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 





Subscribe to The Manufacturing Executive on Apple Podcasts, Spotify, or our website.

Listening on a desktop & can’t see the links? Just search for The Manufacturing Executive in your favorite podcast player.

But once you are connected, you arecontinuously getting information, but I can tell you something about how thisindustry is performing, how these customers are using the equipment tosee what of the new things they've seen out of the data that could be used in amarketing perspective in welcome to the manufacturing executivepodcast, where we explore the strategies and experiences that aredriving midsize manufacturers forward here. You'll discover new insights frompassionate manufacturing leaders who have compelling stories to share abouttheir successes and struggles, and you will learn from B to B sales andmarketing experts about how to apply actionable business developmentstrategies inside your business. Let's get into the show, welcome to another episode of theManufacturing Executive Podcast, I'm Joe Sullivan your host and a Co founderof the Industrial Marketing Agency guerilla. Seventy six, when I thinkabout market research, some things that come to mind are collecting and sortingthrough demographic data, studying industry reports, conducting customerinterviews and Voice of Customer Work, evaluating potential audiences forviability and paid media platforms, and maybe looking at what search enginedata can tell us here's one more for O m's and particularly for machinebuilders and that's machine data. What can you gather from an io t enabledmachine that can help you not only service your customers better, but alsoidentify trends in the market and buying behaviors among subsets of youraudience most of the manufacturing sectors, just starting to skim thesurface in this area, but my guess day will dive into the topic and hopefullyspur some ideas that might help you get out ahead of the pack. So on that note,let me introduce him. Yan pingle joined Ingersoll ran in late two thousand andeighteen is the digital solutions. Leader focused on its industrial andcompressed air systems. Business he's responsible for connectivity to theentire portfolio by delivering digitally enabled solutions andservices for customers, product strategy, innovation, design, io t edgeto cloud and machine learning, digital twin or a combination of passion andjob responsibility. For more than fifteen years, Mr Pingle has been inthe technology segment of the industrial manufacturing space workingon making manufacturing software enable optimization integration and insightsinto the production and manufacturing supply chain. He has specifically beeninvolved in product design for data collection, data, visualization dataanalysis for process and event, data for manufacturing and over the lastcouple of years specialized in new technologies for data management suchas Io t, I iot cloud machine learning, digital twin and big data, and has heldpositions in global portfolio, leadership, Global Product Strategy andglobal business management over...

...different periods of his career. Hegraduated from the Technical University of Denmark with a master's degree inelectrical engineering and computer science and is a certified a scrumproduct owner CESPO, as well as scaled agil framework certified adulis andproduct managers product owner and experienced in the user. Experience innewer experience, design, design, thinking and innovation, he is and hasbeen an active member of I triple e Isa is pe AE, Ash, R, ae m SA and otherindustrial organizations yeah and welcome to the show. Thank you verymuch, I'm glad to late to be here. I'm thinking is going to be a fun littlesession on some new topics, I'm hoping yeah. I think I think it will be forsure, and- and you know I pulled you in here, because you and I first met whenI came on as a guest in industry for Pino Club, which is something you and afew other really smart folks are doing, and I've been doing for a while now onclub house. So is a few months back and somebody on from your crew asked me tocome in and talk kind of about. You know transforming the marketing mindsetin this digital era, but I would I love for you to start up by doing here istalk a little bit about what you and some of the other co founders are doingwith industry. Four Point: Oh Club, especially knowing that probably a lotof our audiences not super familiar with club house and really it's lessprobably about club house than just what you're doing in the industry forpointo world so I'll hand it over to you, oh thank, but so this before on Oclub, I've been part of it for probably four months now. It started. I think,eight months ago it's really good local perfection, as in the manufacturingindustry, with a goal of engaging diverse, wor, wide talent to workcelebate, what we call the Goble of Lusians before my know, and so within agroup we have professionals in this area and then to clock house we hostrooms every week where we have very different topics and segments with inde Dusty for Porto. Well, I'm so to speak, and we invite key guests thatcan contribute with a specific aspect of something, and you are part of thatwhen we had. I believe what we call the collaborative forgot, what this aboutpeople and process etc Ih, but we have technical rooms. We talk about thefuture of the industry for Meno. We have fireside chat, we tip we take anexecutive or or key influence and have a have a good talk there about whatthey're doing and when we everything's going, and then we also have a Mondayroom with his women in manufacturing, which is a very well visited group fora lot of discussions around women's oppositions and and and their way tokind of get forward in in the manufacturing industry. So right now iseverything we do is on Cobhouse, which is the audio only avenue for havingconversations around different topics,...

