Philipp Stelzer: Hello, and welcome to another edition of the RevOps Lab Podcast. Our guest today is Justin Norris, senior director of marketing ops at 360 Learning. Also, you may know him because he's the host of RevOps FM. So, hopefully, you've heard already to his podcast. And with us here is also Janis today. So, yeah, warm welcome to everyone.

Justin Norris: Great to be here. Thanks for having me on, guys.

Philipp Stelzer: Great to have you. Yeah. Justin, could you introduce yourself?

Justin Norris: Sure. Yeah. So I've been in the marketing ops, marketing RevOps world for about fifteen years now. Kinda moved into marketing ops via the marketing automation world. So spent a lot of time in the Marketo community, worked in consulting there for a while, and then about three years ago, came back in house. So today, I'm running marketing ops and BDR ops at a company called 360 Learning. So kind of a RevOps scope, sort of up to where the opportunity is created is how I like to think about it. And I also host a podcast, as you mentioned, RevOps FM. Find it wherever you get your podcasts or at RevOps dot FM. And, yeah, that's just a passion project, something I like to do to learn, get to talk to fun smart people just like what you folks are doing.

Philipp Stelzer: Yeah. Excellent. Yeah. We're gonna link to the podcast, of course. We're not afraid of competition. The more the merrier. Right?

Justin Norris: Yep. Yep.

Philipp Stelzer: And, yeah, could you briefly describe what 360 Learning is, what you guys do, how you operate?

Justin Norris: Yeah. We're a corporate learning management software. So companies that want to train their employees, all sorts of different training, whether that's compliance training or their skills that certain employees have and you wanna bring those skills throughout the organization, increase the skills within your company. That's what 360 Learning's for. And it's really oriented around collaborative learning. Meaning, like, you know, if you have ten frontline workers, you have ten consultants in a particular role, and one of them is really great, you're gonna enable them to spread their knowledge to the other people to create a course rather than kind of a top down approach, which is very often what we see, where you have sort of a learning and development person pushing out course material that may not always be relevant. So it really brings that collaboration into the mix.

Philipp Stelzer: Okay. Great. Yeah. Sounds good. Today, we want to talk about signals. We want to talk a bit about outbound, about outbound technology, how AI is changing that. And also, I think, like, just in general, like, how outbound has changed over the last couple of years. I think it's, like, a space where a lot of stuff is happening. AI SDRs, I think, is one of those, like, you know, clickbait statements that you see a lot going around. So I'm just curious, maybe you could explain just to get started, how do you do outbound at 360 Learning at the moment? Just to get us started a bit there.

Justin Norris: Yeah. It's a good question. I mean, so roll back the clock. Like, I think everything starts with this predictable revenue playbook that everyone's familiar with. You know, you get so many SDRs doing so many calls and so many emails. You're gonna get so many meetings, so many pipelines, and that was the playbook for a decade or more. That's the playbook that VCs love because it feels, like, very predictable and plug and play. You're just gonna stack up these Lego blocks, and it's gonna work. And my experience with outbound, you know, I supported teams on some more high touch ABM type programs during my consulting career. But really my experience begins, let's say, three years ago, working hands on with BDRs, starting off more manually, but with a lot of different signals and just sort of trying to figure this out. And, you know, I've even seen during that time the change of going from, like, you know, x percent reply, like, even, like, five percent reply rates and seeing that shrink down. So I think even in that short window of time for me, being able to see the impact of that model kind of breaking under the weight of too many people using it, people starting to tune it out, the market changing, whatever the underlying reasons are, just seeing that outbound model stop working. Or not working with the level of efficiency that would be needed to really scale and justify it. So the question really goes, where do you go from there? And I think that's a question, at least personally, that I'm still learning about, still trying to answer. My operating philosophy right now, if I had to sum it up, is how can I give my teams flexibility to try as many different things as possible? Because we really are still learning all the time, like, what works in today's market. So have a system where we can have some programs that are kinda like these work or they work to an acceptable degree. So we're gonna keep them on. And then let's try new things, and let's experiment. So that's principle number two is experimentation. So let's have, like, the biggest menu of options we can, and then let's experiment and see what works. And I sort of view my role as a RevOps person in that process as being able to drive those two things and enable those two things for my peers in our business development team and on our demand gen team who I am working with.

