Ep. 167 - How AI Is Changing the B2B SaaS Search and Buyer Journey

Guest: Ayse Guvencer, Fractional SaaS CMO & GTM Executive

As AI changes how B2B buyers evaluate software, many marketers are missing the forest for the trees.

In this episode, Ayse Guvencer, fractional CMO and GTM advisor to SaaS and MarTech startups, joins Ken Lempit to deliver a blunt reality check: AI is disrupting the search journey, pricing models, and even how buyers interact with your brand—before your SDRs even know they exist.

We unpack:

 ✅ Why Google is now a navigational tool, not a discovery engine
✅ How AI-driven tools like ChatGPT are shaping shortlists and decision frameworks
✅ The rise of Generative Engine Optimization (GEO) and what it means for visibility
✅ Why “Service as a Software” is redefining outcome-based pricing
✅ What AI browsers and agentic advertising could mean for reaching the “hidden figures” on buying committees

Ayse also shares practical guidance on how marketers can stay ahead of AI-native competitors, avoid falling for adtech black boxes, and reframe their role in this fast-changing ecosystem.

If you’re a B2B SaaS CMO or CRO trying to stay ahead of the curve, this is one of those episodes you’ll want to listen to twice.

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Transcript

Welcome to SaaS backwards, a podcast that helps SaaS CEOs and GTM leaders to accelerate growth and enhance profitability. Our guest today is Ayse Guvencer, a fractional CMO and GTM leader for SaaS firms seeking to scale, who brings experience in operating roles and at top marketing services companies to her clients. 

Ken Lempit: Ayse, welcome to the podcast.

Ayse Guvencer: Thank you so much for having me. I really do look forward to diving into some of the topics with you today.

Ken Lempit: Yeah, it's gonna be awesome. But before we dig in, could you please tell our listeners about your practice and a little bit more about your background?

Ayse Guvencer: Sure. So let me start with my background. I've been in marketing for the past 21 years, so I've seen an industry and how it evolved from paid search to paid social to programmatic. I'm very 'techy' background as well. So I do know the inner workings of the digital system. I've been on the client side, the Google Tech side, as well as the agency side.

So I do have pretty good knowledge of how each individual pieces operate. And currently I'm running my own company. I do give consulting, as well as act as a fractional CMO to a lot of the AI startups and especially MarTech startups in the SaaS space.

Ken Lempit: Yeah, you know, that ability to marry the operating experience like a domain other than marketing with the marketing, I think is awesome. You know, whether it's a tech background or you know, an operating role in these companies makes a big difference in the, how pragmatic the advice is. Right. How actionable it is.

So that's really awesome. We have a really, you know, action packed set of topics we wanna get into. So I wanna get us started and, let's talk about artificial intelligence, sort of like everybody's talking about it all the time, but we have some great, I think takes we're gonna make here.

So let's talk about how AI driven search behavior is fundamentally changing the way B2B buyers. Research and evaluate software solutions. What's, what's changing that marketers need to be ahead of, not just catch up to, but get ahead of.

Ayse Guvencer: So I think one of the challenges as marketers we have is being able to map out the entire buying committee for SaaS. Average enterprise buying committee is around, average of 22 people, give or take, depending on the employee size of the companies. You do have your C-Suite business units managers, but you do have your hidden figures such as legal, procurement, data privacy.

So further away from a product a buyer gets, they need a little bit of a difference brand assurance, correct? So every SaaS decision maker I talk to, follow a pretty similar journey. They either go to Claude or ChatGPT, they search for products on brands. They compare them. They ask for a decision frameworks and they shortlist brands.

This doesn't mean every core decision maker will be swayed with what an AI tells them, but according to MIT research papers, the trust in AI is steadily increasing. So the consumer journey. Starts with an AI engine, and once you shortlist the brand, you go to Google search for the brand name and go to the website.

So this makes Google a navigational search engine moving forward. So it doesn't play a very active role in the research process, right? So, if this is the current behavior we're seeing and with AI browsers coming into the space, we'll be able to fill in demo requests and schedule meetings. Then we're looking at a completely different cracked user behavior for B2B and for SaaS as well.