...and ours of there is intestinal, andprobably one of the ten most visited clubs on themanufacturing industry on club house right now, yeah. I think it's reallyfantastic what you guys have been able to do on that platform. You know I kindof Dabble in club house a little bit when it was getting really hot. Youknow back around the beginning of this year late last year. I remember exactlywhen I was popping in there, but it seems like there was this influx ofpeople into club house and everybody starting clubs and a lot of them justkind of dissipated or died up, but you guys have kept a really great communityalive and I think it sort of demonstrates the passion around thesetopics that you're talking about and frankly, the brains behind the peoplethat are joining these conversations so and highly recommend anybody who'slistening here. If you haven't to go see what these guys are doing onIndustry for point on club house, just really great smart conversations on thetopic with a lot of thought leaders in the space- and you can join right inand be a part of these conversations, so pretty cool stuff yeah. You can findus, of course, on flotost. You can find a something then you can find ontwitter and we also have a web page industry for Ero Crop Com, where youcan see our schedule and be able to join any room that you would beinterested in so yeah a lot of ways to contact us. I brought this up at thebeginning, rather than the end, when I usually kind of give people a nextplace to go, because I think it's a good lead into the discussion I wantedto have with you today when I came on to your club house session and talkedkind of about shifting the marketing mindset in the manufacturing sector.You said something that really caught my attention and you kind of talked alittle bit about this idea of n w. We have so much data that we can gatherfrom machines at this point in time in manufacturing facilities, and youprobably a lot of those things started just for operational purposes and tounderstand how the machines working, but as all this data builds. This alsocreates a marketing opportunity to you know how do we harness all this dataand then start using it to target audiences accordingly, based on withthe dates telling us and influence other parts, O your marketing strategy,so you're going to do a way better job explaining this than I can. But it wassomething that I hadn't really thought about, even as a marketing guy. In thisera and in the manufacturing sector, and that's why I really wanted to haveyou come in and break some of that down? Tell us tell us what you're talkingabout when you mentioned that it's also been interesting to me when we startedso my background is in data process data manufacturing data, the normalplace, for that is called a historian, which is a engine collect data from allmanufacturing equipment and it's very different in the typical database,because it's really like in a lot of scenarios like every minute I'll get anew temperature, for example, or I get a new pressure or get a new flow orsomething like that. And then then you look at trains and see what's going onwith that. So in Isa, when this whole big data discussing him up, which islike started probably five. Ten years...

...ago, in the general manufact generalindustry, around marketing consumer analytics Etcet Tome, pig Tatan, nowthere's so much data now. For my space, a lot of data is not necessarily bigdata, because if you look at manufacturing there is probably not aarea where this more data than in manufactures. If you look at historicthat collecting data from all these different genes on an entire plant,there is tons and times and times of data, but you can't just apply thetraditional big data and Lytic to it. The way you've been used to in like aconsumer marketing aspect, so there I see. Typically you have demographicsand then you look at behaviors, and so so you can fuck example go on Googleand say I want to get a subset of your users that are twenty five to fiftyyears old mail and they buy reusable shaving plates. Some like that. Sothat's behavior demographics, and now you can look at the data sevens and seewhat you can. You can do that and then you can start on marking, camping, andso, when you initially had to say, the sciences coming in to the manufacturingindustry said well give us all your data and woke up we'll go, find somecorrelations in that information and be able to tell you something that youprobably didn't know now. The problem here is that understanding traditionalby behaviors, O demographics is fairly easy. I mean more or less. Everybodycan kind of understand that concept around that when you go into a machineand you got to understand, behavior is the round a machine when, when does ithave a temper just spike or a pressure drop or whatever right, it's verydifferent, and so what I've seen a lot with these big and lates companiescoming into a manufacturing and and trying to come up with something thatcan help them understand something with new insides, what they will come outwith his inside that is already known on the plan for it's collations. That'salways in the data it can be thermodynamic. It can be fission allkinds of stuff that everybody really knows that works on the machine. Theyknow of these kind of things because they worked on even seen so many years,and so what you're trying to do is to kind of look at it in a differentperspective, trying to go back to this idea of to befit and behavior, andinstead of just looking at the data you're looking at trying to identifysome behaviors that you might be able to see in the day or so so for game.TEMPUS pit is a behavior or pleasure. Job is a behavior, and so so you mightthen be able to identify some correlations between things you didn'tknow, and there are tools today in the industry that are looking at still kindof visual inspection. You have an an event something happened, and then youlook at that information to see what what can I see from the data kind of aminute before five minutes before half not before to something there? And thenyou can say that that's kind of like a pattern and can then that the assistantthat can goes with it and find similar...

