The Manufacturing Executive
The Manufacturing Executive

Episode · 3 months ago

IoT Data: An Untapped Goldmine of Marketing Insights w/ Jan Pingel


Traditionally, marketing teams in manufacturing have depended on CRM supply data to get a picture of their customers.

However, there’s an untapped well of customer insights that many overlook: IoT data from manufacturing facilities. 

Using this data, marketers can target specific campaigns to specific customer bases and create messaging that truly resonates with customers.

In this episode, I sit down with Jan Pingel , Product Leader of Digital Solutions at Ingersoll Rand , to talk about how IoT is opening up new opportunities to get a deeper view of customers and learn valuable things you don’t normally learn about them.

Join us as we discuss:

-Industry 4.0 Club 

-Using IoT data in marketing

-Data privacy and data security concerns

Resources we mentioned during the podcast:

- Industry 4.0 Club 





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But once you are connected, youare continuously getting information that I can tell you something about how this industry isperforming, how these customers are using the equipment to see one of the newthings they've seen out of the data that could be used in a marketing perspective. Right. Welcome to the manufacturing executive podcast, where we explore the strategiesand experiences that are driving midsize manufacturers forward. Here you'll discover new insights from passionatemanufacturing leaders who have compelling stories to share about their successes and struggles,and you'll learn from B tob sales and marketing experts about how to apply actionablebusiness development strategies inside your business. Let's get into the show. Welcome toanother episode of the Manufacturing Executive podcast. I'm Joe Sullivan, your host anda CO founder of the Industrial Marketing Agency guerrilla seventy six. When I thinkabout market research, some things that come to mind are collecting and sorting throughdemographic data, studying industry reports, conducting customer interviews and Voice of Customer Work, evaluating potential audiences for viability and paid media platforms, and maybe looking atwhat search engine data can tell us. Here's one more for Om's and particularlyfor machine builders, and that's machine data. What can you gather from an Iotenabled machine that can help you not only service your customers better, butalso identified trends in the market and buying behaviors among subsets of your audience?Most of the manufacturing sectors just starting to skim the surface in this area,but my guest today will dive into the topic and hopefully spur some ideas thatmight help you get out ahead of the pack. So, on that note, let me introduce him. yenpingle joined Ingersol rand in late two thousand andeighteen as the digital solutions leader focused on its industrial and compressed air systems business. He's responsible for connectivity to the entire portfolio by delivering digitally enabled solutions andservices for customers. Product Strategy, innovation, design, IOT edge to cloud andmachine learning, digital twin or a combination of passion and job responsibility.For more than fifteen years, Mr Pingle has been in the technology segment ofthe industrial manufacturing space, working on making manufacturing software enable optimization, integration andinsights into the production and manufacturing supply chain. He has specifically been involved in productdesign for data collection, data visualization, data analysis for process and event datafor manufacturing and, over the last couple of years, specialized in newtechnologies for data management, such as Iot, Iot, cloud, machine learning,digital twin and big data, and has held positions in global portfolio leadership, Global Products Strategy and global business management...

