[00:00:00] Toni: What are your options if you want to scale past 10 million ARR?
[00:00:04] Maja: like GTM engineering is how it's called and we are literally using AI in order to use the data enrichment as well as heavy automations in order to try to scale what is already working, hopefully, and just like be better in outreaching and ABM
[00:00:19] Toni: That's Maja Voje. And with her, we're talking about motions, go to market engineering, and A. I.
[00:00:26] Maja: right now the entry barriers for AIs are insanely low. And if you can like make sure that you can do like some sort of per result pricing or that you are literally replacing a human out of the normal business process, you are just half a Crazy competitive advantage in comparison with old time players.
[00:00:46] Toni: Today's episode is brought to you by ever stage, the top writer platform to automate sales commissions. You can create a single hub for your reps to track all their deals, earnings, and performance history thanks to EverStage's seamless integration with Salesforce, Microsoft Dynamics, Slack, and MS Teams.
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[00:01:17] And now, enjoy the show,
[00:01:19] Maja: Right. Because like LinkedIn commenting strategy, for example, which was like so cool a year ago, right now, 80 percent of all the comments that I'm getting are AI generated and I don't
[00:01:29] Mikkel: Yeah.
[00:01:29] Maja: a computer genius to figure that out.
[00:01:33] Mikkel: The thing is I try very intently to do something that shows this is not AI. So I will comment on something that just wasn't in the post at all. Or something like, Hey, what happened to your profile picture?
[00:01:43] It was
[00:01:44] Toni: what I'm waiting for is that, you know, when you, you know, click the like button and then you get those different options, those emotional options, one of those options should be a robot. So then you can,
[00:01:53] Maja: I love it.
[00:01:54] Toni: when you, when you see
[00:01:56] that this is like a, you know, a robot message, you just can flag it as a robot basically.
[00:02:00] Maja: Oh my
[00:02:01] god.
[00:02:01] Toni: that's just me, you know, that's just me.
[00:02:04] But Mikkel, what do we want to talk about today?
[00:02:07] Mikkel: so I
[00:02:07] had another kind of segue and now I needed to figure out how do
[00:02:10] we go from here?
[00:02:11] No.
[00:02:11] So we have Maya with us on the show today. Maya, thanks a bunch
[00:02:14] for joining.
[00:02:15] You talk.
[00:02:16] Share if you haven't bumped into her yet on LinkedIn, we just talked a bit about LinkedIn. She has a ton of, I would say, colorful frameworks that look pretty
[00:02:24] awesome. Very easy to kind of understand what's happening there.
[00:02:27] That basically helps you understand your go to market way better, right? So we wanted to get her on to help us talk a little bit about.
[00:02:34] How do you scale past 10 million AI? How do you go beyond just the, the common go to market playbook? Especially now we, we touched, we broached the subject. I would say there's a lot happening.
[00:02:44] I think Tony and I got a bit tired after a year of just talking about how difficult it is to do outbound with basically everything from the amount of buyers being involved the sales cycles, extending win rates, decreasing, and then AI came around. Kind of a lot has happened. And I just wanted to start by asking you, you work with a lot of teams. What are some of the struggles they're facing right now? What's, what's happening in the, in the trenches that you see where it's, Hey, this is, yeah, this is really difficult right now.
[00:03:12] Maja: Right. So, I already had a disclaimer that I cannot like be super geekish about AI per se, but okay, let's go through like seven go to market motions that I have defined and just like comment what is going on in the space and guys feel free to just like fill in this with your thoughts and examples as well.
[00:03:30] So the first one is inbound. You literally creating content in hopes to attract some clients. Well, that's been getting increasingly difficult to do organically. We have already said what the LinkedIn algo is doing. So like in last six months, the organic reach on LinkedIn has been done down for 50 percent on the personal profiles.
[00:03:52] What else is true is that the longevity of the posts like before, you know, it was like one or two days when we were dealing with this value commenting and making sure that we are extracting some leads out of this. Now, the duration, the lifespan of the. post is approximately one week or even more. So just like the longevity of us being like forced to nurture these posts, it's much longer.
[00:04:13] And I think that the same play is happening everywhere. So inbound getting increasingly hard. There is so much AI content out there. Algorithms are shifting towards promoting paid solutions for this type of tools. So, I mean, I have Insane admiration for everybody who can still like fight the organic reach with linkedin pages The only successful examples in link bound in my portfolio are literally like founder led communication, right?
[00:04:42] That there are personal profiles and SDRs doing social selling so that we can squeeze some of the good revenue out of this one.
[00:04:50] So the next one is outbound. You guys have like kind of very pessimistically set up the grounds for us how to talk and think about this. But yeah, I have been like obsessed with clay for the last year or so.
[00:05:03] I don't know if you guys have been following their story. So their current valuation is 1 billion and they are killing it. Not only are they doing like album plays, but also like ABM plays. I have been like doing this for outreaching even podcasts and making partnerships with accelerators. So at this point, like for data enrichment, clay is like literally the name of the game for me when I'm trying to do personalized outreach.
