00:00:05.73 [Announcer]: Welcome to the Analytics Power Hour. Analytics topics covered conversationally and sometimes with explicit language.

00:00:14.59 [Tim Wilson]: Hi, everyone. Welcome to the Analytics Power Hour. This is episode number 283. And it’s the show where finally, finally, we’re going to answer the question. Does size matter? I mean, data set size, that is. I felt like the explicit rating we have for this show made it safe for me to make that joke, but Prudence suggests that maybe I shouldn’t take it any farther than that. So for the past 15 years or so, the business and analytics worlds have been obsessed with big data, collecting it, storing it, and deploying increasingly sophisticated models and techniques to glean value from it. Some people have even gone back to school to learn more about it. And we’ll get to that in a little bit. Yet arguably, many businesses are awash in small data, small and mid-sized businesses and many nonprofits, for instance. As podcast listener Barrett Smith put it way back in 2024, when he proposed this very topic, quote, what are the right analytic tools for small organizations to use on the data they have to make decisions? How do we, as analysts, help these organizations be as data-focused as the big orgs?” So what can we do with data measured more in kilobytes rather than terabytes, or petabytes, or exabytes, or even yodabytes, something I learned as I was prepping for this very show? It’s fun to say, though, a yodabyte. Simply deriding it for its laughable, teeny weeniness seems like a missed opportunity. So let’s talk about it. So I’m joined for this little discussion about small data by my co-hosts Moee Kiss and Julie Hoyer. Welcome to both of you.

00:01:59.77 [Moe Kiss]: Hey, hey, howdy.

00:02:01.53 [Tim Wilson]: That’s coming in, coming in strong with the enthusiasm, trying out the throwing it to two people at the same time and seeing who will try to out polite the other one.

00:02:10.44 [Moe Kiss]: Yeah, see if we fight for the mic.

00:02:12.50 [Tim Wilson]: Yeah, neither one of you is used to talking over people like I am right now, as I am talking over you right now. Oh, good lord, we have a problem. But of course, we do love to bring on a guest who’s put some thought into whatever topic we’re covering. And that actually proved to be a little challenging given the nature of this topic. So when Barrett proposed this topic, we’re like, that’s a great topic. Who can we have on to talk about it? and outside of just us trying to riff on it. And so it just sat there for over a year with us pondering it until we saw that Joe Domoreski had written a medium post titled, How to be Data Driven in Marketing, even if your small business doesn’t have a lot of data. So we pretty much picked up the phone and reached right out to him. Joe is the owner of Country Fried Creative, which is a full-service creative digital marketing agency serving the Metro Atlanta area. He regularly publishes pretty great content on Medium in the marketing data science with Joe Domoleschi publication. I’ve actually used this content as a last call at least once on the show. Welcome to the show, Joe. Well, thanks, Tim. Howdy, y’all. Hello from Atlanta. You are a Southern native as polite by your first words spoken on this podcast. I take it.

00:03:31.51 [Joe Domaleski]: Absolutely. I like to tell people I’m a Southern pollock.

00:03:36.00 [Tim Wilson]: Okay. It feels like the Southern has kind of taken over the whatever the Polish lineage there is. I think so. A little bit. We’re excited to have you. We’re excited to have this discussion. Maybe we can sort of kick things off by Joe having you tell us kind of what prompted you to write like an article digging pretty deeply into the challenges for businesses that don’t have like so-called big data to work with.

00:04:05.43 [Joe Domaleski]: Absolutely. And before I jump into that, just want to say it’s really great to be on this podcast. first indirectly met Tim, reading his book, and Tim needs to tell the listening audience what my claim to fame is with your book. I was the first to find a mistake. That’s how much I read it. And we won’t say what the mistake is, but I’m also friends with his co-author, Joe Sutherland.

00:04:48.62 [Tim Wilson]: I would suggest that anybody who would love to see if they could find the mistake, if you go to analyticstrw.com or amazon.com or target.com or walmart.com and search for analytics the right way, you can get your own copy of the book and see if you too can find the error that Joe made.

00:05:05.16 [Joe Domaleski]: It’s a great book, though. All kidding aside, enjoy this podcast. I’m normally listening to this podcast, Walking My Dog at 5.30 in the morning, so getting educated and enjoying the All the great guests you had so it’s an honor to be here. What led me to writing about small data? I’ve been a small business owner for 22 years. As we’ll talk about in the show, everything skews toward big data and big algorithms. What about the little guy, the little person who just doesn’t have a lot of data? Quite honestly, in my blog, a lot of the things that I like to write about are topics that are either not covered a lot or they’re not covered in a way that is kind of relevant to a small business. As a small business myself, I also wrestle with the problem of small data.

00:06:10.38 [Tim Wilson]: I have strong thoughts on this. Do you have clients who are coming to you saying, can you run machine learning algorithms? They’re thinking that they need you and your team to dive into their data and you’re like, guys, your email list is like 150 people. That’s not… not going to be workable? Are you running into that in a day-to-day basis, or is it more controlled by the agency saying you’re using what you can to help serve the clients with whatever they already have?

00:06:46.95 [Joe Domaleski]: We are so far down on the spectrum. Let me reframe that question, Tim and Julie and Moe. In some cases, I am literally doing battle on some very fundamental issues of I don’t believe in marketing or marketing is the same thing as sales or the only thing that matters is revenue or what’s a dashboard which I know is a favorite topic of all of yours so in many cases Even getting somebody to understand what data is, is kind of fundamental. And I’m kind of late to the game. When I started the business, we started as a web design company. And of course, with 22 years of history, we’ve seen things like social media, search engine optimization, email marketing, and all the things we think about with digital marketing come on their own. And it used to be that we could sell on sex appeal, honestly. Oh, I need a new website. Make it pretty. They didn’t care about analytics. I’ve seen in the last five years in the small business space, and there’s a lot of different definitions of small businesses. Let’s just say 10 million in lower in sales, just so people can kind of get a picture. they’re not thinking about these things. In some cases, I’m actually having to educate them on the fundamentals. Moest of them don’t even know what machine learning is, Tim.

00:08:33.86 [Moe Kiss]: Then do you see that the problem that they’re facing is fundamentally, there might be the first step, which is like, they’re not using data at all, or there’s a fundamental misunderstanding or trust. Do you also see the next step, which is, are they making the best decisions they can with what they’ve got? Is that the evolution that you have to go through?

