Data Is the New Oil: Clean Data, Smarter Decisions | 83

Welcome to the Oil and Gas Sales and Marketing podcast, where every week your hosts, Mark LaCour and Matt Bertram share proven strategies and real-world tactics to help you connect with customers and close more deals. Let's do this. Howdy. Welcome back to the Oil and Gas Sales and Marketing show.

I'm your host, Matt Bertram. Mark is not here with me today at the Cell Oil Conference. He was here earlier, though. I actually have a lovely guest, Robin Sturges, with Infofluency. Correct.

And Robin and I actually have met before and know each other, but we are not sure where. And we both love data. And I love AI, and salespeople need to get with it. And they need to leverage data. Agreed.

It's so powerful. Data-driven marketing is why I made the switch from sales to marketing to get more leverage, to get more targeting, to get more information, to be able to make better decisions, target where I'm going to go, what I'm going to do.

Like, data is power. I think there was that switch, right? Data is a new oil. And I think that slowed down because of the labeling of data. Everything was getting labeled. People were putting in data marts,

and they were investing in servers and all kinds of stuff. And then the excitement, you couldn't do much with the data very quickly. And salespeople don't like data entry, okay? They do not. They do not.

CRMs are a big pain point because you have to label the data properly when you put it in there. Correct. Data in, data out. Like, junk in, junk out. Yes, it's meaningless if it's not correct.

Yeah. I would love just for you to set the table what you're seeing going on, and we can broaden it to software companies. A lot of software companies are trying to sell into oil and gas. Oil and gas seems to be like a target on everybody's back. These days, everybody, well, not a back, a target going forward.

Sorry for my analogy, everyone. A target that people are going after because there's so much leverage that's going to happen of what's going on. And when oil's up on the upcycle, they see it as a big, what people are saying, commission breath. Everybody on this.

Yes, big opportunity. Yeah, a big opportunity. So a lot of people are going after that market. And I think that oil and gas is starting to change. And I think all that confluence of people and technologies coming together and people are starting to change, and it all starts with decision making

and it all starts with data. 100%, yeah. Let me start by saying thank you for having me. It's a pleasure to be here. Data is something like glue where you can argue with it to an extent, but if you have done the work to ensure that it's accurate,

it can make a statement that is widely agreed is accurate, which means that decision makers can follow a direction that is dictated to some degree by the data. And it can be a unifying factor. But to your point about sales and marketing, I thought I heard you say that it's really important that those two functions work together

and data can be something that, again, is a unifying factor there, where sales can appreciate the value of marketing through seeing the data and the impact that marketing is having and vice versa. And marketing can appreciate what sales is doing with their data that moves the organization forward. And that's true in every industry, but I think it's very true in oil and gas,

where there is a lot of volatility. It's not this steady as she goes industry, as we all know. It's high highs and low lows and everywhere in between. And at least in the Houston area, it's so pervasive. There are so many companies that are in business to serve that industry. And the ripple effect is real.

And an outer circle of service providers who are also serving those companies who then serve oil and gas. And so it's an industry that makes a big impact. So when those swings do occur, it's felt pretty widely. But circling back to data, I really do firmly believe, which is why I'm in the business, that data is power.

And it can make an enormous difference. Not everyone agrees with that. Not everyone invests in the data and the effort and the expenditure that it takes to ensure that the data is good. But when that is true, the data really can power some pretty incredible decisions. And I would love at some point to talk to how AI fits into that.

But I'll take a breath. I say this a lot, whether it might not be true or not. But I always feel like data tells a story, whether we know what that story is or not. So if we don't understand that story, we don't have all the inputs. And so with sales and marketing, or when we're working with clients, I'm starting to have them fill out a sheet of what else are you doing

that could impact this positively? Oh, we ran a radio ad. Okay, like why are people searching for your name all of a sudden? Like you need to figure out why there's a deviation from what was happening. And it's about measuring what's going on. So when I'm dealing on these like multi whatever teams of there's old school,

like terrestrial radio and still TVs still getting run, I think streaming TVs way better. But I don't know that market. I'm not the specialist in that market. But certain industries, certain companies are spending a ton of money to do that. And it's creating brand lift and they have a share of voice in that space. And they're getting that.