[00:05:30] However, whenever we are talking about outreach, I prefer to just like operate with smaller databases and just like having this better figure it out. So in terms of go to market motions, I do think that account based marketing and selling and outbound is being like a little bit consolidated at this point.
[00:05:50] Because if we just like go there and say, hi, I see that you liked Emilia's post. Would you attend my webinar? These playbooks no longer work because they are just like dumb shit. So yeah, that was like a little bit of a detour to what is going on in the outbound and ABM space. ABM, of course, like multiple touchpoints, multiple different sellers.
[00:06:12] I have been heavy on implementing these playbooks with AI SDRs. When you are identifying your traffic on the websites, and then you are like making these social selling sequences on either LinkedIn or like with email. But what is super interesting is that we are targeting multiple decision makers in a decision based way.
[00:06:31] tuning. So a typical sequence would probably go like first, you know, I just want to see if you're alive, if you're a little person and have like a little bit of response for you from you. If you want to continue this conversation, the second one would be like a pitch preferably backed up with a case study, or I could be still adding the value depending on what I am selling here.
[00:06:53] And the last one, like I would usually do a three or. Four stage sequence would be like, Hey, are you even the right person to talk to, or should I be moving to somebody else? Go ahead.
[00:07:03] Toni: this, so on this one, right.
[00:07:05] Kind of, you mentioned the, the keyword AI SDR kind of in there and, and actually in more in a, in a, in a framework of, you know, what I would call inbound. Some people call it warm outbound. I would say, right. Kind of. Do you, do you, so first of all, what is action AI SDI in this context then?
[00:07:19] Right. Because many people think an AI SDI is an email gun that you just shoot out
[00:07:24] stuff on LinkedIn and on, on email kind of you using it in a very different way. So kind of
[00:07:29] maybe explain to us how that, how that works and how that AI SDI thing morphed in you know, and, and how are you applying this actually?
[00:07:35] Maja: Gladly. So we could be sourcing from either our website traffic or through our engagements on LinkedIn posts. Or if we are completely new to the game, we could even be doing this for our competitors or beloved influencers in the space. What we are trying to do is literally we are trying to define ICP, right?
[00:07:54] So when it comes to ICP, I am using a lot of data enrichment right now because this type of like very dull, Descriptions of a target audience, which are like company in food and beverage industry with 20 million RRR, with like 500 employees, these things are spent. Like, I literally need to be able to generate more context.
[00:08:17] for personalized outreach with either AI or let's say certain contractors that we hire in order to do data enrichment. And then it's literally like doing this in batches, right? So when I craft an outreach sequence or like, let's say a warm outreach sequence or all bound, however you want to call it I never go and spray this message to like thousand people at I always go and do it in batches because I want to Be able to improve it based on the initial responses.
[00:08:48] So currently I have been playing with one a I, which is an SDR, but doesn't want to be called this way. And if you can like budget for it, Eva is great. So from artisan, this is something that I would definitely like to do. And the other tool that I have been testing for social selling, especially is link bound.
[00:09:09] That's you literally being able to import all of your content iterations. The this data capture lasts for approximately like half a day because it's analyzing literally all your LinkedIn history for six months or even a year, however you want to call it. And then you've got a list of like. Most engaged audiences.
[00:09:28] The problem with this list, if you don't filter it, is that there are a lot of AI comments right now, as we mentioned, and there are a lot of people who are just like doing the value commenting technique. So I definitely want to throw this one for my ICP filter as well. And then I can rock and roll. Like I literally using AI for all of the sequences and data enrichments for approximately eight months now.
[00:09:52] Toni: But actually kind of, so, I mean, let's maybe get a little bit tactical here. Right. We're kind of already kind of going through some of the vendors that are using there. We're kind of heard, Hey, there's a
[00:10:00] tactic here and, and, and yeah, I know. I know. So that's why I kind of, let's take a step further. You're kind of the so I'm just trying to kind of work through the. The workflow, right? Kind of their stuff happening on LinkedIn might be on your own profile, on your, on your you know, a company page on competitors, on influencers, you go and you, you know, use those tools to, to, you know, you would say scrape information, actually kind of scrape data and kind of get
[00:10:22] Maja: It's a bad word. We don't use it.
[00:10:24] Never. Always. Always compliant. Always compliant.
[00:10:27] Toni: I was, I was stumbling. I was stumbling over that as well, but kind of, you, you retrieve information, you know, one shape of form. And then you use that to build lists, very condensed lists, right. Kind of very kind of, you know, whittled down and maybe it's just a hundred or maybe it's 200 or something like this. And then basically, you know, per list that you have, you then craft and tailor a message or a sequence that comes on that, right.
