Latest / The Prompt / Ep. 569 | Jon Miller on how AI is breaking and rebuilding B2B go-to-market

Transcript

Ep. 569 | Jon Miller on how AI is breaking and rebuilding B2B go-to-market · The Prompt

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This is on base your one stop shop for solving your most
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pressing B2B go to market challenges.
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Each week, host Chris Moody and Paul Gibson talk to sales and
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marketing leaders to get you in depth insights and creative
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strategies to set your revenue teams up for success.
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Hey, everyone, welcome back to the Home Based Podcast.
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I'm your host, Chris Moody. Today, I'm very excited to be
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reunited with John Miller. We've been at hundreds of events
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together. Probably John, we competed for a
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little bit based in some of the content marketing automation
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space, but really excited to have you here to talk about
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rethinking growth with AI. Thank you for joining us today
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and welcome to the show. Thank you.
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I mean, it's obviously a fan of you, fan of demand based, fan of
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the podcast and excited to actually be a guest on this.
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So thanks for having me. It's weird.
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I mean, you were kind of one of the the main powers behind all
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the content. Now we have you here, it's the
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full circle journey and that's it.
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The first question we ask is really about your journey, and
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you can hit any of the high points or low points that you'd
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like, personal or professional, but tell everyone a little bit
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about what led you to what you're doing today.
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Well, I always like to start with the fact that I studied
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physics in college and actually thought I was going to go become
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an academic, you know, full time physics professor and
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researcher. But I took a detour into
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management consulting just to try it out and realized I liked
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it and have, you know, but used the physics, the quantitative
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background kind of throughout my career.
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I sort of fell into marketing technology.
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By happen chance, the consulting firm I worked with ended up
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buying this old mainframe campaign management tool, which
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they like redid as a client server system and spun it out
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and that that company into being successful.
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It's called Exchange, and it actually went public in the late
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90s. So when I was graduating from
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businessschoolinthemiddleofthe.com bubble, everybody's going to
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tech. I had never thought about
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working in tech myself, but that's what everybody was doing.
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And there was this company called Epiphany that was a
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pretty hot startup that was trying to build a campaign
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management software tool. And literally the fact that I
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just even had the loosest connection to this other company
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that they were a competitor to was enough to convince them to
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give me a job as one of their first product managers.
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So I started at Epiphany and that was like, I think the
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hottest IPO of 1999 went on to become one of an early winner in
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the marketing technology space. But then the, the Internet
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bubble crashed and Epiphany along with kind of everybody
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else crashed along with it. And in 2005, we ended up selling
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Epiphany for honestly cents on the dollar compared to where it
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had been. I didn't want to take a job at
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the acquiring company. And so I remember calling my
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wife one day and saying, like, Hey, I'm going to take the
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severance package instead of of joining this firm.
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And she freaked out because she was pregnant with our first
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child. You know, not that long ago we
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bought our first house, so we had a mortgage to pay.
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And I was like, no, it'll be fine.
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We'll, we'll work it out. And I spent a couple months
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interviewing for some rig their jobs, but I also had some
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conversations with Phil Fernandez, who was the president
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and chief operating officer of Epiphany.
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And he and I got talking that, you know, there ought to be a
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new kind of marketing technology company and this new software as
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a service thing that was coming around in 2000 and five, 2006
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seems like it's a pretty good way to sell marketing
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technology. And so long medium story short,
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sorry, long story medium, we we decided to get together and
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founded the company that it became Marketo.
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And the vision was to make powerful marketing software as
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easy to buy as Google Adwords because marketer, we recognize
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that marketers have money to spend, it's just as OpEx and not
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CapEx. And software as a service would
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allow us to give software in a way that they could buy the way
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they buy trade shows or Google dollars and things like that.
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So I think most people know the Marketo story, built that.
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I was the first, you know, CMO there.
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But obviously as a founder, you do all sorts of things in the
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early days. And I stuck with Marketo for
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almost 9 years through posts, through the IPO.
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But the thing about being a founder I realized is that if
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you're not the CEO, the bigger the company gets this more
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narrow your role gets. Just it's by definition you just
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keep taking hats off and giving them to other people.