Janis Zech: Yeah. I mean, I think the stats are quite staggering. Right? If you look at — and I mean, it's always a question, you know, what data can you trust here and how many touch points do you need and, you know, what exactly are the right benchmarks. But I'm curious within these last three years, I mean, like, your conclusion to focus on enablement of flexibility and being able to run experiments, you know, why is that the focus? How did you come to that conclusion now?

Justin Norris: I think just because I don't feel confident to say, like, oh, this is what works. You know? Whereas in the past, maybe like, we bought Sixth Sense and started using their intent signals. And maybe in the past, you know, you could just do that and say, alright. Sixth Sense says this company is in market. I'm gonna call them and be like, hey. Like, this is us. This is what we do. Buy something, you know, in a few emails, and that could work to an acceptable degree. And I just don't think that works anymore, and that's not a knock just against Sixth Sense per se, but I don't think that those signals are silver bullets. So we want to try different things. And, ultimately, if I observe my own past buying experiences, which are the things that I like to do — they're not universal, but they're the things that are closest to me. When I look at, like, what did I buy through outbound? I bought Cognism. Why? Because they called me, and I was thinking about, oh, how do I get more mobile numbers? And then my phone rang, and it's like, hey. Like, we're Cognism. We do data. I'm like, well, they have my mobile number. It couldn't be that bad. And so we started a conversation. So I bought Cognism that way. They got me at the right time. You know what I mean? And I think that's to some extent good fortune, and you'll hit enough people at the right time if you do volume, but I don't think that scales out. I bought UserGems through an outbound motion, and that was a bit more of — we had a great AE there, and he reached out. It wasn't the right time. He kept reaching out. He was very good at building the relationship over a period of about seven months in combination with ads, in combination with the brand presence that they had. And to me, I think that's probably the way forward is that it's not about, like, oh, there's this magic signal, and we're gonna — every time we push this button, like, you know, five out of ten times it's gonna work or even three out of ten times it's gonna work. I think it's about building relationships over time, patiently, diligently, combining it across multiple channels, increasing your brand presence, and then starting to see those things come to fruition. That's at least the hypothesis from what I've seen.

Janis Zech: Yeah. Which, I mean, I fully agree with. Like, you have probably multiple touch points through different channels that will impact the potential of conversion to a meeting or a trial. But this is super complicated to operationalize. Right? So, like, the underlying technology stack needs to probably significantly change to orchestrate the whole thing. So I'm curious what were some of the learnings there for you, and how has your view on the technology stack changed throughout these three years?

Justin Norris: Yeah. That's a good question. When we bought Sixth Sense — I mean, at that time, the technology landscape was very — that was about two years ago, and it was kinda like a Coke and Pepsi market. By which I mean, there was, like, Sixth Sense and there was Demandbase. Maybe you would look at Terminus, but really Sixth Sense and Demandbase were, like, the two players at that time. It's like, oh, you're gonna do ABM? You gotta buy one of those things. And we did a thorough evaluation as best we could. But I have to say, and I've experienced this reaction from other ops professionals too, that you sort of feel like you're gonna log in to the product and it's going to enable the process in a certain way. Like, it's gonna have an A, B, and C of, here's your accounts, here's your contacts, here's what you do. And I actually don't feel that it really does that that successfully. It lets you run ads. It lets you define segments. It's pretty good at that. It brings in the signals. It has its model. But you're kind of on your own in terms of — I think the orchestration product is kind of subpar. And you're kind of on your own in terms of, like, how do you stitch these workflows together? And there's a lot of complicated things that need to happen. You need to have a set of accounts. You need to get them into your CRM if they're not there already. You need to assign those accounts to a particular rep. You need to get contacts for those accounts. You need to have some sort of research process. You need to put those contacts into a cadence, and then you need to run the cadence in your tool of choice, SalesLoft or Outreach or any of those things. So that is super hard. Not hard — it's very achievable, but it requires either a lot of uploading lists, which I hate to do. So I'm always like, how do I automate my way out of that problem? Because it's a problem I personally don't wanna have. Or it requires a lot of thoughtful automation. And so we've stitched together a few different things in the past. The future that we're going towards — I was mentioning to you guys in the preshow that I've moved away from Sixth Sense, did a big evaluation of the market, which I think is just a super interesting space right now. We went from, like, the Coke and Pepsi market to — you've just got a full aisle of, like, you know, sort of craft sodas or craft beers, whatever you wanna think about it. Just all these different options that are kind of overlapping, kind of not, kind of playing in different places. And to spoil the punchline, we ended up going with MadKudu, which I think is a great product. We're just rolling that out. But there's a lot of other interesting players there right now. And I think what a lot of these newer tools are doing better is thinking through, like, the workflow aspect of it and providing interfaces for reps to use and ways of automating it, hooks that you can use either to automate in the platform or send an outbound webhook to something like Zapier that can automate a process. So they're being more thoughtful, I think, about that. And, you know, we're still work in progress over here, but I think we're going to end up in a better place is my feeling.

