Sean Zinsmiester, VP of Product Marketing at Infer on Creating Soulful Marketing with Data

Sean Zinsmiester, VP of Product Marketing at Infer on Creating Soulful Marketing with Data

Sean Zinsmeister is the VP of Product Marketing at Infer, a predictive analytics and AI platform for enterprise companies. As a full-stack marketer well versed in channels across the entire marketing funnel, Sean’s career spans from time as a writer to marketing operations and product marketing.

Sean is also a cohost of Stack and Flow, a podcast featuring news, interviews, and discussions on sales and marketing automation.

Here are some of the topics discussed in this episode.

Measuring Brand Awareness Marketing

Brand awareness marketing, frequently associated with emotionally driven print, broadcast, and billboard advertising of the madmen days, frequently has a bad reputation among digital marketers because of the difficulties it presents in assigning attribution.

However, it’s now possible to quantify the success of brand-awareness advertising with technology such as Infer.

“Brand advertising just used to sort of be like feel-good marketing, but now we can really start to generate some measurable results from it. That’s a really exciting use case to sort of look into the future to understand branding used to be the softer, more intangible part of marketing.”

Not only does this improve the ability for digital marketers and advertisers to create an accurate attribution model. It also allows storytellers and creative marketers to optimize their channels and demonstrate a quantifiable justification for their work.

Seek Quality Over Quantity

As many brand awareness marketers know, unfortunately there is still sometimes too much emphasis on the number of impressions marketing content receives instead of focusing on the quailty of those impressions.

“I think that one of the trends that we’re starting to see is I think that there is why there is a new interest, and I say “renewed,” but maybe it never went away… But talk of customer experience, I think, becomes very, very important—customer-centric marketing and customer experiences. How are we structuring the customer journey around this? Are we invading their privacy? Are we too much in their face? Or are we sort of helping them with good experiences?

I think that at the heart, that’s what a lot of marketers are after. They want to create content that you’re going to read and enjoy. I think there’s a quality-versus-quantity argument. And unfortunately, I think that too many times, quantity ends up winning out, and that’s why spam becomes easier. ”

Seeking quality over quantity doesn’t just improve your marketing. It also improves the quality of your users, generates higher LTV, and ultimately creates a better case studies and use cases for your product.

Avoid Soulless Marketing

A central part to creating quality marketing and product experiences for your customers is to ensure that your branding, messaging, and call to actions create meaning and have soul.

“ I think that the marketing executive who is the empire builder that builds these lumbering marketing departments that are very slow moving, not agile, and can do great things ends up being very budget-heavy, and I think that they start to lose some of the creativity as well.

That last word, by the way, is something that’s very deep and meaningful to me because, especially as we’ve talked about automation and AI in a lot of these programs, I really want marketers to understand—not to get too meta, but think about what is the soul of what they’re doing right now. I think that I don’t want technology and adoption of technology to create soulless marketing, and I think that you see it a lot out there where stuff is.”

The tools now available to us as marketers have the potential to disrupt the human touch that creates quality marketing. Keeping this in mind and creating quality for your target users can help mitigate this effect.

Transcript

Note: this transcript has been lightly edited for clarity.

Ander: One of the things I like about doing these podcasts hereon Advertising Influencers, is I get to go somewhere new and fresh within the Bay Area, and here I am today at Infer HQ in Mountain View, with Sean Zinsmeister.

Sean, thanks so much for joining us.

Sean: Yeah, thanks so much for having me.

Ander: And what exactly do you do here at Infer?

Sean: I’m the head of product marketing. I’m in charge of anything around our go-to-market strategy, messaging and content creation, and all things involved with how we bring our products to market.

Ander: Awesome. And I explained a little bit about it in the introduction, but how would you describe Infer?

Sean: That’s a good question. We are a predictive analytics and artificial intelligence company, and we help sales and marketing teams focus on their best opportunities. We help sales people understand better who to sell to. We help marketing teams know who they should actually be sending their campaigns and focusing their marketing spend.