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And after a while, Marketo started feel less like my baby
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and more like the job I worked at.
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So I had that sort of itch to leave and start something new.
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So in 2015, I left to start Engage You, which was along with
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Demand Base, an account based marketing platform and they'll
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engage you up for about a little over five years.
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The funny thing, you know, is even though Engage You in Demand
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Base for both ABM platforms, we rarely competed head to head in
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deals and we actually had a lot of companies that were using
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both because what we did was incredibly actually
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complementary, you know, between the two.
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So early or late 2019, early 2020, Gabe became the new CEO of
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Demand Base and he and I started talking and a little bit kind of
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we both shared our Rd. maps and frankly everything that Demand
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Base wanted to build for the next three years was the things
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that engage you had. Everything engage you wanted to
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build for the next three years was what Demand Base had, right.
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And we just, we were compelled that the vision of the combined
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solution would make the best platform out there.
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So in mid 2020, during COVID, we somehow managed to get a deal
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done and, and brought engage to demand base.
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I initially joined as the chief product officer, led the
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integration and then in 2021 became the CMO.
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So that was when we brought you on board.
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And obviously we helped to evangelize account based go to
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market, you know, at the time. But even after a few years of
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doing that, a demand base, that itch came back.
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You know, I was like, this is a good job, but it's still a job
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and I want to start something else new.
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So I transitioned out of demand base in late 2023, spent some
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time figuring out what I'm going to do and, and in 2024 I started
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a new company. So we're still in stealth.
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So I can't tell you all the details of what we're doing, but
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Needless to say, I think that the legacy marketing technology
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platforms, including Marketo are not innovating very fast.
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And I think they're certainly not keeping up with the age of
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AI. And that creates opportunities
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for some new solutions. And that's the story from
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Physics to Epiphany and Marketo and Engageo and Demand Base and
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now the new thing. I love it, and I think you've
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been branded as a lot of complimentary things in the
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past, but one is futurist. I hear that a lot.
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You've always had a great ear to the ground of understanding
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what's next. You've made smart moves based on
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that. What stands out to you about
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this moment in AI and go to market strategy that makes it
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unlike previous shifts in marketing technology?
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Well, it's not just shifts in marketing technology.
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I think most people would say what's happening in AI is
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probably the, the largest shift, you know, in, in technology that
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we've seen at, you know, if not the largest ever, at least on
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par with, you know, the rise of the Internet.
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It's interesting for two different dimensions.
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You know, the first one is the new capabilities that AI
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creates. We can make software with AI now
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that we could not have imagined making before.
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I like to somehow look back in history to the rise of the
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Internet as an example, right? Internet 1.0 companies took pre
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Internet business models and move them online, right?
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So you could publish content in the newspaper and now you can
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publish it online, right? You could sell stuff in a store
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and now you can buy it at Amazon.
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That's kind of where I think we've been with AI as a product
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capability the last few years, right?
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You could write an e-mail in the old days, and now you can have
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AI write the e-mail for you. The way we're marketing is not
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fundamentally changed, but we're using AI to support it.
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Back to the Internet, though, what happened over time is new
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businesses that could not have existed without the Internet
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started to emerge. Whether that is social
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networking or even things like Uber and Airbnb and whatnot,
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like fundamentally Internet native businesses that that
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required this new technology to exist.
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I don't think we've seen the emergence yet of the AI version
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of those companies. What is a business that, you
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know, required these new things as opposed to just doing things
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that we did before faster, better or cheaper?
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So that's the first kind of, I think big change, right, is the
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new capabilities that will come to bear in technology, you know,
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to allow new kinds of companies, new kinds of capabilities.
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But the second thing I alluded to is AI is also profoundly
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changing how people buy. You know, we're already seeing
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this with zero click search, right?
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They're getting their their results right on the search
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engine or in their LLM chop out of choice as opposed to, you
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know, going through to the web pages.