00:08:56.72 [Joe Domaleski]: Moe, when you’re talking to very small businesses, Many of them don’t have a marketing department, or they have a one-person marketing department, or they’re outsourcing it, which, of course, I’m happy with that. We’d love to be their marketing department. I think the first step is really, and this is to analytics pros like all of you who are way on the other side of the analytics knowledge spectrum, you know, cliches like you can’t manage what you can’t measure. You know, we’ve heard that zillions of time, but it’s fundamental to, you know, even the smallest of organizations. And so, you know, I think for many small businesses, it’s that education part that, yes, marketing can be measured, which sounds fundamental and self-evident, and yet so many small businesses don’t even think about that.

00:09:56.83 [Tim Wilson]: But this is maybe going to take a little bit of a turn because now I’m fascinated. Take email marketing because small businesses are running on, they have limited, they don’t have a marketing department. They don’t have often dollars sloshing around that they can say, directionally, it has to be good enough. My experience with consultants who do analytics support or digital support for small businesses and a little bit of my own experience is they’re being really, really, they’re like, we only have, we’re spending $4,000 a month. And should we put that in Google, in Facebook, or in an email marketing? Like, are they not coming saying, we want to spend as little as possible with you and when you do something, if you do an email marketing campaign for me, tell me whether it worked or not? Like, are they asking that question or are they just saying, you’re a line item?

00:10:58.85 [Joe Domaleski]: Yeah, some are. And then others, we’ve got this concept of a minimally viable product, right? We’ve all heard of that. A couple of weeks, a couple of months ago, I don’t know, they all ran together. I wrote an article, I applied that and I called it minimally viable marketing. And so, you know, what I tell people is there is a minimum level It’s kind of your basal metabolic rate, if you will, of just, I need to have some type of presence. That’ll be a website and social media and some other things like that. Layered on top of that, of course, are all the different things one might do with marketing. What I’ve seen for a lot of Executives, which may just be one person, the business owner or a small management team, is there really isn’t awareness of the numbers they already have. You guys already know this, but a lot of small business. You mean you can tell how many people went to my website? I mean, you would be shocked, maybe not, that many people don’t even fund it.

00:12:10.84 [Tim Wilson]: I give you a note, Rick. It’s not Rick.

00:12:12.64 [Joe Domaleski]: They got rid of my link, Chowner. Why did they get rid of link counters? And I don’t know how many people go to my site. You know, that sort of thing. And so a lot of times it’s just uncovering what they already have. You know, when you send that email, you’re actually collecting data without even knowing. Oh, really? So it’s just getting our arms around that.

00:12:31.89 [Moe Kiss]: So talk to us. Once these companies kind of start down the path of the minimum… Oh, geez. minimal viable marketing. There you go. They’re starting to have this small data set. What are some of the techniques that you work with them on so that they can use the data they’ve got to inform their decisions?

00:12:55.69 [Joe Domaleski]: Yeah. I think, Moe, once I can get a client past that initial barrier that marketing is different than sales, Yes, I need to track certain things. Then we start to look at things related to the quality of the data. the volume of the data, and when I’m talking to many of… And I’m still very much involved. I have 12 employees, which makes my company bigger than two people, but I’m not a 50 or 100-person agency. We’ve got a nice little niche. I still do a lot of the selling involved in our services. Rarely do I see data or analytics be the lead. Normally, we’re going to start a campaign and try to do some more awareness of that. Normally, it is in response to a specific pain point where that is involved, but the client’s not aware of it. But once we engage a client, I think the first step for us is to educate the client on the data they already have, and they don’t even know it. Then we can start to look at the volume, the quality, and some of the things that I talk about in the article. I normally like to pick one thing to focus on. to try to prove the point. It could be the email open rate, if it’s a nonprofit, you know, a conversion rate, if it is a social media manager, you know, maybe it’s the reach, you know, very fundamental things like that. And I think once there’s recognition on the part of a client, Hey, there is this thing called data and we actually have some. Then we can start to explore different issues related to that data.

00:15:01.99 [Julie Hoyer]: Can we actually go back because you started to touch on it and I know you talk about it in your article more in depth. I wanted to chat about this idea of not enough data that you had covered and you rattled off just now. different categories of not having enough data. Because it’s interesting, I hadn’t even thought of when we were talking obviously like small data sets, I didn’t even think of just this broader idea of not enough data. So when you are working with clients and they realize, oh, I have some data, even if they had a large volume of data entry, it sounds like though you run into a lot where they still don’t have quote unquote enough data. to what we’re used to, like, at larger organizations.

00:15:52.74 [Joe Domaleski]: Exactly. You know, case in point, a large email list in terms of subscribers, some of the people that we’re working with, might be 10,000 people. Moest of them, most of our client base, 2,000. like our local Chamber of Commerce. I’m a former board member of the Chamber, and they’re very engaged, and so what we see is perhaps a higher open rate, but we have a lower overall, I think I called it an article, an N, but just the number, the shared number, and when you’re dealing with very small, But we’re working with a nonprofit that literally started up a year ago. They have 100 people on their email list.

00:16:46.55 [Tim Wilson]: Now, what do you do with that, right? But is that the sort of thing that if you say you have 100 and you can talk through the math of saying, imagine if you had 200 or 500 or 1,000. So does it go? Do you wind up sometimes having a discussion of like, If marketing matters, then it’s who you can reach matters. We know how many you can reach with an email. Maybe we should consider trying to grow your email. list. And if we’re going to grow the email list, then we’re going to need to measure whether we’re growing it and what the cost is, or is that not really how the… Tim, you actually nailed it.

00:17:29.36 [Joe Domaleski]: And I had this very conversation last week with that nonprofit that just started up. They were focused on getting donations and other marketing goals, which are important. And I told the executive director, I said, probably the most important thing you need to do right now is grow that email list because you know the donors are going to come and go Who cares if you have five likes on your Instagram posts, but you need to build that marketing database. So even with small data, and even before we try to figure out how to work with it, that is a finding in of itself. We need more people in that database. We can do better marketing. And so there is value there, even with that low volume data set.

00:18:17.71 [Julie Hoyer]: And then another angle, though, is to say, like, when you’re the other example that you brought up, you know, if their list is more like 2,000 people or 10,000 people, like, that’s not necessarily tiny sample size, depending on what you’re trying to look at, but that’s to get better.

00:18:31.02 [Joe Domaleski]: No, now we’re starting to have some decent numbers, yeah.

00:18:33.07 [Julie Hoyer]: So then, though, you know, do you ever run into they simply have, this is like their email address, I sent them an email, and then they have nothing else about them, because I know What can you do with just a few data points, even if it’s on 10,000 people?

00:18:49.32 [Tim Wilson]: Which was one of the things, when you said small data, I always think of the number of rows and Julie’s getting to another point that I think- The number of attributes.

