BOOST Your ERP Featuring Jake Cook from Tadpull

What happens when you combine the worlds of physics, marketing, and e-commerce? You get Jake Cook, our guest this episode, who shares his unique perspective on leveraging data science for e-commerce marketing success. Currently teaching at Harvard Business School and the University of Montana, Jake dives into the importance of data structures, actionable insights, and how the five Cs of e-commerce can propel your business forward.

Featuring Jake Cook, President of Tadpull

Jake has spent his career spanning technology, digital marketing and analytics for companies such as Microsoft, Google, Kickstarter, Caterpillar and DonorsChoose.org. As a cofounder at Tadpull, he works closely with leadership teams to help them scale eCommerce businesses leveraging data and customer-centricity. An adjunct professor since 2007, Jake teaches innovation and digital marketing courses at Montana State University and teaches the ‘Pillars of Profitable e-Commerce’ course in the business analytics graduate program at the University of Montana. Jake is also member in the eCommerce Fuel Capital Group. He holds a MA in Marketing and a BA in Physics from Drury University.

Discover how to build an "insurance policy" for unpredictable events by owning your data and using it to make informed decisions. We also discuss the ripple effect of a CEO's attitude on company culture and the changing expectations of employees in the post-COVID world. Plus, we delve into the potential of AI and robotics to revolutionize both creative tasks and financial decision-making.

Finally, don't miss out on our conversation about the importance of staying up to date on the latest features of your ERP software. Jake encourages listeners to join the LinkedIn group ‘Boost Your ERP’ for valuable tips and tricks to maximize the benefits of your investment. Tune in and unlock the power of data science to transform your e-commerce business!

Transcript

Speaker 1: 0:00

Well, welcome to Boost your ERP podcast Today. We got Jake Cook from Taphole with us today, so welcome Jake.

Speaker 2: 0:09

Glad to be here. Thanks for having me.

Speaker 1: 0:11

Yeah, thanks. So the Boost our ERP podcast. As always, we're just focusing on really things in the next week ecosystem and different partnerships that we have and, just like everybody plays in the same space, we all need to learn from different lessons that different perspectives that people have. So really just want to focus a lot on best practices, just things like you don't know what you don't know And one of the things that, jake, we've been working together for years and I've always been fascinated by our conversations and you really do bring such a unique perspective to the next week road And just really excited to have you on because you bring that just a different light site into some of the things that we in this week take for granted. So I'm excited to have you on the podcast today. We're going to get into some questions, into some kind of remotely technical stuff. But first tell me a little bit What is your background, jake? I mean, i've known you for years. I'm like where are you from? Where did you go to school, all that stuff? Tell me a little bit about that.

Speaker 2: 1:19

Sure, So I grew up in Northeastern Wyoming I'm a fifth generation super small town And I ended up in Bozeman, Montana, for swimming. So it was a swimmer from could have swimmer starting at age five, some all the way through college. That kind of went to school on a swimming scholarship for a division two school, Drury University in Springfield, Missouri, And I got into it was a liberal arts school. So I was very confused as a student. I started off in sociology, did business, But I had a high school physics teacher that kind of blew my mind with showing what you can do with science and math And so, yeah. So I ended up getting a degree in physics, was going to do a dual degree with mechanical engineering at Washington University in St Louis. He did engineering. I worked full time as an intern about nine months in industry for in a biotech company And then from that I got my masters in marketing. This is like 2002 to 2004, kind of when the consumer, consumer, internet really takes off. You know broadband comes on in a big way Yeah, Pre Facebook, pre YouTube, And so, so, yeah. So I kind of fell backwards into kind of the digital e-commerce space from there.

Speaker 1: 2:26

So what made you fall into marketing? So what, what, what drew you to marketing?

Speaker 2: 2:31

You know it's funny, when I was working for that, trying to figure out what I can do with my life, i would work in this R&D lab for this French company And the engineers would come in and review like what they were producing and in the lab and complain, the marketer, people can't explain it, and then they would leave. And then the marketers would come in and be like, well, they're building something that nobody wants, so it's going to know how to use And so. But having been in that room on both sides and hearing both of those conversations, you could see like they both have valid points on what they're what they're saying And I was like, huh, so maybe there's this kind of weird area where you kind of span kind of technical and business, and so that's kind of why I decided to go back and do and do marketing And I was going to a PhD in physics after that. I love teaching but I dropped out of a PhD program And then I just kind of once I found kind of digital marketing. It mapped to physics really cleanly because it's like the site. There's data that comes off the back end of that, which is really fun, So yeah, but you're still involved with someone with academia now.

Speaker 1: 3:27

Is that correct?

Speaker 2: 3:28

Yeah, so I teach at Harvard Business School. I do some guest lecturing there on analytics and customer analytics and do some research in case study development with some professors there, and then and then I teach a create, teach a couple of e-commerce courses, one for University of Montana and one for computer science here at Montana State.

Speaker 1: 3:45

That's super cool And to me and I really want to dive in I'm going to ask these questions because it's such a good one. I mean, do you see really where that because you know, i remember the 80s and 90s and marketing and things like that Do you see that there's really pretty big adoption of like there's a science behind marketing or is there still more of a? it's a field business, not a science business?