...over different periods of his career.He graduated from the Technical University of Denmark with a master's degree and electrical engineeringand computer science and is a certified scrum product owner CESPO, as well asscaled agile framework, certified agilest and product manager, product owner and experienced inthe user experience, in user experience, design, design, thinking and innovation. He is and has been an active member of I triple e Issa,is spe Aee, ashrae m yes a and other industrial organizations. Yeah,and welcome to the show. Thank you very much. I'm glad to behere. I'm thinking it's going to be a fun little session on some newtopics. I'm hoping. Yeah, I think I think it will be,for sure. And and you know, I pulled you in here because youand I first met when I came on as a guest in Industry for pointno club, which is something you and a few other really smart folks aredoing and have been doing for a while now on clubhouse. So the fewmonths back and somebody on from your crew asked me to come in and talkkind of about, you know, transforming the marketing mindset in this digital era. But I would I'd love for you to start up by doing here,is is talk a little bit about what you and some of the other cofounders are doing with Industry for point no club especially knowing a probably a lotof our audiences not super familiar with clubhouse, and really it's less probably about clubhousethan just what you're doing in the industry for Point No. Worlds allin the over you. Oh, thanks for so. It is the formof Club I've been part of it for probably for months now. It's started, I think eight months ago. It's really google professionals in the manufacturing industrywith the goal of engaging diverse worldwide talent to accelerate what we call the globalof loostion industry. For I know, and so with any group, wehave professionals in this area and then to clubhouse. We host rooms every weekwhere we have very different topics and segments within industry for point o realm,so speak, if we invite key guests that can contribute with a specific aspectof something, and you are part of that. When we had, Ibelieve, the what we call the collaborative for God what, this is aboutpeople and process etc. Right. But we have technical rooms. We're talkingabout the future of industry, folk know. We have a fireside chat we took, we take an executive or or key influencer and and have a havea good talk there about what they're doing when we every things going. Andthen we also have a Monday room which is women in manufacturing, which isa very well visited group for a lot of discussions around women's oppositions and andtheir way to kind of get forward and in the manufacturing industry. So rightnow is everything we do is on Clubhouse, which is the audio only avenue forhaving conversations around different topics and ours... there is industry, for weknow, and probably one of the ten most visited clubs on a manufacturing industryon club house right now. Yeah, I think it's really fantastic what youguys have been able to do on that platform. You know, I kindof dabbled or in clubhouse a little bit when it was getting really hot,you know, back around the beginning of this year, late last year,and I remember exactly when I was popping in there, but it seems likethere was this influx of people into clubhouse and everybody's starting clubs and a lotof them just kind of dissipated or died out. But you guys have kepta really great community alive and I think it's sort of demonstrates the passion aroundthese topics that you're talking about and the, frankly, the brains behind the peoplethat are joining these conversations. So at highly recommend anybody who's listening here, if you haven't, go see what these guys are doing on industry,for point, on Club House. It just really great, smart conversations onthe topic with a lot of thought leaders in the space and you can joinright in and be a part of these conversations. So pretty cool stuff.Yeah, you can find us, of course, on club house when youcan find something in you can find us on twitter and we also have aweb page industry for CIAL Clubcom when you can see our schedule and not beable to join any room that you wouldn't be interested in. So yeah,a lot of ways to contact us. I brought this up at the beginningrather than the end, when I usually kind of give people a next placeto go, because I think it's a good lead into with the discussion Iwanted to have with you today. When I came on to your clubhouse sessionand talk to kind of about shifting the marketing mindset in the manufacturing sector.You said something that really caught my attention and you kind of talked a littlebit about this idea of we have so much data that we can gather frommachines at this point in time in manufacturing facilities, and you know, probablya lot of those things started just for operational purposes and to understand how themachines working. But as all this data builds, this also creates a marketingopportunity to you know, how do we harness all this data and then startusing it to target audiences accordingly based on what the data is telling us andinfluence other parts of your marketing strategy? So you're going to do a waybetter job explaining this than I can, but it was something that I hadn'treally thought about, even as a marketing guy in this era and in themanufacturing sector, and that's why I really wanted to have you come in andbreak some of that down. Tell us, tell us what you're talking about whenwhen you mentioned that? It's also been interesting to me when we started. So my background is and data process data, manufacturing data. Their normalplace for that is called a historian, which is a engine that collects datafrom all manufacturing equipment and it's very different than the typical database because it's reallylike a lot of scenariss like every minute I'll get a new temperature, forexample, or I get a new pressure or get a new flow or somethinglike that, and then then you look at trends and see what's going onwith that. So in Asian when this whole big data discussing him up,which just like started probably five ten years ago in the General Manu fact generalindustry around marketing, consumer analytics, etc.

