it still potentially struggle with. And so, like, when we're talking what AI is good at, it's good at analysis as long as the data is kind of structured and well formatted. It's good at writing out report, brainstorming strategy stuff, of consolidating documents into summaries and isolated or a smaller, more concise documentation. And then there are some things where it's not necessarily, and again, this may be outdated by tomorrow, but there are some parts of this that it's just not quite perfect at yet.

26:37Like, for example, advanced processing of data within like an unstructured format. So, for example, I can pull out a Google Sheet with all these rows and all this session data and pull the same thing for the previous time period. And I can feed both into analytics and have it compare and contrast and find the, do the calculations of page differences and then sort it and spit it out on the other end. I don't specifically know exactly how it's doing it, but there is a very specific exact way that it should be done. And so from a predictability perspective, this is something that's better to be handled like with different types of code.

27:22So you can feed both in and process them that way and get the data out through the code. And then you know it's reliably going to be exactly how it should be set up. So that would be one example of I don't think I'd hand the whole data processing thing, if it's an unstructured thing yet, to AI. but I would take whatever that output is and I would be comfortable handing that off to AI to analyze. 100%. I think it's that trade-off between rigidness and flexibility. And there's cases you want flexibility in a thought process and analysis. And there's cases where then you got to get 90 % accuracy, which is not acceptable.

28:02And so you want to do the rigid stuff as much as possible because it's consistent. And then you want to use the brain where the brain provides value and not risk. Yeah, that's perfect. So we've got our SOP that's based on our approach. And then we've identified what steps in that whole process is AI ready. So now the next step of this whole thing would be to build the actual integration. And so there are a variety of tools you can choose from. uh make and n8n are probably the top two uh you can probably zapier to an extent i i like make a lot largely because of how it integrates with different google properties uh it's a little bit simpler there's a there's a learning more bigger learning curve with n8n than with make so if you're just starting out even just trying out and make and then maybe graduating up to n8n n8n does offer a lot more functionality in terms of what it can do and on the technical end of it so pick your poison uh i would say i like i have most of my stuff built in make though largely so clients can easily understand exactly what's going on too um but and so what i can do here is do you want me to do a quick screen share and kind of show yeah absolutely let's dive into and see how uh what happens behind the curtain of the wizard of oz uh okay so all right let me know if you can see that yeah i can by the way for those of you who are driving or doing the dishes or something and you cannot watch the screen right now uh we'll explain everything that's on the screen but those of you who can if you're on spotify or on youtube then you obviously can watch okay all right so here is so this these What is this?