Speaker 2: 4:08

You know, as you'd imagine, kind of like at a Harvard Business School. There's a faculty there that's very focused on, like, marketing science, marketing analytics. We work a little as some faculty at the Wharton School at University of Pennsylvania and these are kind of but they're kind of more quant based. Yeah, you know, really deep on quant based research methodologies. But in academia in general, i think when you look at what's going on in the world with marketing and digital like there, there are a lot of them are, you know, just way, way behind. Yeah, i mean, talk more about this, but it's just to do great research, you have to have data and it's very hard to give a for for them to get access to really good cutting edge data. So, yeah, so there's always that challenge on that, but it's definitely a lot better than when, i think, i started first teaching in 2007.

Speaker 1: 4:52

So, yeah, it's way better than it was five, 10 years ago, for sure, yeah, And I think that's one of the reasons why, you know, when I attended one of the seminars the sweet world where one of your customers was talking about different data, analytics and stuff like that And that was one of the things that drew my interest in. what you guys were doing with your customers is, you know, taking that, that that rich data, you know, in doing something with it other than just your standard reports. So you know, so that's really interesting. So tell me a little bit about tadpole and how. how does tadpole play into that? Or, or how did tadpole come about?

Speaker 2: 5:29

Yeah, So we started almost 10 years ago around this idea that data customer data in particular, but lots of data blended together gives you great insights. Yeah, so we founded the company, initially doing user experience research, kind of qualitative user experience research like you know our very first client was donors choose in New York and they had this huge, interesting challenge because it's a crowdfunding platform for teachers Yeah. So if you have a school district and you want to go on and get paint brushes for your art class, a lot of these teachers are reaching to their own pockets and paying for that. Yeah, and if there's amazing opportunity, we have so much capital to deploy for the teachers. But they have a huge challenge getting people like teachers to on board onto the platform. And that was one of the first like wow, if you can change an onboarding flow or how someone goes through some forms to sign up for an account, and what does a teacher think about who's busy in a classroom and doesn't have a lot of time, even over the lunch hour for 20 minutes or trying to fill out this form. So that was kind of the first like really getting like empathy for an end user and then using, like you know, user studies and then running prototypes off that. So from there we just kind of grew and e-commerce continues And e-commerce continued to explode and data did too. Yeah, yeah, yeah. So today, yeah, we have a software product, then integration to leave that suite, and Shopify and big commerce and claveo, and then we have our own kind of We call a first-party pixel, so a way for these smaller merchants to build their own really powerful data sets, similar to what an Amazon's building on their side every day. Yeah, and then we so yeah, so that's that. And we have a services arm that takes that data, kind of guides clients through these very specific stage gates to kind of scale their e-commerce business and a very kind of predictable methodology.

Speaker 1: 7:14

Yeah, well, that, like, is a always enjoyed working with you guys, because I always come away learning something From from the marketing world and from the application of it every time I talk with your team is so it's really interesting.

Speaker 2: 7:29

Well, likewise, and as I tell my grad students, i teach a data science class and 80% of data science is cleaning. Munging data and Partner like GDO across the table, like an algorithm, is only as good as a clean, as a psychic car. If the gas is dirty It's just gonna sputter on the side of the road. So you got to have got that data, got that data out of the, out of the ground, and refine it and get into the car clean.

Speaker 1: 7:51

So yeah, yeah, super critical for our work. Yeah, and it's interesting is the more and I've had some, some of our delivery team who says some go lives and and recently and just talking with the team, the delivery team and you know, just talking about data and blah, blah, blah and how Important it is for that data to be accurate prior to a goal, live and just thinking about the analogy that you know, like that's with the body, you know, if that's with the body, the data is the blood, is the life blood of that ERP And you can have all of those elements in the correct order, but until you have the right item data, the customer data, the pricing and things like that, it's just so, it's just it's not optional, you know, you know to have clean data and the importance of that. so Well, i got some questions for you that I always ask everybody. So the first one I'm gonna ask you is You know, being in the tech jobs, so this may be a tough question, but what's the best technology advice anybody ever gave you? You?

Speaker 2: 8:58

know it's disposable Technology is disposable and get used to it. So it's gonna move quick. And you got to have a mindset that you're gonna build something But you're gonna throw it away, probably. Or you know, ie upgraded But I think that idea it's easy, especially when you're building technology, to really get Emotionally attached to this, like artifact you know, and thinking that it doesn't need to change or grow. But I Think technology is always gonna. The Darwinian nature of it means you're always having to kind of Ah, you kind of get comfortable with the.

Speaker 1: 9:30

Yeah, in the CFOs on that are listening to your cursing you right now because I've capitalized this, this great product That we built out. That is gonna, but that really is. But that really is true, you know, because if you look at a fax machine, you know, or a beeper, or anything of that nature.

Speaker 2: 9:49

So and I think, disposable of the sense of you know what you bought and thinking you can just you know, for example, yeah, the ERP system.

Speaker 1: 9:55

Okay, great.

Speaker 2: 9:56

Now what do we add, what do we both on to that? or how do we take that data and diffuse it across your organization? and then you know And then also say, for example, examples, maybe you're feeding that data in your e-commerce site, but it loads really slow on mobile or whatever. Yeah, yeah okay, now we it's technically works, but it does need to get kind of disposed of an upgraded.

Speaker 1: 10:15

So Correct, make it load quicker or whatever so but and it's really true and you know as a good mindset Do you think that? I'm just curious from your perspective? Does technology ever get to a point where it's more fluid and that that it is more? I don't say organic, but you know it's a little bit more. That foundation is always there that you just build upon. Do you think it ever gets to that point, or no?