...patterns that are like not ever sentclose to what this is and then you can try to use that. But from when you talkabout really non visual inspection type of analytics, you got to think a littlebit differently, and so they decide is today kind of have to involve thoseestimes to make sure that they take away that already known correlationsand then get into kind of evaluating. What's in the data, and so what reallyis interesting for a marketing perspective? Is that a traditionalmarketing process and campaigns? You would look at the traditional data thatyou would have, which would be supply, change, cram data. So you can look at.I know. Customer stay part. This machine is now ten years old. Based onour assessment. We should probably offer them Xy NC right, but you onlyknow when you sold them, you don't you might not know what it when using tevenfor hasn't been standing still for ten years. So but if you start looking atit and start forming data in our regular basis, you can now know muchmore about what that company or what that customer has done with thatmachine. And so you haven't both away to say well, there's no reason to sendout a campaign to replace a motor on a machine if it don't even have utilizedcompared to other machines rig. So that's one thing you could look at, buteven going a little bit deeper. Maybe when I was working at Rock Wal, we hadthe downturn in the two thousand and sixty thousand and seventy thousandeight. It was query interesting because, like just looking at the at the salesnumbers, we could see when customer started shutting offservice when they started not buying parts for stock and then when theydidn't buy equivalent anymore right. We could kind of see that, but when nothing about it, there is much much more you can see. You might be able to seethat is in our entire industry is slowing down, for example, or they'repicking up or you can not start if you, if you stilluse the demographics around the the customer base and you have as muchinformation as possible. You know size, demographics, location. What's theweather pattern, you know it's the summer or winter there, whatever allthat kind of stuff, and then you started looking at their behaviors andthat could be machine behaviors. They could also be like just looking at alike how loaded are the machines? You can now really target some morespecific campaigns around that information to the right customer base,and you can refer to something that they actually can resonate with. Theycan vention ate with hey. We know that because of the the for fires, forexample, you are slowing down something and we can help you with expence youcan. We can help you with like rental equipment or something like that, andwe can validate that. That's actually happening based on data. We can seeit's right now in the OM space and manufacturing of San untapped area ofinformation, because what we use the Ortas today is to better service arecustomers, and we also usee it for...

...indeed to understand. If, if there'ssome patterns around the motor that we started using three years ago isfeeling a lot more than the one we used before etcetera. So we use that forsure by the way, that's the obvious thing to use the data forts, but from amarket perspective, the the there really some opportunities there. Thatcould be very, very different where, in the past we have mainly used like C NSupply data to understand you know how old is the equipment at the custom?My has what is the next time they should maybe upgrade or whatever, butnow we can actually see some of the the used data that we can use and thentarget some more specific campaigns and especially also may be a voy campaign.So there's no reason to have a campaign into an Ilustre, that's cony, slowingdown where you would want to talk with the industry. That's actually pickingout plane yeah. I think it's really smart, I'm curious. Do you think thisis? This? Is a trend? That's where we're kind of at the very beginning ofthis, or you know, I'm not. A manufacturing operation is Guy Right,I'm a marketing guy who works with manufacturers and I'm curious. Do youthink that, like are we just starting to skim the surface of this or thebigger companies like an Ingersoll rand, where you are like for further downthat path? Already, I think everybody has had. This is giving the surfaceright now, it's not a traditional marketing area. I think even mostcompanies out of work for a marketing is still looking at. You know. Yes, youhave leads that will drive some campaigns and we have traditionallylike most of those used. The UPITA data use the age of the equipment. We mighthave some usage data either by when we go out and service equipped. We canrecord that the one hour, for example, and they can have a good idea of howwell the machines been been going and use, but really looking at the IO T.Data for marketing is something new. We are looking headed from what we callinside perspective, but it's still very, very customer focus so, for examplelike if we can tell a customer that their behavior right now is not good.They might be like short sighting machine or something like that, and wewould want to tell them that you they would want to either change somesetting or behaviors or something like that. That's something we are doingright now, but dose same types of insight could be used for rockingperspective. That's one! That's where I think some people are starting to thinkabout you kind of mentioned this a few minutes ago. You said something alongthe lines of you were interested in the things that you wouldn't necessarilyknow like. What are the other examples you can give up that like what things?May you not realize, or going on that you could gather through some of thisdata. If you do want to additional big data analytics it's about findingthings, you don't know it is taking a big day. I set understanding all thethe demographics and look at the behaviors and eye could see somecorrelations around like we're, taking it thirty five from fifty year old thatare buying these be specific five plate,...