Ander: Yeah, and you guys have a lot of integrations: Eloqua, Marketo, HubSpot, all those other SaaS products.

Sean: Yeah. We like to think of it as a fabric solution. We want to make sure there’s AI everywhere to make sure that it’s not just trapped within an application that you’re with. We want to be able to actually share that intelligence across your entire stack. We call it an “open-architecture approach” to building product.

Ander: Oh, I like that messaging quite a lot.

And, what is an example of a use case someone might use Infer for? It would be awesome to hear that in the context of advertising if you have one.

Sean: Yeah, there’s a couple. So, at the basics, I think it’s important to zoom out and understand why the technology is even useful in the first place. Like, “What are we doing here?” I like to call it that we’re really in the midst of a data storm right now. There’s so much data, and there’s one thing that marketing and sales teams are really good at doing, which is collecting more and more data.

Ander: For better or for worse.

Sean: Exactly. And really, what it comes down to is to be able to actually sift through this data and actually derive meaningful conclusions from anything, to be able to drive action. “Actionable intelligence” is the military term, I would say.

It’s very difficult to throw enough humans at these problems to do this at scale, so one of the primary functions of our application is filtering, being able to take all of the noise out. We have a lot of spam algorithms that we use. We’re doing things like if you’re detecting keyboard strokes to understand “Is this a legitimate form input?” and things like that, so you’re really detecting patterns and things across how users use the keyboard, doing different things on natural language processing to understand “Is this bunk or this a good prospect?” essentially.

And that’s first because if you think about it, at the heart of it, it’s really expensive to hold a lot of data. The more data that you push through your stack, the more inefficient that it gets. It’s like putting oil in your car. You want to make sure that things are running efficiently. That’s the infrastructure piece behind it. That filtering mechanism is really, really important. I would say that that’s probably important to understand that’s the no-touch application of artificial intelligence.

One of the dirty secrets that marketers like to hold to their chest is the incredible amount of waste that they generate with their programs—programs that don’t generate quality prospects and that don’t lead to opportunity gains. But also, the feedback mechanism in the past has been very, very slow, and that’s really important to understand.

If we ran an advertising campaign probably 10 years ago, we would have to wait for the entire lifecycle of the campaign to complete to understand what falls out the bottom. We’re looking at the furthest downstream metrics, which might be closed won revenue and things like that.

Ander: And just for some context, 10 years ago, Facebook didn’t have an advertising platform.

Sean: Yeah. I have a way that I look at marketing automation, if you look at the full timeline of things… I was looking at this the other day. In the ‘90s, it was about all email marketing that became the craze. It was all about direct marketing, moving from offline direct mail to email marketing and applying those things.

In the 2000s when we first started to see the birth of these early marketing automation platforms, social media then began to take on in the next few years coming in the 2000s. Mobile then started to take on, and I think that now we’re in this next revolution around AI and predictive analytics and things like that.

For a lot of these advertising programs, we can get really deep into this stuff, too, because if you’re talking just about brand advertising… Let’s go beyond Facebook for just a second because I actually think that what’s interesting about Facebook is that those platforms are relatively advanced. There’s a lot of intelligence involved with how they’re targeting already. AdWords as well.

Ander: Absolutely.

Sean: Google is a huge AI domain, and they’re doing a lot of incredible stuff behind the scenes. Lending a technology like ours would add optimization, but there’s already a lot of good stuff going. Same with things like AdRoll and advanced programs like that.

But one of the things that’s really exciting is to look beyond just the digital advertising and look at things like how we’re applying AI to things like visual recognition and logo recognition. So, if you think about being able to train these intelligent models with understanding “Can we know when our brand shows up with people posting thousands of things in Instagram?”—what we would like to think of as static image, but we’re now able to pluck data from it and understand patterns and understand “Where is our brand showing up?” Now you’re starting to be like, “Wow.” Now we’re starting to apply AI to understanding.