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We've already had some example of this in our e-mail inboxes,
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right? If if you have Google, Gmail,
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the priority inbox basically says, hey, this e-mail is more
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important than that e-mail. That's going to happen even
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more. We're going to have AI agents in
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our inbox reading the emails for us, processing them, drafting
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replies, and basically telling, OK, of all the emails, here's
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the seven that I really need you to look at.
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So when that starts having our inboxes, what does that mean for
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traditional e-mail marketing metrics?
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Open rates, click rates? Like they're going to go the
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same way that search traffic is going because of 0 quick search.
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So you got these two forces going on the same time, right?
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Like fundamentally new things that are possible coinciding
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with fundamental changes in how go to market needs to work.
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You called me a futurist. Thank you.
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I, I don't know, but I'll say I don't think I don't have all the
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answers yet. I don't really think anybody
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does. I think we're in that phase
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where we're kind of all figuring this out together.
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Yeah, I feel that too, John. This is the second podcast today
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and I shared this on the first one before we hit record and
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that figuring it out point is so important.
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The questions we have to ask ourselves and how we need to
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think about planning and what we asked for with budget, what our
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big bets are. Because it's a totally different
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construct with what we can do with AI and where we need to
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focus people to try to hit our numbers, to try to be more
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creative, to try to figure all those things out.
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As as we think about that figuring it out phase.
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What are some of the lessons that you think are our leaders,
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all of us need to carry forward thinking about AI that you've
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learned from the waves you've been through.
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And the, I do think the.com is the best example because in 99,
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I moved to Raleigh shortly after 99, but I remember hearing
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stories of Red Hat's initial splash and Ferrari and
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Lamborghini would park cars for sale outside of the office
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because it was such a gold rush. And I think we're filling this
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energy and surge and we don't really know exactly what to do
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with it. So I'd love to hear the lessons
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you're thinking of that we all need to make sure we're
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listening to. I think one of the lessons is
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that, well, they talk about this thing called the jagged frontier
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in AI. The jagged frontier is that AI
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is really, really good at helping humans with some things,
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and it's really bad at other things.
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You can write a perfect sonnet in iambic pentameter, and yet it
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can't even properly count the number of words in a paragraph,
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you know, or do basic math. And so I think one of the most
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important lessons is you just have to use this stuff every
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single day and try it and start to build that intuition around
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where the jagged frontier is. How do you get the best out of
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this? And it's just like how people
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figured out e-mail, right in the early days, right?
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The the people who did lots of experimentation with the e-mail
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were the ones who got ultimately good success out of e-mail.
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I think we need to do the same thing with with AI.
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And I think this is true whether you're a marketer or, you know,
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even like an engineer whose vibe coding.
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Of course, if you're vibe coding, you're going to see the
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same experience that a marketer sees when you're writing
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content, which is if all you ask for is like, write me a blog
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post, you get a pretty mediocre blog post, right?
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If all you ask for is, you know, write me some code, you're going
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to get mediocre code. It takes time to really learn
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how to use these things properly and effectively to get great
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results, to apply your wisdom and your experience to get the
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kind of results other people couldn't get, you know, using
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this tool. We just have to do that, you
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know, just like people had to learn how to search properly
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back in the day. Yeah, I think some of the
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parallels between us being on a podcast and what we have to
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think about as we listen and asking great questions.
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I find myself in AI, the better the question I ask, it will help
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me better solve what I was trying to solve.
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So instead of just the prompt, which that's where I started
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when I was first using AI, it was do this, make this, I will
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start with, I'm trying to do X. What are the things I should be
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thinking about to do a better job of getting what I want for
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X? And I yeah, personal example, I
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did that for my 14 year old baseball highlights.
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He had 7 strikeouts last night. I have all these videos.
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I don't want to go through and clip everything together.
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So I was just, hey, how can you make a highlight reel that I can
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send to him so he doesn't have to do it himself?
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Yeah, that's it. Don't use AI like a faster
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typewriter. Use it as actually, you know, a
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new form of intelligence that helps you think better.
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Right. Very important distinction.
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What do you see as the biggest opportunities using AI to have
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true impact and go to market execution?