I can't speak to that. But the thing that I can tell you is they haven't made that revolution to, I guess, web 2.0 is what I'm calling it. And they're relying on me for what the data means in the analytics, right? And they're asking me to look at the dashboards to understand what's going on. And then to bring it all the way back to AI so quickly, I can bring everything back to AI

almost immediately. Same. Right? So throwing that in an MPC server and taking that unstructured data because I can just load it in. And I can get all kinds of trends and insights that they wouldn't have. I have only started to be able to do that recently.

But previously, I would have to figure it out. But analytics is so critical. That's how you make decisions. And I like to say also to my team or clients, I'm like, I have a best guess of what I think we should do based on previous experience. That previous experience is just data that I have that I'm leaning on.

But I don't know how the buyer behaviors change. I don't know how this product's going to hit. We need to test a bunch of different ads. We need to test a bunch of different creatives. And then people don't search the same way I do or whatever I feel. Let's let the data tell us.

And getting yourself out of that saying not everybody searches like me. Some people listen to podcasts. Some people read content. Some people read books. Some people watch TikTok. A lot of people watch way too much TikTok.

Like it depends how your, what modality is that you're consuming. And even more and more people are using LLMs or chat GBT. That's actually something at EWR, which is the sponsor of this podcast. I don't bring it up much, but EWR is sponsoring the sales and marketing podcast. We focus on LLM visibility and entity based. So I'll give you, you'll appreciate this.

And I'm dabbled in it a little bit on previous podcasts. If you look here, I've written five, six books. Okay. Matthew Bertram. But if you asked me what my name is, it's Matt Bertram. Okay.

Those are two different entities. Also, I've had different jobs. We've renamed our company. We've renamed one of our podcasts. My other podcast, The Unknown Secrets of Inner Marketing. Now it's best SEO podcast.

All these things are floating around out there. And so if you're from a knowledge graph standpoint, trying to map all this together, and there's ambiguity around who it is to make it even worse, there's four or five other mats in different industries. Doctors, CTO dealing with AI that speaks, musicians, all kinds of stuff. Yeah.

So brand online in the LLM visibility is something that I've dealt with for a long time. I speak about SEO, it's a natural progression. And we're seeing the jumps of people don't want to, this is all data for any of you sales and marketing people listening, how people search online, how are you're tagging and labeling the data? Like when you're filling out your LinkedIn profile, you're basically labeling data.

And if you leave fields empty, or if there, there's starting to be some security issues of your birthday, right? And putting, so I used to put like a one day off for my birthday. So it would protect what's going on. But now that's an issue because that's, could be a different individual because the data alignment. So the labeling of data in all these data marts to bring it back has been really critical because

the LLMs are just a bunch of AI and it's just machine learning. And it's about data labeling. That's essentially what SEO is about data labeling. And now with LLMs, it's unstructured. So it's starting to make projections and guesses. And there's ambiguity sometimes if it's not clear enough.

And in a different way, I'm dealing with data all day long, just like you are. I don't know. I'll let you respond any way you want to. Yeah, there is a real component of garbage in garbage out, which has been true in every expression of data for all time. CRMs, it's the biggest issue I see with most clients.

100%. But that is exaggerated with the advent of AI. And for all the power of AI, it does accentuate the influence of the bad data. So it's something where I spend a lot of time in education and raising a flag and trying to help people understand that it's even more important now,

more important than ever, to ensure that your data is clean and is accurate. If you're relying on it to make business decisions, really critical strategic business decisions, you really got to be sure that data is accurate. And we are moving for our clients and our services more and more towards an AI-powered delivery of the results and the insights, the meanings of their data.

And because AI does pull from so many different sources, both structured and unstructured, it's just even more exaggerated that it's accurate. So let me ask you this, I'm starting to see tools in the marketing space and sales space go-to-market strategies, synthetic data. So basically, you could build a customer profile and go, how are they going to respond to this ad?

How are they going to respond to this message before I blast it out on LinkedIn or something like that? And then also the degradation of data because AI is feeding AI is like a big issue when we're talking about in the public realm. Right. I think you deal with more controlled data, like company data, proprietary data,

but it's just a macrocosm of what's going on. There's this proliferation of data. That's right. That's happening. The other, this is a little side tangent, but we are, as a collective society, more and more often asking AI to create data, to fabricate data for some purpose,

which is not, there's nothing wrong with that, but it's interesting. It's an interesting dynamic that you add that to the data landscape is that we are now adding to the models data that's just completely made up because we asked it to be fabricated. So I have another podcast, as I mentioned earlier, and I brought on the head of originality.ai, and they do plagiarism checking was their big business. Interesting.