Speaker 2: 10:40

I think, i think data does to some degree. I mean data changes all the time, but I would should be just to be clean with my language here. I think data structures get to a point where they can be very stable for a business.

Speaker 1: 10:52

Yeah.

Speaker 2: 10:52

Yeah, and what you can do on top of that data structure in terms how you ingest it and exchange it, do all that fun stuff and transform it. All That got voted, the ETL work, that stuff, i think can That some there's always gonna be. You know, bugs in the system and quirks, but you bet, you bet. More importantly, i would say, is the culture that comes out of that too. So the culture, how they use the data and how you take that and get people to Okay this, this is broken. Who's gonna fix it in by when? Yeah, and then having the rhythms and the roles against that. I think that's another way of like Wage that the technology was, tell you what it should get fixed or upgraded and things like that, but a lot of times is, you know, like the sits in a dashboard deep in the vowels of a company that nobody looks at.

Speaker 1: 11:36

Yeah, yeah, yeah, and to me and and I've always believed in the nest we world. You know, and, as I've done, dealt with nest week for years and years that it's such a great platform For capturing data. Yes, but really, where that Can see, or where that, that place that we all want to get, is like, what is the actionable data From that? because data should be leading to an action or a correction or something that nature, not just a static. Here's my, you know my inventory turns. That's great, but what are you doing with that inventory turn or how is it affected?

Speaker 2: 12:14

100% and I think of data is kind of like tombs dashboards, like tombstones, right. Yeah, i'll tell you when something lived and died And that's about it, right?

Speaker 1: 12:22

You don't know was it a good life a bad life.

Speaker 2: 12:24

Yeah, you know you want to be. I think with data To it should be more of an EKG. So you know we're checking it. Okay, how's our EKG doing? What's?

Speaker 1: 12:33

you know Well, we have high blood pressure markers.

Speaker 2: 12:34

Yeah, what's your markers on the markers and how do we kind of adapt off that And then, especially with data, just as a common practice I see all the time. How did this compare? Oh, we're up 30%, 30% to last year, last month, last week, and then all the things that influence that where we were back in stock or we're out of stock or all that kind of parts that live beyond kind of a Just a simple static dashboard. But that's the conversations, and data should really drive Conversations to get results from it. I feel like so correct.

Speaker 1: 13:06

You know you mentioned the data structure. That's interesting to say that because I know whether it's a new implementation or a new subsidiary that's coming on. One of the first things that From a vendor and that's we partner is that we always want to say, okay, can you get us the export over coming with, and we're very in the large times. People say okay, well, what format do you want and what fields do you want? And you know we're very clear about you. Know like dump at all you know, I'm like. And it like every single field that you have on there, because you do the, the, the doubles in the details, because you're like, okay, we expect on vendors to have the name, the contact, the address, the bill to all that. But then when you see those other fields, you're like what's that? What's that field, what's that field, what is that field? Well, that's how we track this. Okay. Well, why do you? why do you do that? And but there it is. The anomaly is, yes, you're correct that I think that the data structure is probably kind of set, you know, when you look at master records, but some of that Ancillary information is really what's driving the conversations.

Speaker 2: 14:12

Well, and I think that's where you have a, a great expert like a gbo in there that can bring some creativity. Yeah, yeah, you did it this way and you set that up, you know with maybe it was an on-prem system or quick books or whatever, And it's like well, did you know you? could do this, and I think that's where a net suite can start to really shine and be powerful, it can also be kept. You have to be kind of careful too, because if you don't have an expert in there helping you think through, you can get real rats nest too.

Speaker 1: 14:35

Yeah you can't, yeah, yeah so you know and that is one of the questions that we asked I'm like okay, do you still need this data? You know, do you? do you actually need that historical data? Is there a value to that? So, but yeah, that that is. That's really a lot. I love that a technician, but it's going to go away. Don't get locked into it and keep and keep.

Speaker 2: 14:55

Fluid is why here is keep fluid, keep fluid and updating, and I think that especially with big digital transformation projects, big capital outlays to do it, it's like I'm all this money, now We're, we're done, right, and it's like, yeah, you are. But it's kind of like going to the gym And be like, okay, i'm in shape now. It's like, no, everything degrades. Entropy is everywhere. Second law thermodynamic rules the world and so like you'll have to kind of fight that entropy and go back and upgrading things as you go through it.

Speaker 1: 15:20

Yeah, no, that that's, that's great advice. So then the follow-up question is is what's the best business advice, non-technical? What's the business when you're a business owner yourself? you deal with business? What's the best business advice you've ever heard or received?

Speaker 2: 15:34

it's not, it's kind of I mean, sounds kind of trite, but no with. you know, when you see a wave paddle and get, just go, and you know You got to be in front of those waves to really see big opportunities. and um, even like you, look at a Warren Buffett right, he says, when I see something I don't just kind of commit, i put all my chips on the table. So I think a lot of is like building your spidey senses, if you will, your intuitions and some decision criteria to really think. if you are in front of a wave, and that's yeah. And if you've ever been to the ocean when you feel that pull of that wave going out and you're like that, i think you're kind of listening for that in like the business and the economy and then really starting to paddle.