[00:10:48] Maja: Sequence, always a
[00:10:49] Toni: is that how it
[00:10:50] Maja: a one gun, kind of. Yeah. And based on what we are getting out of these results, we are adjusting the scripts and this is like seriously good because you can literally be doing it for any offer that you want to test. For example, I am working with like one of the AA platforms for agents builders, and we have had these.
[00:11:10] Lead magnet, which was like how to do your voice A. I. It was not even created by the team. It was created by one of the other content creators. So based on these people that have been engaging with that type of lead magnet, we have identified our I. C. P. Fit and based on enriching this data of who these people are and how to best start a conversation with them.
[00:11:33] Everything is heavily AI assisted at this point. We have been sending down the sequences and actually booked out of 200 messages, something like 45 calls. I don't know the exact thing, but this was like such a brilliant campaign to do. So yeah, this is stuff that I've been supremely passionate about and so far it has been working well.
[00:11:52] Now all the.
[00:11:52] LinkedIn algorithm secretly prohibits the scraping of the data. These vendors, like what we saw, that Apollo page was suddenly blocked from LinkedIn. So there are realistic risks. If you are using this type of sequences now, the beauty of this one is that as long as you are not like scraping data from LinkedIn directly, if you are using like other sources providers, you are kind of, you know, playing it safe, but if you want to be perfectly compliant, do that manually.
[00:12:21] Wink.
[00:12:22] Toni: yeah,
[00:12:24] no, I know, I know. I mean, so the thing is Mikkel and I, we used to work in social media management basically kind of, you know, building those, those companies. It was Falcon social nodes brand watch. And we are keenly aware of all the terms of service for those social media, social media platforms basically.
[00:12:38] Right. And, and scraping for LinkedIn was always kind of the red blaring light. And as, as I saw more and more people doing it, I was like, how does this even work? Kind of what, what kind of workaround are they using? And it turns out they weren't, they were not using a workaround, but I guess the only one is to you know, to do it manually, I kind of have a Chrome plugin.
[00:12:56] I guess. Right. I'm
[00:12:57] not sure kind of what the
[00:12:58] Maja: Yeah. So the
[00:12:59] heaviest, sorry to interrupt, but what would be like immediately publishable are like automations. So for the actual delivery of the messages, Hey, rich is like. Safe to use. Some of my folks rely on LAM list so the, where it is getting tricky is mass delivery of these messages. Most tools will just like keep and do like these sequences.
[00:13:23] This is the text that you'll be sending out now. Good luck, wink. So yeah, this is the tricky part usually.
[00:13:28] Toni: Interesting. By the way, Maya, would you, I mean, you kind of, you, you kind of the GTM strategist, right? But some
[00:13:34] of.
[00:13:34] the stuff that we're talking about, it
[00:13:36] Maja: the
[00:13:36] Toni: sounds almost
[00:13:37] like, so I mean, that's why I wanted to plug it. But some of the stuff we're talking about almost sounds like, you know, GTM engineering, right?
[00:13:43] I mean,
[00:13:44] Maja: Yeah, oh my God.
[00:13:45] Toni: right. What's, what's the do you have feelings around this? It's like, oh, don't call
[00:13:50] me that. Or yes, do call me that kind of what's,
[00:13:52] Maja: Passionate feelings.
[00:13:53] No, seriously, guys, as we were just like walking through kind of like go to market motions, so repeatable and scalable ways how to get clients. I do see that this industry is evolving in two different directions. So the first one is to be like super human, like to literally post personal stories, which cannot be replaced by AI.
[00:14:15] The other thing are events. Like to genuinely form the relationships with people and I mean events were never my forte. Like when I go for an event I would rather lock myself in a toilet to avoid the crowds just like
[00:14:28] Toni: Or be on stage. It's like either on stage or in the restroom. It's like,
[00:14:32] yes.
[00:14:34] Mikkel: What about the parties? Come on.
[00:14:36] Maja: I have two dogs. I live in the countryside. I mean, yeah, most people would think that I'm a very extroverted person, but guys, the older I am, the more awkward am I when it comes to the socialization. So I would never shy away from an intellectual debate. But if there are like 40 people just like circling around trying to ask me something, and just like, My husband, could you get me out of this please?
[00:14:59] So yeah, it's a special skill. And I think that we can learn a lot from our older colleagues who have been more experienced with this type of work to just like do the social skills better, right? Because as like, okay, we are probably like millennials, but Jen James, for example, this is like something that we have to train heavily when it comes to sales goals and like more old school salesman.
[00:15:23] So telling you still working, like, it's incredible to do that in yeah, if you're not talented, like, then you need a coach or somebody who will guide you through how to just like do the best out of these type of engagements.
[00:15:35] Toni: So that was the, that was the human side.
[00:15:38] Right? And then you were like, let's lean into the
[00:15:41] Maja: the engineering side.
[00:15:42] Toni: yeah. Well, I mean, it's, it's, do you have like something else, a kind of comedy or was that basically kind of what we talked about just previously?