But now it's, is it AI or not? Okay. That's the sole focus of their business. You would find this so interesting. As of 2023, there's Grammarly AI. There's going to be fingerprints on AI on all literature, probably in mass going forward.

So human written writing is going to be very narrow or very far and few between, because everything, a co-pilot, right? People are sending emails. Great. AI is integrating with our daily lives, and we don't even know in everything that we do. In everything, which is not to alarm people, but it is a reality and it's a factor.

And it becomes, how do we use it? That is the crux of it. How do we use it responsibly? How do we use it? How do we use it to augment our humaneness, our own wisdom? Human in the loop.

Yes, 100% human in the loop, because if we don't, we are going to fall off the rails in bad ways. So one of the things that you said to me before we got started that I wanted to go back to, because I think it's absolutely critical, there's so many times that people look at a snapshot of data, and they're like this, they're pretty dashboard or whatever, and they're like, here's your report, okay?

A dashboard or a snapshot, like a screenshot of data at any given time is not a report of analysis of data. Now, you can take data and load it into an LLM, and you can start getting some really interesting output and analysis and trend lines, and you can start to map the data where it's not two-dimensional. And I don't see anybody using this stuff. It's mind blowing.

Yeah, I know. But I think it's a matter of, it's a lot of things, but a couple of the factors. I think technology is happening at a pace that people cannot keep up with, because when I got back into technology, and even like when the new iPhone update or whatever comes out, like you got to stay on top of it to understand what's going on, because the language and the glossary of definitions of what you need to know, the framework to put it all together

is massive. Actually, I wrote a book called Search, Do I Really Need SEO? It took me probably a year plus to understand how it all fits together. And so that's what I wrote out. I was like, this is my brain map of everything. But once you understand it, now we can pivot it, we can change it, we can have the language, we can talk about it, we can shift it. But until you understand the overarching factors, or like prompt engineering, I took five courses on prompt engineering,

because I'm like, I'm using this thing all the time. How do I like ask it differently? How do I change the outputs? Like, people are starting to use technology, just pick it up, like without put the contacts in your phone, like label this stuff so it's useful in the future. I just think people are moving fast and maybe don't know at all. That's what I see at least with like my parents, it's just, the technology is moving so quickly unless you keep up with it, it passes you. Yes, it becomes, you don't know what you don't know in

terms of you are unaware of what is out there for you to leverage. My parents are a great example too. When I just showed them Claude and you can ask it anything, it can do this or that, answer these questions. We did a family. Mind blowing. But you don't know that those things are available until you're shown or told. And that contributes significantly to the lack of usage and lack of adoption in a lot of ways. I find myself in our business doing a lot of work to just educate. Educate. Yeah. Share with people, hey, this is available to you. Consider it.

I did share this previously, so I'll keep it brief, but there was an aha moment with my wife when we were planning a vacation trip and she was like, you haven't helped whatever. And I know where this is going. Yes, I do. So I was able to find where we're going to go based upon all different kinds of metrics, what there is to do for the kids, where the restaurants to eat were. And she was doing all this like manually. And I think that was the big move for her. Yes. Because she stopped chat, GBT and everything. And then when I started hooking up my emails up to

Claude, she's like, what are you doing? What are you doing? Yeah. But it's to experience something that's very relevant to you, like planning a vacation. It really does illustrate the power and the applicability in your life. And I think the more we start to see that in business and the more business leaders are leveraging AI in a business context, I think the more the needle will move because it really can be such a powerful tool for an executive who uses it well. Robin, I want to hear more. And this is a personal interest of what your company does with it.

Okay. Yeah. So I'd love for you to talk more about that. Okay. At the core, we believe that it is possible to collect in an automated way the data across an entire organization. And organizations have data spread out all over the place. There's so much technology. There's a platform for every possible use case. So that puts the data everywhere. Rather than try to put that data all in one system and migrate to something that's the silver bullet, rather than do that, it's our belief that if we just co locate all of that data in a single place, a warehouse,

basically, and we give that data back to the decision maker in an environment that yes, includes KPIs, yes, includes dashboards, yes, includes charts. And then you power it with AI and you put that in an environment where the decision maker can literally access the data points from every part of their organization, from their website to their marketing materials, to their accounting data, to their CRM pipeline, the whole thing. And then think of the power that they can leverage to make amazing decisions. So that was a lot of what was discussed. I took