Toll a big data. Now there'sso much data now. Well, my asthlee is that a lot ofdata is not necessarily big data because if you look at manufacturing, there isprobably not an area where it's more data than in manufacturing. You look athistory, is that collecting data from all these different machines on an entire plant. There is tons and tons and times of data, but you can't justapply the traditional big data analytics to it the way you've been used to inlike a consumer marketing aspect. So there I see. Typically you have demographicsand then you look at behaviors and so so you can, for Exama,go on Google and say I want to get a subset of your users thatare twenty five, two, fifty years old, male and they buy reusableshaving blades some like that. So that's behavior demographics and now you can lookat that data set and see what you can you can do with that andthen you can start on marketing campaign. And so when you initially had thesedata scientists comming into the manufacturing industry said, well, gave us all your dataand we'll go find some correlations in that information and be able to tellyou something that you probably didn't know. Now. The problem here is thatunderstanding traditional buying behavior us, the demographics, is fairly easy. I mean moreor less everybody can understand that concept around that, when you go intoa machine and you got to understand behaviors around the machine. When? Whendoes it have a temperature spike or a pressure drop or whatever. Right it'svery different. And so what I've seen a lot with these big and aLID, these companies coming into a manufacturing and come and trying to come upwith something that that can help them understand something with new insights. What theyreally come out with this insights that is already known on the plant floor.It's collations that already in your data. It could be thermodynamics, it canbe physics and be all kinds of stuff that everybody really knows that works onthe machine. They know these kind of things because they worked on the machineso many years. And so what you're trying to do is to kind oflook at it in a different respective of trying to go back to this ideademographics and behavior and instead of just looking at the data, you're looking attrying to identify some behaviors that you might be able to see him the data. So so, for kind of temperature spike is a behavior or pleasure dropis a behavior, and so so you might then be able to identify somecollations between things you didn't know. And there are tools today in the industrythat are looking at still kind of visual inspection. You have an an event, something happened and then you look at that information to see, Wall whatcan I see from the data kind of a minute before or five minutes before, having before the something there, and then you can say that that's kindof like a pattern and can then that the system that can that can gothrough the data and find similar patterns that...

...are like ninety percent close to whatthis is and then you can try to use that. But from when youtalk about really non visual inspect and type of analytics, you got to thinka little bit differently, and so data scientists today kind of have to involvethose Assamese to make sure that they take away that already known correlations and thenget into kind of valuating what's in the data. And so what really isinteresting for a marketing perspective is that a traditional marketing process and campaign is youwould look at the traditional data that you would have, which would be supplychain crm data. So you can look at, I know customers, theybought this machine is now ten years old. Based on our assessment, we shouldprobably offer them x y and see right, but you only know whenyou sold them. You don't you might not know what it's when using thatmachine for has been standing still for ten years. So, but if youstart looking at Iot and start pointing data in our regular basis, you cannow know much more about what that company or what that customer has done withthat machine. And so you haven't both the way to say, well,there's no reason to send out a campaigns will place a motor on a machineif it's only been half utilize compared to other machines. Right. So that'sone thing you could look at. But even going a little bit deeper,maybe when I was working at rock wall we had the downturn and the twothousand sixtyzero seven, tyzero eight. It was very interesting because, like justlooking at the at the sales numbers, we could see when customers started shuttingoff service, when they started not buying parts for stock and then when theydidn't buy equipment anymore. Right, we could kind of see that. Butwhen not thinking about Iot, there's much, much more you can see. Youmight be able to see that as in our entire industry is slowing down, for example, or they're picking up, or you can now start if youif you still use the demographics around the customer base and you have asmuch information and possible. You know size, demographics, location, what's the weatherpattern? You know it's other summer or winter there, whatever, allthat kind of stuff, and they started looking at their behaviors, and thatcould be machine behaviors. Paper also be like this, looking at at likehow loaded all the machines. You can now really target some more specific campaignsaround that information to the right customer base and you can refer to something thatthey actually can resonate with. They can resonate with hey, we know thatbecause of the the forest fires, for example, you're slowing down something andwe can help you with X, Y and Z, you can. Wecan help you with like rental equipment or something like that, and we canvalidate that that's actually happening based on the data we can see. It's rightnow, in the OM space and manufacturing of an untapped area of information,because what we use the out days today is to better service our customers andwe also use it for onded to understand...

