Plurio by Elly Analytics: why B2B SaaS breaks and how AI-agents replace it

Speaker A: Nobody wants analytics. Actually people don't want to think about this. They want just answers. We tried different things. The plan was to make three sales per month and we could do this, but we didn't find the way how to make 10 sales per month. We don't want dashboards, we want answers. We don't want all the complicated things. We don't want to see the graphs or the numbers and to find what's working or not tell us what is working. We decided not just how build AI solution that will make the AI first marketing, the native marketing possible, but also start to be the AI first company to start using AI agents. It's not about okay, I need to learn how it works. It's about try the first use case you have in your real life.

Speaker B: Welcome to Rebuilding SaaS Marketing a podcast by Digital Hunch. I'm Renata, a growth expert and co founder of the marketing agency for SaaS products and together with founders from across the SaaS world, we're figuring out how marketing works in the age of AI. In each episode we unpack one founder story. What worked, what failed, and what still keeps them awake at night. Today we we are joined by Evelyn Agorotnikova, head of marketing at Ellie analytics and Plurio, an AI native platform combining revenue link attribution with a performance marketing agent that learns from your business data and acts to increase the revenue. As uh, the team's first marketer, Evelyn guided Ellie through several restructures and rigorous hypothesis testing. Steady evolution, not a pivot. We'll dig into the journey and lessons from three years of experiments. Hiring as a growth channel, 150 plus sales calls per quarter and a content agent that builds trust. Uh Evelyn, nice uh to have you here. Tell me a little bit more about your product, about the audience that you're targeting. So we're just starting, starting the conversation.

Speaker A: Hey. Hey. Sure. Uh, first of all thank you for having me here and for inviting for your beautiful podcast. So happy to be here. Originally we were making marketing full funnel marketing platform for a very special audience for the consumer brands and consumer services that have a lot of problems with stuff that make, that helps them to make data driven decisions. Most of the ah, tools or solutions they're created for E commerce, some of them for uh, hard B2B products but uh, there are just a few options uh to have something really good and workable for the space, uh, where you have um, uh, a couple of funnels or your funnel is very difficult or um, you have a lot of touch points, uh between your first click and the revenue you get. So in that space, uh, you need to have a really great data, uh really strong attribution. And the dashboards, not just that are beautiful but that get you answers. So uh, that was the one thing we started to build one product. We started to build for that kind of market, for that kind of industries like healthcare, like uh, all home services, B, uh 2B, uh subscription businesses, uh, real estate, uh, what else? Car dealerships and so on.

Speaker B: Yeah, talking about the B2B, uh analytics. I know it's usually very painful to track everything from user to the real sale even, especially when you have for example different funnels, sales service funnel and demo, sales demo, uh, calls funnel and usually it's very hard to understand what gives you growth. Uh and yeah, I uh, believe that your service was um, perfect for that,

Speaker A: is perfect for that. Totally agree. Yes but it's still, you know, it's still very situated, uh, it's still very hard to implement because the sales service is not working here. Uh by the way because you uh, don't it's sometimes it's always a black box for that kind of businesses and because they need that a really deep understanding of what's working and how the attribution works, how the data streaming works, how your data is aggregated. Uh, to answer can I believe that data to make the decisions today? Because my revenue will come just in a week or two weeks and I need to make my decisions about buying uh ads uh right now. So self service doesn't work here but uh, the only option for that kind of business is actually that actually works. Uh, before Ellie that was uh, to build that kind of data, uh platform and uh dashboards by yourself with your team in house. Uh yeah. On the other side that's also challenging because you need to hire the team, you need to have your deep expertise to hire that kind of team that already did that things a couple of times and they know how it works and they know and they will focus just on that kind of marketing analytics. It's expensive, especially in the US market. So uh, what we did, we had a team that was uh, building all that marketing analytics for the companies. Uh we were responsible for data implementation, for data streaming, for attribution. We were you know like uh, like internal team for that, uh, for that companies to build that, that analytics for them. And we are responsible for the data from day zero to end. So uh, that was the idea.

Speaker B: It's like fractional analytics uh team where usually we have a fractional marketing Team. And I suppose in this uh, stage of your product, uh, it was for you a uh, fractional analytics, uh, team, right?