00:18:57.51 [Moe Kiss]: Yeah.

00:18:58.05 [Tim Wilson]: Yeah, like you talked about in the article, I was like, oh, it could be small data because it’s-

00:19:03.17 [Joe Domaleski]: Yeah. I mean, if we’re looking at a classic data frame to use a Python term there, when we say small, yeah, it can either be in terms of features. That brings up a good point too. I was talking to another client of ours, and we were trying to determine the optimal layout for a contact form. Now, if you’re needing lots of features, then you want to ask everything, right? Fill this thing out. If any of you have ever adopted a dog, the Humane Society is one of our clients here in our neck of the woods. I’m a dog lover, love dogs. Have have low key he’s a therapy dog all over our social media and they look at the form to adopt the dog and because we had to put this on the website i mean it. I think it was almost as much as a FAFSA form. So, you know, my kids are grown and gone. Half your audience is like, oh yeah, that FAFSA, that’s awful. The rest are like, I don’t know what a FAFSA is. Oh, you will.

00:20:23.61 [Tim Wilson]: That’s financial. He’s pulling the American financial aid form for college.

00:20:29.61 [Joe Domaleski]: I had to stick one on your mode, the financial aid form. I don’t know what the essay stands for. Basically, when your kids go to college, you have to fill out a form to document all your revenue as a parent. And it is, it is, and it’s a small business owner. it’s like an audit. I mean, it’s worse than tax form. Okay, so you fill this thing out, and okay, now we’ve got 50 things we know about our target market, right? That’s too many. But hey, if I just have a name and an email, is that really enough? And so, yeah, sparse, we might not have enough rows, but we may have too much or too few columns. And in most cases, it’s too few.

00:21:19.21 [Moe Kiss]: But then do you also find that the businesses are… Do they overemphasize decisions with not enough data. I’m just thinking of that form example. We either have two fields or we have 200, but we think it’s working because people are filling it out. Tell me about how they critically evaluate that. when it is such a small sample like that’s the thing that I mean I feel like I have the inverse problem where people assume that everything is significant because we have so much data.

00:21:55.86 [Joe Domaleski]: Here’s what’s interesting in working with small businesses and you know for those listeners that are consultants with a handful of clients you know this this episode is going to resonate with you because you encounter these problems a lot it’s not talked about a lot it’s not taught about in schools and so in many cases Moee we’re dealing with and I don’t want to make small business owners or small enterprises sound like you know they fell off the back of the truck and they don’t know what they’re doing uh although you know I’ve been doing this for 22 years I still don’t know what I’m doing I’ve been making it up as I go along somehow somehow figuring it out but you know in many cases they don’t know what they don’t know So they’re literally, you know, go back to the, you know, okay, you’re tracking this data, you actually have, you know, we need to look at this. Many of them don’t know that, you know, three, fours of the people are abandoning the form. So they’re just looking at the forms that come in. Oh, this is great. We had 20 people fill out the form last month. They have no idea that maybe there was 200 that gave up on it. And so part of engaging a client where they’re at with the small data is to create awareness that, you know, here’s the big picture of what’s going on. And I would submit that even the absence of data is kind of a finding in and of itself, right?

00:23:26.05 [Moe Kiss]: What sounds tricky there is like they’re making some like pretty simple mistakes. That’s like a really hard conversation to have around whether you’re overreacting to small changes, whether you’re not actually looking, say, at the data that you’re not collecting because folks are dropping off or they’re using the wrong metrics. How do you start to navigate that conversation without kind of sounding like a jerk.

00:23:53.34 [Joe Domaleski]: You know, it’s not easy. And I, you know, would say based on some of the interactions you guys have had with some of your other guests and just what you all do for a living, right? In analytics, whether you’re talking, whether I’m talking to a peer, a fellow small business owner who I can appeal to owner to owner and say, you know, look, I understand where you’re at. Let me help you. Here’s what we do. We take our own medicine, or maybe you’re presenting It’s kind of like emperor’s new clothes, right? You’re talking to the big boss and they don’t have any clothes on. And you’re just trying to get them to have a fundamental grasp of something, you know, very basic. And so you do have to approach it delicately. One thing that I’ve tried to do, and I guess this is the best place to insert this than anywhere else is, you know, I decided almost two years ago at the, you know, tender age of 57, to go back to graduate school. And I am currently a master of science and analytics student at Georgia Tech. Part of the reason I am doing that, number one, is so that I get educated, not on the small data, but, you know, on all the cool machine learning things. But the other aspect of it is to have that brand where when I’m speaking about analytics, I actually have some academic credibility. It’s not just Hey, I’ve got gray hair. I’ve been doing this for a while, but I can actually, you know, legitimately look somebody in the eyes and say, look, I’m literally studying, you know, state of the art stuff here. You don’t have to do all these things, but at a minimum, you need to be doing X, Y, and Z. And so that really kind of served as the basis for writing that article, because even in grad school, everything is large data set. It’s like, hey, what about me? What about the little guy?

00:26:04.03 [Tim Wilson]: I’ve got a little bit of an axe to grind. You’re articulating this as a more extreme kind of lockout than I was thinking that starting, and maybe some of this is your experience, and I’m trying to, as I’m thinking through the anecdotal interactions I’ve had, I feel like it’s more small businesses or small analytics consultancies that have gotten kind of enamored by Either this is what my platform, my CRM, or my digital analytics platform, or whatever I have, my media platform, it’s just telling me stuff. I don’t really understand it, but it’s giving me numbers that are telling me I should pump more money into it. kind of in a mode of where we have to have more data. We can’t do anything because we don’t have enough data, because there’s so much talk out there of, oh, you got to feed the model, you got to feed the beast, you’ve got to build the big data warehouse. Whereas, and you make this point in your article, and I mean, I would, Matt Gershoff has sort of made this point, like if you have no data, then you’re making a decision with no data. If you have small data and you do the simplest dumbest little line chart and draw a conclusion, you may make a big mistake. You may say, I’m not thinking about confounding or seasonality or something, but it seems like overall you’re gonna be in better shape using small data that’s noisy and messy. And sure, the more you know, the better, the more you know and think about something like seasonality, that’s better. But shouldn’t there be an encouragement to say, start with what you have? Have somebody who’s holding your hand a little bit that’s keeping you from over interpreting something. But it sounds like your experience has been more, they’re not even asking that question, which I’m struggling with saying, but they’re paying somebody $500 or $5,000 a month to do stuff. And how are they feeling like there’s any accountability that that’s a worthwhile investment if they’re not looking at data?