So the brand visibility in AI engines becoming more and more important. Yes, the traffic is little, but if a buyer is going to ChatGPT and getting brand recommendations with comparisons, features, pricing, as well as an entire decision framework, they're already swayed to a short list. So the impact of being and appearing as a visibility in AI engines

are really becoming an important piece of the consumer journey, and I don't think a lot of marketers are catching up on this trend because we seem to be very focused on consumed with internal applications of AI like automation, generation, measurement, optimization. As well as what job functions would evolve, but I think we're missing the big piece of consumer behavior changing, and I think that's something we need to really focus on as marketers.

And there are some kind of web developer companies or some marketers looking at generative engine optimization, which means you're trying to optimize your brand visibility in AI engines. But I think this is still a very early stage kind of focus for marketers, which. Marketer's role is to catch these trends in consumer behavior.

I think we're not really doing a very good job when it comes to this currently.

Ken Lempit: I think that's a really great insight and I totally agree with you. You know, we can see. The drop off in the search, but we know people are still looking for stuff, right? So where are they getting the information. The flavor of the last couple of years was the, you know, the dark web, right?

Ayse Guvencer: Mm-hmm.

Ken Lempit: off web, you know, user groups, Slack groups, you know, other ways to communicate. But, the search behavior is migrating and definitely migrating either to Google's own AI summary or I've had plenty of people say to me, Hey, I, I just use OpenAI and you know, I go and ChatGPT and I say, you know, find me the best accounting software for manufacturing or whatever it is, and they get a whole result that keeps them off the search engine.

As we've come to understand what a search engine might be. So, if we look back at SEO, I mean, that was a good ride, right? Marketing services firms, they were able to get some good money for close to 20 years on SEO is, is that a big opportunity for my brethren in the agency space?

Ayse Guvencer: It really is, and I come across these posts regularly that SEO is dead. It is not. The change of the name might be necessary to include different platforms. Let me give you one example. When Amazon Marketplace started, or TikTok put in shoppable plugins and Instagram as well, there was a huge influx of retail traffic that Google lost.

So that was a change in search behavior. Then we have people trying to book their holidays on booking.com, so that's another search behavior. So SEO cannot only cover Google anymore. And one thing is ChatGPT is pulling its results from Bing, and this makes Bing one of the most prominent kind of search engines, which is interestingly, they don't have a big market share.

So when you're talking about SEO, it's a search behavior optimization at the end of the day. And now we're seeing multiple platforms where consumers are using and more heavily relying on AI engines. So SEO is not dead to anybody still thinking about this or discussing this. It's still a huge part of content and brand visibility, but I wanna make one thing clear with AI overviews at Google where we see the results being pushed down significantly, and some of the publishers and some other kind of functions being pressured to be in AI overviews.

We've seen a 40% drop. In organic traffic to top sites. Now Google is rolling out AI mode, which is gonna shave a little bit more traffic from the top sites again. So if we're just focusing on Google, I think we're leaving a lot of the opportunity on the table.

Ken Lempit: Yeah, it's, I mean, there's lot to unpack there. I'd recommend people listen to this little segment twice. Because when, you know, when we do paid advertising on search, Bing is, you know, 5% of the action. But I think what you're uncovering here is how important it is to know your positioning and, and how Bing is seeing you, even if you're not gonna advertise there.

and as well, you know, you said something twice that I think might have gotten past people, which is this. People asking the LLM for help in decision support, right? Decision framework, as you called it, or, or a selection framework. And I, I think that calls for us to create content. That might look a little different than we used to and, and free it from being gated.

Like one of my favorite pieces of content going way back was the model request for proposal. And we would put that behind a form so that we could get the lead knowing people were hunting for whatever the thing was that the RP was aimed at. Now you almost have to put that content out as freely as it can be presented so that the LLM might find it.