Speaker A: Right, yeah, something like that. So yeah, it's not just a platform, it's also the service. So yeah, that's what we get. And uh, now we are uh, transforming. We stopped to be just the platform, just the dashboards, just the analytics. And now we are transitioning to the next stage. So now we have an AI agent that is connected to the data that our clients, they believe in. So it can be two options. They can connect to our data platform that we're building for them, or they can, if they have their own, uh, and they already built it by themselves, we can connect our agent to their platform, uh, and database, uh, and uh, after that, uh, that agent it thinks, but also does for our clients. So you can automate uh, all the performance marketing with one AI agent that is uh, trustable because it knows your data that you believe and you can check it and it checks itself all the time. And uh, it can act for you in the ad platforms.

Speaker B: For me, as a founder of a, uh, marketing agency, it sounds a little bit scary, but I know that uh, actually of course it cannot work without experienced marketing manager anyway. But yeah, it's the future that sometimes I'm afraid of, but I believe in it, uh, nonetheless.

Speaker A: Yeah, it's a very interesting way and I think it will be one of our challenges to break this uh, fear with uh, the marketers that it can be scary. AI is scary, yes. We are always like, can I believe that information or not? How can I check it? And so on. So we're building that system with that knowledge because we were marketers, we were performance marketers, our founders were, I was also so understand how. And I had an experience when uh, I, uh, uh, added the automation platform to one of my businesses. Uh, I was working with, when I was working in an agency, I don't remember, it was like $10,000 went away in one day. So we know the problem.

Speaker B: Yes.

Speaker A: And that's why we're building the system, uh, in the way that it can be possible.

Speaker B: So let's move to the time when you just started. I know that the most intense phase of your experimentation with growth in marketing so started when you had to enter the US market three years ago with no brand awareness, with no local presence, which is usually, you know, the worst case scenario, and no partner network. What was that experience like and how did it unfold for you?

Speaker A: I guess it was four years ago and we moved from Established market. We were working in uh, that time, uh, at our local market, uh, to global without brand recognition, uh, without no establishment, uh, community round product, uh, without local presence. Um, we had 30 people in the team. Most of the team was uh, on the product side and uh, on the service side. So uh, we had actually no marketing. I uh, was the first person that was where we started what worked in the local market. We had partners, we had uh, lots of events, we were participating webinars, um, we made our own vans. We had a very good known expertise of our founders in the market, in the digital market. And uh, it helped us to generate sales. And we were bootstrapped at that time and uh, that worked great. But when we moved to global market, everything changed in one way.

Speaker B: Uh, tell me more about it. Yeah.

Speaker A: Okay. So what we realized that uh, nobody knows us and we don't have a community that can tell about us. So the first thing we decided to do is to uh, try to get that kind of people who can tell the story about Ellie. Uh, so we started to build the partnership channel. And uh, we didn't have any experience on building partnerships uh, in the US market and global market. Uh, and we didn't have that kind of experts that could introduce us to that guys. And we even didn't have that um, ideal partnership portrait in our heads. But we had some hypothesis there. So we separated uh, that hypothesis. We thought okay, it can be tech partnerships. So with the complemental tools it can be some co marketing. But we still didn't have the name, the brand. So it was not so easy. But we decided to check it as well. We had something like we could go to Microsoft Marketplace or Google Marketplace or some somehow uh, connect with them. But it's still in our mind. We were going there, but uh, there is a separate story. But uh, the funny thing was that during all the experiments, I will tell about them a bit later. We understood that our ideal partners are the guys who are like fractional simules or some guys, small agencies that have um, 2, 3, 4, 5, 7 clients. They know them, they are uh, like fractional CMOs for them and they are closing that role and they uh, have deep understanding on performance marketing, on the importance of marketing analytics and data for making decisions. So they're very experienced and they had really uh, great expertise in that part. So we started to look for that guys and we talked a lot with them. We needed to have a lot of calls at that moment to make a lot of calls. The first calls to understand if the story we're telling about Ellie is valuable for that new market market because we knew that it was in the local market, but now we didn't know how to tell it in the right words. So we needed to have a lot of calls, uh, and to talk to a lot of people to get feedback and to understand uh, what's working, what's not, how we need to tell the story, how we need to tell uh, about our values.