Brand advertising just used to sort of be like feel-good marketing, but now we can really start to generate some measurable results from it. That’s a really exciting use case to sort of look into the future to understand branding used to be the softer, more intangible part of marketing. It always kind of worked, but we were sort of trusting this halo effect.

Ander: Do we get lift?

Sean: “Somewhere? Don’t know where. It could be nowhere.” The truth is it’s usually an orchestration of a lot of different things. I think that’s the other thing that a lot of marketers forget, too, is that if you’ve been any sort of business school 101 marketing class, the reason that they teach you the marketing mix is that it is a mix. Your approach should be a diversified portfolio to borrow from the financial and securities. You’re going to have some high risk/high reward programs. You’re going to have some sort of bonds programs that are just steady, wins the race. Maybe those for you are AdWords and Facebook—traditional advertising that we know we’re going to get a decent amount of performance marketing, and it’s just going to be steady and fairly predictable about how we’re getting that back.

And then there might be programs that you go along with. Maybe that’s your big media spend—your TV buys and things like that. But pushing them all together, that’s where you sort of get that power of that marketing mix, and I think that that’s something else that people are starting to realize, and as they now start to apply AI, they can now start to intelligently analyze their marketing portfolios to figure out “OK, what’s working and what’s not? Let me double my efforts on this program, dump this one, and now I have a better opportunity to reap the rewards of the money that I’m spending,” if that makes sense.

Ander: Yeah. AdWords, Facebook, Quantcast, AdRoll… All of these platforms have fairly intense and powerful analytics that are built in, but there’s a lot of synthesis of the data that needs to be done after that data has been acquired, you know? You might need a business intelligence person to do something like that, especially when you have something as qualitative as identifying how often your brand is showing up on social media.

Sean: I would say it’s not even what happens with the data after, but some people forget that, actually, the hardest job that we do today when it comes to AI and building predictive models is the data preparation part. Everybody’s data, no matter what business that we work with, the amount of data clean-up that you need to actually build a coherent model, a good model, one that’s going to be highly predictable… There’s missing data. There are things that have been entered wrong. I mean, if you think about the amount—especially if you’re an organization that lacks what I call “governance,” where you have frontline salespeople doing data and the amount—of percentage of mistakes that you can calculate just from that, or things that are missing because you don’t have conditional logics set up in your Salesforce or whatever your CRM is to say, “Hey, you can’t move on to the next step without answering this question.”

Or you get silliness, like at my past company, where they’re asking for title, and when they went back and did the analysis, they actually figured out that the #1 title was Mr. and Mrs. because it wasn’t job title. Naturally, the user was like, “What’s my title?” and most people were just entering that. The lack of the word “job title” as that label for that field as an input form just led to all this data corruption that had to be gone, go back and clean up.

That’s a really, really big problem that I think we’re going to start to get more intelligent by leveraging technology to clean it up, but a lot of this stuff has to be done by hand. There is infrastructure that kind of helps automate it, but the amount of data preparation to even get to that point? People are familiar with the garbage-in-garbage-out type of formula, and that doesn’t stop as soon as you run the program. That’s kind of an ongoing process, and I think that if you looked at where marketing operations professionals spend the majority of their time, cleaning up, organizing, segmenting, purging data is a really heavy task, and it doesn’t stop once you launch the program.

Ander: No, certainly not. I really like that example of title versus job title and how important that is in qualifying a lead or making sure you’ve got all your information in your CRM, whatever it might be.

What are some other examples or maybe observations you’ve seen from customers here at Infer or elsewhere?

Sean: Sure. I think the most popular observation is a lot of people have been getting into technographics. We actually wrote a piece that was published because people are familiar with firmographics and demographics, and these are how you make up criteria of a profile when trying to build that persona in your ideal customer, if you will.

Ander: Sure.