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Because I, I think initial waves of AI usage, they were all
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efficiency based. But if you think about hitting
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pipeline, hitting revenue, where are some of the opportunities
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you're using AI? So let's start with the the
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changing buying dynamic that I alluded to.
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Sending more emails will not be, you know, even if they appear to
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be personalized is not going to work and stand out when
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everybody can do that same tactic.
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And we also get really good at sort of, you know, saying this
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is clearly still just AI slop. It's probably worse than not
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trying to personalize in the 1st place.
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So it really comes down to using AI to bet, to get better quality
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instead of better quantity. The way I sort of like to think
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about it is we've always wanted, we've, we've had this dream
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since my days at Marketo and beyond of, you know, the
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proverbial right action over the right channel at the right time
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for each person. We talked about that at Marketo,
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you know, literally like 18 years ago.
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The problem is that the traditional approach for doing
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that, whether it was, you know, Eloqua or Marketo or you name
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it, looks like some kind of Vizio diagram or set of rules,
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right? If this happens and they're in
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this industry and they are this persona and this is true and
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this is true, then you should take this action.
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And the more ands you want to do there to take into account more
15:33
nuances of situation, the more your process flow ends up
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looking like spaghetti, you know, to the point that is
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literally an unmaintainable set of rules.
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And so because of literally the limitations of the technologies,
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we haven't I think ever actually delivered on the promise of true
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one to one personalization. Now I think the real opportunity
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for AI and 1:00 to 1:00 personalization, as I just sort
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of alluded to is not write me an e-mail for Chris because again,
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I just maybe AI guess there eventually where it's good
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enough, but I don't think we're there today.
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But what I think AI can do is work through that super complex
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combinatorial math of, you know, all the possible segments that
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somebody might have and all the different personas and all the
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different behaviors and all the different actions and almost
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like mix up the right playlist of actions to take that's going
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to be relevant and engaging for that person.
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That sounded complicated. So let me use an analogy to
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explain it. When you go to your Instagram
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feed or your TikTok or whatever is your network of choice, you
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see a different feed of posts. And I see, and that's because an
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AI is sort of determining what are we are going to engage with
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and find relevant. The algorithm isn't creating new
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posts. AI isn't spontaneously
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generating. Here's a reel I created just for
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Chris. Right now.
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It's looking at this library of all the possible reels it could
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show, and it's saying this is the best one to show him right
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now. And then what's the best one
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after that and the best one after that.
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The difference between Instagram and go to market is Instagram's
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just trying to keep you on the page longer, right?
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To show you more ads. They only care about engagement
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in B to B. We care.
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What is the sequence of actions that's going to move this person
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closer to revenue, right? How do we get there?
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Not just that person, but they're buying group closer to
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revenue. And to the extent the AI can
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start to string together those playlists, to me that is the
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promise of 1 to one personalization.
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It's not mail merge personalization, but it's
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actually truly relevance at scale.
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I love relevance at scale. That's a great phrase.
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Saying evolution in the context of humans is not where I'm
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trying to go with this. But I do think there's a huge
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change in the roles we need to play as AI.
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Take some more of the heavy lifting.
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I'm curious how you think our roles should evolve based on
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everything that we're talking about.
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So the fact that we can use AI to understand things that before
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may have not shown up in any diagram, chart, data set that we
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had. You know, I like to sometimes
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talk about the nuance between what I call the artist and the
18:20
tactician when it comes to kind of go to market.
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If we want to simplify it to personas that are maybe people
18:25
listening, it's the demand Gen. field marketers and it's the
18:29
marketing operations specialists.
18:31
But we can generalize that to, you know, outside of just go to
18:34
market to kind of, you know, other pieces, you know, of AI
18:37
and or other other pieces in the enterprise.
18:41
So the artist, their job is to create the most compelling,
18:46
offer experience, content, you name it, that's going to really
18:52
connect with and engage some segment of the ICP that
18:57
companies going after. And I think in many ways, that
18:59
is a uniquely human task, right, of understanding the market, of
19:05
creating and developing relationships, you know, of
19:08
creativity and even craftsmanship.