Ayse Guvencer: Exactly. There's also the piece of being asked whether shall I create a decision framework for you? That's the next recommended question. You hit yes on ChatGPT, and then it fills out that decision framework then asks you whether it should score it for you and you hit yes.

So at that point you have a scoring based decision framework completely filled out for you. Already swaying your decision to shortlist which brands. So when we're thinking about these things, we need to understand from a consumer behavior, what they're searching, what kind of content that they need will be decided by the consumer, not by the brands at this point.

So, structured content any question, long-term question related content, pricing, any of those structured content would help immensely for the brands because it's not just traditional SEO anymore, or not just traditional content creation anymore.

Ken Lempit: And you know, you can imagine what those other kinds of contents are like comparison us versus brand X, brand Y, and brand Z. Making those things easily consumable, so that when somebody goes into ChatGPT and says, Hey, tell me the difference between Zero and Intuit for accounting,

Ayse Guvencer: Yeah.

Ken Lempit: The machine has as much as it can get, you know, as much as it might need to create that comparison.

So that, I think there's a lot there. I mean, we could, we could lock the episode down now, we'd be done, but we got, we've got more to go. We, we talked in our prep session about AI native startups and there's sort of two things happening at the same time. There's existing brands, existing solutions incorporating or bolting on or creating copilot AI aspects, and then there's entrance into the space that are starting from scratch, and they're starting with an AI perspective. So the AI startups are, are disrupting a lot, but effectively pricing is one of the places that seems to be most confusing and controversial. 

Ayse Guvencer: Mm-hmm. 

Ken Lempit: we talked about outcome-based pricing and I, and I think it would be great to talk about that. 'cause I think that's one of the, I think that's one of the things that's gonna be confronting buyers.

And, and builders of software solutions is trying to come up with new pricing. So could you give us your take on outcome-based pricing strategies and what that's gonna look like? You know, at the moment of purchase,

Ayse Guvencer: Sure. So they're already resource and usage based pricing by companies. So when you take a look at Snowflake, for instance, their pricing is based on usage. Based on different aspects of functionality and how much compute power you use. So when you have a pricing model like this, then you, the buyer can actually forecast how much data that they're going to use, how much compute power they're gonna use.

So that's already based on tiers. But with AI native startups, I've heard a great term. It's not software as a service anymore. It's service as a software. I was looking at pricing for one of the AI startups I work with. So they are. Charging based on tasks completed. So, I do understand it's a little controversial for some of the most kind of established SaaS businesses to look at this from a seat based or subscription based pieces.

But when you, unlock an outcome-based pricing, you also unlock time to value faster and a lot of these startups will have free trials and premiums, which already shortens that time to value. So if the pricing is more towards an outcome-based task space, and if you're seeing the value a lot faster, you wanna increase your usage, right?

So that's one aspect of it. But I think in our prep session we talked a little bit about. The hesitancy from some of the SaaS businesses to this sort of pricing. Not being able to predict how much that something is gonna cost, having fixed kind of prices or budgets when it comes to tech. And I completely understand those concerns as well.

But with this type of pricing coming from AI native startups and we're gonna have agents also doing tasks. And getting the pricing based on the task that they complete. Then we have two different kind of pricing models coming from established SaaS businesses versus AI native startups. So I think we're gonna have to find a happy medium, but if a buyer is paying for the outcomes that they are generated, and if they're happy with those outcomes, the usage is gonna go up and they're gonna start doing more tasks.

So I would love to hear what your thoughts are on this one.

Ken Lempit: It's interesting, I think, if you're, reflective of actually how we got here there are aspects of software pricing that have been by the Drip. You look at things like API calls, right? They're consistently priced in tiers or even, you know, by the call. So we've kind of crept toward this even before AI. So I think your point about hybrid arrangements or, you know, still working on budgeted arrangements, especially larger companies, they sort of wanna know what they're gonna spend to get something done. So I think we'll see maybe per agent pricing and agents will have capacity, right? They'll be governed as to how much they can do in a given time period.