Speaker 1: 16:17

So Yeah, no, that's great advice And to me that kind of leads to my next question that may line up with this is that when you deal with businesses and every business has had customers that are just like, wow, this business just crushed it They get it And other businesses are good not to diminish them, but everybody deals with customers like, okay, i love what I do, but if, like, i didn't work here, i go work for that company or work with that company, what those companies? I'm sure you have them. What are they doing? right that you know that you don't see other businesses do? Is it tie in, kind of what you're saying before?

Speaker 2: 16:56

You know it's funny, i teach this kind of a model. We call it the five Cs of e-commerce. Yeah, and the first thing I would say for the ones that kind of crush it and you could abstract it it's a little bit in the ways of digital stuff, but basically they have capital, right, they're not trying to like, take $2 and turn into $1,000, right, they invest accordingly and against a conviction or a thesis. I think the next step is they've got a culture that's obsessed on customers. Yeah, and everything they build from a product or customer experience kind of anchors off that you know down to. You know shipping confirmations And what is that experience? We click over to track that order or whatever, and then they build great products. And I think that's the thing with the internet is, if you build garbage products, you know it's easy to knock it off and put it on Amazon. Yeah, and then you know it's really easy, like all of us, if it's a new and up and coming brand and it's a high price point, you're going to go look at the reviews, you're going to go to Reddit and you're going to go to YouTube. So you, within 15, 20 minutes, have a really good triangulation if this is worth investing in And I think some people think that like the old days of commerce where you could kind of hoodwink people like God, you know, no one will find out.

Speaker 1: 18:03

It's like oh, yeah, yeah, Yeah Yeah. You can outmarket the customer, right? You know, in the old day and you could just like it does this and you're like it's so hard to return or I can't get ahold of somebody on the phone that you just you buy him, what do you do? You just kind of live with it. There's no recourse back to that. Now you have that recourse to reviews.

Speaker 2: 18:24

And the patience to back that capital and time like it takes a lot of people, i think, and I live more in the, obviously in the e-commerce you know, on the general, space. But when it comes to like direct to consumer or D to C, a lot of people think, well, i just got to put up this site and I just have an ATM in my business. It's just going to throw out cash, right. It's like it takes a lot of time. It takes years to get something to compound and not linear growth. But if you want to get exponential compounding growth, that does take time.

Speaker 1: 18:48

And I think that's back to that kind of cultural component of the way leadership thinks about the capital and when they expect returns and we're not going to build a new website and put in an ERP and then we're going to make millions of dollars in three months, it's like, hey, you know so And I guess follow a question on that, and I do see that as well Those that treat their customers while you see it And to me, i've been at sales in part of my background I could always tell great companies by how companies treated their vendors, because I was at sales And it's just like this whole methodology. We'll use a word earlier that I think has been a great buzzword industry is empathy. We have empathy is really, and to me, like when you know, we hire primarily on business experience for our consultants. You know, in our managed applications, our business department, the number one criteria is you have business experience, have you lived in that shoes? Because it's extremely hard to have empathy if you haven't lived in that shoes. Where your website's down, or you got this and I'm dealing with an image issue And you know when, when you've lived in that world and they call you up and you know, or somebody's trying to close their books tomorrow and they've been a controller and like I got to get this deck to my board, and when you've been that controller, you're like, oh, i know what you're at, i'm gonna drop this and we're gonna get it out the door, you know. So to me that empathy is really cool. But how do you see you know companies because they're always say start at the top. You know it's rare to ever see a great customer-focused department. You know our business not have a start at the top because it takes so much energy and it takes an investment. You know, like I said, you see an investor relationship, it's a customer relationship. How do you see those get that down to that next level? Have you ever run across where you see a good transformation from here down to here?

Speaker 2: 20:49

Yeah, i think that Peter Drucker quote you know that management guru, peter Drucker that said you know a culture strategy for breakfast. Yeah, peter Drucker, michael Porter, one of those two, but that culture piece you know, the most strategic, smartest, amazing. you know business plan analyst, blah, blah, blah. But you know how does the CEO treat the person in the front desk? Yeah, how do they treat a vendor, or, to your point, and then how do they treat, you know, a junior intern right, and I think that all just kind of like will emanate out. And in this new world post COVID, i mean, people have lots of options where to work. they expect a lot more in terms of the cultures they work in. Yeah, and so you know there's. you know that old, you know nice guys finish last. I would argue that you know.

Speaker 1: 21:34

That's changed.

Speaker 2: 21:35

That's changed a lot. That's changed a lot. Yeah, exactly So yeah, yeah.

Speaker 1: 21:40

And to me, what's interesting is, as we talk about this whole industry and the paradigm is, in some way, e-commerce really started to drive that, because so much of this self-service, i can get more data, i can give more feedback, i can give all that that. There is this 360 degree loop. Now that wasn't there before. You know, you didn't have all that.

Speaker 2: 22:04

Yeah, i think and depending especially in B2B, like some of that speed if you come up natively in e-com, like you know, and then you know if you're seeing like a Warby Parker or Albers, they're kind of going the other way right, come up in e-com and then they back into stores. But that culture coming up in e-com is built on agility, speed. You know, scientific methodology applied on like a 24 hour basis, you know. I mean they're going through that And then you know they'll back into traditional retail that way. And so for other companies, i think we're in an interesting time where we're seeing a lot of baby boomers and people retire. There's a whole digital thing coming And some of our clients like, oh, i don't want to deal with that, and they're either building a succession plan, you know that next generation that believes in tech and data and all those things, the ones that don't I think they're. It's going to be a really interesting opportunity the next few years because, you know, coming post COVID, we all got trained to buy something on our phone And you look at the buying power of Gen Z and you know if you have it, if you're selling electrical supplies and you're an electrician and you're 25 years old on a job site as a journeyman, who are you gonna you know, i need these outlets covers, whatever. You're gonna do that on your phone, probably increasingly. Just use AR to take a picture, download, load and get a shipping confirmation for your boss And like, if I have to call or fact, i mean, i mean it's almost yeah. So I think there's a huge opportunity in B2B and kind of these transformations coming.