00:28:53.71 [Joe Domaleski]: You know, don’t, right? They don’t see the value in it. And I think by, you know, I often tell people, you know, bad breath is better than no breath, right? Never heard that before. Never heard that before. All kinds of Southernisms. But let me add a corollary to that. But it can be so bad that it actually knocks everybody out, right? So there’s this fine line, right? And so when it comes to data, yeah, start where you’re at. And normally, I never encounter a business leader who doesn’t have some sense of financial numbers. And I think that this is a place to kind of differentiate. And so we literally had an accounting client once. And the phone call started, you know, the marketing manager brought us in and said, I need some help. And, you know, we need marketing. We’re a services company. And my great, we’re a services company too. And, you know, we’ll set up a call with the CEO. And the CEO starts off the call. Joe, I think you’re wasting your time. I don’t believe in marketing. Literally, we started the phone call and I said, okay, we have nothing to talk about. And then the marketing manager’s like, wait, wait, wait. And, you know, I think they were posturing a little bit.

00:30:28.42 [Tim Wilson]: Isn’t that for an inspiring employee? Yeah.

00:30:31.55 [Joe Domaleski]: And, you know, we started to have the conversation. And once the foot was in the door, the truth kind of came out. Well, we got burned and, you know, blah, blah, blah, blah, blah, blah. And I started appealing to this person who was a CPA on a financial basis. I said, you know, I actually have an MBA in finance. I know financial statements. You want to talk about ratios or whatever? we can do similar sorts of things with your marketing it was like a light bulb went off she was like really. I didn’t know what I was paying all this money for. I wasn’t getting any results. I said, well, you’re actually tracking the numbers. And so I think from a data literacy standpoint that, you know, money talks, right? And so even if you can get it on those terms, you know, and then come over to marketing and it could be any other data, right? I mean, that’s my background is marketing, but it could be operations. What I find though is that marketing of all the areas of a business, I think many times that’s the last to get the analytics treatment. Other areas of a business typically inventory and you know operations and logistics and finance and those sorts of things and even sales But but marketing sometimes in a small business is late to the analytics party.

00:31:54.82 [Moe Kiss]: That’s so interesting and And I can see how I mean I can see the fact that I mean particularly like operations and finance to some degree can’t function without data, but yeah, I don’t know. I’ve experienced maybe the inverse where those areas are like, I would say less mature, definitely established first, but less mature. That’s a really interesting perspective, and I can see how for small companies that would be the case.

00:32:24.46 [Joe Domaleski]: With a small business, a lot of times, Moe, their sphere of influence might be a town. or a county or a suburban area or metropolitan area and Frankly, they can get by with projecting. We won’t make this into a marketing class, but we’ll toss out there. When I went to marketing school, we had five P’s of marketing. I think they’re currently teaching the kids four P’s of marketing. I read an article somewhere, there’s seven P’s of marketing. I don’t care how many P’s there are, but let’s just go with one of them.

00:33:05.62 [Tim Wilson]: I only know four. Yeah.

00:33:07.39 [Joe Domaleski]: Okay. Yeah. Well, yeah. And, you know, promotion, let’s just say it’s promotion. A lot of times their ad spend is kind of low. They don’t have to spend a lot to maintain an online presence in a, in a, in a small town. And so, you know, so they have back to that minimally viable marketing. They’ve got a website and they’ve got social media. They’ve got email. They may or may not do. Google pay per click, and maybe they do. And so there’s that recognition of that. And so that creates another small data problem, right? Okay, we’re thinking about advertising, but we’ve never done it before. Now we have zero data. Now what do we do?

00:33:50.31 [Tim Wilson]: But they’re not hearing. Again, maybe where I’m floating in Columbus, I know analysts who support coffee shops that have three locations. I’ve got a cousin in Colorado who supports a lot of service plumbers and electricians. For years now, it’s been kind of a local SEO and even a local search engine marketing, because they’re saying, why would somebody know Tim’s plumbing? I need to, when they’re searching for plumbing, they hit this, but I pop up, that I show up in their location. And my sense has been, in the case of my cousin, she’s not an analytics person, she’s kind of a web web design, website, web dev shop, but she’s also kind of the content marketer and she’s often saying, how do I use the data better? My plumber client wants leads and he’s paying me for his digital presence and what am I supposed to do? A lot of this feels like there’s kind of a between place where the agencies that are providing the service, they’re not big enough to be staffing full-time analysts. They may not be snowflakes like you who say, well, I’m going to just go get another master’s degree to learn about it. I feel like I’m seeing much more where there is a hunger and an interest, but a lot of kind of fear and uncertainty about where does this plug in and then relies on Google’s, whatever Google’s current term for their local business thing is Google Business Center. Is that what it is? I don’t know. I feel like that’s been rebranded a few times. Yeah, that they just say, well, I’m just going to log into that and hope that insights emerge and that feels wrong. I don’t know.

00:36:03.32 [Joe Domaleski]: Yeah. Well, you know, what’s interesting and, you know, of course, we’ve, you know, gotten what almost halfway through the podcast and not said those dreaded letters AI. But, you know, we’ll go ahead and Take the opportunity to stick that in here. There is some thought. Google had some pretty big announcements at their marketing conference a couple months ago about making advertising, in particular, a little bit more self-service. So, you know, with AI augmentation, you don’t have to know anything about analytics. You go in there and tell it, you know, hey, I’m a plumber and this is my target audience and let it, let it do its thing.

00:36:48.36 [Tim Wilson]: Yeah, I’ll believe it when I see it because, you know, keywords for, you know, 10 years, right?

00:36:55.19 [Joe Domaleski]: And it’s just not, it’s not there yet. And, you know, any of you that, you know, that are listening that have ever had to set up an online ad campaign or whatever the user interface is, you know, rather, rather dense and thick, but, but, you know, to your point, Tim, you’re, you’re absolutely right. And that’s one of the things we’re trying to do with our firm is find that sweet spot at a small company. They’ve got that one person freelancer that’s kind of advising them on stuff and they get much beyond that and you know now what do i do sort of thing. Versus a big company which may have i gotta tell this story you know i’m working with a client right now who’s on the larger end of the spectrum they’re actually a national company. And they are just headquartered down here. Now, don’t fall out of your chairs when I’m about to tell you. And those of you that are driving while listening, don’t drive off the road. Runners don’t run off the path and into the bushes while you’re listening to the podcast. This is a national company. Somebody set up a dashboard and the marketing manager can’t even get access to it. But the executives don’t want to change the platform. And so you talk about they have data. It’s actually being displayed in a dashboard. They don’t want to change the contract. So we may end up going in there and setting up something. to help bring the data to light as kind of a side gig. So it’s like, don’t mess with what’s here, but we’re flying blind. And we need something to steer. And I’ve never seen anything like it.