So, you know, if you need a lot done, you're gonna have to subscribe to, you know, more agents. So I think there's gonna be. Ways to get at some predictability, some consistency. But I, I think the, the downside for the software companies is also the upside. So if task-based pricing became the norm and we did a good job, then we get more tasks.

But if our, if our work is not so good, right? If it's a mixed bag, I think the financial results could be a mixed bag too for the software company where adoption could be, variable, you know, by user, by department, by location. So, a traditional, like an ERP system, you know, has sort of enterprise wide pricing, enterprise wide budget, and, and it's a pretty fixed engagement.

I mean, there's a lot of moving parts, but in the end of the day, there's sort of a fixed budget that SAP is gonna get its hands on for each of the things it sells. I can imagine that being very disruptive to publicly held companies if their financial results were impacted by how good their expense management component is, or their maintenance management component.

So I, I think it's gonna be controversial and the larger firms are gonna kind of insist on a minimum annual commitment. You know, there'll be some new language around this probably is, is I think where it's gonna go. 'cause, you know, how is SAP supposed to support Ford Motor Company when they don't know what the revenue's gonna be?

Right? How do they deploy support worldwide? So, maybe smaller companies we're gonna be, you know, more aggressive with this. But I, I think the big guys are gonna have to, everybody's gonna want some predictability. I think Ford wants to know what its ERP is gonna cost, and I think if SAP is gonna deploy solutions engineers worldwide for Ford, I mean, they sort of have to know what's on the table.

So.

Ayse Guvencer: Of course. But you know what I was thinking when you were mentioning it about mixed bags of results, correct? So, an average LTV for an ERP software is around what, six to seven years, right? Give or take. 'cause it's not a very easy system to onboard and it's not a very easy system to offboard, 

Ken Lempit: It can take you half that time to get off.

Ayse Guvencer: Exactly, exactly.

But when you think about not all teams utilizing every single functionality or ERP training or ERP software and not getting a good value out of it. Where comes the retention, even when you're in a subscription service or a seat based service? So I think if we're discussing mixed bag outcomes, that's not just for the pricing, that's for retention.

And I think innovation from a product perspective for any SaaS company is crucial, but you can lose your clients very easily if it's a mixed bag of results. Even the seat based or a subscription based system, you have no.

Ken Lempit: Yeah, sure. I mean, the, the smaller SaaS play has always been, you know, eat around the edges of the major solutions, right? So if you're a better expense management, you know, company than what SAP provides. Good chance you can start to make inroads in some of their customers who might be unhappy. Sales tax computation, another one of those like special purpose SaaS where people are doing fine, you know, picking off pieces of you know, a large scale ERP deployment.

So yeah, I mean, you know. Pricing is controversial. If you care about pricing, if you're just trying to get work done, you probably don't care as much as we do. But, but I, but I, but I think it's gonna have an impact on publicly held companies, and I think there should be, people should be looking out for that.

The AI firms are gonna force some change on the marketplace. I think that's, that's kind of where I'm, I'm landing on this. I wanna take a look at a couple other topics we talked about. You mentioned AI browsers, and I'll give you credit. You know, I'm not the most well read guy, but that's the first time I heard of an AI browser, and then obviously it's a whole category I wasn't even looking at.

So I bet I'm not alone. What's an AI browser? And how are AI browsers and things like agentic advertising gonna impact how companies communicate their value props? What do we need to know here as marketers? And it sounds to me, it seems to me like it's coming soon.

Ayse Guvencer: It is, and let me explain this this way. When you go to Chrome, you do all the work as the user. It's a passive way of working with a browser. Pretty much you do the heavy lifting. It doesn't really provide any feedback to you, so you're basically, In a passive relationship. When I talk about an AI browser, I'm talking about an active browser.

So imagine that you are doing some tasks, but you ask to purchase a plane ticket, it's gonna go and do that for you. From a SaaS perspective, it can fill out demo forms, it can schedule meetings for you, right? So this doesn't only impact marketing, this impacts sales handovers. So they're not here fully yet.