[00:15:50] Maja: Yeah, I do think just like to finish the thoughts. So yeah, like two trends that I've been seeing. One is like hyper humanization target things that we have talked about before. And the second one is automate the shit out of everything because like literally you want to scale this and everybody's using this type of tools.
[00:16:10] I think at this point it's like. How our parents had to learn how to use the computers, right? So here I definitely want to be on the right side of the history. I'm not saying that it's AI or diode this stage, but yeah, the GTM engineering play has been something that I want to invest heavily into.
[00:16:29] I mean, it's tough because it's so, so difficult to just, like, keep up with everything what is going on in the. space. I literally like to say that every day that I spent on social media in AI makes me feel five years older. And there is this amazing stat by Harvard Business Review that like, whereas 84 percent of companies think that AI should be a part of their enterprise strategy, only like 20 percent of them are using this on a daily basis.
[00:16:56] So there is a lot of talk and limited amount of actions. So I do think that if you will go into this stuff some more, like GTM engineering is how it's called and we are literally using AI in order to use the data enrichment as well as heavy automations in order to try to scale what is already working, hopefully, and just like be better in outreaching and ABM ing.
[00:17:19] Toni: last point from my perspective, and then Mikkel is going to probably kind of take us
[00:17:22] forward here, but I think the one mini piece of the sentence that you kind of just mentioned is really, is really key. Scaling what already works. I think what
[00:17:30] I've seen many times kind of go kind of straight against the wall. Is rolling out some AI stuff
[00:17:36] and basically kind of experimenting, you know, with that. But the idea should actually be, Hey, you figure something out, maybe through unscalable means and then use AI in order to kind of take this forward. Right. So
[00:17:47] if you.
[00:17:47] do it the other way, I believe you just, you know, I don't think you're doing it right.
[00:17:52] Maja: Love it. And we should also mention the importance of persistence here. So usually it takes us two or three months to get like legitimate results out of the actions that we are experimenting. So just like in terms of resource allocations, what we do is we spend 80 percent of our time, resources and budget on Scaling what is already proven to work and maybe like 10 or 20 percent of the staff should be what I call moon shots
[00:18:18] Toni: Hmm.
[00:18:19] Maja: by AI place.
[00:18:19] For example, in my line of business are the moon shots for now, but yeah, we have already normalized some place and this is something that we have added. To the 80 percent of the stuff that we are doing. I do think that this is a very good equation, because right now, I mean, the landscape is changing so fast, and everybody has this fear of missing out every day, like, that I am alive.
[00:18:41] There are seven new competitors for my clients, so we are just like, persistence, build solid food business fundamentals, and just like make sure that you spend a lot of time talking with your customers because whenever you have a problem to solve it's likely that you are going to get very good insights if you just talk with like 20 of your ICPs.
[00:19:03] Oof.
[00:19:07] Mikkel: some of the, the challenges and changes with inbound without bound. We've not talked about the other I would say there are three big motions. The other one, which is PLG. Have you seen or noticed anything kind of changed there as well? That's,
[00:19:19] that's kind of making things very difficult.
[00:19:21] Maja: No, seriously, like the product market fit for like big companies has been severely disturbed, disturbed by AI space. Brian Balfour from Reforge wrote beautifully around about this. And right now the entry barriers for AIs are insanely low. And if you can like make sure that you can do like some sort of per result pricing or that you are literally replacing a human out of the normal business process, you are just half a Crazy competitive advantage in comparison with old time players.
[00:19:54] So this week I have been talking with a very like traditional SAS. They work in HR space, in talent recruiting space, and they are launching like AI features. Very interestingly, this company hasn't changed prices much for like the last five years or so. And now as they were kind of forced to introduce AI features to the product, they will be rolling out an entirely new So it's going to be called something differently.
[00:20:22] Why? Because A it will be so much easy to upsell the existing customer base into new offers. And this will be like their spin off. I have been seeing this type of work being done as joint venture as well. Or even like as a product, which are bright products. And just like the lead acquisition mechanisms to the main products.
[00:20:41] But what is super interesting about this one is that it's also unlocking like market, which was not previously obtainable to them. Meaning beforehand, they were integrable with a couple of solutions with a couple of like those stuff that HRs use. And now as they are building this type of new AI enhanced offering, they will suddenly play better with other AI.
[00:21:04] core solution providers. So that's literally an opportunity to attract so many more clients into this one. I mean, right now, 90 percent of my portfolio are companies that are active in the AI space, and I don't want to sound like a gold bullet retriever, AR or die, but something big is going on and we should pay attention.
[00:21:26] Mikkel: No, I agree. I also wonder like now there's going to be a bunch of business, right? That are just maybe struggling to reignite growth coming from this phase. We've been through an AI is exasperating some of that. What do you recommend those companies do if, if, if there's like a go to market leader listening right now and maybe growth hasn't really picked up yet to the level it should be at and they struggle to kind of reignite what are some of the steps they could take to start truly scaling past just 10, 10 million now?