19:10
And then you might even have an audience in mind when you're
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doing this, right? You might say, maybe it's just a
19:16
dinner. This dinner is for VPS and
19:18
above, right? Who live in Chicago, right?
19:21
You could be more nuanced than that.
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That's the artist job. But today with marketing
19:27
somebody then so also has to go and decide, well, who am I going
19:31
to actually go into the database and mail this thing to right And
19:34
like, well, I need to fight. I'll do a selective VPS in
19:36
Chicago, but I can't mail these people because I got this other
19:38
thing yesterday and I got to exclude these people because
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this is going on. And well, I don't want to over,
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you know, and you stand up with like this complexity of trying
19:47
to figure out what to actually send people and when, and
19:51
especially when there are multiple things you might want
19:53
to do. I might want to invite them to
19:55
the dinner. I might want to send them a
19:57
request to be a reference. If they're a customer, I would,
20:00
I might also want to let them know about my new product
20:01
announcement. Demand base does this, right?
20:03
I remember, I don't know if you guys still do this, but we used
20:05
to have a weekly meeting we called air traffic control,
20:08
right? Where it's literally like, sit
20:10
down, like we're really all upcoming campaigns and how are
20:12
we going to manage this complexity, You know, all that
20:14
kind of stuff. So that's, you know, that
20:17
there's a tactician whose job it is to figure all that stuff out.
20:21
But that's a perfect use case for AI, right?
20:24
As we just said, let the A, just like the Instagram algorithm,
20:27
let the AI figure out. We're going to show this to
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Chris at this time, and we're going to show this to John at
20:31
this time. So you end up with this
20:34
partnership right between the human and the AI.
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You know, where the humans are about understanding the market
20:40
and being creative and creating experiences.
20:43
And the AI is about handling the data and the tactical
20:47
optimization and a journey orchestration.
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And I so wrap that up. I, I, I think it's a pretty
20:52
example of just what we're alluding to earlier, right?
20:55
Like a human by themselves can do good work and then AI by
20:58
itself can do OK work. But usually I think the best
21:01
outcomes are when humans and a IS can actually collaborate,
21:04
using their own strengths to deliver something that's better
21:07
than either can by themselves. Absolutely.
21:09
I love that and it feeds nicely into what I want to talk about
21:13
next. And I'll give you the topic so
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you can be thinking, but it's on the the organizational or
21:20
cultural changes to pull this off.
21:22
And the thing that comes to mind for me and a lot of SAS
21:26
organizations that skew tech heavy, there's always been the
21:30
notion of shadow IT and folks who go out and buy things and
21:34
don't tell everyone else. And we've got this cool tool,
21:36
but three people use it. AI feels similar to me because
21:41
you may have an organization where two people are using it
21:44
well, and then you can just go down the maturity of how they're
21:47
using AI. And it's not a cultural norm.
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There's not governance, there's not a conversation around, hey,
21:55
what prompts are you using? What's working well for you?
21:57
What are the use cases? So I'm curious, through all the
22:01
changes you've seen, you've been at innovative startups, you've
22:04
been acquired, you've seen all that multiple times.
22:08
What are the changes that we need to make as organizations to
22:12
make sure that we're adopting an AI first go to market model?
22:16
Yeah, I mean, the technology is in many ways the easy part and
22:20
and the hard part is getting the humans to kind of, you know,
22:23
work with the machines. So as I alluded to, I think
22:26
we're all still figuring some of this stuff out.
22:29
But I think one thing is like super clear is that it starts
22:33
with leadership setting the expectation that your work
22:39
involves and requires you to partner with AI and everything
22:43
that you're doing. And then I think once that you
22:45
know, and then leadership passing, make sure then the
22:47
tools are available. I've gone and given speeches at
22:50
conferences, you know, and talk about all these cool things that
22:53
you should be doing with AI. People come up with me
22:56
afterwards and like, you know, my company doesn't let me have
22:57
access to ChatGPT. I'm like, maybe you need to work
23:01
somewhere else because that company is not is going to fall
23:05
behind. So it starts from leadership
23:07
allowing it and basically really saying like this is what the job
23:10
is and then training and skill development.