Speaker 1: 23:29

Yeah, and I agree when we talked about that a lot is that so much gets pushed towards the B2C. You know the B2C, but that B2B and just thinking about, you know, putting the mindset of let's just use cell phones, imagine your life without a cell phone. Yeah, It's just like you can't go back. You know, your brain is expanded and to me, once, those companies that are done or doing, or still doing and building out their B2B, you can't go back, you know, because there's just no better way to do some of that, you know.

Speaker 2: 24:07

Yeah, And it's gonna start at the core with data, right, If I'm that electrician on a ladder, do you have it in stock? Can I get it overnight? It's holding up the whole job site, Correct. And then if it says there's 45 in stock, live in the ERP, pushed to the front like you got that sale or better yet, it's an auto subscription model. We know you need electrical outlets every 15 days you're on the thousand, you know, yeah, yeah, so yeah, No, that's very All sorts of real estate opportunities, i think coming from that space.

Speaker 1: 24:38

Yeah, and that leads into my next question is I always say that you know, on your, from your perspective, you have a totally different perspective than a CFO, ceo, you know, director of marketing that's working with you, just because of your experience and what you guys do, and you know I see it all the time that you know, as an S&P consultant, if I was running a business, i have a totally different perspective because I've sat on this side of the bed for nine years And to me and at times we try to actively do that through our consulting and our managed application services okay, hey, i used to own my own business, so I know this is tougher, et cetera, et cetera. So what is the thing that you're sitting on your side, you know, not talking to any specific customer? you're like, hey, if I were in your shoes, i'm like, do this or don't do that. What is the perspective that you see that never working in your job before? what do you see that your customers should see?

Speaker 2: 25:40

I'm trying to tie together the two themes we just spoke about. One is empathy for your customer. Really get out and see it through their eyes. Is that a demographic that's changing? What are their habits are going? really, we're in a really turbulent, interesting time, right now in the industry, the market share is up for grabs for who's gonna win and build the best experience. And once you have done that work with your executive team or your board and you have conviction around that, then go deploy the technology to deliver an experience that will meet that customer's expectations And be patient. That's gonna take a few years to do it, and so, and underlying that, i think, is AI And you know, i think, the biggest thing I've seen and been working in space for a long time but chat GPT is the first thing where people non-technical nerds can get in there and see what it can do. And we've had different waves of technology. We had broadband, we got the cloud, we had mobile. This is that next wave. And if you have the data and you're building on it back to like I think you know we talked earlier about second-hand this is a wave to paddle out for for sure.

Speaker 1: 26:44

So chat, gpt is this? I mean, in 10 years from now, are we gonna look back and say this is when everybody pivoted.

Speaker 2: 26:55

I think it's a great example. People get you know and then they obviously have to be a little bit like oh, it's sentient beings And you know it's not like. you know, we're not quite in the Terminator stage here, but I think there's things here of like, when you look at AI, a lot of people thought AI would come for the radiologist or the finance people, like that's the first piece that would get automated out. And what AI has actually shocked everybody is it's gone after creative Things that like creating images or writing a book or writing text. and you look at Codex which OpenAI has. you like, just talk to the computer and it writes the quick it will write code back for you, right? So there's some incredible things and moving really, really quick on that And so, and that's what robots should do, that's what automations have done throughout history, right? So instead of, like you know, saddling the horses to ride to town over an hour, we can do it in five minutes, which frees us up to go. you know human beings to go do other like fun, creative things. So I don't think it's something everybody. there is some whole ethical issues on this, but I think it's. yeah, it's an amazing wave, it's here.

Speaker 1: 28:00

Yeah, and to me and let's go a little deeper in the AI because I think it's a really interesting subject. You have a little bit more context than I think the average person does on it. What do you see? You know you mentioned finance, you know stuff like that. Where do you see the practical application? You know there's that knee jerk well, we're gonna lose the AI battle through the machines, terminator, stuff like that But what is the practical? Where do you see the practical application of AI?

Speaker 2: 28:29

Yeah, that's a great question. So let's start with a very common thing How much more money are we gonna make in two or three years? Yeah, right, and you would say that you probably could say you know, it's probably gonna be driven to some degree off how much we made two or three years ago. Yeah, yeah Right. And so you know this is linear or linear regression. What we would call it in different times but basically, what's the slope of that line gonna be? Yeah, we were, we built this into one of our tools, a forecasting tool where we can take what they call time series data, where we take a date, you know a date, we sold this much right, time series date stamps, right. And then this lives in any transactional data set you can find, yeah, and you can run it through these really powerful algorithms and it does some incredibly amazing math underneath the hood that will abstract out things like Black Friday, cyber Monday or you know what. We had an inventory hiccup three years ago. We were out of stock for 90 days that the robot can figure out. Ooh, how much do I weight that into the model and all this kind of stuff? And what it spits back is it's like upper and lower bounds And typically when you're in the analysis it'll kind of fan out because the robots are super accurate five years out, right, yeah, but it can be darn accurate in the first 12 to 24 months. And if you have like five years of transactional data you can have, like we're gonna be in this range. So if I'm the CFO, it's like ooh, i feel comfortable pulling a line of credit, knowing these are the upper and lower bounds of what we might do for revenue. Yeah, and so you can use that data to kind of think and make some good financial decisions off of. A very common problem everybody has is how much are we gonna grow Or are we gonna grow? And you'll see. Do the other thing too, like ooh, it's kind of flat but it's tanking. In three years you're gonna be down 30%. And that can be kind of shocking too, because it's buried in the data the butterflies flapping its wings, that's creating the hurricane and the robots are helping us figure out where those hurricanes are gonna originate.