[00:21:52] Maja: Cool.
[00:21:54] So I like to see about product market fit as this cycle, right? There are a couple of elements that you need to nail and some of them are like more expensive to change. Some of them are less expensive. And I don't know, I'm a pragmatic by soul. And I always like to start with more inexpensive ones.
[00:22:12] So the first thing that I would usually check is if our messaging and positioning still makes sense as there are new competitors and just like so many good things going on in the arena, I would be like doubling down on. Seeing if like my landing pages are still converting. Well, if I should be doing some tweaks in messaging, super easy to test this with ads, I have been doing like shit, a lot of meta ads advertising, and this is just like so easy to nail.
[00:22:39] So this one would be an easy fix. You can figure it out with using usability tests or running a AB test, or just like having a kick ass messaging and positioning workshop. Meaning, are we still relevant is how we are talking about ourselves still resonating with our target audience. This is where I love to start because everything else would evolve around my decisions around that.
[00:23:03] The second thing that I would go and review immediately are the offers. So first of all, thing that I do is that I ask my clients. What are the competitive alternatives that I have to fight for, for their budgets, right? And sometimes these things can be like some sort of DIY things like textiles. Other times it can be freelancers or agencies.
[00:23:24] So in terms of just like how competitors are launching their offers, I would go back and revamp my pricing and packaging. Why? Because right now the There are some pricing trends that we have to talk about. The first one is whereas people still like predictable costs, there is this pressure to not to overpay for things, right?
[00:23:46] So cost per result and like a little bit more of an outcome based pricing value based pricing is kind of something that people anticipate to have. Then, like the other trend which is going on in pricing right now, especially with Sorry, you will hate me again, Michael. But with AI products is to literally be as firm as pricing per outcome, meaning if you are longing like offering like lead generation tool, you would be charging for lead.
[00:24:13] Is that always possible? No. Oftentimes we have to use proxies or like serving different packages for different personas. The way how I usually convince my companies to just like revisit the pricing, launch some pricing experiments is literally as your product is getting better, you should be charging more because it's creating more value.
[00:24:33] So literally what I would love people to do is to revisit their pricing every quarter or something like that, if it is still relevant. if we are like leaving some money on the table. One of my favorite ways to test this one out are upsells. So for me, the 2022, 2025 is literally about the adoption and making sure that we are squeezing more revenue out of existing customers because acquisition has been going indecently well.
[00:25:00] So if there is an upsell that we could be running or some sort of like other payment mechanism for incurring revenue, I would strongly prefer that to just like. Keep the lifeline and yeah, there are a bunch of other pricing stuff that we can buy it too But I'm explaining something else. So what else would I change in order
[00:25:20] to figure?
[00:25:20] Okay.
[00:25:21] Toni: just to kind of interrupt you a little bit here. So you mentioned the pricing per outcome, right? And this is an, you know, everyone is thinking
[00:25:27] Maja: a dream that's not always
[00:25:29] obtainable, yeah
[00:25:29] Toni: I know, but that's actually almost what I'm trying to say. Like everyone is thinking about this. And, and, you know, when I reasoned through this, it's like, well, it's, it's a little bit like, you know, look at your workforce, right?
[00:25:38] Kind of who in your workforce are you, are you paying per outcome? And the only people that really come to mind is kind of salespeople. Like they have a commission, they get kind of, you know, for the outcome that they're generating, they're getting paid. Right. And I think if you're building a tool around that, you know, mindset, I think the whole paper outcome makes a ton of sense.
[00:25:57] I think for some of the, for some of the other areas, I mean, I know Intercom does you know, you know, outcome for, you know, paper outcome for resolving tickets and stuff, but for all the other stuff, it feels a little bit more alien. Actually kind of to, to think like this and pay like this. So this is one piece.
[00:26:14] And then the other piece is like, well, I would kind of like to know how, you know, how much money I need to put down into my budget line here. And I don't want to be surprised suddenly in six months.
[00:26:23] Right. So do you, so you said more and more people are expecting, you know, cost, you know, price
[00:26:28] Maja: Not expecting, but experimenting with. This is like a critical difference here.
[00:26:33] Okay, so the concept here is called the value metrics, right? Imagine like this product as a driver of like a value added, and then you capture some of this value adding, a. k. a. pricing, in order to build a sustainable business.
[00:26:46] So, value adding is just like this. I call it holy grail of pricing, but not always can we measure the sales. I mean, if you're a payment processor such as Stripe or PayPal, you can just like sit on this money. It's not difficult to grab it, but often you have a conflict of interest here. For example, if I'm making more money with my email marketing software, they would be inclined.
[00:27:08] to like charge me more, but in other ways, like I would be, I would like to keep my cost predictable. So they are using a proxy here. Whereas the ultimate value metrics usually is the revenue that we are that's good branding. They could be looking at other indicators, how they could predict that I'm making more money with this product.