Speaker B: Let's just stop here because I have questions. How did you understand, you know, I want to first talk about partners a little bit more. How did you understand that you need this fractional cmos? Why uh, did you decide that you need this kind of partners? Not, I don't know, other agencies or some other people that work with clients as well?

Speaker A: It's interesting question. Uh, it was four years ago, but I believe that it was because we made a lot of uh, calls and um, we had a really great calls with tech partnerships, with potential tech partnerships, uh, guys that were uh, telling like oh my God, we have a great solution and you can uh, strengthen our solution, uh, and make deals with our clients and everything is fine. But uh, what we got that the only thing we could offer to that partners was our product. And uh, sometimes when they came to our, the partnerships, uh, managers, they came to the, to the team, to the account executive, sales reps and so on and told like, okay, we had a really great solution, let's try to implement it to our clients. They told uh, uh, who are they? So that was the problem. Uh, so, and then we moved to um, we had that hypothesis that uh, we need to find a small agencies as well and we need to find someone who is working, who is understanding the business so good inside the process and who has the really good expertise uh, in the product we're creating. Because we knew that it's difficult to sell even, even for us because uh, we know it, we know how to tell about some of the details and uh, answer the really hard questions about how we are connecting data, why it's good, why it's better than the others can offer and so on. But it was difficult to replicate it for, for the others. The next step was to uh, to understand how we need to tell about the product in what words, uh, what values are really valuable and um, how we need to position our product in the new market.

Speaker B: So I love, I love this theme because usually, and actually I think we discussed it with another founder in this podcast that usually everyone wants this silver bullet or magic uh, pill or something, but they Usually talk about channels, not about the positioning and values and everything. And I'm so happy that you brought it up. Please tell me about it. How did you understand what kind of positioning you need and what kind of valid proposition you need to show on your website or in your communications so that people believe and want to talk to you?

Speaker A: We're still in the process because I think it's the research that never stops because you always need to check is the value you're positioning to the audience, is it still a value? Or maybe something changed, like AI happened and now everything changes. Yes. And you can't say. It's one of the things that now we can't offer right now. Just a marketing analytics platform. Nobody wants analytics. Actually that's one thing that we understood during all that calls that uh, it's very hard topic. It's very difficult, it's very difficult to understand, to implement, to use, to make decisions, um, to see the, the answers behind the data, the numbers. Right. So every, that step is very difficult to. And you. And people don't want to think about this. They want just answers. Just tell me the answer what to do. So, and I'm so happy because of the EA era, um, that helps us to make that transition from just dashboards that can show you the answers but also the give your answers, tell you what to do and do. That's amazing. So what we did, uh, when we understood that we need to have a lot of calls to understand, to check uh, what's working, what words are working and what parts of products are working for the audience. We knew that we need to make calls and a lot of them. The first thing we started to do is to check hypothesis what channels can bring us that first calls. Because we knew that the marketing PayPal audience is the hardest audience to get. They're always overwhelmed. Yes, they're very busy. They have a lot of outreach emails or emails in LinkedIn. Everybody wants to talk with them but they didn't. They don't have any time for that. Our ICP were like marketing CMOs, uh, head of marketing, uh, growth managers, um, founders of medium uh, sized companies and CEOs. So the hardest part and uh, to start generating calls, we decided to invite experts from different outreach channels. Uh, so we invited one for email outreach. Uh, he was a really great expert in uh, automation of the process. Uh, and we found him from the community.

Speaker B: Community.

Speaker A: All of them we found in the community. We didn't uh, uh, make uh, position. Uh, so it was like that uh, one uh, for the LinkedIn outreach and one for Upwork. We believed in that. Three channels and two of them through automation. Yeah, like I told, they were really great experts. We didn't take some guys, uh, who just trying to find the value, how it works. Right. So, uh, and we give them freedom to do anything with, uh, our help. My job was to build a system that will help them generate leads and generate first calls. We had a lot of experiments. I think we experimented during one year. And uh, what worked for us, it was very interesting. One thing, uh, when we moved from just inviting to the demo, we implemented the other funnel in our email outreach. It was uh, inviting to um, the interviews. And there were real interviews. We were talking about, uh, the goals of the companies, asked a lot of questions about them, about their budgets, about what they're doing, what they like. Do you have the analytics, how it works for them? Please show us. So all the stuff that helped us to understand the needs of the market as well. And we showed our product as well. And if we saw that the conversation is really good and interested in continuing this, this conversation and uh, they want us to show them the demo, we could schedule that call and show them. And actually that funnel worked much better because I think, because nobody felt like, you know, somebody will show me the demo, they want to sell me something. It works really great.

