Sean Lane 0:01
Hey everyone, Sean here. Before we get to the show today, I have an exciting announcement for you all. My go to market services, business minded light Consulting has a new name, new look, new feel, beacon GTM. And we are still doing all the same type of work at Beacon GTM, we are helping founders and revenue leaders improve their go to market execution. One of my partners is a former sales leader, one of my partners is a former customer team leader. And with my background in OPS, we've got a pretty good chunk of the go to market spectrum covered. So we're excited to unveil this new brand for everyone to check out, head to Beacon gtm.com To learn more about it. Alright, that's enough of that on with the show.
Hey, everyone, welcome to operations, the show where we look under the hood of companies in hyper growth. My name is Sean lane. When I look around the tech marketplace right now, I'm really encouraged by the fact that we are clearly making the transition from Ai hype to AI execution. We're moving past the days where people would just put AI or machine learning on their website in order to garner some extra interest. And we're getting into real concrete use cases that drive outcomes that companies and those outcomes just happen to be leveraging AI in order to get there. We're all about tangible examples on this show. So I wanted to talk to someone who is living and breathing those real use cases every day and could share them with the rest of us. I found that someone in Kyle Coleman, CMO of copy AI, longtime listeners might remember that Kyle was on the show back when he was the CMO at Clary. And now he's spending his time building copies go to market AI platform. Also, by the way, he just launched a podcast of his own that's called Future proofed, you should go and check that out. After you're done listening to this one. I caught up with Kyle just a couple months into his tenure at copy AI. So what's great about this conversation is that he's on just as steep of a learning curve as the rest of us when it comes to applying AI to our go to market execution. In our conversation, we talk about what Kyle means by designing for more upstream AI workflows. We cover why operators are critical in creating leverage through AI, and how his team wants accidentally created a blog post about how to use AI to write obituaries. Let's jump right into those real world use cases though. As someone who is new to his business,
Kyle Coleman 2:26
I wanted to know what Kyle had learned about AI use cases that he didn't really necessarily expect coming into the business. So one of the biggest challenges that we have that I have, as the Head of Marketing here at this company is demystifying the art of the possible which is to say, show people what they don't know about what they can do with AI because I think what certainly happened to me, Shawn, it seems like it happened to a lot of different folks is chat GPT launched, you know, 1218 months ago, whatever it was, and the CEO calls us emergency all hands and says you need to use AI and everybody's like, okay, and so they went and got a chat GBT license and they logged in and they see the blinking cursor. And look, what do I do? Like how do I act? And so there needs to be more prescriptive. pneus I think there needs to be again, some demystification because right now, the use cases, it's not that they're not obvious. They're just not really well prescribed. And they do require a certain skill set, whether that's prompt engineering, or some sort of Systems Integration, or a combination of those two things. And that's what has been really eye opening to me is just like getting a menu of options as a starting point, versus trying to invent from whole cloth, what you're trying to do with AI is a totally different approach. And that's something that's been really, really interesting to see from my own personal journey. I can talk about the specifics, but I'll pause there and see if that resonates. Totally.
Sean Lane 3:50
And I think, you know, giving people a starting point to help them understand the art of the possible is true of any new technology, right, have any new use case. And so you all seem to be focused on primarily marketing and sales right now in terms of those use cases. So help folks go through that demystification process and help them understand what might some of those starting points or some of those starting problems might be? Yeah,
Kyle Coleman 4:17
sure thing. So a lot of the use cases that I was thinking about prior to starting a copy AI and I think a lot of sales people aren't used to seeing revenue operations people are used to seeing is from the existing SAS vendors, whether that's clarity or outreach are gone. So a lot of these vendors have these use cases that are really useful, but they're useful to the super downstream end user. So the individual AE can get a AI generated meeting summary and a follow up email or the individual SDR can get, you know, an outbound written email or something like that. And then that's very useful. But what I have seen and what I'm excited about for operations people is these use cases that are way further up the stack, like you can enjoy Direct AI into your CRM in ways that I just really hadn't thought about. Because again, the art of the possible it just hadn't it was opaque to me. So now seeing the way that AI lead enrichment, AI account enrichment, AI lead scoring, and the way that that can then dictate so many downstream processes and make so many people not just more efficient shot, but more effective. Because the leads are better enriched, the lead scoring is more qualitative and more of a thinking agent that saying, this is a good lead, because XYZ reasons I can base that rationale off of this super robust set of information about the lead and about the account, I can match that to what you know, my company views as a high quality lead. And now I can have a much more flexible, strategic, thoughtful lead scoring system. And that has so many benefits for everybody across the organization. So just like thinking like that, and moving AI up the stack, and having operations people being the ones just like you do with so many other systems, being the ones that are architecting, the way that AI is infused across the go to market, that's the big unlock for me. And that's where we're starting to see go to market teams really achieve that velocity is when they can move things up and have aI affecting way more than just individual downstream use cases.