a Harvard course for executives on AI and they were talking a lot about structuring that and thinking about how many different places you have to go to get the data and how many marketers, for sure, log into so many different platforms. And then they're trying to, how do we synthesize this? And then people want to know, okay, I'm running all these different campaigns, like how do I bring it all together in a dashboard and see it? Which really moving the needle? Yeah. So there's a lot of issues with that. The biggest horror story that I've heard,

and I'd love to hear your opinion and safeguards to this, is like AI governance of a big company did that, but moved really quickly. It was very early. And the HR data was available to everyone at the whole company. Oh my gosh. And the legal was available to the whole company. Oh my gosh. And so it was a big mess. That's disastrous. That is absolutely disastrous. I will not claim to be a cybersecurity, security expert by any stretch. We're not talking about, yeah, we're not talking about cybersecurity. We're talking about internal people at the couple. Yes.

Accessing the data in the data warehouse where there weren't proper permissions. And I think that people were seeing the big picture so much that they needed to slow down and take some time to understand what they're agreeing to and what they're doing to understand where this data is going, how it's going to be used. You need to know how it all works before you AI first your whole company. Yes. 100%. Because like you said, there is so much risk in that, but there are also very available solutions to that. Permissioning is not hard, but you need

to be mindful and you need to be careful because to leverage the data landscape I described where you literally have all of the data in one place. Good for the CEO. Good for the CEO. Very powerful for the decision maker. You don't need the whole organization to know what everyone makes, what the compensation structures are for everyone, disastrous for the company. So it's easily addressed, but there needs to be an intentional strategy around the security. Going back to something that maybe people, that might be more relatable to talk about this, but

then we'll go back to CRMs is chat GBT five came out. And for those of you that don't use chat GBT a lot, chat GBT four remembered everything was amazing across all the searches. That was the differentiator for me with quad. I had to keep it in the prompt line, the discussion line or whatever, the thread, the thread, that's the proper term. And chat GBT remembered everything. When five came out, I thought it was due to like data usage and they were like, maybe pumping it back to recursive learning. That's what I think. But I asked it. I said,

why aren't they have a fuzzy memory on stuff? And that's where all the hallucinations came in. And all that I think is people are getting hurt. People are getting hurt. And if you remembered everything to that point, they only remember something. If you ask them to remember it and pens it, I really don't like that. I wish I could go back to chat GBT four, but they're saying that there's safety issues associated with it. And you have to map it. And if anybody's listening and doesn't know what I'm talking about, you need to get up to speed.

What is great is that it's not hard to get up to speed. There's a lot of great content out there. Deep learning.ai has been really helpful for me. Yes, lots of great educational opportunities and to just try some things and just put the cloud app or the chat GBT app on your phone and experiment with it. But people are just playing with it. You can use this for real business cases. I was actually talking to a VP of innovation the other day. I might have shared this story previously. And I'm seeing this in public eye too. There's a fall off like

over investment in AI, but he would maybe read that article or whatever and we're on a project together. And he goes, yeah, for our business, he's like out of publicly trade coming. And I was like, we haven't found any use case for AI. And I was like, and I was like, interesting. Yeah, I like canceled my next meeting. And I was like, Hey, let's talk for the next 30 minutes. Yes. And I can give you like a bunch. Yes. And then so I think there's an issue around education around this. And if you're listening and you're a salesperson, imagine if you don't have your

eyes or imagine if you don't have your voice. Like that's how powerful this is. It helps you see. It helps you understand. It helps you enrich the data to have better conversations. It helps you to know what the pain points might be. Because you can understand their 10k report down to what it like the knowledge there's existential crisis with like Bain and company in the center. Because all the data that you pay them a ton of money for right now you can get at your fingertips going back to these reports that we're talking about. And so you incorporate that into the

learnings of the data and the projections with the visualization with maybe a chatbot right answers some of the questions. If you have questions about it, knock your socks off. That's so powerful. It's so very powerful. And I agree that there is definitely an education component to this. And I think there's also some misunderstanding, some suspicion, some fear on the part of a lot of people who people are afraid it's going to replace their jobs. Yes. Or afraid that it's inaccurate or afraid they're going to be misled or and you will be