Speaker B: It was the incentivized interviews, traffic when you pay for the time. But first of all they need to fill in some forms, some surveys. So you have to.

Speaker A: Okay, so yeah, I mean that even without any incentive, it worked. Uh, we saw the better conversions there and we continued to do this. So we use that information both for our education and understanding the market. It also could bring us the demos and sales.

Speaker B: How much cost were you able to

Speaker A: do with this approach, with this kind without paid, uh, incentive, without Amazon cards so that we started to use later, we could schedule not a lot of calls, maybe around, I don't know, 10 per week.

Speaker B: Still many?

Speaker A: Still many, yes. But, um, what we noticed that not of them were really qualified to MQL or scale for us at that moment. Yeah, it was one thing. And what we decided to do in our outreach, email outreach worked really great. So it's one of the hypothesis that could generate us a lot of calls, uh, LinkedIn outreach and Upwork. At that moment they were not stable. And uh, that's why we focused on email outreach. And what's uh, next? Um, we decided to build a system with, uh, email outreach. We scrapped, uh, a lot of data, sourced it, collected into the Base and tried to score uh, using the uh factors like if we know if they have some ad spend, uh, we used similar app for that but it wasn't ideal. But we use it like a signal if they have uh, for example the new hired Simeo or head of marketing because it's always uh a door for us to come and to introduce us. It was a lot of things like that. And uh, what kind of revenue do they have and all the stuff that will showcase us uh, if they are pretty big enough for implementing such a solution. Uh, because that's one of the main criteria. Because if you are still spending 2, 3, 5,000 per month for your ad spend, you don't need actually that kind of for one channel, for example, that kind of analytics, uh, advanced analytics, you can use something more simple and it will work, good work. But when you're starting to spend $100,000 per month uh on ads, it's starting to be very important to understand what really brings you money. And without the data and just in the platform data or with this simple one where you don't know how the data is working for you, it's starting to be impossible. And uh, the second step we did, we decided to make this list of contexts as wide as possible.

Speaker B: So did you want to narrow down the.

Speaker A: Yeah, yeah. To narrow down this list. That was one of the strategies we decided to use. Uh, we started to send a lot of emails and we were joking that at one moment we thought that now everyone knows the analytics because they got um, emails. We tried not to make them our mails like a spam. So we always tried to bring some value in our emails to uh, not borrow the guys. So it wasn't like we sent 10 emails, uh, we could answer no as well. We were trying to test the wording, the value we are measuring. After that we tried one really great thing for us and I think we will come back to it a bit later when we will build our content, uh, machine and a bit uh, of friend awareness. So uh, what we did, we implemented the uh, uh campaigns where we offered Amazon cards to talk with us. So we still use the demo funnel and the uh, interview funnel, paid interview funnel, uh to invite people to talk to us. And after they agreed we send them the link to the uh, quiz uh with uh, 10 questions and a small video about adding we asked to go through. And um, uh so during answering that questions we got information about the business and uh, some information to score the guys. So finally we got information about the ad spend because we Couldn't get it from somewhere, uh, I mean the real one that we can believe, uh, the source of truth for that. So. And uh, uh, after answering their questions we could invite or didn't invite that guys to the interview. And after that we started to get 150 calls per quarter. We had just two account executives at that moment and uh, half of Siva, uh, the founder of analytics. And uh, they were busy with that calls all the days. So uh, and we got a lot of information there because um, especially on the paid interviews, people, they like to share the information about the business, their goals, what they struggle with, uh, uh, what they're waiting for from their marketing, from their analytics. And we got a lot of information there and at that moment we understood that the product we are selling is really difficult to scale because um, there is a small distance, maybe I don't know, weeks between the teams that spend a lot on that paid. And they already made all the tests they wanted to make. They already tested, made a B tests test as creatives, learning pages, products, uh, value propositions. They did everything and they, but they need to grow and okay now they need the answers what's really working and uh, before they started to build that market analytics by themselves or use some other solution and even if they don't

Speaker B: like the solution, you mean, you mean the moment of realization? Yes, when they need this. Marketing analytics is very short.