Sean Lane 6:23
This distinction between optimizing for the downstream end user use cases versus designing the entire end user experience further upstream, was one of my biggest takeaways from my conversation with Kyle. Now, whether you're using copy AI, or some other vendor, the idea of operators getting to mold and design all of these workflows that are happening under the hood. And then you just surface the magical outputs to the AE or the end user. That's super compelling to me. And look, maybe that's just my bias operator's perspective. But even with that bias, I actually think that Kyle's examples, don't narrow down who can contribute to these use cases, it's actually the opposite. Now, you don't have to be a data scientist in order to play a role in the lead scoring model. These AI driven workflows empower you or anybody to get creative, and then make leverage for the rest of your team in order to achieve your goals.
Kyle Coleman 7:22
Here's Kyle with more. That's exactly right, John. And so let's use if you're selling to salespeople, this is I think, a pretty good example, because sales, people's titles are completely arbitrary. You know, you could have a sales director, that's not a director of sales, they are an individual salesperson that is an account director or an account executive or whatever it may be. And so it kind of a classic lead scoring model, if you're using Marketo, let's say, you would have some title based scoring that says if their director or above, give them 10 points or you know, something like that. And then what happens is you end up passing off a lot of individual contributors to your team for follow up. And that's maybe not ideal. But in the AI enrichment world, the AI is able to go to that person's LinkedIn profile, look at their work history, look at their work experience, look at their, what they're responsible for, infer where they are up or down the hierarchy, and then make some sort of assessment on their ability to, you know, evaluate or move a deal forward or be a buyer, whatever it may be. And so as an operations person, you're able to define those things, crisply and easily. To your point, you don't have to be a data scientist to go in qualitatively, our lead scoring model, literally, Shawn, what it says is the most senior sales and marketing people, that's what it says, And the AI can go and you know, write the queries and do the inference and do the research and everything else. And then the confidence that our sales team has in the lead scoring model actually exists, because they know that we're screening out the right folks, screening and the right folks, etc. And a lot of that is totally controlled by our one person operations team.
Sean Lane 8:56
Do you think that that also kind of lowers the barrier to entry for companies that might not have like a treasure trove of data that they're sitting on, right? Like I'm thinking about earlier stage, companies who might naturally look at a lead scoring or target account exercise as very difficult because they don't have, you know, a ton of historical actuals to lean on in these types of exercises, it seems like this kind of might open the aperture a little bit up for them to be able to leverage AI to help make some of those inferences. And by thinking about that the
Kyle Coleman 9:28
right way, 100%, John 100%, there's so much information about a person and what's always been, I'm just going to keep on this hobby horse of lead enrichment, because it's been such a pain point of mine for so many years, and makes a lot of sense. And so the other factor here, Shawn, is it's not just that person. And yes, we know a lot about them individually, and their work experience and their title and the role and all that, but it's the account as well, that makes a huge difference here. And so similarly, the AI can go and look at not just the website or a 10k or something like that. I can go and look at real live news. What is going on this week today at that company? What's the good news? What's the challenging news? How should I then inform the lead score based on the personal information we get about the person and their role and their title, their persona, but also what's going on at that account? Is there some sort of indication that this account is in the market for a solution like ours, and why and so now the AI can reason, uh, you know, and find the intersection of that Venn diagram that says, Mr. or Mrs. salesperson, internally, I'm handing you this lead, because XYZ reasons for the lead ABC reasons for the account, go get them you have everything you need. And to your point, that's a much more robust, quote unquote data set than most especially early stage companies ever have. And getting that data, it used to be either very expensive or not possible, or very manual. And
Sean Lane 10:53
I think it also probably helps a lot with the maintenance aspect of it, right. So like, well, back to the Marketo example you're giving before with the point system, every six months, four months, depending on the company, maybe 1218, someone that has to go back and revisit that model and say, okay, to the factors that we pick 18 months ago still make sense? Or have the weightings shifted? Is our data stale, right. And I would imagine that this also makes the ongoing maintenance, but also the more real time adjustments to the model a lot better for those end users. Because ultimately, you don't have to have this massive group exercise to make those updates
Kyle Coleman 11:37
100%. And here's a pretty easy example, you are working for a company and you are introducing a new product line, whether that's something that's developed organically, or whether it's via m&a In the old school world, let's say let's just use copy as an example. Right now, we're primarily focused on selling the sales and marketing people, what if we develop capabilities where we now need to go sell to a more technical audience to a product person, and the old school, we would have to go and create a whole new lead scoring model that says Product Manager or CTO or all this other stuff. Now, we just change two words in our lead scoring model and say, the most senior sales, marketing or product people. And then like we're done, we're done. And we can monitor that. And then we can score or look at the results of how well our our tier one, two and three leads, converting the you know, from lead to opportunity, and do we need to make or sharpen our lead scoring model in any sort of way, if we start to see our tier twos, converting at a higher clip than tier ones be pretty surprising. But it's a very easy way to monitor that thing.