00:38:37.70 [Tim Wilson]: So maybe to shift gears a little, I feel like we’re kind of laboring in the getting from zero to one data point. And maybe it would be useful to say, OK, let’s go to where there is a small data set. And we’ve already talked about two definitions of small, one number of rows, one number of columns. And I guess you can have a lot of columns that are sparsely populated. So you go through in your article a few different aspects of that. But what are if an organization, there is a person and entity that is saying, I have data. It is not a lot of data. Where should I be starting and what should I be cognizant of? Maybe let’s not worry about the pitfall so much first, but what can be done effectively with small data?

00:39:43.51 [Joe Domaleski]: Yeah, that’s a great question. use what you have sort of mentality. Some is better than none, more is better than some, but if all you got is some, let’s use that. And I think the first step in that process We have two interns working with us right now. What’s cool is they are also fellow students at other universities. So they’re my interns, but I can also say, hey, I’m a fellow student. And we’ll put them on projects, okay? And what do you think? Again, they are in master’s degree programs in analytics. When we start showing them small business data sets, what do you think their first reaction is?

00:40:32.59 [Tim Wilson]: We can’t get statistical significance with this. We don’t have… Yeah. Oh, really?

00:40:38.48 [Joe Domaleski]: Okay. Yeah. The initial reaction is normally, we can’t work with that. And it’s kind of a teaching point to them. Oh, yes, you can. And you’re going to have to because it’s all we got. So let’s figure out how to make use of it. In the article, I outlined some different techniques and one of the things that we talk about, and again, to somebody who’s familiar with analytics, some of these things are or, you know, are going to sound familiar. You know, I think the first thing, you know, let’s call it gut instinct. Let’s call it, you know, naive forecasting, you know, basic benchmarks. I think this is where, you know, experience can help provide some clarity on what you’re looking at and what you can do with it. Case in point, okay, we had 50 email opens on the last campaign. Well, Joe, that’s just not statistically significant. We can’t do anything with that. Or even the corporate client, hey, here’s what we know and it’s all that we know. And I said, well, that’s kind of a starting point. We know something. It’s better than nothing. Now, that may be noise, but we can apply some common sense to it and leverage what we know to try to make the most of I guess it’s kind of like being in the kitchen, right? Being short of ingredients, you know, when in doubt put more flour in there or put an egg in there, you know, that always makes, well, I don’t, I don’t know how that works, but my wife always figures out a way, you know, okay, you don’t have to go to the store. We’ll, we’ll figure out, I think she learned it from her grandmother. We’ll just use the ingredients we have sort of saying. And I think that’s, uh, that’s, that’s true with, uh, you know, with data. Another thing that I like to do is just kind of aggregate data into into chunks or clusters or groups. Sometimes that really provides some clarity. Instead of looking at one big blob, let’s see what’s in common. I’m a Gen Xer, so maybe I fit into a certain category in the marketing data and it’s not on the same level as maybe a Gen Z or like my kids like millennials. So, you can do good old-fashioned classification even with a small dataset and get more insight than just looking at spreading it too thin in a big blob. That’s been very effective working with little datasets.

00:43:23.37 [Tim Wilson]: As you were talking, there’s an upside to small data is that you can actually look at the data. Like if you’re dealing with a million rows, you can do distributions and you can do some EDA and try to get a handle on the data and maybe pull and look at some of it. If you have small data, you know what? You can look at every one of those leads that came in and see which were garbage versus which ones weren’t. And I think that goes with the, you know, if the master student says, yes, but you may have selection bias because your form’s too long, Good knowledge to know, but this is back to bad breath or no breath, I guess. If you have a small data set, if you have qualitative data, or even if you want to collect qualitative data, If it’s a small amount, you don’t have to come up with some fancy natural language processing to try to assess the sentiment. You can read 100 comments. You can read 10 comments a lot faster.

00:44:31.22 [Joe Domaleski]: That’s right. In fact, ironically, I am taking analysis of unstructured data this semester along with the simulation class, which I’ve done before. I’ve actually written some on my blog about some sentiment analysis or restaurant reviews, but you’re absolutely right. When you have that small data, maybe 50 people opened your email, you can actually know who they are and reach out to each one of them and ask them more about, did you find it useful and that sort of thing. Can’t do that if you’re dealing with, you know, 100,000 email opens or not. You can’t do it easily.

00:45:08.12 [Moe Kiss]: Yeah. And you also mentioned controlled micro experiments. Can you tell us a bit more about that?

00:45:13.71 [Joe Domaleski]: Yeah, you know, that’s a fancy, you know, I tried to jazz it up when I put in the article, but I, you know, I think, I think at the end of the day, you know, we were all kids once and I remember I’ve always been somewhat of a science and math geek. You know, I used to just put things together, little experiments, right? Steal stuff from the kitchen and try not to blow up the house or hook up electrical parts and try not to, you know, short out. Although I think I once did stick a paperclip in a light socket. My dad was not happy about that, but it was a cool. That’s what siblings are for, to be blamed for the… Yeah, and I have a younger brother, Dr. Chris Domalescu, who has a PhD in this stuff. So, you know, he’s a smart guy. But I am the big brother, and don’t you forget it, Chris, if you’re listening. But, you know, I think micro experiments, again, if you’re small business, you’re agile, you’re flexible, you don’t have to have a steering committee, you don’t have to have, you know, 20 approvals. just try something out. You know, you can do it in a controlled manner and see what works. You know, whether it’s a little A-B test or just, you know, I like to use the term a bake-off, right? You know, here’s a sample, you know, you test this and you test this and see which one, you know, seems to work better. And it may not be scientific, but it, you do kind of pick preferences. This happens a lot, you know, on the design side of our agency, too. How do you measure a logo effectiveness? I mean, really, okay, there’s been papers written about that, but at the end of the day, you get a small committee. Do we like it or not? You know, what do you like about it? What do you don’t like about it? And so, I think in similar manner, you can do that with, you know, other more qualitative marketing things.

00:47:13.96 [Moe Kiss]: So one of the things that I was listening back to an old episode and it was funny because Tim’s Last Call was a podcast that I love. It was about choiceology and it was talking about natural experiments. And so I wonder if this comes up as well. I’m sorry, Katie Milkenman’s a genius, but the episode was essentially like looking at past I know like an example might be like, I don’t know, we didn’t have $4,000 that month so we just didn’t do any marketing. And so you use it as like quote unquote a natural experiment to see what would happen if you turned off marketing. It’s like, have you found that like looking back over historical incidents is like something that you can derive like meaning and direction from?