[00:27:28] And one of the very good ones would be number of subscribers. So they will probably charge me per number of. Subscribers as a proxy that potentially I'm getting more value out of the product. But yeah, pricing, I mean, pricing is fun guys. Pricing is something that we can talk about for two hours.
[00:27:44] Toni: yeah, no, exactly. And I think, but to your point, like, I think this is relevant for folks that are a million in AR, but it's also relevant for folks that are 10, 20, 30, a hundred million in AR. I
[00:27:55] Maja: I was working with a 7 billion company last week, and we were like literally launching 10 pricing experiments, mainly upsells, mainly like tweaks on displaying what is the best selling package and how to see that, like, it's still being relevant for the competitive solutions and a lot of upsells and just like this FOMOs.
[00:28:15] Toni: Can you, so, I mean, since you're saying kind of, this was like an, an actual case. Obviously, you know, don't go too much into detail, but people might be like, wait a minute, pricing experiments. That sounds interesting. How does, how does that work? I don't need to shift everything around and bet everything on, on black and then hope you know, how, how does that work?
[00:28:34] Maja: Okay, that's a little bit different. from your geography. So some of the states might have like some sort of legislations that you cannot be using, like as much pricing parity and pricing experiments that we would want to. But the easiest way how to start thinking and testing about this is with offers that are not public.
[00:28:55] Because if you have any sort of service components to your business. These prices are not saying non transparent, but you negotiate them one on one. Right? So I don't, I'm not saying that you should be creating more of these legacy deals, but the easiest way how to just like challenge your thinking around pricing is to make offers, custom offers that are converting at more than 70%.
[00:29:18] It's slightly more expensive and go in another iteration with a little bit stiffer offer. So how I usually do this is whenever I see that something is converting more than 60 or 70%, I just like bump the price for two 20 or 10%, whatever I feel comfortable with. Because whenever I'm presenting this, my voice should not be shaking.
[00:29:37] I should still feel that I'm delivering this value added. Now for the pricing pages, what is super interesting is that you can A B test this. Just like last week I caught this company, La Grote Machine from Mahina, I think it's called from France, that they are doing A B testing with just like displaying their free version on the pricing page or not.
[00:29:58] And this is not even you messing up with the price. It's just like literally showing three or four packages or your website, right? So this is like, Very easy experiment to do. It is like literally something that you can do with any A B test tool out there that you integrate to your website. Now for the more complex ones.
[00:30:17] And what is like super interesting to explore, I think it's pricing parity. So on my website, I have been using like discounts, variable discounts based on geographies. Why? Because different countries have different willingness to pay. How do I justify this? Like, how do I think about this? It's literally like I tell this to people like seriously, if there is a person from emerging markets, they are probably struggling with the budget a little bit.
[00:30:43] So in total, that's a much like higher proposal to their budget. And that hasn't backfired to me. This is okay. If you are selling something really, really, really cheap, but when it comes to like more B2B products, what we'd love to do when we are coming to pricing experimentation. So for me, the easiest things are upsells.
[00:31:02] And upsells are just like more of the same that they have already agreed to buy. So if you are new to pricing experimentations, upsells is where I would love to start.
[00:31:12] Mikkel: you know, it's funny. Like I feel like at least maybe I'm, maybe I'm a little bit wrong, but at least in software, it feels like we're not that serious about pricing when you compare to traditional B2B where you have
[00:31:24] Maja: Oh, compare it with e commerce where every cent you
[00:31:27] have to
[00:31:28] Mikkel: Yeah, exactly. But it truly, if you're like a shipping company, it really matters what your costs are in,
[00:31:33] in relation to the price and what the demand is.
[00:31:36] And, and, and, you know, it's, I think what's this price intelligently had a stat around the companies that revisit their pricing and packaging. Once a quarter, they outgrow their peers
[00:31:45] Maja: Yeah, that's right. That's
[00:31:46] Mikkel: percent or something. Yeah, Yeah.
[00:31:48] And it's, and it's just crazy. And it was I think another entrepreneur I saw, he, he had a I want to say three SAS companies in his portfolio and he just killed a freemium because he saw based on testing that actually, you know, he gets more paying customers by not having freemium and he doesn't care about the vanity
[00:32:05] metrics of, you know, free users.
[00:32:06] So it was like a natural decision. So I think yeah, I love the pricing and packaging bit because it's to your point, it's, you, it doesn't cost you that much to do, to test
[00:32:16] Maja: it's,
[00:32:16] the closest thing to money in your
[00:32:18] Mikkel: Yeah, no,
[00:32:19] exactly. It really is. So, but what if you so let's say you also have an outbound engine running or similar where there are a lot of costs, there are a lot of people, but it's just not delivering the returns at the rapid pace.