if you don't know if you don't know how to ask the right questions, prompt it correctly. But yeah, you got to understand why there are hallucinations. And also a lot of the stuff that's getting sensationalized about it, the fear of it is you can't outsource your brain to it. There's the MIT study showing the brain shrinking, but you need to use it as more reach, like it gives you greater exposure if you use the tool better. And also some of the scary stuff that's out there, I'm not sure exactly where the prompts were or how they got it to be that way, but

it shouldn't really, it doesn't really have a, it doesn't have a life of its own. It's not, no, it's recursively telling you whatever you've input in it, but it is programmed to help you do it better. That's right. So if you're trying to do something not so good, it's going to help you do that. Unfortunately, it has a lot of power for good and a lot of, yeah, it's like a gun. It's like any kind of tool. It's any kind of tool that's powerful. There's two edges. This is double-sided sword. That's right. Exactly. I'd like to go back a bit to the human in the loop, because I think

there is this fear that this is going to replace me or this is going to start to replace our intelligence and the machines are going to run the world. I think it will. Eventually. Maybe eventually, but the human in the loop is so important because AI is just a tool and it used well. I believe that it can encourage the human intelligence, the human application of what AI can suggest. And let's just say you're trying to solve a business problem and you turn to AI for that. And the AI can pose, let's say, three different solutions to that problem and explain,

give rationale for why those solutions are suggested. Then it's the human with their wisdom, with their knowledge, their past experience that brings that decision to bear and acts it out. It's not that the AI is saying this one of the three solutions is the thing and going to do it. It's just a tool for the decision. And humans have to map that workflow today. We can go really deep into orchestration, but I don't think that that's where a large majority of people are at. I think that integrating this into business processes, as you talked about, with the human

in the loop component gives you a massive amount of leverage. AI is not going to take your job, and I've seen AI on its own. We'll start to run rampant on some things, but somebody using AI will absolutely take your job. Well said, well said. Yes, that is exactly right. And we're kind of at this point, similar to where we were with the internet and its advent, is that it is happening, and you can choose to embrace it, leverage it, use it well, or you can choose to put your head in the sand, and that person who is using it well,

yes, that person is going to be the one that takes your job. I mean, that's from a hiring standpoint, or like if you're thinking about switching sales organizations after you get your big commission, or something like that, the skill sets that people are going to look for, I'm seeing it. I'm looking at resumes. I want to know, okay, what tools, what automation tools, maybe not agentic AI completely, but what automation tools are you using? What certifications have you got to just show me that you have any idea of what's

going on? Right, right. Maybe in the interview, what kind of use cases, and you can ask them really easily questions to see where they're at. That I'm finding is, if you're smart, okay, if you're smart, I can teach you stuff. If you have the right mindset, that's still important, like that's still important, and the right, but to be open to learning, because there's a lot of organizations that have friction against it, or friction against change, or this is viewed as a threat, so you don't have everybody rowing at the same direction. Those are the things that are

going to take on organization much faster than AI. Yes, I love your phrase openness to learning, because a person who is embracing AI is showing that they literally are open to learning and embracing what is available out there to be leveraged. Well, you're going to have to learn it an hour later. That's right. Now as you learn it before everybody else, it's not a fad. It's like the internet. The internet's like, I remember people making those claims, and who knows where it's going to go and what's going to catch on first. I've seen some models and where it's going.

I would love to hear some use cases of implementations that your company's made, and what it did for those companies. Absolutely. One of the places that we are putting a data product into play that is AI powered is in the M&A deal evaluation space. This is something that serves a buyer. Let's think about someone in private equity who looks at many, many deals, and it's a data rich, data intensive exercise to evaluate any manner of high number of companies that they might consider buying. The use case is that the AI can ingest the data on those

potential acquisitions and make sense of it very quickly, raise red flags, show strengths, very efficiently distill what would be a decision point. Basically, increases that time to decision so that the buyer or potential buyer can evaluate many more deals and make a higher quality decision more quickly. I mean, short selling. Stuff's not making sense. Long-term investment portfolios, crypto trading, anybody out there, LLMs can help you a lot. If you have access to data points and you've got to map it, you've got to give it examples to feed it, you can get some very compelling

outputs if you know what you're looking for. That's right. Machine learning has been used in e-commerce selling for a long time. You can sit it on top of an ERP system to just enrich product titles and enhance what's going on. There's tuned LLMs to search for products and ask questions. I mean, every aspect of marketing and selling has to do with AI. Really, for me, however long ago it was, I looked at this and said, as a sales and marketing person, there's no choice. Let me just eat the elephant one bite out of time. That's right. Where do I