Speaker 1: 30:16

You know, there's so many questions that when you talk through some of the stuff that asked, well, the first one is like and we've talked about this before one of the things that draw on each other is those businesses. is that, you know, Nesweet, you don't have to be a large, mid-market or enterprise to be able to afford an ERP. Okay, You know, and so it's really opened the door for those smaller to medium-sized businesses to say I'm gonna be on a true ERP system that has a lot of horsepower in it to process my operation and stuff like that. So when we talk AI, you know, is that same truth that it doesn't have just to be for a big business or a large market? Is that available on a practical scale for smaller businesses?

Speaker 2: 31:02

That's our whole mission behind Tadpole. The same tools as an Amazon or a Netflix or a Walmart has to build that we wanna equip. You know we say we're kind of. Our job is to try and be Gandalf. We're just helping Frodo get the ring to Mount Doom. Yeah, yeah, we're just really like Gandalf And I think that you know, helping the hobbits, yeah, fly back and like or Luke Skywalker or whatever, like we're kind of the Obi-Wan Kenobi's where like, ultimately, we wanna give them the lightsaber to fight this stuff, And so, yeah, it's absolutely within the reach. The thing is is this stuff sounds hard and nerdy and what is machine learning and you know neural networks and it has all this buzzwords and acronyms, yeah, but I think about the internet, consumer internet 20 years ago, and it was, you know, http, blah, blah protocols and like that was nerdy, It's like okay, you know it's the way we can send some information over connection. Yeah, and so I think the forward thinking leaders are. I don't understand AI. I don't quite understand get it, but could it do this? And that's what I think. I think we try and work with our execs or our boards or people that just assuming. if you had a magic wand and you had this, what would you wanna be able to predict? Yeah, what patterns would you wanna identify? And if you start with a really open-ended question like, oh gosh, how much do we lose in stockouts? Yeah, yeah, well, if we have that ERP data, you can run a model on that really quick and say, well, the demand loss on that is hundreds of thousands of dollars. And now we have some data to drive a business decision on how to deploy the capital.

Speaker 1: 32:25

So So do you see, from just I'm just trying to baseline just an average. Do you feel like an average next week customer has enough data So long as they have some longevity, you know, take some time. Do they have enough data to feed those AI models?

Speaker 2: 32:40

Absolutely. You can do some very simple thing with customer transaction logs and a friend of ours, dr Peter Fader wonderful human being basically has done a lot of the bleeding edge work in the world around this. A lot of his and Dan McCarthy's work is around using customer transaction data to predict the value of the company. You basically start at the atomic level of the customer How much do they purchase, how frequently they purchase, how recently do they purchase In each one of those little things, the time between when they purchased, how much it was and what the cadence of that is. You can develop off margin data, a discounted cash flow analysis on an individual customer basis, wow And you roll that all up in a group or a cohort. Year over year you can start to see what the value of the company is And if you ever want to shop at or raise money or whatever, you've already got that in your back pocket as an operator And that's just basically very simple order data. That was in NetSuite.

Speaker 1: 33:42

So and I know you work a lot with PE firms or those PE firms using some of the stuff that you guys have built out to help kind of identify that value of that.

Speaker 2: 33:52

So we'll start big, broad brushstrokes with that machine learning to predict traffic, revenue, revenue to the site, and then that's kind of the top. And then we'll kind of zero in on the individual customer base And we might find that, wow, the customer base in 2019, total garbage, like they're not loyal, we're losing, 95% haven't bought again. By the way, we spent massive amounts on online marketing. Acquisition costs for our lifetime value is like way out of whack. But 2020, whatever we did then And then like oh, it turns out we actually used email more than paid ads And we built an email to like nurture those. We weren't buying those customers after we had them for a repeat purchase And so yeah, so that's kind of there's kind of layers we'll peel back of the onion to get to.

Speaker 1: 34:34

To get there And then. So then to me, you know we talk a lot about change management. You know, whether it's an ERP or any tool, is your your? your moving people's cheeses. You're looking, asking people to look at different things, and one of those keys is like knowing where you're going to be, you know. So if you had somebody that's like, hey, okay, i want to go down this AI path, i'm going to spend X amount of dollars to get there, What does my world look like in like two years? I'm just curious what does that world look like for those companies that successfully do that?