Sean Lane 12:36
My guess is that some people listening right now we're having a little bit of a look in the mirror moment, because I'm positive that some of you have avoided projects like lead scoring or target accounts. For some of the now defunct reasons that Kyle just ticked off our datasets too small, someone will have to maintain and update the model, or, Oh, we're about to launch a new product to the market. And so our current model won't work. In a world where these excuses go away, ask yourself, what are the barriers that you could remove from your team's day? Or what are the low value activities that they're currently doing that you could completely eliminate? These aren't new questions, by the way I get it. Operators have been asking these for years. But now we've got more powerful ways to answer them. According to Kyle, it's again about that upstream experience, knowing which questions to ask, and leveraging the customizability that is now available to us.
Kyle Coleman 13:33
This example is top of mind for me right now, given my most recent experience at Clary. And now the way that we're running our Opportunity Management here at copy, a lot of what you've seen from other revenue technology vendors, is, you know, we can take a sales transcript, and we can find medic information, and you know, whatever, it's useful. In some cases, it's useful, but it's not super configurable, it's not necessarily your process is maybe not configured for the way that your buyers want to buy. And so now, a lot of those use cases are useful super downstream for the individual rep or for their manager and their coaching moments. And again, useful. But imagine a universe Shawn, where the revenue operations person is getting to say, here is what good looks like at different stages in our deals. And I can define this qualitatively with sentences with you know, natural language, I can say, I know that in stage one XYZ needs to happen my entry criteria, this my exit criteria is this. I want the AI to go read through my CRM history and build a model of what good looks like and then apply that model to what I know needs to happen to move a deal forward. And so now instead of the AI forecasts being a downstream individual, a your sales manager exercise, now it's an org wide exercise, and the revenue operations person has skin in the game and their fingerprints on defining that forecasting methodology, defining the way that the AI is doing. Its research and making its inferences providing its feedback and strategies and having an org wide impact. And so now the operations person is responsible for defining that org wide. What does this forecast what should a deal score be at this company, by segment by industry, by whatever it is configurable, again to your business. And then that is the definition that's informing the insights that the AI gives you. And that's a totally different paradigm than, again, what most I believe most operations people are used to seeing or haven't seen for the last 12 or 18 months from the AI capabilities.
Sean Lane 15:35
If I keep pulling that thread, right, and I know what good looks like, what do you think the output of that looks like now for those end users? Right? Like, instead of just a score that says, you know, your deal is a 74? Like Good luck with that, right? Like, what do you think that then becomes if the DevOps folks are doing a good job at that kind of upstream design? What does it look like then for the end users? Yeah,
Kyle Coleman 16:02
so again, totally configurable for you for the operations person, the way that we have it internally here, Sean is we use Salesforce as our CRM, and we have this deal scoring AI workflow that's running in the background, evaluating every deal. It scores, the deals based on the way that our operations person to find out one through 1010 Being good one being bad. And it says, Okay, this deal is a seven. Here's why it's a seven. You talked about a timeline, but we're pretty close to that timeline. And we haven't exchanged any paper yet. And by this point, in a deal, I would normally expect to see some multithreading happening, but you're single threaded with this one buyer who has authority, but you should get in touch with this persona and this persona, in order to strengthen the deal. And so it gives a score, it gives a rationale as to why and it suggests real meaningful, bespoke to that deal. Next steps that are again defined by the operations person who configures what a deal score looks like.