23:13
It's exactly as sort of as I alluded to earlier, right?
23:16
Whether you're writing content or writing code, it takes time
23:19
to learn. How do you use these things to
23:22
get superhuman level output out? Like AI won't do it by itself
23:27
and you won't do it by itself. So how, you know, invest in the
23:30
training to help people really learn and understand you, all
23:34
that kind of stuff. So that includes the sharing
23:36
prompts and, and, and things like that that you're talking
23:38
about. Although as a sidebar, I had
23:40
something a minute I wanted to say earlier.
23:42
There's this thing going around these days that is even less
23:44
about prompt engineering. And now it's context
23:47
engineering, right? Because the new models are
23:49
pretty darn good at figuring out what to do, like reasoning, you
23:52
know, so your prompt doesn't need to be quite as much do
23:55
this, then do this. Then it's like quite chain of
23:58
thought. You don't have to do that as
23:59
much yourself in the prompt, but what you do need to do is give
24:03
it all the context. And so training people on
24:06
context engineering I think is actually really important.
24:08
Yeah, I love that. There's a video that my wife and
24:11
I both watched separately in our feeds, which is always
24:15
interesting when that happens to the AI and the algorithms.
24:18
But it was a teacher and the prompt that she gave her class,
24:22
which was an elementary school class, it was described how to
24:26
make a peanut butter and Jelly sandwich.
24:28
And she would re enact or, you know, visually show them what
24:34
they described with peanut butter and Jelly.
24:36
And if they forgot a step, she's actually putting it on her arms
24:40
or whatever. And it was such a simple visual
24:42
that felt very similar to what we're talking about, because
24:47
without the right context, missing a step, setting the
24:50
stage, you could be solving something in a very different
24:53
way than what you intend. And I I do think there's a
24:56
parallel that we can find there. That's all, all accurate, you
25:01
know, it. It also, I think just to me,
25:03
like just gets to the point of like almost like as I said
25:06
earlier, with technology, the easy stuff is to use AI to
25:09
automate the stuff you're already doing, right?
25:11
The harder stuff is reimagining what could I not do before that
25:15
I can do? I, I think the companies, not
25:18
the, not outside the tech vendors, but like just the, the
25:20
users, the marketers who asked that question and, and really
25:24
kind of reimagine some things are the ones that are going to
25:27
be the most effective, the most successful in the age of AI.
25:31
Yeah, I have a few questions left before we jump into the
25:34
high pace lightning round. You know, insert pyrotechnics.
25:38
I need that in my office, like shoot off some fireworks.
25:41
But the first is on storytelling.
25:44
You've built incredible brands. You've been on the forefront of
25:48
trying to create new categories and explain how to take very
25:52
complicated solutions and make it a little easier to execute
25:55
and hit your result. And you're doing it now.
25:57
You're building a stealth company, so you'll have the
26:00
storytelling and the brand building that will happen as
26:03
soon as you can talk about it. How do you approach storytelling
26:07
for complex products? So I've always, since my marketo
26:13
days and engagement demand base, you know, approached creating
26:17
content that was really focused on what the audience wanted to
26:24
hear or needed to hear more than what I wanted to say.
26:27
I always say it needs to either be educational and valuable or
26:32
at a minimum entertaining to the target personas.
26:36
We made some fun entertaining videos back in the day at Demand
26:38
Base as a perfect example of, you know, it's OK, just if it's
26:42
entertaining and fun. You know, way too many companies
26:45
get so wrapped up in who they are and what they do that they
26:49
miss out on just you know, what's valuable or entertaining
26:53
to my persona. When you embrace it that way,
26:55
what's really interesting is it gives you the freedom to talk
26:58
about way more stuff than specifically narrowly focused on
27:02
what you do. So that's one factor.
27:05
I think another factor is I think way too many people ignore
27:09
the emotion. You know, stories work
27:13
inherently because there's emotion, there's tension, you
27:16
know, there's some drama that needs to be unresolved.