Philipp Stelzer: I feel like this is gonna be the show where Janis and I have the least amount of opinions because we're so pipeline management forecasting focused and kind of further down the funnel.

Justin Norris: You gotta get the pipeline from somewhere.

Philipp Stelzer: Yeah. No. For sure. For sure. And I think, look, this is such an important topic. I was so looking forward to this conversation because, you know, I think we'll learn a lot here today. But if you think about kind of what you just described, right, so you went from kind of a bundled solution to an unbundled solution, there's more best of breed almost, so kind of, you know, the typical cycle of software bundling, unbundling is happening again. If you would basically describe the different unbundled segments, right, like, how would you describe them? How would you draw kind of the software map? Super curious now.

Justin Norris: I love that question. Because that's the way my brain works, and it's not easy right now. So the hot word is signals, and I think signals is both as useful a term as any and also probably very reductive and it will become, you know, a laughing stock soon. People will — oh, signals. You know, these terms have these hype cycles and they become cringey, but whatever. If we just think of a signal as like a data point about a company, either something that they did, first party data, or something that we know about them that they're doing or that they're thinking about, then I think it becomes useful. And you think about, okay. There's all these different data points, and I need to come up with some way — or think about, like, alright. What if we targeted companies visiting our pricing page? You know? It's an age old signal. So you're like, alright. You can probably get that in your marketing automation platform. You say, alright. But what about ones that are, like, reviewing us on G2? It's like, alright. We got G2 for that. They're like, okay. But maybe they have, like, a project in their 10-K or in their annual report that's related to our value prop. So it's like another thing. So you can see how very quickly that becomes, like, chaotic. And, again, for a RevOps person, you don't wanna be like, alright. I got, like, ten different lists from ten different places pulling them in. So where I'm going with that is some sort of signal aggregation platform. A platform that can ingest first party data, third party data. I think Common Room is another great option there. They are doing a great job at making a wide menu of signals available and bringing them together and kinda showcasing how that works. I think MadKudu is also doing a great job there, and it's one of the reasons we went with them. I think UserGems is another company to look at. They kinda started in the job change signal, like, focusing on that one signal, and I think I've probably seen that the future is gonna be like, you can't just focus on one signal because someone else is gonna come in and integrate that. Even if they only do it, like, seventy percent as good as you, it'll start to feel commoditized. So you need to have more. So they have quickly expanded into a menu of additional signals as well. So I think of the signals as one layer. And then there's another layer that you could think of as, like, signal evaluation, signal analytics, like, the efficacy of those signals. And, again, this is, I think, more rudimentary. You might think about, like, MadKudu has scoring built in. They come from a scoring place. Sophisticated scoring. A machine learning driven model where they're saying, like, okay. Of all these different data points we can see, these are the ones that we think are important to make someone either a good fit or have a strong propensity to buy. So that's like another aspect. That could be a separate tool or that could be all part of the same tool. And then you have the execution layer. Like, how do you actually take these signals and start to do something with them? And this is where, like, everybody's got something that's called a copilot. You see it in Common Room. You see it in MadKudu. You see it in a few other places where you have, like, your hit list of either accounts or contacts that you wanna work on — if it's just accounts, some way of sourcing those things. So with MadKudu, for example, you can stitch in, like, your ZoomInfo or your Apollo or they're building a Cognism integration for us. So it'll be good. So you can bring them in through Cognism. With Common Room, they have, like, their own universe of, like, a prospecting universe that you can use to pull in contacts. You start with accounts that have some signals. You bring in those contacts, and then you connect them into a cadence. And, usually, then you're moving over into, like, your SalesLoft or your Outreach or your Lemlist or your SmartLead, whatever it is that you use. And then there's another piece just to round it out, which is, like, where does AI play here? And part of that can be sort of what you might think of as, like, the Clay piece, which is using a combination of AI intelligence and web scraping to go and fetch information either for the purpose of signals. So you could say, like, go and read all these 10-Ks and find companies that have initiatives related to whatever, buying a certain category of software or doing a certain thing, hiring people or whatever. So that can be used for signals. It'll also be used for the personalization and the writing of the email. So go find me something about this person. I think it has mixed results so far. But using — bringing the AI into the writing layer. So it's kind of the research layer and then the writing and composition layer. And then there's the full blown — we talked a bit about AI SDRs. There are AI SDRs. Like, there really are. They're both email and phone. I had a phone conversation — I forget the name of the company or I would give them credit, but it was kind of a fairly credible phone conversation with an AI speaking to me. Like, it was weird. I sort of hope the future doesn't go there, but it was eerily okay. Like, kinda did better than a lot of BDRs would on the phone. And then there's the data enrichment piece as well. So sorry. That was a lot of different categories, but it just gives you a sense, I guess, to your question.