Sean Lane 16:58
I'm glad that I'm talking to you, early in your copy AI tenure, because you have I'm imagining are on this crazy steep learning curve right now. For the ops folks that are listening to this, who want to join you on that curve and figure out, just go back to the very beginning of our conversation, the art of the possible here, like, help them like what are you turning to? How are you learning, like,
Kyle Coleman 17:23
there's so much garbage out there about AI, I want to sift through that and get to the part where like, people can actually use this and bring it back to their companies. Right, right. And so I should have said that all that deal score information is getting written back to fields in our opportunity records. So now everybody has full access to what is the aI think is happening, and our forecast call Shawn are super interesting, because there's nowhere to hide. For a salesperson. I've seen more often than not, when the human is disagreeing with the AI score, the human is like, Yeah, okay. I actually have a, I have a multi threaded enough, okay, I gotta go do that. So the way that I'm learning is we have this concept of workflows in our product. And what workflows are shown is effectively their actions that are chained together to produce some prescribed result. And an action is effectively a single prompt that you would send the LLM. So let's use a pretty simple account planning workflow. Out of the box, if you go to copy AI, you can start a free trial right now, if you wanted to just go to copy AI, sign up for free. Out of the box, we have the sales workflows that are pre built that say build an account plan or do Company Research. And when you click onto that workflow, you can see the breakdown of the chain together actions that are input A, so the user is the input the URL for the company. And then the set of actions are go scrape their website, go look at their press releases, go look at recent news reports, and then format an output, go look at industry terms, or whatever, and then format an output in XYZ sort of way, all five of those steps that I mentioned, if you could go do that and check GPT. But you would have to be the one doing the prompt engineering, you'd have to do on writing the prompt to the LLM, copy, AI does all that for you. It's all pre configured. Now you can go and you can tune it. And you can say you know, but format the output this way or that way, or add this piece of research or whatever it may be, you know, I want you to, I'm selling the startup. So go look at CrunchBase and pull out their investors, you know, whatever. And so that's the difference is that you get these pre built workflows, and this menu of options where you say, Oh, that's cool, I didn't even know I didn't even think that we could do that. And then you can go do it right now immediately, with little to no effort. Or you can go and you can build your own workflows. So let's say you want to run a workflow where you take a sales transcript and turn it into a LinkedIn post, or take a podcast in your case, Shawn, and turn it into a LinkedIn post. You can go into our workflow builder module, and in natural language you say, given a transcript from a podcast, write an SEO optimized blog post and a LinkedIn post to promote it. That's all you have to do you write that sentence, you say, build the workflow, and then copy is going to go. And it's going to build the right actions with the right prompts, chain them together, and then you've got your workflow. And now all you need to do is copy paste the transcript, and you get everything else you need for the rest of time. And so like demystifying the art of the possible with a prebuilt menu of options, and once you get a taste of what's possible, I've noticed that the inventiveness that individual users and myself has, it just becomes exponential, and there are infinite use cases. And then they go building those use cases to make it natural language. And like codeless is really, really helpful. Isn't it amazing
Sean Lane 20:38
that the more things change, the more they stay the same? Yes, we have this amazing new technology. And yes, companies like copy AI and open AI are inventing new and exciting ways to leverage that technology. But at the end of those workflows, there's an end user who just wants the answer. They just want an output. And they do not care how that output got to them. If an account plans bits out in a format that is useful to them with complete and accurate information that they didn't have to go and manually research themselves. Guess what, that's what's most important to them? Not the prompt engineering, not the underlying large language model. It's just the answer. And we as operators can play a role in crafting the experience that gets to that answer. And Kyle told me that he's hearing the same sentiment from the customers that he's talking to
Kyle Coleman 21:30
100% churn, and let's just keep using account planning, because it's something that SVP of Reb ops at a company called tropic was his name is Anthony, I was working with him the other day. And he's a former enterprise seller, enterprise sales leader, and he made that transition into revenue operations and strategy. And he is super anal about account plans. Like he has his concept of what good looks like he has very crisp rationale as to why that is, and importantly, Shawn, all of the enablement that he will then give to his reps cascades out of that account plan, so you damn well better know, their strategic growth initiatives, so that you can go and execute this type of outreach in this type of sales meeting and this type of discovery in this demo, whatever. And so as the operations person would Anthony can do is he can go into our build account, plan workflow, and he can customize it for the things that he knows he needs for his sales methodology. And then he is the one instead of checkout the change management, instead of trying to go to 100 individual reps and saying, Here's how to read the 10k, here's what you need to pull out. Three out of 100 people actually do it. Now, you've codified best in class, and you have it automated, so that, you know, you're giving your sellers the best shot to go show up well in a cold email or show up well in a discovery call or demo, or whatever it may be. And that's the power that red ops people are gonna have now is, it's not just about the systems integration. It's about the thoughtfulness and the things that need to happen to truly affect the sales cycle, which is what the best ops people do anyway. And now it's going to be much, much easier for them to do that.
Sean Lane 23:04
The other thing that I think comes out of it, whether it's an account planning use case for the forecasting use case you were describing before, or research on an account, everything we've been saying for years around look like this tool is going to be the thing that allows you to do the stuff that you actually want to do like you want to actually sell this is the thing that's going to allow you to get back to selling and focus on high quality activities, versus low quality ones. And I just don't think we, you know, obviously things have gotten better, but we just didn't have the full capabilities to actually do that. And so now if you have you know, let's say with your manager, a account plan, review where you're supposed to get together with the AE, the CSM, the account manager, whoever it might be, instead of being interrogated for the first half of that about why you don't have the right information required for an account plan, you guys can actually spend the time planning for the account strategy. And the same thing for like, if you're going into a deal review, instead of the rep scrambling for like, man, what happened on the last call did I ask for the neck than the right next steps? Like I forget who the economic buyer versus the champion is like that stuff to me feels like the stuff where you can click a button, and you have what you need. Exactly.