...if if there's some patterns around themotor that we started using three years ago is failing a lot more than theone we use before, etcetera. So we use that for sure right away. That's the obvious thing to use the data force. But from a marketingperspective, there there're weally some opportunities there that could be very, very different, where in the past we have mainly used like crn supply data to understand, you know, how old is the equipment that the customer has, whatis the next time they should maybe upgrade or whatever, but now we canactually see some of the usage data that we can use and then target somemore specific campaigns and especially also maybe a void campaign. So I there's noreason to have a campaign into an industry that's currently slowing down, where youwould want to target the industry that's actually picking up. Right. Yeah,I think it's really smart. I'm curious, do you think this is this isa trend that's we're kind of at the very beginning of this or youknow, I'm not up a manufacturing operations guy, right, I'm a marketingguy who works with manufacturers, and I'm curious to think that, like,are we just starting to skim the surface of this, or are the biggercompanies like an ingersoll round where you are like for further down that path already? I think everybody is at this skimming the surface right now. It's nota traditional marketing area. I think even most Commons of work for what marketingis still looking at. You know, yes, you you have leads thatwill drive some campaigns and we have traditionally, like most others, use the supplyteam data and use the age of the equipment. We might have someusage data either by when we go out and serviously equipment, we can recordthat the run hours, for example, and they we can have a goodidea of how well that machine has been been going and use. But reallylooking at the Iot data from marketing is something new. We are looking atit from what we call inside perspective, but it's still very, very customerfocused. So, for example, like if we can tell a customer ofthat their behavior right now is not good, they might be like short cycling machineor something like that and we would want to tell them that. Youthey would want to either change some settings or behaviors or something like that.That's something we are doing right now, but those same types of insights couldbe used from marketing perspective as one. That's where I think some people arestarting to think about. You kind of mentioned this a few minutes ago.You said something along the lines of you. We're interested in the things that youwouldn't necessarily know, like what are the other examples you can give ofthat, like what things may you not realize are going on that you couldgather through some of this data? If it's still a traditional big data analytics, it's about finding things you don't know. It is taking a big data set, understanding all the demographics and look at the behaviors and then you cansee some correlations around. Like we're thinking it's thirty five from fifty year oldthat are buying these specific five blade razors.