Philipp Stelzer: Let's just collect real quick just for the listeners. So, basically, you have the signal aggregation platforms, right, that essentially aggregate first, second, third party signals that might also be, you know, then kind of the Clay model where you actually kind of have a lot of flexibility around kind of scraping information. Then basically taking action on that. You have the sales engagement platforms that are usually the execution layers. You have the sales intelligence tools. Right? So the data enrichment tools that then sometimes get combined. I think that has been the case for many years already. Some tools like Clay make that maybe a bit easier. Right? But it also comes with the cost, let's say. And then you have kind of AI copilots that sometimes sit in these tools or separately. Anything else? Or is that a fair representation, or how would you adjust that?

Justin Norris: Yeah. I'm learning here on the podcast. That was a decent summary. There's the analytics piece. So whether you have some kind of scoring or even just a way of measuring the efficacy. Like, anyone who's run one of these plays has known — you can say, alright. We got ten meetings or ten opportunities from outbound this month. Like, what was the thing? Which play did they come in from? That's actually really hard to track and to measure. So there's that piece as well.

Philipp Stelzer: Yeah. No. Just wanted to agree. Right? Because I think this is typically also with, like, just marketing campaigns in general. Like, this whole cohort analyses they need to run, which are hard to do. First of all, it's hard to do a cohort analysis, like, really rigidly with all the gaps in data that most sales motions have. And then the other piece is you need to have, like, a good n. And I would argue, like, most of the motions don't have a big enough n in a short amount of time to actually really be statistically significant. It's more like always like, hey, here's this trend we're seeing. It could be actually working or not. Maybe. You never really know. You need to be a company at a huge size with a very fast deal velocity to actually really do that properly.

Justin Norris: Agreed.

Janis Zech: Yeah. Yeah. I wanted to add one more. Right? Like, I think it's then also to have the AI agent to actually take care of all the outbound work, at least partially. Right? I think the phone calling, that definitely is creepy, but I can totally see a use case for this and it will become better. But I think just like an agent that prepares the emails or, like, basically, like a service that does this really, really well — so maybe even sends it out, right, automatically at the perfect time. If you can trust that.