Speaker 2: 35:06

I think you can start to have some well. First of all, you can sleep a little. Ai is not perfect, right? There's? always yeah, So that's one thing sometimes people feel like Oh, this is like GPS And it's like you know it's, it's it's ordinal directions, right, but it can be pretty accurate And it's like it's all going to cut off that data. So the better data we have, the more accurate. It will predict to some degree. So yeah part of it, I think, in fact follow up with Zillow. you know, Zillow let the robots buy houses and then basically they had to lay off 25% of their staff. So yeah yeah, and, but the robots had never seen something like the COVID housing bump right. So the models were trained and they never seen and it's just running and running and running. So it's like bye, bye, bye, bye, bye, and then yeah, yeah. So there's there's some cautionary tales, but there's a kind of a crawl off of running AI And the first step is build a data warehouse or just build good, clean data And we say it's just like an insurance policy, it's really cost effective. And if you follow what Apple did to Facebook, if you don't think there's value in owning your data, i mean Mark Zuckerberg's got a huge problem right now because Apple can cut off access to their data and their entire business models and Jeopardy. Yeah, so that's the first step. And then from that, once you have the data clean and organized, working with like a GVO to get that clean and organized, then you can bring in the nerds to say you want to be able to understand how much well customers buy in a couple of years, how much are we losing with stockouts? That's, our margins. Should we run ads for products and what's the right ratio based on the margin data? Yeah, and then from that you can kind of like, kind of level up and the clients that kind of buy into that vision. You know, we've seen incredibly. It takes years, especially in, you know, seasonal swings, but three or four years you see incredible compounding.

Speaker 1: 36:50

Yeah, and to me what's interesting is, you know, as soon as I ask a question, you know my head is like, well, covid, and you know, no AI, no tool is going to predict COVID. But for me, from my perspective, the thing is, is that when you get to COVID, if you do have that data and you need to pivot, at least you have data to help assist in how to pivot. And those that don't have that, that are forced to make those decisions without having any data. when you get to that injunction point, then it goes purely off feelings and gut And then you start. that's the rolling of the dice.

Speaker 2: 37:29

Yeah, that's your analogy of a patient. You know, my knee hurt, i tweaked it. You know I fell on the ice. Well, we're just going to cut it open and see kind of what's going on in there, And you'd be like you know, like you know, you want an MRI and an X-ray And you want to see, you know. So there's a part of it of, like, you know, if the business is the patient, you got to have a way to diagnose, you know, do the diagnostics on it And so, yeah, that's where I think people get a little overwhelmed, which is understandable, just if you can think, you know, if you take one thing away, just get the data clean, get it organized and start building that as an asset. And why? that's not gap principles. I do feel personally and I've talked to Pete about this at Orton a lot, i do think, changing accounting standards. where you look at data as some component of the balance sheet, i think it's hard to put a value. but when you look at customer lifetime value or individual customers, we're already moving in that direction in the research And what's fascinating is the models they ran. they're within. I mean, they're incredibly accurate for, like Wayfair, they're pulling Wayfair's public data sets and they're predicting like 2% or 3% of earnings calls. So again, we're already there. The futures here is just not widely distributed, as the old saying goes.

Speaker 1: 38:37

You know and I know we're both big fans of MPS or Net Promoter Score, and to me that was one of the things that from a NestWeep perspective, we had certain customers that were doing that were. We were doing email surveys, mps surveys out of NestWeep periodically to be to be customers and things like that and recording what they were as a customer are they a detractor, a promoter, all those things And we're storing that on the customer record. And to me, what was so exciting is that you're able to go look at your sales for the year, since you have your customer records And not only is it just a number of okay, our MPS is 77. Well, that's great. What percentage of your sales are to promoters? What percentage of your sales are detractors? Because that maybe you may have a 77, but your top five customers that 80, 20 rule 80% of your business come from your top 20. What if those top 20 are detractors? But the bottom is like that's my promoters, but if you lose two or three of those, to me that was the best when you started to see that blending of a technology to try to put some measure to what that value is, and I know you can do more with it, like you said, but that's really to me it was a great start of like hey, we got the customer data, we know what they are, we got their sales data, we know what items they were buying, we know what the impact is on our balance sheet.

Speaker 2: 40:18

Yes.

Speaker 1: 40:18

Now let's start to blend that together And I think that's really interesting. when you start looking at that And to me, if I was buying an organization, from my perspective, you're like, hey, what would I do differently Before? I never buy a system. there's a lot of things you have to sign, but I'm like, let me see your data.

Speaker 2: 40:34

Absolutely. I'm going to start with customer data. It's that blended. there's kind of a trifecta, at least in e-commerce, of data. You can use customer data, the campaign they came from, how you acquire them. Really, you need to have the product catalog, the ERP data A lot of people have campaign data. They don't really marry that well with customer data. Only the best of the best. This is what Amazon has done for 20 years. They married that with the catalog data. But you have a great point there. I think MPS is a really good way to get changing cultures. Yes, our software will capture MPS at checkout and then if someone gives us a two out of 10, the promo code didn't work I got an email we can catch for that. E-mails goes out at 10.02, it's going to work across the globe. Oh, shoot, go in, fix this thing. You're catching it at 10.05, and you don't disappoint any of your customers. That can be one thing. That feedback loop of the actual customer can change cultures a lot. We have a really good point in that too, which we call customer heterogeneity, which is a really fancy mouthful, basically saying like can you look at values? it's all spiky right. There's not like this smooth distribution. Yeah, what are our detractors? We had MPS. Blah, blah, blah, ooh. Did we Our promoters? we're not growing promoters year over year, we're actually losing promoters. Well, there's another one of those EKG signals like we've got some high cholesterol here. we got to change our diet and get going Yeah.