...races wit, but we're finding out thatit's really the thirty five to forty five lips doing or whatever I e sothings like that, the looking college all they find something completelydifferent, that they're starting to buy less of them, or something like thatright. That's just a traditional thinking there right when you go intothe manufacturing and process data, you got to be careful not having that samecounty because again, as I said before there, a lot of correlations that arevery well known, that you don't want have an example that, from one of myprevious jobs I have when we were working with one, a gat which was aLigorio, a gas producing Australia and they have one of those big companiescome in the said. They give us all your data and we will just go through it andlook at it and then come back with some really great insights that you can usefor your business right and then they got the data porly three months andthen it came back says: We've found some very interesting colation in thedata. If you increase x, you will get more wide, and so they look for what isaction. What is why? And they said well exes, but they didn't really know asthey didn't have really dementit. They just looked at the data sate and sowhen they, when the come customer look looked at the data, I could see thatthat what they found out is that if you put more natural gas and you can getmore Ligavit Al Gas out- which of course like you know, a right, and soyou really need to involve your ses to eliminate all those non collationsfirst, because then everybody will be interested in the things that theydon't know right. So if you can have enough demographics and behavior dataon 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 the sin. What Anaica the physics about? What reallyabout the behaviour side, that there might be something that customs aredoing, that you didn't understand or really knew that they were doing inthat particular way. And so that's what you would let want to find out is thosethings that you can either use to. imprve, your equipment, ormaybe you have the equipment, change so better operate under those behaviorsthat you see the customers, do it or those behaviors are this bad? I meancould also be that right. So if you can kind of tie so behaviors to a lot oftrips and warnings on the machine, you can tie those things together. That'sthat's a that's a bad thing that cut was doing so what we will be looking itis. You know, set pon changes when that's the custom of actually makingset pon changes and what is the effect of those, but that's just for myProsessi, your perspective, looking at the actual data, but if you put in bigdata Olitic, the idea would be that anomalies can be found more romatically, as opposed to somebody have to say I'm thinking this is the case. Letme look it to data, but instead now we can see hey here's some new anomaliesthat we didn't know about before and now it's true the reverse. I found aproblem and now I can look at the solution, whereas the normal is. Ithink I have a solution of RECEIF. The problem is there so Yan, I'm going togo home tonight and most likely, my...