Justin Norris: Looking at that use case, there's another tool that I'll give a shout out to, which is EvaBot, and it's a good example of how these are, like, slightly overlapping but not fully the same. So, like, EvaBot — really it's not like detecting new accounts for you. It's presuming that you already have an account universe, it's presuming that even you already have a contact universe. So it's, like, kinda downstream from the signal aggregation, the contact sourcing, all that stuff. And what it's doing — hopefully those folks will forgive me if I misstate their value prop. But what I took away, at least, was it's looking at these contacts and companies, and then it's doing that AI research, sort of that Clay type web scraping and AI stuff. And it's taking, like — alright. I know that Justin works with 360 Learning. We sell an LMS. These are the sorts of use cases they have. These are the sorts of value props they have. Now for these companies, these are the things that match that. So, oh, this company has this sort of initiative and this sort of thing. So here's the angle that you should take in talking to that company. Talk about — emphasize this. So it kinda gives you messages. It does that work of forming those connections and thinking through. And it can do a first draft of the email and whether AI is gonna be better or not at the BDR — it's probably only a matter of time till it gets decent at doing that. But I think that part is really hard. That's the part that a skilled seller, I think, has been able to do and give them an advantage versus just somebody who's brute forcing it being like, buy my widget. Buy my widget. You know? Like, I noticed you went to this university. Buy my widget. Like, really, really superficial outbound versus someone that can be really thoughtful. Like, hey. I noticed you're in this sort of industry, and these are the kinds of pains that you have, and we have helped other companies with these very specific pains. I'm showing you that I know you, that I understand you very well. So I can't speak to how effectively it really does that in practice, but I think that is an interesting application today. I call it, like, forming connections between value props and real world pain points for a very specific company.

Janis Zech: Yeah. I think one part that is really challenging as an, like, AE SDR — sort of like what you were alluding to, like a SDR who's really good at building relationships essentially, or a full cycle AE. I'm not sure how you would define that. I think one problem, at least that I've experienced, is you're really becoming overwhelmed quite often and you have so many options of different sales motions and things you can try out. It's really hard to just nail one really well or just to have, like, that rigor and focus to just execute against that. So, like, when I'm listening to you, what I'm thinking about here, like, how I would envision sort of how you are thinking about the future of outbound and where you would like it to go to is sort of to have all these systems plugged together so that essentially as a full cycle AE or, like, advanced SDR, basically, what I do is I have, like, I log in to my application or whatever, and then it just tells me, like, those are, like, the ten accounts you should focus on, and these are the three motions that work best in the last thirty days. So try those, and you need to add, like, a little bit of research here to personalize it. So, like, to what level do you think would you see that automated?

Justin Norris: Yeah. I mean, it's not far away, so I can sketch out a little bit how we're thinking of it using MadKudu. So MadKudu has a copilot, and the cool thing about it is that you can define different playbooks and deploy them to different teams. So let's say you have a team that's mostly inbound, but they do a little bit of, like, call it warm outbound reactivation work, leads that have come in before that are showing activity or maybe opportunities that were closed lost that we wanna reactivate. You can kinda have their inbound plays set up. Like, here's your hand raisers. Here's your webinar leads. Here's some leads from a trade show. Typically, today, these are often, like, all these different reports. We're kinda sending BDRs or SDRs in all sorts of directions. Like, follow up with this hand raiser, and here's a report of webinar leads. And by the way, go do this other thing. Like, it's very fragmented. I would hate being a BDR for that reason. So this provides a way to bring all those things together. Now for a more senior — it's called enterprise SDR or BDR — their page might look a little bit different. You could have a different set of playbooks for them. Could be, you know, whatever, other types of intent signals, or these are companies in a particular vertical, like a vertical campaign could be rip and replace. So companies that we know are with competitors that you might pick up in different ways through web scraping, through job posting analysis. And so I think of, like, the ops function as helping to measure all those different plays, like you said. So not necessarily automatically surfacing them, although that's an idea that I'm sure could happen in the future, but doing the analysis and saying, like, these are our champions. Let's say, we know that these plays are working at an acceptable rate, so these are on lock. And every day, they're gonna go in and through some background automation, we're gonna be constantly refilling those buckets with a certain number of accounts all the time. And then maybe we're gonna have challengers, plays that we're continually thinking up. Maybe we meet biweekly, and we come up with ideas, and we roll them out, and we have these experiments that are in motion. And so alongside these champions, we have challengers. And those challengers might do really well. Some won't. They'll be discarded. And the ones that do well, maybe they'll join the ranks of the champions or maybe displace existing champions. So you're kinda constantly running that. That's the vision at least — to have the best plays always in motion and to make the best use of your team's time. I think that is where ops and then wiring all the stuff together to make that happen — that is where ops can really add a lot of value in this process.

Janis Zech: It sounds to me like you are still thinking of different motions for different target audiences and accounts. So kind of separating out the inbound versus pure kind of, like, let's say, like, pure high intent inbound versus warm outbound versus, you know, segments. Right? So enterprise, SMB, mid market. Is that right, or is that also falling apart?