Speaker 1: 41:58

To me and I really do agree that you look at it is to me I've always thought that, like MPS, gets you in the ballpark because you're starting to see where you're at. What's interesting and I know we talked about this a little bit in the past that, from a net sweep perspective, one of the I call it the canary in the coal mine, it's the park cases, because in that suite on the case record, you can actually track that item. Now I have my item. I know what my case issue is broken product, late delivery, blah, blah, blah. Manufactured defect, what have you? well, now I can tie that to an item. Okay, well, my item is connected to a vendor record in net suite. So now I can say, okay, looking at historically in 2022, here are my top item defects. Now these are related to my vendor and how many support cases do I have based off that, which gives me a lot more ammunition as I go to my vendor. But also started to identify those trends And to me, like the support cases, like hey, you got to do all these phenomenal things, but I'm like, no, just track the data points on the support case, which is gonna facilitate a lot of things, but it's just that, one little thing that allows you to say I know from a CEO and I log in to net suite and I can see my support key issues, manufactured bad products. Click on that. How many cases do I have? And all of that information, because it is, goes back to your tombstone analogy what happened in this last week?

Speaker 2: 43:33

What died What died, and I think and that's just a great example of using the data to start to change the culture Correct, and we're talking about empathy for vendors. But if a vendor is like, hey, you're putting my business at risk by not delivering product on time or high quality, and I have a, and then in oh no, we're ship on time, we hit it. And when you say no, over the last five quarters you've declined our lead times are increasing 20%. So we need to have a conversation around this, because we know from our other data set of lost sales by stockouts or whatever, right, and I think that's it's so much easier to start with an objective conversation to have when you have that data and the trend line data especially.

Speaker 1: 44:10

Yeah, well, that's really cool. Well, i know we're coming up here on time. That to me. My last question I have for you, jake, is if somebody wanted to start working with you guys to start in this area, what are those first steps that they need to take? What do they do?

Speaker 2: 44:26

You know the first thing we'll do is we say we move at the speed of trust And so we have a very we try to design a very affordable option. You'll have some data science dropped into this. You don't need to know what this stuff does. But, you know, for a very normal amount we will go through and basically audit all the analytics. We'll run some time series or some predictions on what we can think. Well, also, people think through the staffing, like an org chart, for example, and then we can kind of say, okay, what do you need Here, here, here, and at the end of that they have a really good blueprint for what they need to do over the next three years. And if TAPL is a part of that, you know, like we say, we're gonna kind of try to win that business as we grow. But versus showing up with some hey, we got this big vision and this, you know, sticker shock type of thing. It's like let's bring everybody along, cause again, e-commerce is so new It's not taught really in MBA programs. You can't go get an exec ed training. We're working on some exec ed training programs for this, but it's a very nascent field And it's so interdisciplinary with finance and ops and marketing, that you know we're gonna try and bring you along over two or three weeks with some of these principles that we teach it in some of our grad courses. And then from there you know our clients have a really good stance And sometimes it's like hey, we're not quite ready for that kind of horsepower, we're gonna wait a year. Some clients, especially with PE, it's like sweet, we've got 12 sites, we're gonna do these three this month or this year or whatever, and then we can kind of stage, gate it and then put it at a minimum. We're dropping the software and build the data sets And that stuff, but it's built again from. We'll go first. We tend to use it as a loss leader, but we'll use the data and our methodology and our IP to hopefully win the business At a way where I think our clients we care about lifetime value a lot And so we don't want clients that are in or out and this stuff takes time to compound. So that's what we start And then from there it's like, oh, we need some help with the RP. Well, we're gonna give Todd and his team a call over there, and we've done that a lot of times And we'll need to. On the other side, we're kind of like a baseball team. We need a pitcher and we're kind of the cashier So.

Speaker 1: 46:22

Yeah, And to me and those people that haven't had a one-on-one call with with Taphole, highly recommended. They bring so much to the table and I've said in other podcasts and other things like great consultants just don't provide knowledge. They ask great questions And I know that, Jake, with just the conversations we've had that have been customer specific, just the questions really do make you start to think in a different way. I've always enjoyed those conversations because I've always felt like every time we've been on a joint call I've walked away knowing more than I did prior to that call, So thankful for that Likewise.

Speaker 2: 47:00

Yeah, no, i think of us as a general practitioner, but we're gonna call on the orthopedic surgeons. Yeah To the RP.

Speaker 1: 47:05

Yeah, so how do they get a hold of you, jake? If they want to get more information, how do they get a hold of you?

Speaker 2: 47:12

At tadpolecom T-A-D-P-U-L-Lcom. The poll stands for empathy, pulling customers, pulling data in and using that to scale businesses. So yeah, tadpolecom, jenny and Chris would love to chat with you. They have a whole easy step through quick 15, 20 minutes walk through what we do and, if we can help you, as we say, try to turn data into dollars.

Speaker 1: 47:32

So yeah, yeah, and they're fun to work with. I've worked with both of those And for those that are new to a GVO, go to shellofficecom. We also have a LinkedIn group called Boost Your ERP. Go ahead and go there. We have a lot of tips and tricks, new concepts. Just posted an article in there on support cases. Like I said, one of the most flexible records in Neswy and Moffton, most overlooked in Neswy, and you already paid for it, so use it. But with that, jake, as always, pleasure. Wish you the best and we'll talk to you soon. Awesome Thanks, todd. Appreciate it, man, no problem.