Sean: And it’s now starting to understand “Well, what can we understand about you as a buyer, given the technology that you use?” And by the way, I don’t actually think that this is siloed to just the B2B customers. I think this is actually something really exciting where now we can start to bring in this buzzword soup, the Internet of Things, where we have connected devices. What applications are you using on your iPhone or your Android phone or your FitBit watch or smart watch and these types of things?

Ander: Your smart shoes and jacket are probably coming soon. All that data.

Sean: It was a mentor of mine that mentioned to me: We create so much data exhaust wherever we go. We’re walking around, we’re exhibiting a GPS signal that follows us everywhere if you have tracking on. I mean, we’re generating so much data just by walking around, and your phone is counting your steps and all this stuff. The amount that we can actually understand about the different technologies and how to understand our profile from that had been a really exciting advance in targeting, especially for things like advertising.

A very simple example is you have a Salesforce application that you want to be able to identify who is using this Salesforce CRM, OK? So, there are ways that you can detect that by crawling the web. There is also behind-the-firewall data that you can obtain from third parties. You build that list, you put together your advertising program, and it’s very, very target, and that’s a very simple one-to-one. We have this product, and it fits this profile, given this technology.

Now, does it matter what the title of that person is? I mean, it should because it’s going to create a more personalized message, if you will.

Ander: Sure.

Sean: And personalization is something that I think a lot of marketers get confused about.

Ander: I completely agree.

Sean: It’s really more about “Is this relevant to you?” It’s more just like, if you just purchased said product on Amazon, and then you’re on Facebook and you see an advertisement for the exact product that you just bought, that’s not really relevant to you.

Ander: No, no.

Sean: Where was this an hour ago or during the time of purchase? The timing mechanism, I think, is still something that a lot of these systems are struggling with today. Amazon is pretty darn advanced and has an incredible amount of data, and even they’re not picture perfect when they’re following the customer journey, as it were.

I do think that technographics and your technology profile is incredibly powerful stuff, and I think it’s a new way that marketers are starting to examine how they do segmentation, without getting too, too granular.

I think the other exciting trend is how we’re starting to analyze engagement and behavioral analytics. What’s going on? How are people interacting with your content, with your website, with your things? What kind of signals can we project from here to make sure that we’re delivering our messages at the right time? Especially when you’re talking about assets that you own, those signals can be highly predictive.

There’s another new trend that I think is an exciting frontier, although I think that it’s in its early days, which is third-party intent data as well. OK, it’s not just about what you’re doing on my websites and the things that I own, my own media sites, but what’s going on on these third-party publisher sites? What are people researching about this topic that I care about? How do I target those people? And you can see how that can be very attractive for a marketer to be like, “Jeez, well, if I’m selling big turbines or cyber security or something very popular right now in the news, I want to know what accounts that I should be looking at for people who are actively researching that.”

Now, I would argue there’s an incredible amount of false positives around that. There’s not enough coverage of those signals for it to be predictable, coverage meaning there just aren’t enough of them. You can’t just draw conclusions based upon these single data points that are out there, but it does give some interesting insight for the marketer that they didn’t have before. It might be a pin light in a dark cave as it were, but it still is something, and I think that that light is going to get brighter and brighter.

Although as we do that, I think that the conversation and dialog around data and privacy is going to continue to prevail. I think that we are very, very liberal when it comes to this conversation, especially here in the United States, and if you look at what some of our allies, like Germany and Canada, have started to really crack down on… To send unsolicited emails to somebody, which this sort of thing is commonplace. “What about it? Everybody is on a list somewhere.” I mean, you could be sued in some of the European Union.

Ander: That’s wild.

Sean: Which is unbelievable. I think it’s going to be interesting to see where that conversation ebbs and flows and how willing people are to just give up their data.

Ander: I had a really interesting conversation a while ago with Tyson Quick, our CEO at Instapage. It was actually the very first episode of this podcast. We talked about how a lot of people see this data collection, see this really hyper-focused targeting of ads to them as possibly a little bit creepy with personally identifying information and all that stuff like that.