Amazon Echo, is going to be flashing,yellow at me in my kitchen and Alexis going to tell me that I need to ordermore diapers for my new born or more cake ups for my cure and it's prettyconvenient, but I also you know I can't help it to cringe a little bit when Ithink about how much Amazon and Google and other big you know, tech companiesknow about me and I'm just wondering: Do you see hesitancy from customersabout giving back? You know data to the O em or do you think that'll be anincreasing sort of worry for one reason or another? Do you think that thepositives that come from that outweigh any sort of privacy concerns, but dataprivacy in Dar security is definitely very important topic Toras considerboth as a customer and a vendor. I won't say that Causas et necessarilyhesitant, except for some specific sectors like the power industry, lifescience, but cause Deverill they're, keenly interested in how MS and vendors can help them at thesame time protect the data and their privacy. It's interesting, though, thatcustomers they generally would rather not share any date at all, and I seethe farmers at say the less. He shares the better right, but at the same timethey would still want to know what are the best practice in the industry andwhat can we see out of our data? So it's kind of like a two phase scenario:What they want to share it little possible, but want to get as much outof it as possible as well, and then, of course, it's important that they alsowant the customer or the Om to be able to help them as best as possible andagain the more data they share there, the more you get out of it, but it isevinly. A specific area of concern. We've had various discussions withcustomers around speciality data, privacy and security. So it's about foranybody that has solutions in the it and I out space. They got a document,there's whole security paradigm. What are they doing and he also evendocument you know: Have your environment being tested by anyofficial test companies each other, like that, this change of the Mota thatwill test your your system make sure it's secure. You got to make sure thatday data is encrypted all the way through from when it was generated atthe edge all the way into the cloud and then an de privacy side, they're,definitely differences. So from our perspective, our equipments istypically considered as a as a resource like electricity. It's confessed are aselectricity, so the data that we would collect can tell very little about whatthe customs actually doing with the equipment or what he, what they'redoing in their plan right, we can get an idea of it if they might be doingmore or less. We could get an idea of what this shit would be, but then youcould just park outside the factor and so when the lights on- and you wouldknow the same thing like so there's not a lot of concern for my customers ofwhat date we are collecting, whereas if you are an am for like manufacturingequipment like bottle lines, mixing thanks then becomes a bit different,because now you potentially also have... You have the recipe of what'sgoing on in the manufacturing within your system, and so you got to be verycareful about the daily your collecting, what you collecting it for so when Iwas working at some of the like rock Olman honey wall, and we were talkingabout historian, so I historian would typically be collecting the processdata or not necessarily like the recipe data or the production formation or theprodom information that would tively be in an me system or something like thatright. So you could take the process data from Historia and say you can kindof. If you are really smart, you can probably identify a little bit ofwhat's going on, but it's Tellin be hard, and so that's the same level. Ithinkin O em that are pulling data off the machines they're, not so muchinterested in like diversity and the actual products that have been made andhow many and all that kind of stuff they're interested in the machine theyjust did in windom machine breaks or when it has a warning and what was thetemperature at the time. I was the pressure flows and, and what was itright before and so in a m has to be a little bit concerned about at leastcommunicating what they're collecting what they ad they're collecting and, ofcourse, what the correct men data for- and it's typically very obvious whenyou start having a connection with the customer, because you are you're goingto be calling up to custom and say hey, I can see your machine is down, and Ican see past this particular air cold and I will be coming out. You knowlater today was his part and install in and get the machine back up and moneyagain, and customers are very happy about that right, whereas if you wentahead and says well, you are producing this particular product, and if youchoose to do this other product, you would probably make more money. That'sa different discussion than that's to ally some that could be left within thecompany right within the in the customers and that's what they'rehiring these big firms to do for them. Instead, right and the day the stateswould in the company, so you got to be very careful about what Dan are youcollecting and what are you collecting it for and then, when you startthinking about because some customers would want to say well what can youtell us what's best practices of this equipment? How do you best utilize it?How you best structure your set point or the control of this equipment? In myindustry, for example, there you got to really be careful about. Well, if I'mgoing to be doing someone that you have to anomalies anonymized, the data yougot to look at here is five hundred pieces of equipment in this paticularindustry, and here is some general trends of how this five hundred pieceof equipment is being controlled or being one or being medalist right. Soyou have to look at that perspective as well, and it's very very certain bybecause, for example, why we have customers connecting into our system tosee the data, but they can only see their own. They can not see anybodyelse's information. They can go into a quarter. They can see exactly whattheir machines are doing right now, but they can not see anybody else's, and so,if you're going to start looking at that level, you got to be very carefulabout how you passes that data, and you got to think about anonymizing thatinformation to start seeing what you when you can see, because you can see alot and you got to be very conscious...

...about what you do using that data forthat you can see and, for example, like when you're looking at the specificcustomer data, it's ally about you servicing that equipment for theircustomer and so you're, looking at the at the actual machine of foeman dataand alarms and trips and trying to figure out how what's going on in themachine. And how can you you know, what are you going to do fix it? If didn'tits propense like or is meant before? Like short side, I you he our behaviors,that it's not necessarily about what corage being manufactured on a machinebut really how they use it using it. If there's something that you can see,that's not right, then you can use that like what you got to be very, verycareful about the day you're pulling in and typically I would say most most arejust pulling in that machine performance data yen. Is there anything?I did not ask you about that you'd like to touch on today or is there any? Youknow anything you'd like to say to manufacturing leaders out there who areyou know it's kind of intimidated by all the new technology that's emergingand the data that's available to them and aren't sure where to get started.Yeah I would. I would, I would definitely say, start engaging themarketing side of your company with the I o Cito Week. So there's the is totransformation and did it so whatever right and it's from working perpectlyabout the website and getting leased and understand something from these,but on the discide there's another digital side there right if you're,starting to get data from your equipment on a real time basis, theinitial project will typically start from a service respective like beingable to better service. Your customer able to me be more proactive and alsobe able to like, in our case, for example, it's a lot of it is aboutreducing truckles. So if I can know as much about the machine before I come ona site, I might have a very good chance of bringing the right heart and fix itright away right. So that's the first initiative around that whole thing, butif you start engaging marketing and say okay, now, we've got this data for it acouple years like Welwa. Can it tell us if I have a market campaign around theage of equipment? It's really about how much this has been used. Can the Iot dhelp me give some more aspects or information around that that I canbetter utilize for a market campaign and then once you get marketing alittle involved there they're going to have their own kind of aspects oflooking at the information I say. Well, I can see this or this or this, and Inever even knew that we had that data, because it's I mean that's it's a itcan be a cold line of information for marketing. As long as you make sure youkeep it anonymized as much as possible from a marketing campaign perspectiveease, so it's industry, specific behavior type specific. But it's notyou not going to go to a marking campaign specific to one customer,because you can see something you got to be very careful about that, ofcourse, like it has to be trend, type data you're. Looking at but thereis Imean a lot of cases. If you are...