Right, but we're finding out thatis really the thirty five to forty five that's doing or whatever. Right. So seems like that they're looking college or they're finding something could be different, that they're starting to buy less of them or something like that. Right. That that's just the two traditional thinking. They're right. When you go intothe manufacturing and process data, you got to be careful not having thatsame concert because again, as I said before, there are a lot ofcorrelations that are very well known that you don't want to have. Example thatfrom one of my previous jobs, I have or we were working with oneof gaps which was the liqual financial gas producer in Australia, and they haveone of those big companies come in the said Hey, give us all yourdata and we will just go through it and look at it and then comeback with some really great insights and you can use for your business, right. And then they got the data for like three months and then a gamebanks this. We found some very interesting collations in the data. If youincrease x, you will get more. Why? And so they look,but what is x and what's why? And they said, well, Xis what they didn't really know because they didn't have really the men data.They just looked at the data data, and so when they, when thecontent customer look looked at the data, they can see that that what theyfound out is that if you put more natural gas and you can get moreliquefied natural gas out, which, of course, like you know, Duh, right. And so you really need to involve your sames to emanate allthose known collations first, because then everybody will be interested in the things thatthey don't know. Right. So if you can have enough demographics and behaviordata on your machines and on your customer behaviors around how they used machines.You might find some interesting aspects that you didn't know, and I'm not justtalking now about the Simbold Anamy, that the physics about it, but reallyabout the behavior side, that there might be something that customers are doing thatyou didn't understand or really knew that they were doing in that particular way.And so that's what you would will want to find out, is those thingsthat you can either use to improve your equipment or maybe you have the equipmentchange or better operates under those behaviors that you see the customers doing. Orthose behaviors are this bad. I mean it could also be that right.So if you can kind of ties behaviors to a lot of trips and warningson the machine, you can tie those things together. That's that's a that'sa bad thing the customers doing. So what we will be looking it is, you know, set point changes, when as the customers actually making setpoint changes and what is the effect of those? But that's just for manyprocess in your perspective, looking at the actual data. But if you putin big data analytics, the idea would be that anomalies can be found moreautomatically as opposed to somebody have to say. I'm thinking this is the case,let me do with the data. But instead now you can see,hey, here's some new anomalies that we didn't know about before, and nowit's true reverse. I found a problem them and now I can look atthe solution, where as the normally is, I think I have a solution thatwe see if the problem is there. So yeah, and I'm going togo home tonight and most likely my Amazon Echo is going to be flashingyellow at me and my kitchen and Alexa's...

...going to tell me that I needto order more diapers for my newborn or more cakeups for my cur Eg andit's pretty convenient. But I also, you know, I can't help atthe cringe a little bit when I think about how much Amazon and Google anotherbig, you know, tech companies know about me. And I'm just wondering, do you see hesitancy from customers about giving back, you know, datato the OEM, or do you think that'll be an increasing sort of worryfor one reason or another? Do you think that the positives that come fromthat outweigh any sort of privacy concerns? Well, data privacy and day securitiesdefinitely very important topics always consider both as a customer and as a vendor.I won't say that customers are necessarily hesitant, except for some specific sectors like thepower industry life science. But custom every they're keenly interested in how Om'sand vendors can help them at the same time protect the data and their privacy. It's interesting, though, that customers state generally would rather not share anydate at all, and I see the problems. Has To say the lessyou share the better, right, but the same time they would still wantto know one of the best practice in the industry and what can we seeout of our data? So it's kind of like a two phase scenario.What they want to share for the part able, but want to get asmuch out of it as possible as well. And then, of course it's importantthat they also want the customers or the Olym to be able to helpthem as best as possible. And again, the more data they share there themore you get out of it. But it is definitely a specific areaof concern. We've had various discussions with customers around specifically data privacy and security. So it's about for anybody that has solutions in the Iot and I wouldspace. They got a document their whole security paradigm. What are they doing? And it also even document, you know, have your environment being testedby any official test companies, etcetera like that. There's tone of the modethat that will test your system make sure it's secure. You got to makesure that day data is encrypted all the way through from when it was generatedat the edge all the way into the cloud, and then I need aprivacy side. They're definitely differences. So from our perspective, our equipment istypically consider as a as a resource like electricity. It's compressed air or iselectricity. So that data that we would collect can tell very little about whatthe customers actually doing with the equipment or with it, what they're doing intheir plant. Right, we can get an idea of if they might bedoing more or less. We could get an idea what their shifts would be, but then you could just park outside the factor and see when the lightson and you would know the same thing. Right. So there's not a lotof concern for our customers of what data we are collecting, whereas ifyou are an om for like manufacturing equipment like bottle lines, mixing tanks.Then it becomes a bit different because now you will potentially also have you youhave the recipe of what's going on in...