00:48:01.16 [Joe Domaleski]: Absolutely. And I think this applies to small or large data sets, right? Data tells a story. One of the interesting things, and I don’t know about all of you, your respective organizations or companies, but we saw a massive uptick of business during COVID. Why might that happen when most things were buckling down and cutting costs and we don’t know if we’re going to be here tomorrow? At first blush, you would think that that would impact everybody equally, but it didn’t. We saw almost a 30% increase in our billings to customers. Now, why might you think that it, you know, that happened during COVID?

00:48:52.59 [Julie Hoyer]: Well, my guess is if you’re working with small local businesses, a lot of people, you know, the larger organizations, if I’m remembering correctly, had the supply chain issues or, you know, things shut down, they couldn’t have the big warehouses. And I remember there was a big push of like, Go buy local, order from a local business, help them out when people can’t go into their stores. So I think there was like a little boost for a while, right, from that?

00:49:16.99 [Joe Domaleski]: There was. I think Julie, that was part of it. I think another part of it. What did we all do? Now, I let my staff work from home. We have two physical offices, but I let them work from home. And they’re all creatives, and they like that. They keep some morale up. But everybody worked from home, for the most part, during COVID. And if you were a small retailer a restaurant mom and pop shop or whatever you were invisible if you weren’t online so everything pivoted people are driving down the road to see your sign. They are now sitting at home and they’re on Facebook or Instagram or whatever. And so they had to pivot their marketing to online. And it was more than an aberration. I mean, it was sustained over about two and a half years. the uh and so as you were suggesting mo you can look back and kind of tell the story at first glance you’d say everybody kind of had a down and like no it actually didn’t you know people needed our services more than ever because restaurants, we’re converting to pickup, right? So, hey, we need you to update our website. And if our website isn’t updated, we’re invisible. And we’re going to do curbside pickup and those sorts of things. And so, yeah, data tells a story. And even if it’s a small data set, it is worth looking at that.

00:50:46.99 [Tim Wilson]: But that does get to one of the pitfalls that I really liked that you had in the article about ignoring context and external factors. And this doesn’t seem like, to me, it’s not tied to necessarily small or large. The number of anecdotes I have of somebody just ignoring an externality and looking at the data and saying, this thing jumped way up or this thing plummeted. People were aware that COVID was going on. People were aware of tariff stuff, but there’s other things that happen. Are they aware of what their competitor is doing? And are they aware that if your data looks very surprising, An analyst, the first thing would say, it’s probably not a miracle. We found the perfect marketing message, and this has caught fire. Something might be going on that I may or may not be able to influence. This email campaign did wildly better than any of our past email campaigns. I need to stop and think about my business and the operating context first and see if I can think about it. Again, maybe there you have the nice thing about small data is you can say, I can really go look at what they did. Did a bot get a hold of an email or something like that? It does seem like there’s the same challenge scales down that does go to a little bit of a data fluency that if it’s easy to sort of overinterpret the data, the statistician’s reaction will be, well, you need more data, that’s not necessarily the right answer. You just need to be thinking about what’s generating the data and what might actually also be going on.

00:52:42.89 [Joe Domaleski]: Yeah, toward the end of the article, I think I summarized some of these things that I commonly see that are mistakes with small data, and they’re not necessarily limited to small data. I think you’re right, Tim, they applied other things. Probably the number one thing that I see dealing with small data are people killing campaigns too soon. You know, it’s the classic, we’ve been running this for two months. Why haven’t we set sales records? We’re gonna kill it. And, you know, we know it takes time for things to work. Looking at the context, which we talked about, you know, another one is kind of the inverse of that, which is, you know, okay, we had a great, we may just made a great sale. It must be the marketing. As much as I would love to claim credit for that, Okay, that’s a single data point. We can’t even draw a line with that. We can just look at it and be amazed. Can we at least have a couple more to kind of draw a thing? And then, of course, people waiting for perfect data, and I think that’s the trap many analysts and data scientists fall into is this this mythical thing. So I have an intern right now that’s working with us. His name’s Sam. Great guy. I’m going to have him listen to this. And I want to encourage him. He’ll graduate at the end of the year. We’re looking at something, some of your listeners probably heard of it, maybe you guys, just a classic marketing mix model. We’re looking at the Meridian model. It’s an open source model that’s put out there. The sample data set that comes with the GitHub package, the marketing spend is like $240 million. Now, I don’t have a client that even their annual revenues don’t get anywhere close to that. And so we’re looking at this and Sam was, I had him looking in the model and I had him present this internally to our staff. And I said, we’re going to run some of our client data through it. And, you know, okay, a marketing spend on, you know, per month, even for a medium sized client might be $5,000. So, you know, $240 million, $5,000. You know, what do you do with that? Of course, Sam’s initial reaction. I’m not sure we have enough data. Oh, we’ve got enough data. We’re going to make the most of it.

00:55:21.16 [Tim Wilson]: Wow. Well, on that note, uplifting as it is, but it’s a good segue to, we’re going to start heading towards wrap, but Before we do that, we’re going to actually take a quick break with our friend Michael Kaminski from ReCast, which is an MMM and GeoLift platform helping teams forecast accurately and make better decisions. Michael’s been sharing bite-sized marketing science lessons over the past months and the coming months to help you measure smarter. So I’m going to turn it over to you, Michael.

00:55:58.42 [Michael Kaminsky (Recast)]: The synthetic control method has been called the workhorse of causal inference. Synthetic controls are used to generate causal estimates in situations where large-scale randomization isn’t possible or is too expensive. When we’re doing causal inference, we’re always trying to compare some treatment effects to a counterfactual, what would have happened without the treatment. The synthetic control method is a method for creating a counterfactual. It can be used in experimental contexts where a researcher has intentionally manipulated some treatment, or in quasi-experimental contexts where a researcher is trying to evaluate the impact of some change that wasn’t intentionally manipulated. The idea behind a synthetic control is simple. We want to identify control individuals whose pretreatment behavior most closely resembles those of our treated individuals. So the idea behind synthetic controls is to create a weighted average of potential control individuals that best match the treated individuals before the treatment. In the case of geographies, you might imagine the best counterfactual control for Houston is a mix of Austin and New Orleans and Dallas, and that that mix is a better counterfactual than any of those cities individually might be. There are lots of different methods for creating these synthetic controls, and correctly estimating the uncertainty in the causal estimates can be quite tricky. But when utilized in the right experimental context, synthetic controls can help practitioners run statistically powerful experiments even when large-scale randomization isn’t possible.