start? Because it was overwhelming. The biggest point, is there anything else going back to like dashboards and CRMs that you might want to call out that you're seeing? Because I feel like that's the biggest pain point that I see when I start to come into an organization as a fractional or something like that. I'm going like, where's all the data? And it's all fragmented. Right. Your CRM or your project management tool or your ERP system is the heart of your business. If you build workflows to that, that's right. That's where you start. Yes. So I'll say two

things about that. One is that every organization has data. Not every organization is using their data. And you need to expose the data in some way that is useful to you. And if you're at the point where simple dashboards are where you're at, and that's your next step, take it. That's okay. Take the step. Take the step. Whether that's just something as super basic as creating a dashboard in Excel. Okay. If that's where you're at, do it. If you can go a step beyond that and you can use truly an analytics platform like a Power BI or a Tableau or something, do that.

But even beyond that, that's... Or a looker. Or a looker. I mean, whatever is that next step for your organization to visualize and understand the data that's in your organization? Absolutely. 100% do it. The ultimate direction that we're all going and that analytics in general is going is an AI-powered analytics platform that takes us beyond a dashboard that has to be pre-built for a purpose. Purpose built for something. Whereas you put AI in the mix of that, then the world is open to any question that you could possibly ask and you're not reliant on the

dashboard that was created for a single purpose. So last question, because we are out of time. We're way over time here, but I would love to continue this discussion with you, maybe on my other podcast. Where do you think SaaS is going? Because now you have AI that's so purpose driven by what you can get exactly what you want. There's a lot of people listening that are paying thousands of dollars or hundreds of dollars for all kinds of SaaS tools that they're band-aiding it like duct-taping it to make it work for themselves. I mean, the vibe coding is not there yet because

you got a vibe code to make the changes unless like it's difficult, but it's getting very close and there's a lot of enhancements in these tools. But now you can build a tool that's purpose driven or purpose built for you specific to you that you own that you're not paying licensing on. You're using a tool that you had to fit somebody else's idea in their head of what should be fit for the masses. And now you can take a tool and customize it for just your needs. I feel like that's where the SaaS market's going is as people get up to speed, they're going to start

building custom tools that are purpose fit for exactly what they want. So if I was a SaaS company out there and that's kind of what Google's doing with AI overviews, they're trying to put something up there to slow down that transformation. I don't know, that's the way I see it, but I'd love your input on that. I don't have the answers to that 100% honestly, but I do think there will be a lot of transformation and change in the SaaS model. And I think that the SaaS models, the SaaS products out there who embrace the flexibility and build into their products, the ability for their users

to customize and to build on their own will be the winners. What exactly that looks like? How exactly that morphs and shapes itself? Who's to know? That's the billion dollar question. That's the billion dollar question. But I really do believe that there will be a great amount of movement in SaaS platforms and there will be some big winners and there'll be some really epic losers as well. People need to change, right? There's going to be a lot of people that are riding on their brand. That's the biggest thing that I've seen in marketing is a lot of the big

companies didn't have to rely on any of the changes. They could just ride their brand. And I feel like that's starting to change. Before we get out of here, do you have a LinkedIn fail or tip of the week? A LinkedIn fail or tip of the week. Wow. I will say that in LinkedIn, there is so much content out there that is what we've been talking about AI produced. I think authenticity is the win of the day in LinkedIn and the more authentic posts, the more authentic video, the more people see its AI. Yes. And people see when it's not AI. That would be my point. Is the more authentic,

the more real, the more open you are in your presentation of your message on LinkedIn, the better. I thought you were going to say, export your contacts. Look for like a data answer. No, I didn't give a data answer on that, did I? So Robin, it's been a pleasure to have you on the show. Thank you. It has been a pleasure for me as well. How do people find out more about what you're thinking, what your company's doing? How do they get in touch with you and your business? Absolutely. Absolutely. So we are on LinkedIn Infofluency. You can find us

online at infofluency.net. We would love to have a conversation myself and my co-founder, Alyssa McGinn. We both would welcome a conversation. Thank you for inviting me to share that. Wonderful. Well, everyone, my name is Matt Bertram. Make a difference, not a sale. Thanks for listening to OGGN, the world's largest and most listened to podcast network for the oil and energy industry. If you like this show, leave us a review and then go to oggn.com to learn about all our other shows. And don't forget to sign up for our weekly newsletter.

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