Speaker A: Yeah, yeah, yeah, yeah. So uh, they understood this and before they started to implement it.

Speaker B: Yes. It's almost impossible to find this exact time when you need to go to them.

Speaker A: Like you don't have any good uh, signals for that. Sometimes maybe someone can tell the story in the LinkedIn and we try to collect that information. Maybe someone is trying to hire some marketing analytics guy or something. Ah but it's just nothing uh to make to grow.

Speaker B: When they implemented something, they will never change it.

Speaker A: Right? Yeah, it's very difficult. Yes. Even if you don't like uh, the result, even if you know that there are some weakness in the system you choose and uh, it's not working. For example sometimes the ideal clients they choose uh, solutions from E Commerce segment and it doesn't work good for them because it was created for E Commerce segment that is pretty much different funnels and the sales cycles and the uh, ecosystem they have of the tools they have of the data they have. So uh, even if they understand that okay, it's not working for us in the way we want to, even in that moment, they don't want to change something because they Already pass that way with this person invested too much, uh, money, people, time, resources, everything. Even if we're saying like okay we will do it for you, but we need their attention as well because we need to understand their business, their goals, their marketing. And they need to explain it one well again they don't want to. Yeah, right.

Speaker B: So the same thing with marketing agencies I suppose because sometimes if company hired an agency it will try to work with it as much as they could. I don't talk about you know, this enterprise companies when they make big tenders and every year they change their uh, provider. But yeah, uh, it's very hard for people, I think not only for just companies, it's very hard for people to invest their time uh for something new. And that's why they stick to alter solutions to maybe they're not so advanced but at least they know that how it works and how they can, you know, live with it. I totally understand though.

Speaker A: Yeah, the same thing. Yeah, I agree. Let's recap a little bit.

Speaker B: Okay, so uh, after you started the outreach until this moment when you started to get 150 plus calls like demos or sales calls, how much time did it take to build the system that works this way?

Speaker A: It took us around one year to get to that point. It wasn't like we were building that system during one year. No, we built it pretty much fast. We didn't have a lot of time where it's startup. But uh, to get to that idea, to find what was working to generate that calls, we spent a year testing different hypothesis. That's right. And then uh, we found that magic button. It wasn't magic. And uh, actually um, to build that system it wasn't rocket science. I mean that uh, we were lucky to meet really great guys. It was American agency that showed us that how the system can work, how the scoring system can work. Uh, they um, uh provided their uh platform uh with uh quiz with the questionnaire uh so we could understand uh what questions even what questions were working. What questions were people uh, don't like to answer or did they see our video and, and we saw that statistic there on the one hand, on the other they helped us to build that part. Uh and then I just uh built the scoring system after that questionnaire uh came to ask and actually I liked it so much still because uh, it's automated, it really works great. And um, uh the system that we built brought us really ICPs. So uh. Yeah, yeah on the calls and uh, they had problems, they had budgets, they needed the marketing analytics and they told it and they confirmed it and the size of the business was great and the ad spend was great for us and everything was amazing. So. But the situation was wrong. After that experiment we decided to stop it and um, continue working with the product.

Speaker B: After all this incredible sales calls, hundreds of them, you understood that something just doesn't work. What signals help you to understand that and how did you take this into account?

Speaker A: We had a plan to grow our sales at that moment. The plan was to make three sales per month and we could do this, uh, but we didn't find the way how to make 10 sales per month.

Speaker B: All about scaling?