Opera is one of the browsers working with their area assistance. And I saw a great demo. It's not rolled out fully, but it is pretty impressive what it can do without you leaving the browser. That's the whole point, right? Being able to autonomously run tasks simultaneously without leaving the browser.

Perplexity is working on their one called Comet even though I've heard some controversial, kinda announcements from the Perplexity CEO saying we're gonna target and track everything for hyper-personalization. I don't know how privacy regulators are gonna take a look at that, but when I'm in an AI browser, are they gonna be autonomously working on simultaneous tasks while you're engaged in something else?

And mind you, it's still very fairly new. I don't think anybody knows exactly how that's gonna track and how rankings are gonna look like, so that's still to be seen. But when you're talking about an autonomous active system doing some of the tasks that a marketer does. To get to the brand or talk to sales, that's a whole different ball game in terms of how we should organize ourselves.

So I think that's the crucial piece. It's also has to do a lot with how consumers, how B2B buyers are gonna utilize it, right? So, we're gonna have to understand what that journey looks like before even taking any actions. So that's what I mean. We need to understand these consumer behavior trends before starting automating things.

'cause consumers gonna decide what they need automated, right? So I think that's something we need to keep in mind.

Ken Lempit: So it's the browser then taking next steps for you.

Exactly. So, you, you talked about like booking travel, so like I want an Airbnb near Hartford, Connecticut on these dates and instead of just spitting back results, it might suggest based on your past behavior or a question or two, it asks you, it might suggest two or three for you to choose from and then go ahead and fill in your details and get you done.

Ayse Guvencer: Exactly.

Ken Lempit: That. That's quite a different situation. Having done my share of Airbnbs, it's really, it's a time suck. It would be great if the, if the browser could be smart enough to do that. And I think we go back to the idea of the decision framework for SaaS. Which is, you know, what we're talking about here. To then build a short list, create your queries to these organizations, and maybe even suggesting how they should respond to you.

That would be kind of scary, right? I mean, it's the machine doing a lot of the work.

Ayse Guvencer: But. If you think about it, I don't look at it from a scary perspective. I look at it from an efficiency perspective. So it's gonna create a lot of automation and efficiency within the research and buying process. So I think there's an opportunity for SaaS businesses, even to shorten some of those sales cycles instead of going back and forth in demo request time, scheduling meetings.

So, that's one way of looking at it. So, any new technology brings its challenges of adoption and how we adopt and what jobs are gonna evolve. So I completely understand those concerns. But it's gonna create a lot of efficiencies for sales teams, customer success teams, product teams, as well as the marketing teams.

So there's also that side of the coin too.

Ken Lempit: I mean, it's really interesting. As you were talking, I was thinking, well, maybe my advertising has to be communicating to the machines on the other side, right? So it's not just my content, my website, but I'm kind of wondering if I need to stimulate the machines with some advertising that might create a whole revenue model for us here.

Ayse Guvencer: Exactly. Exactly, exactly. And you asked about agentic advertising, so that's still very fairly new. But currently the way that we, we target, the way that we track or the way that we reach out to buying committee, is slightly different to what agentic advertising can do. So I talked about how legal procurement and privacy and InfoSec teams given when they kind of enter the buying process is relatively later.

Because the core decision makers evaluate the products. They come to a conclusion. They have a RFB, they identify case studies, and then they have a short list of products and then, then that goes to procurement and IT and privacy and InfoSec, correct? And then these stakeholders, as I call them, hidden figures vito about 70% of sales. Because

they don't know the brand. They're far removed from the product and they need a different type of brand assurance. So what if with agentic advertising, we can personalize on the go, messaging to these people? Because in a lot of the cases, they don't leave digital footprints. They're not even in the funnel.

So how do we reach out to them with the current way of advertising or marketing? And you mentioned something really important, right? We have dark social, so they're gonna go to ChatGPT, research brands understand shortlist and go to Slack channels, Teams channels, and email and discusses internally. This all happens without any brand influence or knowing

what the brand should do. So there are gonna be ways for us to influence some of those discussions with agentic advertising and with AI engines and so forth. So I think there's a great opportunity, but we need to jump on it right now.