...the manufacturing within your system, andso you got to be very careful about the data you're collecting or what you'recollecting it for. So when I was working at some of the like rockwoman, honeywall, and we were talking about historians, right, the historianwould typically be collecting the process data, but not necessarily like the recipe dataor the production formation or the product information. That would typically be in an EMAsystem or something like that. Right. So you could take the process datafrom my story and and say you can kind of if you're really smart, you can probably identify a little bit of what's going on, but it'sstill going to be hard. And so that's the same level, I thinkin o m that are pulling data off the machines. They're not so muchinterested in like the recipe and the actual products that are being made and howmany and all that kind of stuff. They're interested in the machine, interestedin whendom machine breaks or when it has a warning and what was the temperature, the time was the pressure flows and what was it right before. Andso I know we have has to be a little bit concerned about at leastcommunicating what they're collecting, what data they're collecting and, of course, whatthey're collecting the data for. And it's typically very obvious when you start havinga connection with the customer, because you are you're going to be calling upthe customer say hey, I can see your machine is down and I cansee it has this particular air code and I will be coming out, youknow, later today with this part and installed and get the machine back upand running again. And customers are very happy about that, right, whereasif you went ahead and says well, you are producing this particular product,then if you choose to do this other product, you would probably make moremoney. That's a different discussions and that's typically something that's could be left withinthe company, right within in the customers, and that's what they're hiring these bigfirms to do for them instead, right, and the data stage withinthe company. So you got to be very careful about what data collecting andwhat you're collecting it for. And then when you start thinking about because somecustomers would want to say, well, what can you tell us? What'sBest Practices of this equipment? How do you best utilize that? How yourbest structure the set points or the control of this equipment? In my industry, for example, there you got to really be careful about what, ifI'm going to be doing someone that you have to anomalize, anonymize the data. You got to look at. Here is five hundred pieces of equipment inthis particular industry and here is some general trends of how these five hundred pieceof equipment is being controlled or being one or being utilized right. So youhave to look at that perspective as well and be very, very certain Bartbecause, for example, like we have customers connecting into our system to seethe data, but they can only see their own. They cannot see anybodyelse this information. That can go into a photo. They can see exactlywhat they're machines are doing right now, but they cannot see anybody else's.And so if you're going to start looking at that level, you got tobe very careful about how you process that data and you got to think aboutanonymizing that information to start seeing what you what you can see, because youcan see a lot and you got to... very conscious about what you useusing that data for that you can see and, for example, like whenyou looking at the specific customer data, it's really about you servicing that equipmentfor that customer, and so you're looking at them, at the actual machineperformance data and alarms and trips and trying to figure out how what's going onin the machine and how can you you know, what are you going todo to fix it? Think did and it's broken up, of course,like or it's meant before, like short sighting. If you can see somebehaviors that it's not necessarily about what products being manufactured on the machine, butreally how they use using it. If there's something that you can see that'snot right, then you can use that like but you got to be very, very careful about the data you're pulling in and typically, I would saymost, most are just pulling in that machine performance data the end. Isthere anything I did not ask you about that you'd like to touch on today, or is there any you know, anything you'd like to say the manufacturingleaders out there who are, you know, it's kind of intimidated by all thenew technology that's emerging and the data that's available to them and aren't surewhere to get started. Yeah, I would would definitely say start engaging theirmarketing side of your company with the digital side. Those weak so there's there'sdigital transformation and did it all whatever, right, and it's from marketing perspectiveit's really about the website and getting leads and unstaying something from these. Butit's on a dish side. There's another digital side. They're right. Ifyou're starting to get data from your equipment on a real time basis, theinitial project will typically start from a service respective like being able to better serviceyour customer, be able to be put the more proactive and also be ableto like in our case, for example, it's a lot of it is aboutreducing troubles. So if I can know as much about the machine beforeI come on site, I might have a very good chance of bringing theright part and fix it right away. Right. So that's the first initiativearound that whole thing. But if you start engaging marketing and say okay,now we've gotten this data for let's say a couple years, right, well, what can it's tell us? If I have a marketing campaign around theage of equipment, it's really about how much has it been used? Canthe Iot Day to help me give some more aspects or information around that thatI can better utilize for a marketing campaign? And then once you get marketing whobeen involved there, they're going to have their own kind of aspects oflooking at that information. I said, well, I can see this orthis it is and I never even knew that we had that data because it'sI mean, that's it's a it can be a gold mine of information formarketing, as long as you make sure you keep it anonymized as much aspossible from a marketing campaign perspective least, so it's in a specific tick behaviortype specific. But it's not. You're not going to go to a marketingcampaign specific to one customer because you can see something. You got to bevery careful about that, of course. Right, it has to be trendtype data that you're looking at. But there is, I mean a lotof cases if you are connected to your...