Speaker A: Yes, it's all about scaling. And uh, we tried different things. We tried to implement audits before we are selling our product. We didn't have self service. That's also um, something that usually helped to grow your sales. But in the same case we had a really small churn. Yeah, we understood that we couldn't scale our sales uh, with the product we have because we need to find a really great insightful criteria that will show us that now the company need us. So we can go to the company and say okay, let's make a demo and uh, let's uh, go to the sales process. We knew that we could do abm, we can try different channels, we can try more and more, uh, and that will give us maybe not three sales per month, but maybe four, maybe five. But it won't be a big difference. And we heard from the audience because we had that calls that it's a difficult product, it's a difficult project. We need to find the time to improve, implement it. We have uh, some barriers and because we at that moment we focused on the channels and we didn't focus a lot on the content. We tried to make it, we tried to build that system but we all the focus built on making the calls. And uh, because we had all that conversations, we heard that information from the audience. So we need to change something. We need to find the way, the more easier way to show our product, to show the value of our product. Because you know when you're implementing it it takes um, around the month. It depends on the business. Sometimes it's two weeks, sometimes it's around more than one month to implement the product. Then they started to get the uh, insights and the value. And it takes time but to make more sales we need to show the value right now or you need to have some amazing products that everyone will want to try. And we decided to go to that part. So we decided uh, happy we because at that time the AI uh came to our world, to our lives, to our work. Yeah, there was very interesting feedback from the especially US market and the US audience that we don't want dashboards, we want answers, we don't want that all that complicated things. Uh, we don't want to see uh, the graphs or the numbers and to find what's working or not tell us what is working. They even asked us, even our clients from the US market asked us okay, now you built uh for us that beautiful dashboards but how can I get answers what I need to see there? And uh, it was a really great insight because for us we saw that insights but it wasn't in our service to be like an agency that will explain everything and do everything for them. That's why we transition to the space, to the agent that can get that answers, can bring that answers on the plate and say okay, that's why your sales broke down or that's why uh, the situation changed. Because I don't know your competitors, uh, uh, they launched new creatives or the company or the product because I don't know uh, your utms were broken because the data is incorrect here. And I need to go and find uh, out why, uh, because um, I don't know, the creator was burned out or something like that. And that's what you need to do next to change the situation and to fix it. That's the hypothesis to test to grow your sales. That's what you need to do. That's the answers that the CMOs want to have to. So we started to build a product and uh, now it's working on the pilot and actually it's so amazing because I believe if I had something like that when I was working in an agency with clients with big budgets, you know, you always felt like so much stress when you don't. You can feel like that decision is correct. You can even be very a great expert in reading uh, the reports and um, understanding what's going on and analyzing all the data. But for example I'm a human. I can't analyze four previous years of data and uh, understand even can analyze uh 400 creatives data but the AI can. So that's the difference. And the other thing is that it can go and uh, change biddings, uh, change uh, stop uh, or uh, increase or decrease budgets on the creators do uh, some other stuff. And that uh, helps me to, not to spend 90% of my time to managing the campaigns but to focus on the strategy. I Think that's amazing.

Speaker B: The thing that you're building and you're going to do marketing for this product is the AI agent that takes all this data that I don't know for how many years we have.

Speaker A: Right.

Speaker B: Analyzes it and also connects to the real ad platforms. And after analyzing everything you have the information from the ad platforms, the data from your CRM or from some revenue streams, it combines it all and makes decisions.

Speaker A: Right. And acts. And acts and acts. Yeah.

Speaker B: This is something else because ChatGPT can analyze something but maybe not, you know, always correctly but it can't act and do some changes in the ad platform

Speaker A: or some other platforms can act but can analyze or don't have access to access to the, to the data. So we're combining all that stuff in one place.

Speaker B: Now here in the podcast of rebuilding SaaS marketing, we have a tool that will rebuild, maybe not SaaS marketing, but you know, the marketing for companies that, that have big budgets and can make real informed decisions based on the whole funnel, not just on ads, uh, uh, information. So and you're here now like this is the moment of when you know the pivot is right now and you're testing the product and I suppose you're planning to do marketing differently because now you have an AI product and you also have AI in your company's processes. So tell me about it. You know now we're getting the talk about all the AI that we can have.

Speaker A: It's so much about Ellie. Uh, because we have some things that always like a principles for us. First of all the work must be interesting. Uh always if it's not, it's, it's not something we're doing. And uh, we don't want just building the solution because we want to build it. We always build something because we knew uh, by ourselves what do we want? All of us were from performance marketing space and that's why we decided to build this solution that will help us to make a data driven decisions. And the same was with the AI. We decided not just build AI solution that will make the AI first marketing, the native marketing possible, but also start to be the AI first company. And what does that mean? That uh, that meant that all the processes, all the teams, all the knowledge base now is located in, in cursor. We choose Cursor. There are some other good platforms I will tell about the cursor because uh, it's working for us and uh, because uh, our founder, I like how he tells the story. The most modern uh, and progressive tools are always made for uh, developers. And uh, that's why Corso is really great for us because it's also updated. The new things are uh, uh, integrated in the Corso, uh, and we can use it. And now we are using it. Our developers are using it, our analytics are using it, our, our uh, product managers are using it, our marketing is using it. And we feel by ourselves how, how impressive it can be when you, when you have an agent that can act. So that's the transition to the product, the agents that can act. So we feel it, we nail it.