Kyle Coleman 24:14
And to your point, Shawn, it's clicking the button and having what you need or delivering what the team needs, where they already are. You don't need to require them to log into a new app or have you know, whatever the what's the status, something crazy, like SAS sales teams have something like 30 applications. And it's like how the heck, like people are spending 11% of their time every week, just switching between apps, like it's crazy. And so if you can as an operations person, meet them, you know, quote, unquote, in the flow of work, if you want them in Salesforce, make sure build all these fields on account records on lead contact records on opportunity records, and have the AI inform those fields right there in Salesforce. If you want them in quip if you want them in Google Docs sucks if you want it in Slack, whatever, whatever your systems are just designed the API to go and hook up the right zaps, or API integrations or whatever to go feed those systems. And now, again, you don't have to worry about change management, you don't have to worry about reps using or not using the product, you know, that your design is getting implemented in the systems they actually use, there's zero excuse because a rep doesn't have to do anything. And then to your point, they don't care where the data comes from, if it's useful data to them, and it's meeting them where they are, you're checking to enormous boxes, which are huge barriers for change. And
Sean Lane 25:34
I think the end result, or the goal for ops people, and I've been thinking about this ever since the last time you and I chatted is leverage, right? Like you are trying to create as much leverage as possible, which is not new for ops people. It is literally why operations teams exist in the first place is that companies believe that by hiring and paying these people, they can get more yield productivity, leverage whatever word you want to use from the rest of the team than it costs to pay that person. That's literally why we exist. And so it feels like
Kyle Coleman 26:09
everything you're saying about ops, being the folks that can design this is perfect, because we're there to create leverage in the first place. And now we just have a different mechanism through which to create that leverage. It's exactly right, we could talk about any of these use cases we've already talked about. But I'll just keep harping on lead scoring because former SDR here, it's near and dear to my heart, my responsibility as an individual SDR managing a team of I think I have like 65 STRS, under my charge at Looker, at some point, it was a volume game, it was like you're gonna get 3000 leads this week. And your team is responsible for calling down on all of them and trying to qualify them in or out. And, you know, this was a time zero interest rates. And we could get away with this and all that. But the leverage and operations team can create now is it's not handing off 3000 leads, it's handing off the right 100 leads that are satisfying the lead scoring and account profiling that we have defined. So now instead of an SDR, frankly wasting time listening to a phone ring for five hours a day. Now they say these are the 100 people, they're super well qualified, they're high intent, they have the right criteria from a lead from an account standpoint, now I just need to focus on these 100. And it's not to say that you should necessarily have fewer STRS. It's to say the work they're doing much more impactful. So you the operations team are going to be providing leverage for yourself, multiplying yourself, but also leverage across every other role, who are frankly, doing things in pretty archaic ways right now, and are doing things in ways that are not scalable. I mean, you look at customer acquisition cost to lifetime value, you look at what is happening to multiples for these teams that are super heavy on their go to market spend, something's gotta give, Something's gotta give. And operations teams can be the ones that pull that lever that really help accelerate and find areas of friction, reduce that friction and achieve real velocity for their teams.