I think there’s an argument to be made. And, although I’m not 100% sure how much I agree with this, although I do see the merit in this argument. I think there is an argument to be made that some people might actually choose to provide more personal information if they are truly getting targeted with things that are of real value to them. I think one of the reasons that people don’t like that information being out there is the stuff that they get advertised to them that’s completely irrelevant to their needs.

b>Sean: Sure.

Ander: Do you agree with that? Do you think that it’s possible that people might be more OK with it at some point in the future? Maybe it’s something where you have analytics built into your car. Your car is connected to the cloud. Your car breaks down, and you start getting ads from local repair shops that can start bidding on fixing your car.

Sean: Right. It’s a really big question, and I think it has so many dimensions to answering it. I think people will be OK until it gets compromised. Part of it is that I don’t think we’ve hit the high-water mark when it comes to cyber security and data breaches and things like that. You know, there are so many connected programs. If you think about how we log into this and that cloud application using our social keys and things like that, and when those get compromised, people can backdoor into our email and then find out all sorts of good stuff that we didn’t really intend the outside world to see.

Ander: Sure, sure.

Sean: I think people are getting smarter about understanding that it’s tough to get the toothpaste back in the tube type of thing. We’re in a place where you really should understand that if you are using a service for free, nothing is free.

Ander: Right.

Sean: You are paying for this with your data, and if you’re getting enough value from that service, say like a Facebook or name your favorite application or whatever, I think that’s your currency, and I do think that your data is a type of currency that people will offer in order to pay for services, rather than offering up their hard-earned dollars.

On the other hand, it’s like there is something very nefarious also going on with data as well, and I think that it’s starting to make people uncomfortable. Even in the news lately, it’s like you have the great pushback from, what is it, 45 states as the federal government tries to gather voter registration?

Ander: That is fresh, fresh news, yeah. As July this year, 2017.

Sean: Fresh, fresh news, and you sort of have to sit back and wonder. There’s a plethora of reasons why people don’t want to do this, but a lot of it is like they’re asking for party affiliation and the last four digits of your social security. This is not stuff that you want to willingly give up, right? You have no idea what they’re going to use the data for, and that’s the kind of key, too. What is this data being use for? Is it just for research purposes, or are you going to be pumped through an advertising machine that you’re not aware of to be bombarded with messages?

When it comes to marketing, in particular, and advertisers, I unfortunately sort of land on the more pessimistic side of the aisle because I think, inherently… To utter the words of another mentor of mine, I think marketers are inherently lazy, and I think that they throw money at problems, and I think that it’s very easy to just sort of push a list into a program, hit the button, and just spam everybody. If a few people get hit with messages they weren’t supposed to or didn’t ask for, well, you know, it’s pretty much harass them until they either unsubscribe or they buy. That’s not a great marketing strategy.

Ander: No.

Sean: I believe that we call it “spam,” and it’s happening all over the place. I think that
those types of tendencies is what has led the European Union to push back on the data privacy acts. Do I think that the U.S. follow suit? I think this is going to take us a lot longer to get there. I think that we’re too willing to give up our data for services today. I think some of it is invisible. I mean, nobody reads the terms of services with stuff. I mean, it’s meant not to be read. It’s literally a piece of content that’s meant to be unattractive for you to read. We don’t 100% understand what’s going on.

The simplest answer for your question is I do think some people will use that as currency to pay for products and services. I think there needs to be more education around what that transfer looks like and where your data is ending up. I think that if more people knew what the end game looked like, there might be a little bit more hesitation to say, “Do I really need this fitness program?” or whatever it is that you’re giving things up for.