...customer with Diote, it is very, very different than the only day that you had was when you sold theequipalment to them, to the customer, right, that's your endpoint of alot of your customer connections without Iot and did your connection to it. Butonce you are connected, you are continuously getting information that I can tell yousomething about how this industry is performing, how these customers are using the equipment, to see what of the new things they've seen out of the data thatcould be used in a marketing perspective. Right, we're seeing customers with thisparticular component doing something much better than once without the component, and I cango to market compaign, to the customer spot, to component and say here'sa case story. If you add a DIS component to your machine, wecan show you a ten percent increase of whatever, right, or decrease ofenergy utilizations. So something that data can tell you much more now than before. I love that. It's a commit's like a completely new will, notnew, but just additional way to do market research within a very, youknow, specific subset. Yeah, absolutely, really smart. We end. Thisis really good conversation today. Appreciate you coming on and doing this andwondering if you can tell our audience how they can get in touch with youand how they can learn more about both industry for point o club and alsowhat you doing it. And you're Sol rant. Sure, so I'm aLinkin. I'm on twitter link receieve my last name, pingle, so youcan find me easily there and from there is reference to every where else.INDUSTRY FOR MY NO CLUB IS INDUSTRY FOR CEA clubcom. And then the otherthing that I think would be interesting too is that this is new right andnot just a marketing aspect but the whole industry for my no. So Idefinitely encourage everybody to not just go on Industry Fau no club, but otherresources there are. You can search industry for when no technologies IOT smart manufacturing. You can check out the standards and and Industry Ong stations like EU.On Industry for my no SIME DOT Org says me, a arc is sayI tripli and I wot wild. They have tons of resources. Well,that could get you into this space here. And then the next step, ofcourse, system start using it for my for marketing perspective. But that'sthis temically not the first level there, but that would be and again alsodefinitely encourage anybody to go on industry for my no club and join some ofour rooms and clubhouse. I learned something. Every single day I'm in a room, even if I'm on a host I there's guest speakers with new aspects, new ideas and new information that I didn't didn't know about. I learnedsomething every day I'm on these room. So definitely encourage everybody to go on. It's a great way to get an hour, two hours every week andget some more information about industry for my no, and different aspects sort it. And it's not just the technology is is also the people, of theculture, etc. said a lot of a lot of different topics that couldbe very interesting to learn about. Beautiful. I can speak from my own experienceabout the quality of some of those conversations, so I would second everythingyou said there. Welly and once again,...

...thank you. Really appreciate you takingsome time out of your day to do this. I think this isgoing to be really interesting, you know, the episode for some people to listenback to and open their eyes to some things I haven't been thinking about. Glad to be here. Thanks to you. Thanks for invitation. It'sbeen it's been great time. It's been fun. You know, everything youdo here is all good information to to the to more ass that the sameaudience. Why it as a screen industry for Pint O club is trying toreach and trying to evangelize on this Cup of information, so great time joinit. Likewise, thanks you on and that's for the rest of you.I hope to catch you on the next episode of the Manufacturing Executive. You'vebeen listening to the manufacturing executive podcast. To ensure that you never miss anepisode, subscribe to the show in your favorite podcast player. If you'd liketo learn more about industrial marketing and sales strategy, you'll find an ever expandingcollection of articles, videos, guides and tools specifically for bedb manufacturers at GorillaSeventy sixcom learn thank you so much for listening. Until next time.

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