Sean Lane 28:00
Leverage and velocity, leverage and velocity. Kyle's point is that we now have this super powerful set of tools at our disposal, and we need to react accordingly to take advantage of them. If his stat is right, and reps are spending 11% of their time switching between apps, then, okay, let's spend that 11% of time on revenue producing activities instead. And just as importantly, He's emphasizing that we have to meet those end users where they are at in order to actually find that efficiency. Again, I get it. None of this is new to operators who have been listening to this show. But the means to the end are shifting. I love how specific Kyle is when he talks about these use cases. And I wanted to go even further into those. He and the team at copy AI are obviously the number one customer of their own tool. So how are they using it internally? Yeah,
Kyle Coleman 28:55
for sure, Shawn. So all of these things I've already mentioned, these are things that we have live. And what's interesting is we have this concept of the AI sales OS, which is effectively the suite of Sales and Operations oriented workflows. And it was homegrown. We, our internal operations person started creating all of these workflows, I was like holy smokes, this is game changing. Let's bundle this together into a product and go sell it as in classic startup. So all of this is real. Let me tell you a different example on the marketing side because there's so many use cases that we haven't really unpacked on the marketing side. And if there are marketing operations, people listening to this, you might lose your minds the way that I did. When I saw this. Every sales call that we have, we use gone for call recording. There's a transcript that's created. We have a workflow that's running in the background that is listening for moments in those sales calls, where the prospect says Holy shit, or Wow or I can't believe that is so cool or My mind is blown. Yeah, whatever. They react in some positive way. So the AI is listening for that. Based on that trigger. The AI listens to that part of the phone call listens to the way that the seller sold the value prop. Listen to the way the process spec communicated their needs and how we would help and then goes and writes a blog post in a format that we talk about. Here's the old way of doing this. What if you could do it this way? Here's how you do it with copy AI. We have a database. Now a content database of this has been this workflow has been running for about six weeks now. We have 3000 articles that were written, that are SEO optimized, ready to rock and roll. And what our marketing team does then is we do the SEO strategy. We say, what are the keywords? We're taking this AI sales or West product to market? What are the keywords that we need to own, we need to own AI enrichment, AI forecasting, there's this laundry list of keywords we need to know. So the article is written by the AI API into notion. We have a notion table now of all of the 3000 articles, we can search that table for the keywords we care about, like enrichment, for example. And then our content team can go and publish it. And it's this unbelievable flywheel that we can create where the more sales meetings and events we have more transcripts we have that creates more content that creates more traffic that generates more sales calls that generates more content, and the flywheel feeds itself. And it's this what we're calling the $0 CAC strategy that's very content driven and content oriented. And this is not crappy AI content. Like if you will go to chat GBT right now and you say, write me an article, that's whatever is top of mind for a CFO and sell them this procurement software with this value prop, you're gonna get a crappy output, because it's all in the fast paced world of b2b procurement. Exactly. But because these blog posts are based on transcripts, the way that our humans are talking the way that human prospects are talking, it's real. And then, if I'm the author of this blog post about cold calling, or cold emailing, or and how AI can help, I can apply my brand voice to it. So I upload a handful of my LinkedIn posts on my blog posts, I tell it, you know how I want to sound, I want to sound like an expert, but also still fun, you know, dropping an emoji here or there. And that's now my brand voice. And so the blog post is written. And then if it says Kyle is the author, it applies my brand voice, it changes the things that needs to change. And now it sounds like me. And it's insane. And so flywheel real authentic content that's always on. And there's a million use cases just like that, but that one, I think is particularly cool.
Sean Lane 32:19
And like the volume of that, like, astounds me. So do all 3000 of those see the light of day?
Kyle Coleman 32:27
No, no, in fact, prior to my joining with a web flow API, we had these blog posts automatically publishing, and we published a blog post of how to use AI to write obituaries. Like, maybe we don't need to be helpless. So it's not fully automated. And, you know, SEO is it's not more is more that you got to be careful with SEO strategies. And so the goal is to create the Bank of content, and then allow our human and SEO strategist to go in and say what keywords that we want to own. Let's find that the content that's most applicable to those keywords, and then let's go publish those things. So there is still a human in the loop process or component of the process. For the time being, I think that's probably still pretty wise to do so that we have some quality assurance.
Sean Lane 33:12
Yeah. The other thing that that makes me think of is kind of the ripple effect on especially a smaller company like yours, how you think about your growth in terms of people, right? Like you're the CMO, you've got this budget, you've got these targets sitting in front of you. But I would imagine, given the fact that you can produce 3000 blog posts without anybody writing anything. The way you think about the future staffing of your team, the way you think about staffing, the entire company is probably very different than even three years ago, right? How are you thinking about that? Like, how are you wrapping your head around what the next two or three years of the marketing organization will look like?
Kyle Coleman 33:55
This is top of mine, Shawn, for so many reasons. I mean, you read any headline from any week from the last year and a half. And it's about rip this or reduction here, layoffs here, whatever it may be, which is maybe a roundabout way of saying, I'm being really careful about where we dedicate our full time employees. And we're where we spend our personnel dollars, because I don't want to make mistakes, so to speak, then we need to unwind at some point compounded by the fact that we're an early stage startup. And you know, every dollar is precious and existential risk if you overspend and whatnot. And so what I'm trying to do is I'm trying to be really thoughtful about what do I need full time employees focused on SPOILER ALERT operations is one of those things because it's, again, one of the highest leverage things that we can do. So I will be hiring for a full time operations person at some point. But maybe I don't need to have a fleet of let's say writers in house. I want to have contractors thought leadership is still important AI can't invent thought leadership. So we need to be focused on that. So our in house people are the more thought leader or oriented writers, and then the contractors that we have punch things up for SEO or will help us with, you know, the technical components of SEO or something like that. And so I'm trying to be really, really careful about what we dedicate FTE spend on and really trying to apply that same. The word that you use, China is the perfect word, which people can give us the most leverage. And who do we need as a full time employee operations is certainly one of them. Content Strategy is certainly one of them. Lifecycle marketing is certainly one of them. And so like those types of roles that are like pretty horizontal, broad impact, I'm trying to avoid specialists as much as possible, I want in House strategy, that AI can help, you know, get a first answer to, and then I want to outsource more tactical type things. And I do not want a team of 40 specialists, I want a team of 10 generalists that can go and make a lot of stuff happen.