27:19
There's other emotions you can kind of play in there, but I
27:22
think the best storytelling kind of taps into an emotion.
27:26
So as just an interesting example for that, like, yes, I'm
27:30
building this new company and stealth, you know, why am I
27:33
doing that? A big part of the reason I'm
27:35
doing this in stealth is not because, Oh my gosh, I'm afraid
27:38
people are going to copy my like idea.
27:40
That's like who? So amazing idea.
27:42
Like, I mean, listen to this podcast.
27:44
You can probably start to figure out what what I'm doing, right?
27:47
The reason I'm doing this in stealth is to create some drama.
27:51
There's a velvet rope up, you know, behind kind of what I'm
27:55
doing and I want to create some excitement like what's going on
27:59
in there, right? How do I get in?
28:00
So that way when I finally do kind of open up the velvet rope
28:03
and, and unveil this thing, right?
28:06
It hopefully it's got like all this potential energy that's
28:10
built up. Now I can quickly turn that into
28:12
kinetic energy and look how look how fast we're going.
28:15
Hopefully that doesn't sound too self-serving, but I just think
28:17
it's an example of a form of BDB storytelling that most people
28:22
don't use right? And it's all about tapping into
28:25
that emotion. Yeah, I think that was perfect.
28:27
One more question then we have some very fast ones this.
28:31
The challenge here is you can only say one thing.
28:34
So it's a limiting factor here. What is the single biggest
28:39
challenge in B to B marketing today?
28:41
That one's easy to answer. The single biggest challenge in
28:44
B to B marketing today is the mismatch between the
28:48
expectations for how people want to measure marketing and the
28:52
reality of how marketing actually operates, right?
28:56
They want MQL, they want to say, show me the impact you're having
28:59
on this quarter. But buying is not a simple
29:02
linear machine. I like to say it's not a gumball
29:04
machine. It's not campaign in
29:06
opportunities out right. It's this complex non linear
29:09
process where oftentimes, you know, the reason why somebody
29:13
bought today is a touch that happened three years ago, five
29:16
years ago, maybe last week. You don't know, right?
29:19
Was it something you did, something that somebody else
29:21
did, a conversation they had in a, you know, at an event you
29:24
don't know? So the, the reality is the, the
29:27
drive for NQLS and the short term pressure that the current
29:31
measurement metrics put on marketers forces them to do the
29:34
wrong thing that I think is actually harmful to the customer
29:37
relationship in the long term. We need to think about new ways
29:41
of how we're going to measure marketing that's better aligned
29:44
to the way marketing actually works.
29:46
Perfect. Yeah, very easy to do, right?
29:50
No. Fair enough.
29:54
I agree with you all right, we have some quick hitters before
29:56
we close out and the most important question we will save
29:59
for last. The first though, what are your
30:01
favorite tools or technology that you're using today?
30:04
Let. Me First off by like just
30:05
challenging the kind of the tool first mentality.
30:08
You know, as we sort of talked about, there's too many
30:11
marketers looking for, Oh, if I just buy the next tool, then all
30:15
of a sudden all my problems are going to go away.
30:17
You know, when instead, do you have the right fundamentals in
30:21
place? Do you have the right product
30:22
market fit? Do you have the right
30:23
positioning? Do you have the right brand, you
30:25
know, in place? Don't worry about what tool
30:28
until you have that stuff figured out, right, I'm going to
30:31
give a you know, that said people want to hear about tools.
30:33
That's fine. Couple that I am big fans of
30:36
these days is I think you use this yourself descript for a
30:39
video editing is one that just you know, has sometimes that
30:42
feeling of like, OK, this is close to a magical experience
30:46
for editing things like this. Not something that's not in go
30:50
to market per SE, but I've really been a fan of recently is
30:55
a company called Crosby, Crosby dot AI, which is an AI enabled
30:59
law firm. So when I have a customer
31:01
contract I need reviewed, I send it to them over slack and within
31:05
3 or 4 hours I get it back with red line markups under fave
31:08
legal ready to go back to the customer, including like here's
31:11
what I should say to the customer.
31:13
But it's not just an AI doing it.