00:57:15.05 [Tim Wilson]: All right, so if you enjoyed that mini lesson, Michael and the team at Recast have put together a library of marketing science content specifically for Analytics Power Hour listeners. For everything from building media mix models in-house to communicating uncertainty to your board, head over to www.getrecast.com slash aph. That’s www.getrecast.com slash aph. All right. That was a fun discussion, and I’m not sure if I’m coming away from it more energized or depressed about the prospects. Hopefully, it’s a call to arms to not dismiss small data from some ivory tower of your ivory tower moe with your canva You probably do have Yotabytes, the data that you’re working with. Do you actually know data scale? Do you talk Zeta, Exo, Yotabytes? You ever asked how much data do we have? Yeah, Yotabytes.

00:58:23.19 [Julie Hoyer]: I thought it was Yotabytes. Where did Yotabytes come from?

00:58:26.85 [Tim Wilson]: Oh, well, that’s your assignment, find out. The last thing I like to do on the show is go around the old virtual podcast bar and share a last call, something that might be of interest to our users of any shape or form. And Joe, you’re our guest. Do you have a last call you’d like to share?

00:58:45.97 [Joe Domaleski]: Yeah, so I’ll do this in two small parts. First of all, I just, you know, for folks out there listening to this awesome podcast, If you encounter small data, don’t be discouraged. You actually, hopefully, you listen to this and you came away with some ideas. You’re not alone because sometimes I feel like I’m alone. Am I the only guy who’s twiddling bits here? But when I’m not doing graduate school stuff or running a small business, I like to kind of head in the other direction and get my mind off of computers and machine learning and numbers. And I have been reading this very addictive book series, and as usual, I’m late to the party. It is called Dungeon Crawler Carl by Matt Henneman. It is awesome. I understand it’s going to be made into a TV miniseries. The premise. How many, how many of you, you all know what Dungeons and Dragons is. Okay. Picture, and this sounds ludicrous, but it’s normally the basis of a great concept. Aliens take over the earth and turn it into a dungeon crawl game that’s a game show and People are trapped in there and it’s like a live D&D and it’s being broadcast across the galaxy and the lead car character Carl His cat can talk and her name is Princess doughnut and it is everywhere Dungeon crawler Carl. There’s seven books the guy Matt wrote it during COVID and self published it and A book publisher’s picked it up, so it’s a great business success story. You can’t put it down. It is hilarious. Is he writing more? He is still writing more.

01:00:40.91 [Tim Wilson]: Okay.

01:00:41.85 [Joe Domaleski]: Dungeon Crawler Carl, highly recommended.

01:00:44.38 [Tim Wilson]: That is, if Michael Helbling was on here, I’m deeply curious to know.

01:00:49.52 [Joe Domaleski]: I mean, I know there’s listeners that are shaking their head going, yes.

01:00:54.91 [Tim Wilson]: Of course. Yeah. Awesome. That’s on my reading list now. Julie, what’s your last call?

01:01:04.06 [Julie Hoyer]: My last call does use the dreaded two letters of AI, but it was a recent article that came out from Cassie Kozarkov, one of our faves. Stop it. Was this going to be yours, Moee?

01:01:19.55 [Moe Kiss]: No, but I did also read it yesterday and I really enjoyed it.

01:01:23.38 [Julie Hoyer]: Yeah, it was really good. And actually, my favorite part, so the title of this one is the alignment trap, why Gen AI metrics spark debate, not clarity. She has a lot of great subsections in there. And actually, why I wanted to use it as a last call was the fact that me and Tim were actually going back and forth and discussing the article. And it was interesting that she makes so many strong points in there and a lot of good points. But in general, I feel like it’s one of those articles that I want to go back and reread. And it sparked a lot of good debate between me and Tim, where I just think if you’re If you’re starting to have conversations about measuring the performance of AI, something we noticed throughout the article and why I want to reread it is like there’s kind of two themes going, measuring the efficiency, quote unquote, of like the model itself, the large language model, like the actual process behind it, whichever one you choose. But then there’s the larger idea, which I was more honed in on and interested in of the actual output, return on investment, did it help you reach your business outcome? Obviously, that’s something we talk about so much on the show. AI, GenAI being another tool to help you get there. It’s a good read. It’s got a lot of good points, and it definitely gets you thinking.

01:02:46.60 [Tim Wilson]: It just seems like it mixed those together where I’m like, no, part of this is not hard.

01:02:54.68 [Julie Hoyer]: Tim’s like, let me drag my soapbox over here.

01:02:57.08 [Tim Wilson]: No, no, no. That’s what I want to be is out publicly saying that I thought maybe Cassie was a little off on something.

01:03:06.99 [Moe Kiss]: So I was going to talk about something completely different and now I’m going to pivot.

01:03:15.92 [Julie Hoyer]: Oh.

01:03:16.24 [Moe Kiss]: I’m going to pivot completely.

01:03:16.92 [Julie Hoyer]: You don’t want to do the Tim and do both?

01:03:19.43 [Moe Kiss]: No, the other one’s like a three-fer. So I had a really interesting conversation with the CEO on the weekend just randomly. And we were talking about AI and creativity. Look at that little flex.

01:03:31.33 [Tim Wilson]: She’s at a professional sports game just hanging out with the CEO as one does. OK, carry on.

01:03:38.87 [Moe Kiss]: Anyway, we were having a very interesting conversation about AI and creativity. Because obviously, that’s something that Canva where I work gives a lot of thought to and fundamentally like humans still being at the centre of it and I was talking about I was talking about how I use AI for generating ideas and just how I feel that sometimes I’m getting diverse perspectives. And he did a game with me that completely stumped me. And I now am like, does everyone know this? Is this a thing that is known or is this a thing that is not known? And I am super, anyway. So we sat there and he goes, there was him and he had his son with him. He goes, open your AI tool of choice, right? Claude, church, APT, whatever. And we had different ones. And it goes, you had to give it a prompt to give you a number between one and 10. Has everyone heard of this? Everyone gets the same number, regardless of the tool. And then it goes, give me another number. And then you go, give me another number. And we wrote our prompts slightly differently because each person wrote it in their own language. And you got the same number. And then it said, give me another number. And you get the same number. And I was just sitting there like brain exploding because I think in my mind, the way I’ve been thinking a lot is that companies that are leaning in really heavily here are going to have this advantage. And I guess kind of where I’m landing now is like, maybe that advantage will plateau at some point if I’m thinking that I’m using some of these tools for more Hypothesis generation and things like that, like maybe there’s going to come a point where a plateau is, but it was just such an interesting exercise to go through that I hadn’t. And I was like, is this a no known that everyone’s aware of?