Sean Lane 35:57
The thing that I could see as being amazing for ops people, but challenging for companies is, I think that the way that a lot of ops people think would fit perfectly into the system that you just described, right? You want people who are curious and want to figure out those different use cases, and then figure out how to connect, you know, the customer marketing thing over here to the product trigger over here and how that ends up in the hands of either the end customer or an internal end user. But those people are hard to find, right. And so do you think about not only the roles you need, but the staffing and the profile of those people will probably change too and asked you, right?
Kyle Coleman 36:38
Absolutely. Absolutely. And the staffing and the profile, that it's changing it in a couple different ways, Shawn, one is, am I looking for experience? Or am I looking for aptitude? I've you know, in it, hopefully, it's a combination of both. But I've always been predisposed to the aptitude side, because smart capable driven growth minded people will figure it out. And should they may not have the Marketo certification, and they may not have the Trailblazer core, you know, but they'll go do it. And you know, I'll pay them to go to it. But I would rather have somebody who is inventive is curious, again, brings that more generalist approach to things is capable or has a track record of connecting the dots, and making an outsize impact on their team versus somebody who is like a super deep expert on one thing that will be useful for us, but may not be a 10x type opportunity. I'm trying to find those 10x opportunity, people. And it's like you said way easier said than done. But sit this is hiring, especially with everything we've learned over the last couple years. Hiring is a slow down to speed up exercise. And especially we're a 45 person company right now shone like every single person makes an enormous difference, or 2% of the company. And so like we have to be able and willing to slow down. Think a bit differently about our evaluation criteria, what we're looking for, it's not just a whiteboard exercise, showing me that you can architect an object joined inside of Salesforce, it's a totally different sort of thing that requires systems thinkers that are not wedded to any specific tool set. And then the new thing is, you've got to be curious about AI. If you aren't already thinking about and are well versed in AI offerings, that's a pretty serious red flag if you're trying to work here. And so like, I'm starting to see this becoming more and more of a thing where people are bringing a sharper POV to the table about what AI can do. And if my advice to the operations people listening to this is, if you haven't done that already, please start. Please start, like follow the right people on Twitter, just do some Googling and play around with these tools, try the free trial of copy AI or chat GPT or whatever. Just like if it's not now it's only a matter of time, until it becomes a requirement for any operations person. So I hope that many many people will start taking it a bit more seriously.
Sean Lane 38:58
Last thing I want to ask you about is the future right? Like we've talked a lot about use cases that sound futuristic, but you're already doing them right now. You've got like the inside look at what's coming. So what Should folks be excited about? What are you excited about in terms of future capabilities that we're kind of on the cusp of being able to harness but we aren't quite there yet.
Kyle Coleman 39:20
The only limiter is data and we're building capabilities I don't know when my product team will get mad at me I don't know when we're what we're really focused on Shawn is expanding the universe of integrations or of data inputs that we can achieve because there is a not too distant future where we can become any company's business brand by connecting to or ingesting all of the data so you can come in to the platform in some future point in time and you can ask any question that you want and you can get an answer more or less immediately it's more like you know, this concept of the the LLM the large language model that these chat GPT and llama and Claude and anthropic are trained against LLM. This is more like an s lm a small language model, that's your business. So the AI we can be that databank that is taking in all of this data and building the model so that your team can come in and build the workflows, ask the questions, like it's business intelligence in a totally different way. And that's, I don't know what that's gonna look like, but the opportunity for it is extremely exciting.
Sean Lane 40:36
Before we go at the end of each show, we're going to ask each guest the same lightning round of questions. Ready? Here we go. Best book you've read in the last six months, ooh, it can only be one, you're gonna break the rules. That's fine.
Kyle Coleman 40:50
Joining a startup, I had to reread zero to one by Peter Thiel such a good way just to he's contrarian in the way he thinks, the way that he foresaw or at least forces me to rethink some things that I held pretty dear. And this concept of breaking things down to quote unquote, first principles and starting with a blank page, and then building thinking out from there. And it's just super interesting for the way that he thinks about systems so useful for startups useful for systems, zero to one, you know, maybe don't like Peter Thiel, for whatever reason, fine. But the thinking in that book is really really good. For the marketing people listening to this. I just reread Made to Stick Made to Stick by Chip and Dan Heath, it's all about how and why messaging sticks in people's brains. And they go through all these different case studies about storytelling and advertising campaigns, or whatever it is. And they break down the six main things that make things stick, super, super helpful, not just for marketing and campaigns, everything, but for internal marketing. How are you going to make people remember what you want them to do, you can list out a boring, dry, 10 Step manual. Or you can architect a story around it and make people actually remember it. So Made to Stick super interesting, and a lot of use cases in work and in life.