[00:32:34] Like how do you just with your go to market mindset, how do you go in and diagnose this to find a path forward? Because we also have a bunch of listeners who might want to. You know, maybe they're growing okay, but they need to, or want to grow faster. What,
[00:32:47] what would your,
[00:32:47] Maja: I would bring in new partners or just like, include GTM engineers to the team. I would literally change the play with this AI personalized outreach because like, you know, the definition of stupidity, doing the same thing again and again and again and expecting different results. Sorry guys. I love you.
[00:33:05] I'm just like being intentionally harsh. So you will remember. So yeah, I would definitely have a consultant. reviewing what is going on and I would have a challenger. So somebody external who would be doing this process differently in the account. Plus with the existing team that I am like kind of stuck with at this moment, I would be exploring new tooling.
[00:33:26] This is how I would fix it. Now you have to also consider that's my bias. I'm never like super into hands on like outreach, how to optimize the sales teams. I'm just like there. This is not working. These are five people that can help you make this work. So, in terms of hands on execution, I unfortunately cannot provide further intel.
[00:33:47] Mikkel: Yeah.
[00:33:48] one thing one thing I picked up you talked about recently is also, and you mentioned it a little bit, it's the whole, you would tap into messaging.
[00:33:55] Maja: Oh,
[00:33:55] Mikkel: And you had this, you had this amazing visualization.
[00:33:58] I forgot where I saw it, but it was basically on cookies. And, and, and we also had Anthony from Fletch on the show. I, so I'm a big believer, by the way, I truly believe messaging is also one of the big levers you can pull. Although it might not seem like it, what you know, what steps would you take here to kind of start auditing this this angle?
[00:34:16] And, and quite frankly, how do you tackle some of those? Folks, I just, I'm just going to say it like Tony, who was like, no, no, it's outbound. What are we even talking about? Messaging all that soft stuff. Like screw that. We're not going to spend time on it. Well,
[00:34:29] how would you navigate this?
[00:34:30] Maja: no, I would literally start this and my philosophy with messaging and positioning is a little bit different. So my background is in direct sales, like literally I started in e comm. So for me, it's promise, conversion, social proof. Let's rock and roll. Right? So coming from a little bit of a different background.
[00:34:49] My core, when it comes to positioning and messaging are always combinations of U. V. P. S. So what's your promise to the customer that you can keep and U. S. P. S. So why are you the best? People hate the word the best, but like why should they choose you instead of competitors? We need clear answers to that.
[00:35:05] So this is where we start. But our first attempts usually suck. So I firmly believe in testing our initial hypothesis of USPs and UVPs with either A B testing with advertising and on landing pages. By doing like mass sales outreach or just like some sort of, even like you can test this if you are adding people on LinkedIn, it's possible, like, what is the thing that you are pitching?
[00:35:32] And if that's not an option, you can still test it on a sales call. So I don't believe that there is anything like any genius that would just like say turn leads into likes. An increased conversion by like 50 percent or something like that. I believe in robust testing. I believe that this should be derived from customer testimonials.
[00:35:51] Whereas founders vision is totally important. The problem, especially in the AI space, where I'm the most active, is that there is this huge knowledge gap in love and knowledge for the product. Right? So we have makers who are literally obsessed with A. I. And we have users who are like, Okay, cool. A. I. Now, what does it do for me?
[00:36:11] Tell me in simple words, let's have these conversations. Make me care about this. So, yeah, this is something that I have been very, very, very obsessed with.
[00:36:20] Toni: So, piggybacking on this actually kind of, because for me, I mean, you're, you're obviously very much dialed into the space. You're kind of thinking about go to market engineering. You're kind of thinking about, you know, using all of those different tools right now, stitching them together. Where, where are we going to go in, in, in three years from now or in 12 months from now, kind of where's this thing going?
[00:36:38] Because obviously everyone is thinking like, Oh no, you know, it's going to replace the SDR and then the AE and then the CSM and then the service wrap and like. What is your vision of how this thing is going to turn out
[00:36:49] as we are adopting more and more AI?
[00:36:51] Maja: Okay, so this is one of the five barriers of adoption of AI that Harvard Business Review defined. So will it take my jobs? And based on the Forbes survey, 74 percent of Americans are literally concerned that in a year AI will do something to their jobs.
[00:37:09] Positioning and messaging is the first thing that we are doing with this. The second thing as a leader is. to literally empower our folks and tell them very honestly that this is a technology change. That is, we have to adjust as the same way as we had to use how to use CRM for the moment. So as a leader offer assistance, like in terms of education, in terms of just like working together on creating pilot projects.
[00:37:34] So the only way how to find I'm not saying the resistant is to be super transparent about this. Actually, a client of mine actually mentioned for copywriting, which was the first area of our work, which was severely disturbed disrupted by AI. So he called his copywriter into the room and say six months in six months.
[00:37:56] We will have this conversation again. Either you will learn AI and you will do like the job of your life Like it will be an elevation for you. You will do so much more. You will literally superstart yourself Or you want, and we will have to say goodbye. Unfortunately, they had to say goodbye because psychological barriers into just like tapping the whole humanity need of being relevant, of being needed, of serving humanity, of having value in this ecosystem, these are some deeply rooted needs.