01:05:33.90 [Tim Wilson]: Can we all do that right now? Because I’ve got caught up and I’m asking.

01:05:40.33 [Moe Kiss]: All right. Give me a number between one and 10.

01:05:43.09 [Tim Wilson]: But we can kind of frame it as long as it’s asking clearly that question, I can put, give me an integer.

01:05:47.65 [Joe Domaleski]: So we all type that in. Give me a number between one and 10.

01:05:50.20 [Tim Wilson]: But it can be something. I’ll give me an integer that falls between one and 10 inclusive or something like that. Sure, Tim.

01:05:56.31 [Moe Kiss]: I don’t know. That would be how the team phrases it. You said this is a line of experiments.

01:06:00.65 [Tim Wilson]: I’m not trying to break this. Yeah, I’m curious. Are we ready? Are we all going to go?

01:06:04.88 [Julie Hoyer]: Wait, wait, wait. Oh, hang on. I’m struggling over here. OK.

01:06:11.32 [Tim Wilson]: Yes. Oh my God. So Moee’s and Tim’s and Joe, you got the same thing? Yeah. Same number. Okay. And then give me another number.

01:06:21.85 [Julie Hoyer]: Julie. Yeah, I got seven. Sure. How about seven? That’s how it answered.

01:06:26.52 [Tim Wilson]: Oh. Well, that’s. Oh. What? What? I got four.

01:06:36.29 [Julie Hoyer]: I got three.

01:06:37.87 [Tim Wilson]: I got three.

01:06:38.45 [Moe Kiss]: Okay. So this is interesting. I got four as well, Tim, but I’ve done this three times now and every other time I’ve gotten three is the second number.

01:06:48.58 [Julie Hoyer]: I even said, the way I prompted it after seven was cool. Can I have another?

01:06:53.42 [Moe Kiss]: I tried to be very casual. And then normally the third number is also the same. So the thing that’s very interesting about this is like, just especially with the intersectionality of creativity, like, are we actually generating more creative ideas or? No, well, Jesus Christ, no.

01:07:13.94 [Julie Hoyer]: Isn’t it all statistically, you know what I mean? It’s all like the statistics of what is most likely in the combinations of the words and the, I mean, I don’t… I mean, it’s work speaking a little bit, but… There is also a very interesting article about Workslop that we can talk about next time on the show.

01:07:31.31 [Tim Wilson]: Yeah.

01:07:32.07 [Joe Domaleski]: Oh, yeah, I slop. Yeah.

01:07:34.68 [Tim Wilson]: I have a whole rant that was recorded that I never put it up. Val said that I could or should, but it was like six minutes and I was like, but who is this for?

01:07:45.24 [Moe Kiss]: So Tim, after our live experiment on the show, over to you. What’s your last call?

01:07:53.60 [Tim Wilson]: So I’m going to go very simple with, and this was a kind of learned about this podcast called Lost Women of Science. Heard about it through 99% invisible. And basically the premise is the hosts go back with people that you haven’t necessarily heard of and they kind of bring them up and kind of talk through them. So specifically the one I listened to was June Bacon Bursey, which was the weather expert who answered the $64,000 question. And it’s just, it is a well done. I’m kind of hooked on the, listen to a couple other episodes since then. But if you’re into kind of history and people who dive deep and kind of go a little beyond the Wikipedia entry on somebody and they tend to have fascinating stories.

01:08:50.59 [Moe Kiss]: Nice. I just put that on my list. There’s also a Spanish version, which is very cool too.

01:08:55.41 [Tim Wilson]: That is true. Yes. They recorded it in both. Yeah. I made the note to do it a while back and I’d forgotten that. Well, this has been a pretty interesting discussion. And I think we all now have a bunch of reading and listening material from the last calls alone. So, all right. But Joe, thank you for coming on, taking a break from your running a company and being a student.

01:09:25.34 [Joe Domaleski]: Well, thanks for sticking up for the small people, the little people, the little data people.

01:09:32.56 [Tim Wilson]: If you have enjoyed the show, please leave us a review or a rating on whatever platform you listen to. I think Joe mentioned before we were recording that he has a podcast sticker on his laptop, and you too can do a sticker. If you go to analyticshour.io. click on the global nav, fill out a little form, we’ll happily send you a sticker or two or three. You can also reach out to us on the LinkedIn, any one of us individually, or the company page, or reach out to us on the Measures Slack, or you can just send us a good old email at contact at analyticshour.io. So regardless, if you are analyzing Yota bytes? Yota bytes, not Yota bytes. Okay, Yota. Think Star Wars, not Seinfeld. Yota bytes of data, or literally dozens of rows of data, or whether you’re just trying to explain and define what data is to one of your clients for Julie Hoyer and Moee Kiss. I just want to encourage you to keep analyzing.

01:10:48.31 [Announcer]: Thanks for listening. Let’s keep the conversation going with your comments, suggestions, and questions on Twitter at @analyticshour on the web at analyticshour.io, our LinkedIn group, and the Measure Chat Slack group. Music for the podcast by Josh Grohurst.

01:11:06.42 [Charles Barkley]: So smart guys wanted to fit in. So they made up a term called analytics. Analytics don’t work. Do the analytics say go for it, no matter who’s going for it? So if you and I were on the field, the analytics say go for it. It’s the stupidest, laziest, lamest thing I’ve ever heard for reasoning in competition.

01:11:25.61 [Tim Wilson]: Jack, it’s perfect record if you’re really feeling. I’m not jinxed to this. I’m happy. That’s a gamble.

01:11:35.66 [Joe Domaleski]: They’ve gotten better because I’m a student there now.

01:11:41.11 [Tim Wilson]: I was going to ask you to put in your address so we could send you a little something, but I’m pretty sure if my writers are right. Is that right?

01:11:50.26 [Joe Domaleski]: Yeah, because I have your face on my laptop.

01:11:53.17 [Tim Wilson]: Yeah. We will not send you anything else with my face on it.

01:11:58.02 [Julie Hoyer]: You don’t hear that often.

01:12:00.04 [Tim Wilson]: I know.

01:12:02.00 [Joe Domaleski]: Well, the quintessential analyst, you know, that was to, it’s right up there with the rest of my stickers.

01:12:08.49 [Julie Hoyer]: Oh my gosh, Helbs is going to be so happy to hear that. It’s going to make Helbs’s week. I’m just going to try to do it all.

01:12:15.02 [Tim Wilson]: See how it goes. Show me Tim. Yeah, just me. It’s all about me. Rock Flag and Yodobites. I forgot to actually come up with something. It had to be that. It had to be that.