Ken Lempit: This kind of brings us back to Microsoft because they also have LinkedIn, right? So imagine kind of combining the power of LinkedIn and Bing and having an agent work against those two resources to find out who are those other people in the organization, you know?

Ayse Guvencer: Exactly, exactly. And mind you, LinkedIn you can use your LinkedIn data to target on Bing already. So, there's the data component within the Bing campaigns that you can use LinkedIn's data to target already, right? So that's also another thing. And I think one thing that I was reading earlier today is Microsoft is sun setting

one of the technologies that they bought, demand side platform, which helps you buy programmatic advertising. So the future as they see it, is based on AI. So they don't necessarily need a DSP at this point. So they're pivoting their entire strategy from a marketing advertising perspective and what solutions they offer to that side of things.

So that's also a huge development, I think, from one of the big tech companies moving away from a traditional way of buying media, towards and AI kind of focused buying journey so that's already happening.

Ken Lempit: We, we didn't prep a lot on advertising, but I, I, I gotta throw you a little bit of a curve ball here. Mark Zuckerberg was on video in the last week, right? All over the internet. A lot of us watching uh, Zuck talk about, you know, there's no need for agencies to buy media or to build creative and.

You know, it's pretty controversial stuff. Should we be thinking as marketers that we don't need expertise in media buying or creative or you know, sort of my belief is these tools don't make art directors out of people like me. You know, I can barely draw a stick figure. I don't think they make writers out of people who are illiterate, you know?

So, and who don't have the experience to know what really motivates people to make a buying decision, to create compelling content. It's sort of, as someone who's written content for decades, I can see when the content could be influential that's generated by, by ChatGPT or others. But what's your take on that, you know, is, is Zuck right and he doesn't need agencies or creative people, or is there still a role for us to craft ideas and, and decide what the imagery should be?

Ayse Guvencer: Let me take a step back and kind of put this in a better perspective. So the way he's looking at things, he's saying, connect your bank account to Facebook ads and sit back and relax and have AI do the work. And we're gonna spit out measurement results. And whenever I hear these kind of propositions from the Googles or the Metas of the world, I cringe. Because you have

relinquished all control to an AI black box that you don't know how it operates, but it's gonna drain your funds from your bank account without, and how are you gonna kind of see the results and justify the results If Meta is kind of completing their own homework. So I don't see how that will be a reality.

Maybe for small businesses that don't have the resources for agencies and creatives that might slightly look attractive. But I would caution any business relying on a media buying strategy like this. And let me, let me tell you another thing. So when I was at the client side Google had a ad format called PMAX.

So basically you set your KPI, but you dunno where your ads show up and you cannot control for brand safety and it eats up your budgets quite significantly. And it's based on AI. So whenever I hear these solutions and knowing the tech behind them and being in this sector for AdTech for the past two decades I would really take these kind of offerings with a grain of salt because,

we're not there in terms of relinquishing all control to AI. And we will still need creatives. We will still need agencies, maybe a different type of agency working with different type of AI tools or AI capabilities. But personally, if I was a business, I am not connecting my bank account to Meta Ads and I don't advise anybody to do so.

Ken Lempit: Yeah, I'm with you. It's like, talk about conflict of interest. And you know, the fact is that Meta could have 3, 4, 5 or more brands all competing for the same stuff. And who's to say that they don't have a pre-existing relationship with one of them, or they give preference to one. And it's not just one black box, it's 5 or 10 black boxes.

And none of the advertisers know how these decisions are actually being made. Seems like a recipe for disaster and by the way, this PMAX performance Max, we've had nothing but trouble with that. We avoid it. It does not do the job. And it's a great example of the AI just running amuck, taking your money right.

Ayse Guvencer: Well, let me tell you something. When you're talking about big agency hold cos, 'cause I worked in those as well. The relationship with these tech companies is slightly complicated. So you're looking at a big holding agency company when the margins are really going down and you do have a lot of rebates and kickbacks from these tech companies.