I mentioned the word “nefarious” because you start to think about what we could use it for—things like healthcare and understanding all my data. “Did I really want it going to my insurance company? Yeah, I love my Strava app, but does my insurance company really need to know about that?” Well, I guess if I’m broadcasting it everywhere, then maybe it’s fair game, but you can see how that becomes a very dicey argument very quickly.

Ander: Certainly. At the same time, though, if I want to share the data from my smart device, my smart watch, or from Strava or any other application, with my doctor or with some health-based service to make predictions and recommendations for my health, then that is entirely to my benefit.

Sean: 100% and that would also assume that there is some encapsulation around privacy for that, right? The pipe leads from point A to point B, with nothing in between. That’s you sending that data through a secure channel to your doctor, and it’s for their eyes only and not to be resold.

I think that the reselling of data is something that a lot of people just don’t think about, how a lot of these companies make money. People are like, “Well, how did I end up on this list?” and it’s like, “Well, you signed up for something!”

Ander: Somebody paid for your email address, yeah.

Sean: Somebody paid for your email address or they scraped it off the web. I mean, you have a public LinkedIn profile that’s not locked down, and in your information, you have our corporate email and your personal email. Well, there are a lot of data companies that have human factories that literally are combing the web, copy-pasting things, and building into a custom schema, the database that they put into a system, and they resell it, and that’s how they collect it. That’s a form of data mining. Any time I get a chance to talk to… Because we deal with a lot of data companies because we’re always looking at “How can we make this a feature of our product? Is this something that fills a gap for our customers?” People think that more data is better. That’s not necessarily true. Is it the right data and all that stuff?

But the key that I always ask is “How are they mining the data? Where is it coming from? Does this feel like shades of gray in how this data is being obtained? Is this a trustworthy source?” and things like that. “How reliable? How much is that data refreshed?”

Why LinkedIn is such a prime data source is because people want to be able to understand when you’re switching jobs, and what’s a natural thing for you to do when you leave this venture and go on to another? Most people update their LinkedIn page, right? That sends signals that are extraordinarily valuable to advertisers because they don’t want to know Sean at Infer; they want to know Sean at company X, and they want to be able to deliver that. Maybe there’s a different offering, given the company that I’m in, and now you’re sort of seeing how the profile changes by one important life change that I made personally. That data source is incredibly rich because most people are fairly truthful, I would want to say, on LinkedIn. I think everybody personally markets themselves, but there are hard data points that we are self-reliant on, probably governed by our own egos perhaps for LinkedIn. But there’s a reason that that objectivity and that truthfulness lives on LinkedIn. That’s what makes it a valuable data source versus something that might curated or made up by somebody else.

Ander: For sure. This opens a brand-new window into an entirely different conversation around marketing and ethics—the ethics of marketing—and we are almost out of time.

I normally ask a question to everyone at the end about where marketing is heading, what the future of marketing is, and how we as marketers can prepare for that future.

But I would actually like to frame that question in terms of marketing ethics. With all of these ongoing and upcoming privacy concerns that consumers have, that professionals have, and that corporations have, what do you think we as marketers can do to continue educating ourselves about those concerns and also to act responsibly as all of these things develop in the future?

Sean: I think that one of the trends that we’re starting to see is I think that there is why there is a new interest, and I say “renewed,” but maybe it never went away… But talk of customer experience, I think, becomes very, very important—customer-centric marketing and customer experiences. How are we structuring the customer journey around this? Are we invading their privacy? Are we too much in their face? Or are we sort of helping them with good experiences?

I think that at the heart, that’s what a lot of marketers are after. They want to create content that you’re going to read and enjoy. I think there’s a quality-versus-quantity argument. And unfortunately, I think that too many times, quantity ends up winning out, and that’s why spam becomes easier. It’s like, “Hey, I have this giant list of a million people, and if I get 0.25% of it, at least I’ll have that amount of wins.” That’s a terrible conversion rate, but so be it.

Ander: Reminds me of the median conversion rate on AdWords.