[00:38:30] And my solution to it, as the social sciences are not developing as fast as we are in the technology space, is to just like Equate your people better for this transition. I don't know what else to say, but this is reality how I see it.
[00:38:45] Toni: On this, on this story. Right. So I kind of, we had also, I forgot her name, the chili paper lady on the show, actually. Yes. Alina, exactly. And I think she also kind of, you know, told, told the story recently of them looking for content person, like a copy
[00:39:00] writer or something like this, right.
[00:39:01] And, and I mean, Chili Piper is very, very prominent, very visible. They get like tons of inbound applicants. And she was like out of 500 applicants, only one. Had like serious
[00:39:13] AI understanding
[00:39:15] and background. Guess, guess whom we hired. Right.
[00:39:18] And it's
[00:39:18] Maja: No, exactly. Exactly. This is the future guys. Such a great
[00:39:21] Mikkel: Yeah,
[00:39:22] Toni: yes.
[00:39:22] Maja: So, just doubling down to this one. If you learn this, it's also impacting your salary severely. So based on the research by my friends in product management, such as Akash Bhush Gupta and Pavel Khurin, AI PMs are paid 3x as much as normal PMs, right?
[00:39:40] So at this moment, now that we are talking, it's still a very fast way, how to elevate your career real fast and how we learn this. Is to literally, we are learning this by doing it. The first thing that I say to my folks who are like struggling to adopt AI workflows. The next time that you want to Google just like go to AI model of your choice, usually chat GPT or Claude or perplexity and ask the same questions.
[00:40:07] Just like replace your need, your natural search for information with that. And by the way, Grook is amazing as well by X. That's a one hell of an engine. But without any more of AI enthusiastic growth, just, just do it. Just do it. And there are like super use cases that you can do. Like seriously, you can build AI agents with.
[00:40:26] It's no code tools. There is lovable where you can like literally tweak the websites together and like AI assisted products with like practically zero upfront cost or something like that. So that would be a skill that I would be personally training. And I am training, there is this super cool stats that you can learn anything decently in 20 hours.
[00:40:48] So just like engineer those 20 hours into the next 14 days.
[00:40:52] Toni: So, so my, my kind of journey on this, and I don't know, I think Mikkel and I started a little bit late on this, but still probably earlier than most
[00:41:00] but kind of my, my journey on this was actually, I needed to understand kind of what this thing is good at and what this thing isn't good at, kind of, kind of what are the boundaries, kind of where does this everything machine and everything magic machine actually fit into my toolbox, right?
[00:41:15] Because it doesn't, it doesn't do everything. But it does do some things. Right. And the only way you will get there is by engaging with it and testing it out and, you know, pushing the limits. And it's like, ah, I didn't get this one. Right. Understood. Okay. You know, not going to use it for this, but then as also time goes by, and this is so mind blowing to me, it's like. Now I kind of do the same testing. Oh, oh, no, not now it gets this now, now it totally gets this. And now it does these things as well. And it's like kind of this space is, is moving really fast and kind of, you know, the only way you will be able to, you know, have any kind of stake in this is like, try it out, kind of stay, stay track and, you know, be, be close to it.
[00:41:51] Basically. Right.
[00:41:52] Maja: I love this. Thank you so much for this insert. This is super important. As makers of AI products, what we should be doing is leading with case studies and like use cases for now in our communication. Because like literally messaging such as build your AI receptionist in 30 minutes, no code required. This is where the mainstream is.
[00:42:14] This is like the language that is resonating with the adoptions at the moment. So just like forget what you already know and double down on what your customers needs.
[00:42:26] Toni: There you go.
[00:42:27] Mikkel: That's it. I just want to say thank you for joining us, Maya. Also, if you enjoyed
[00:42:32] this episode, do not forget to hit. Yeah. Yeah. It goes fast. I know. But also if you enjoyed this episode, which I obviously I'm betting you're doing since
[00:42:40] you're listening at the very end of it please hit subscribe, like follow, share, whatever, download
[00:42:44] Toni: And tell your friend, tell your friend we had Maya on the show and she is selling a lot of awesome stuff. So check her out on LinkedIn. She, she has a lot more followers than I have.
[00:42:53] So that's, that's, a problem. I need to work on that, but
[00:42:55] Maja: Oh, no problem. You just launched a couple of viral magnets. I got you.
[00:42:59] We
[00:42:59] Toni: Yeah,
[00:43:00] Mikkel: Yeah,
[00:43:00] Toni: there you go. There you go. There you go.
[00:43:02] I
[00:43:04] Mikkel: Thanks a bunch.
[00:43:05] Take care.
[00:43:06] Toni: have a good one. Bye bye
[00:43:07] Maja: Take care, ciao. Thanks for listening.