Sean Lane 42:02
Awesome. Next one is favorite part about working in OPS, you are, I think, more in OPS than you ever have been before in this the role. So favorite part about working in OPS, its
Kyle Coleman 42:12
leverage, Shawn, its leverage and its control, you get to control and define what good looks like in so many different areas of the business and your architecture. Maybe this is overstating it. But I truly believe you make or break the company, like you are setting up the things not again, not just systems and integrations and data flows. Of course, all of that is extremely important. But the qualitative side of how it gets done. And it's amazingly exciting. It's also a lot of pressure, you are willing to take that on and you know, take the good with the bad, take the feedback, the constructive criticism, take the wins. Like it's a really exciting role to be. And I truly, genuinely believe that revenue operations people specifically that are responsible for sales ops, and marketing ops, and Cs ops, and all the strategies in between, and of course, everything else that is Reb ops, people that have that wider purview, like you got your hand on the tiller, you're steering the ship. And like if that ship arrives safely, it's because of you. And if it crashes, maybe also is because of you. It's a really high leverage role.
Sean Lane 43:13
You might have answered you the next one already. Flipside least favorite part about working in ops? Yeah, I think so the pressure company crashes, it's YOU the pressure, I
Kyle Coleman 43:22
mean, well, what I'll say I'll answer it a little bit differently. Although the flip side of that coin is definitely true. The least exciting or interesting or valuable thing about working in OPS, is the fact that you get very little credit when things go well, and you are immediately brain blamed when something breaks. And that's a really tough place to be. And so you've got to have thick skin, you've got to have a growth mindset, you've got to be really charitable with people who maybe don't deserve the charity, and you've got to rise above it. And so you know, managing your own thought process, managing your own emotions operating with equanimity. Like that's really hard, and you have to be able to do it because I promise you, you're gonna get far more constructive feedback than you are positive feedback.
Sean Lane 44:05
That's a very good way to put it. It's a very good way to put it. All right, someone who impacted you get into the job you have today.
Kyle Coleman 44:10
Kenan rice is a VC at an early stage venture capital company called Tokyo black, and he hired me at looker. He was the founding VP of Sales and Marketing at looker. And I knew him in a past life before that, and his fingerprints are all over my career, Shawn, like, and I could say the same thing about 10 other people in my life. I'm very fortunate to have so many people in my corner but Kenan specifically, he introduced me he's an investor in copy AI. He introduced me to the CEO and I wouldn't be here in many ways, if not for him and literally, I would not have this job if not for him.
Sean Lane 44:44
That's awesome. Shout out Kenan. Alright, last one. One piece of advice for people who want to have your job someday.
Kyle Coleman 44:49
Say yes to opportunities. Try and create your own opportunities when you can. And when new things new challenges, new opportunities, new you know Whatever work jobs to be done when they present themselves, go run toward that fire. And the way that I operate is, I may not know that I can go do a thing. But I have confidence that I'll figure it out. And I have the support of the people that I work with to say, yeah, he made maybe a little rough, maybe a little failure at the front end of this thing, but he'll figure it out. And if you operate with confidence, and if you have a group of people that you're working with that have that confidence in you, it's up to you to go and find those new challenges. Expand your horizons, find that growth edge, that really is for me, what's fulfilling is the challenge, I don't want to come and do the same thing every day, I want to come and figure stuff out. I want to come and find where the fires are, and go put them out. Again, like it may be something I've had zero exposure to in the past. But I operate with confidence in myself and again, the confidence in the support network around me that I can go do it. And if more people operated that way, I think we would start to see a lot more velocity inside of companies. Because this is a lot about reducing friction and being willing to have the crunchy conversations or go solve the hard problems that reduce that friction, or B, you're going to start to see people with a lot more growth potential who have a more general broad experience that can go solve big problems, that companies.
Sean Lane 46:25
Thanks so much to Kyle, for joining us on this week's episode of operations. If you liked what you heard, make sure you're subscribed to our show. So you got a new episode in your feed every other Friday. Also, if you learned something from Kyle today, I certainly did. Or from any of our guests. Please leave us a six star review on Apple podcasts or wherever you get your podcasts. And of course, don't forget, like I said at the top of the show, go check out Kyle's new show future proofed wherever you get your podcasts. All right. That's gonna do it for me. Thanks so much for listening. We'll see you next time.
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