Sean: Sure. Yeah, yeah. In a lot of these programs, I have to imagine, that keeps going down and down. I also think that marketers need to start thinking about the fact that people have more power to shut you off more than ever right now. They have the ability to block your advertising with ad blockers. They have the ability to delete their cookies, go incognito, and not have you track them around and things like that. I think that part of it, we have to check “How much do we want to forcibly opt in? And if we do opt them into our programs, how much value are we actually creating?” I think that part of that is by evaluating that experience.

I think that the other thing, too, moving towards that more quality argument, is I think something that’s actually probably going to change the way that we build marketing teams in the future. I’m a big believer that smaller teams can do big things. I think that the marketing executive who is the empire builder that builds these lumbering marketing departments that are very slow moving, not agile, and can do great things ends up being very budget-heavy, and I think that they start to lose some of the creativity as well.

That last word, by the way, is something that’s very deep and meaningful to me because, especially as we’ve talked about automation and AI in a lot of these programs, I really want marketers to understand—not to get too meta, but think about what is the soul of what they’re doing right now. I think that I don’t want technology and adoption of technology to create soulless marketing, and I think that you see it a lot out there where stuff is. Anybody can put words to a page. In fact, with deep learning and neural networks, we’ll get to the point where you could probably create a half-decent, readable blog post right now with just a machine and a few inputs. They won’t be able to replace us podcasters quite yet.

Ander: No, no. Not yet. I still have a job. Haha.

Sean: We still have time!

But if it’s just another piece of content you could throw in the pile, was that worth doing when you could have spent more time and maybe a little bit more effort to create something that somebody would truly enjoy? That’s putting more soul back into it, and I think that it’s the same with automation. The ability for AI to be able to streamline through small, tedious tasks I don’t think is ever going to change. That’s going to continue into many other verticals that we’re seeing beyond sales and marketing, but I still want creativity to be the main differentiation, and I think that no matter what your role is in a marketing team or even in an organization, whether you’re a start-up or a longstanding company, I think that there’s always going to be a great point of differentiation for people who are willing to be creative and move beyond the different types of buttons that they can push. I think that’s going to be hugely important.

Ander: Awesome.

Sean, this has been a fascinating conversation. I have learned a lot, and I think I can speak for the rest of us listening that this has been really, really cool. Once again, thank you so much for inviting me here to your office here in Mountain View—that’s where we are right now, right?

Sean: Yeah, you nailed it.

Ander: Yeah, so thank you so much for inviting me down here. It’s been a real pleasure connecting with you. You’re also the co-host of your own podcast, Stack and Flow.

Sean: Yeah, it’s been a lot of fun.

Ander: Please, provide a shameless plug.

Sean: Yeah, it’s StackAndFlow.io. I do the show with my co-host and producer, John Wall. People might know him from MarketingOverCoffee.com. I’ve known John forever since he first started on the scene with podcasting. It’s been a lot of fun, and it’s been a pleasant surprise as well. I didn’t know how enthusiastic people were going to be about the show, and to be honest, one listener was going to be a big success for us.

Ander: I hear you, yeah.

Sean: But it’s been a wonderful channel, and it’s been a great educational device for me because one of the things that’s really helped me do is be a better interviewer, which if you’re a communications professional, it’s all about practice. That’s your bicep curl. That’s the exercise that you just want to keep doing, and I get a chance to do that with John week after week. From members of the Infer family to some of the people that I really, really admire, it’s a great excuse to talk to them and improve those skills.

Yeah, I love any feedback that people have. We’re bringing in a lot of new topics beyond sales and marketing, starting to look at things that are happening in supply chain and really exciting integrations between sales and marketing and supply chain and what that looks like, so we’re really starting to broaden the conversation. Check it out, leave us a review, and let me know what you think.

Ander: Awesome.

Sean, once again, thanks for coming on the show, thanks for having me here at your office, and I’m sure we’ll talk to you soon.

Sean: Thanks. It’s been a lot of fun!