...connected to your customer with it, itis very, very different, then, the only day that you had was when you sold theprovence to the customer. Like that's your end point of a lot of realcustomer connections without it and Antigo connection to him, but once youare connected, you are continuously getting information that I can tell yousomething about how this industry is performing, how these customers areusing the equipment to see what of the new things they've seen out of the datathat could be used in a marketing perspective way we're seeing customerswith this particular opponent, doing something much better than the onewithout the component and not go go to market compain to the customer, SartoComponent, an say he s a k story. If you add a discomposition machine, wecan show you a ten percent increase of whatever right or decrease of energyutilizations or something that data can tell you much more now than before. Ilove that. It's a it's like a completely new on that new, but justadditional way to do market research within a very you know, specific subset,yeah, absolutely really smart! Well, yeah- and this is a really goodconversation today- appreciate you coming on and doing this and wonderingif you can tell our audience how they can get in touch with you and how theycan learn more about both industry for Plano club and also what you're doingat Ingersol rand sure. So I'm on Leadin, I'm on twitter Linga just use my lastname pingle. So if you can find me easily there and from there as we on toe else, industry for Mino Club is industry for so Cocom and then theother thing that I think would be interesting to is that this is newright and not just a marketing aspect, but the whole industry for me know so Idefinitely encourage it, but so to not just go on industry for Pena Club, butother resources there are. You can search. I us before menal technologies,biot's mot manufacturing. You can check out the standards and and industryoverstone like EU and in US people who know s e to excess me arc. I say Itriply an IT. Will they have tons of resource as well? That could get youinto this space here and then. The next tape, of course, is then start using iton my son marking perspective. But that's that's tell not the first levelthere, but it would be in again also definitely encourage anybody to go onindustry for in Oclok and doing some of our rooms and Cophos. I learnedsomething every single day, I'm in a room, even even if I'm on a host. Ithere's get speakers with new aspects, new new ideas and the information thatI didn't didn't know about. I learned something every day. I'm on these roomso definitely encourage it, but to go on it's a great way to get an hour two hours every week andget some more information about inertion, know and different aspects ofit, and it's not just the technology is, is also the people, the culture exceptup to a lot of a lot of different topics that that could be veryinteresting to learn about beautiful. I can speak from my own experience aboutthe quality of some of those conversations, so I would secondeverything you said there will you and...

...once again, thank you really appreciateyou taking some time out of your day to do this and like this is going to bereally interesting. You know the episode for some people to listen backto and open their eyes to some things they haven't been thinking about gladto be a thanks. You thanks for imitation. It's been, it's been greattime. It's been fun, you know. I you do here is all good information to to theto more or less the S, the same audience wide as as we anitoo whenocris trying to reach and trying to evangelize on this shape type ofinformation, so great time join it likewise, thanks on and as for the restof you, I hope to catch you on the next episode of the Manufacturing Executive. You've been listening to themanufacturing executive podcast to ensure that you never missed an episodesubscribe to the show in your favorite podcast player. If you'd like to learnmore about industrial marketing and sales strategy, you'll find an everexpanding collection of articles, videos guides and tools, specificallyfor B to B manufacturers at gorilla. Seventy six ASHLAR. Thank you so muchfor listening until next time. I.

In-Stream Audio Search


Search across all episodes within this podcast

Episodes (83)