31:14
It's more than just they ran it through ChatGPT.
31:17
Then it's actually a law firm that has lawyers that are making
31:20
sure that when I'm getting back, you know, they're following
31:23
their kind of fiduciary responsibilities.
31:24
So big fan of a couple of those tools.
31:27
That's awesome. I love that.
31:28
And definitely strategy first. I think we could have a whole
31:30
separate podcast about how total first leads to turn.
31:34
Kind of starting with that one, absolutely.
31:37
What about Goodreads? We're really generous with this.
31:40
It could be book, blog, newsletter, podcast, any type of
31:43
content. But what are some of your
31:45
standouts that you find inspirational?
31:48
As Aceoi really focus a lot on how to build the right company
31:53
culture and environment that I want.
31:56
So for me, my Bible on building a healthy company is Patrick
32:01
Lencioni's The Advantage, which rolls up his writings from the
32:05
sort of summarizes the ideas from all of his other parables
32:08
that he's written. It's actually more impactful if
32:11
you read the parables first. Things like 5 dysfunctions of a
32:13
team and death by meeting and all that kind of stuff.
32:16
But The Advantage itself kind of brings everything together.
32:20
Another one is Setting the Table by Danny Meyer, the founder of
32:24
the Union Square Hospitality Group.
32:25
He really talks about catchphrases, like just the
32:30
things that you just say over and over and over again to help
32:33
define a culture. And I think that's that's
32:35
really, really powerful. And my last one is Turn the Ship
32:38
Around, which is a book by AUS Navy submarine captain who
32:46
metaphorically turned his ship around from the low performer to
32:49
high performer by basically empowering the sailors to stop
32:54
asking for permission. He told them instead of asking
32:56
for permission to do something, just tell me what you intend to
33:00
do. And that simple language change,
33:02
you know, really, you know, unlocked a lot of empowerment
33:05
for people. So, so those are some of the
33:08
cultural business books that I go to and like to recommend.
33:11
Great examples. The organization I was at before
33:14
Demand Base, my boss, the Advantage was required reading.
33:18
So we talked about that a lot. It's a great one.
33:21
What about the the people who inspire you, who stands out in
33:24
the B to B space that help you stay in a a good mental state
33:29
for what you need to try to go and do?
33:31
1st, I'll give you Chris Penn, who is, I think the, you know, I
33:35
learned more about AI from him then I think perhaps anybody
33:39
else. So he's got a great newsletter,
33:42
a great blog, he's got a course, a book you can buy.
33:44
I highly recommend all that. Kieran Flanagan also is doing
33:49
some really good he's Medium newsletter and whatnot, you
33:52
know, with AI where he just shares super detailed prompts
33:56
that are, you know, even if I don't use them directly or are
33:59
kind of worth seeing how he thinks about, you know,
34:02
prompting and all that kind of stuff outside of AI.
34:05
You might have had her on the podcast, if not, sometimes, if
34:07
not, you should. Kathleen Schwab is an ex IDC
34:11
analyst who recently published a book called Marketing in the
34:13
Great Big Messy World. And she more than anybody else,
34:17
I think, just nails this task of explaining that marketing is not
34:21
a simple linear machine. She says it's not a vending
34:24
machine, but it's complex and non linear in ways that are more
34:29
like the stock market or the weather.
34:31
Great recommendations. We are right on time.
34:33
Within the minute, we have one more question.
34:35
Most important one How can everyone get in touch with you
34:38
after this podcast? Best ways on LinkedIn?
34:41
I post on weekly on LinkedIn multiple times a week, so follow
34:46
me. Feel free to e-mail me, comment
34:49
on my stuff. That's the best way to reach me.
34:51
Outstanding. Well, John, it was great seeing
34:53
you again. Thanks for hanging out.
34:54
Thanks. Look forward to seeing you in
34:55
person in a couple weeks. But yeah, it was awesome to have
34:57
this opportunity. I appreciate it.
34:59
Talk soon. Thanks for joining us for On
35:03
Base. If you enjoyed this episode,
35:04
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35:17
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