Justin Norris: I think so. I mean, ultimately, the inbound outbound distinction is kind of nebulous because everything that an SDR does is outbound to some degree. You're reaching out to persons. For some cases, they maybe they asked you to do it. Some cases, maybe they're, like, more subtly asking you to do it. In other cases, they're not asking you at all. So I think of it on a spectrum, and I think of it as, yeah, you want to separate those out because you're gonna deal with them in very different ways. If somebody comes in and requests a demo, it's the single most important signal that you can have. It converts at thirty to forty percent to an opportunity. So you have to put all of your focus on treating those people, giving them the best experience, responding to them as quickly as possible, etcetera, etcetera. And then there'll be a range, you know, of other signals that some are not important enough to deal with at all, and some of them maybe are much harder and require more work. But because you're really targeting the companies and they're much bigger, it's going to yield a bigger payoff. So we're okay with investing more effort there. So I definitely think about breaking it down. And I definitely think, like, a mid market enterprise split in your team makes sense because those are different conversations, and you want reps that have the business savvy and the acumen to have enterprise level conversations focused on the enterprise and penetrating those accounts. And then more of a high velocity mid market motion. I think that's a different skill set and probably more of a ladder in terms of career progression.

Janis Zech: Are these signals or plays combined with ads or any type of other touch points you basically add on top?

Justin Norris: Yeah. No. I think they can be and should be. It can be combined with ads, can be combined with marketing, like events, ongoing events, marketing newsletters, content. It's all — I'm not suggesting — I think it's very impractical to, like, orchestrate it. Like, first, we do, like, two ads, and then we do webinar, like, something really, really sequenced. But I just think if you're surrounding them with value and appearing on their radar — I think I go back to that UserGems example when I recently did a case study because, like, internally, just really analyzing that sales cycle from my point of view because I thought it was really, really masterful. Shout out to Chase who's the AE there that sold us that. But one of the reasons why I was responsive to his message in the first place was I was really familiar with the brand. You know? And so we underestimate how much brand opens doors on the outbound side and is the difference between — like, you know — this company is cool, and they reached out to me. Like, that's kind of neat. So it's all working together, in other words.

Janis Zech: Yeah. Yeah. And then one other question, deanonymization. I probably pronounced that wrongly, but I hope everybody knows what I mean. Like — and, of course, I mean, you can do it kind of in the RB2B way, right, where, like, you know, it's not an opt in, which is, for example, in Europe is not possible. But then there's many other ways how you can basically get an email address and try to, you know, understand who those users are. How important do you think this is for these plays?

Justin Norris: Yeah. It's an interesting question. It really comes back to the experimentation. Like, we've had, you know, de-anonymization for years. From a first party perspective, if you have marketing automation, as soon as somebody fills out a form, they are cookied and they are known. And every subsequent session in that browser, you know, you were gonna have that history of what they did. And so that's been a part. And then there's the company level de-anonymization, which, you know, people have mixed results with. And the RB2B stuff, which is, you know, illegal in some countries and controversial at least in others from, like, a privacy and ethics point of view. I think my take on it just from — not even alluding to any of those factors beyond just what makes the most sense if you want the web stuff — is to think of it in terms of a waterfall the same way you do with data enrichment because no one vendor is gonna cover all your bases. And I think back to the tech evaluation I did, I think Warmly is clearly the strongest player there. They really focus on the de-anonymization piece, and they do exactly that. They layer their own de-anonymization. They OEM success data. They bring in RB2B. And so they're very focused on, like, revealing to you the largest percentage of your web traffic. Whether that's right to do — whether that's right to do from an ethical perspective or from an efficacy perspective. Like, does it actually work? Because someone's on your website, are they gonna be more responsive? Like, yeah. Maybe sometimes, logically, if I'm, like, checking something out and someone calls me, like, at least I know who you are, presumably. But does it mean that you're ready to buy? Probably not. Like, I look at a million vendors all the time, like, just out of interest. I see somebody post something. I'm like, oh, who are they? What do they do? I'm just, like, a tool junkie. So I look at tech all day long. It doesn't mean I'm gonna buy anything or that I have budget. So I think it's one factor. You know, if you see a big surge from a company, all sorts of different people visiting your site,