So, um, then when it comes to the advertiser's best interest. You don't get that much. You're being pushed by both sides to use PMAX or use formats that are not really beneficial for your campaigns. But there's a huge pressure to use them. So I advise every marketer to understand what these features and products look like so they can actually make informed decisions without being pushed these kind of products and features up to their throats.

That's the, that's the risk I see.

Ken Lempit: I appreciate the, you know, going off script a little bit here 'cause I just think it's such an important topic. I wanna go back to last topic that we prepped on was that, you know. It's not a free lunch building, an AI based software company. And in fact, costs are going up. You know, to deliver a unit of work, it's actually can be more expensive.

I think Uh, open AI's Altman was saying it costs 'em like tens of millions for people just to say please and thank you. Exactly. So the compute costs are really high. There's supply chain issues that impact building out data centers. So what's an established SaaS company to do? What adaptation should they make to their business model to to be competitive here?

'cause they're, it's like they're paying two costs, right? They have to build the new thing and incur the new costs, and they still have their old, maybe it's a marginal cost of businesses lower, but now they've got sort of two marginal delivery costs. So how do they manage that?

Ayse Guvencer: So when you take a look at what McKinsey recently published they're predicting the compute costs and investments to topple an astonishing $7 trillion by 2030. AI needs make up about $5.5 trillion of this. So when you talk about compute, you're talking about middleware, hardware, data center storage, memory, and

within the unpredictable market that we're living now with trade wars and multiple other instabilities, investors are not sure how much to invest and when to invest. That creates a huge instability in the market when big AI companies rely on these types of investments. Because under investing puts you back in the game. Over investing kills your business. So that's a huge issue. And if you are going back to seat prices or kind of subscription prices, so let's say if you are in the data storage solutions business, right? You do incur those costs. And if those costs go up for your business, you're gonna have to pass some of those costs to your clients.

Either the subscription fees are gonna go up. Or some of the other parts and functionalities are gonna go up. So let's go back to the ERP solution example. So, a buyer is in the market currently, and then an ERP cost is around 150 K. But the second that goes up to 250, then those buyers are gonna start thinking again, is that viable option for me?

Shall I even delay this purchase for myself or shall I completely leave the market? When you think about the current companies that have ERP solutions, if those costs go up significantly and there's not much product innovation, then that begs the question, are they going to, are they gonna look for modular based pricing or are they gonna look for resource-based pricing, which are a little bit more favorable?

So companies within this space are gonna have to make two choices. Either increase the costs relatively, but really heavily invest in RND and added value. Or lose some of their clients to more alternative based pricing companies. So this comes back to what that pricing is gonna look like. But the current trade issue we have globally is impacting supply chains heavily.

And when a lot of companies are relying on compute, then that makes it quite difficult to understand how much you need, when you need, and what those costs are gonna look like. And given the tariffs are going up and down with one tweet on through social and the market crashing, it makes it even impossible to forecast these.

So all I can say is we will see, but that's where I see the companies are gonna have to make sacrifices, either heavily invest in RND and increase prices or lose your clients.

Ken Lempit: It's a, it's a tough spot and also implies that we're gonna need. Kind of a revisit or recommitment in the venture and PE community to build these companies. I think this is a great place to land, our episode. Ayse, if people wanna reach you, how can they do that?

Ayse Guvencer: Sure. They can directly reach me on LinkedIn or they can follow my page ELVT Consulting and I'll be more than happy to answer anybody's questions or even give a deep dive into some of the topics we discussed.

Ken Lempit: Thanks so much. If people wanna reach me, I'm on LinkedIn/in/kenlempit My advertising and Demand Generation agency for SaaS is Austin Lawrence, and we're at austinlawrence.com. And if you haven't subscribed to the podcast yet, Shame on you! and please do so wherever podcasts are distributed.

Hey Ayse, thanks so much for being on SaaS backwards.

Ayse Guvencer: Thank you so much for having me. It was great.