Speaker B: The reason why I invited you specifically to this podcast, uh, because I wanted you to show us, you know, this won't be an audio version of our podcast. And if you're listening on an audio platform right now, this is your gentle nudge to hop over to YouTube and see everything with your own eyes. The link to our channel is in the description. But for those who are watching this, I uh, wanted you to show us what exactly you do in Cursor because it's in your everyday life and you're making, I don't know, marketing decisions and you're doing stuff inside the AI agents. So uh, tell me.

Speaker A: Recently I was hiring a team before it, during the four last months I uh, was using Corso as the main platform and as the main space where collecting all the knowledge base and everything. And because we are startup and changing a lot, the previous information doesn't matter already. So uh, that was the latest information team needed to know. And so what happened? It was a really magic because usually you hire someone, you need to make an onboarding. Uh, it's a system, you need to show where everything is located, why it's here. So lots of questions are usually asked by the team. Uh, and at that time I think I spent maybe five minutes just to tell about what kind of meetings do we have and why they're necessary to join them. That's it. Because uh, actually I think next time even that information will be in the cursor. The team, they went to the cursor, they asked all the questions they needed and it answered. And now I will show you how it works. So we have the interface. I usually use the other theme but I know that it's easy to go uh, through it when it's quite okay. So I'm using Whisper Flow. It's amazing tool to just to talk with your computer. Uh, and I will. Hey there. I'm um, in you Tim Member Tally. Um, please let me know what do I need to know about the marketing and Sales about the company, about the strategy. I was hired as a, uh, product marketing manager. So collect uh, information that will help me to start my job. Yeah, it can take some time. Uh, Yeah, I use ChatGPT Max. Okay. It will definitely take. No, uh, we have different, uh, models, uh, here and um, uh, some of them more fast, some of them more clever. So I use it for different purposes. Uh, so while it's generating the answer, I, uh, can show you what we have here. Uh, we have different repositories. It's like the space, working spaces for different teams for different needs. Uh, and uh, uh, I will show you the marketing and sales because in that spaces there are a lot of sense that can be a lot of sensitive information. Information. But so I'm collecting here our information, our AI projects, the called the content and the different strategies we are collecting here. Creatives, uh, experience, sharing something. Here it's the calls, uh, it was the calls around with the other B2B teams that were sharing experience with us. So we can take some notes from there. Uh, different external meetings that is necessary, uh, to go through. And this, um. Okay, here's the call with you. I don't know why it's here. Okay. Uh, meeting transcripts we need to have here. Onboarding, process, outreach, partners, all the list of partners. Our um, script, sales, sales things, reports and um, anything rebranding, basically everything. Yeah, yeah. So we're collecting and you don't need to collect everything in one moment. So it's the process of working with your cursor, as you know, as a very clever assistant, where you need to tell, um, him what does he need to do or what do you want to get and why. And it will. So what it can do, um, it can use all the contexts. It has the rules. The rules helps me not to make him, uh, not to make a lot of halogenises. And um, that's really important. So I can tell him please never use the information for that kind of stuff from the Internet, for example, just use the contest or for meetings. Never, uh, think, never, uh, summarize, uh, the information in your way. Just like I told you to do this with the, I don't know, with the thesis or something thing. Uh, so what do we have here? Let's go through. Aha. In that case, even in that case, he collected a lot of information and I can tell him please make it shorter or just give me that kind of information. Uh, but he decided to make it like that. In that case, uh, okay. Company and product. Okay. Yeah. Okay, here Is he collected some step by step plan but I don't need it and I can tell him okay, I don't want to plan. Just give me the links to the workspaces uh or uh, files that I need to go through and that's it. So that's how I'm working with my cursor. I can ask him it any question and uh, I can tell him what to do or what not to do, what to use. I always ask him for example to when I'm making research uh to show me where is the information is canonical. For example from the strategy space where it takes information about I don't know foundings or um, the uh, what else can be, I know foundings, uh, the numbers or something. It will show me that this information is canonical is the thing that exists. But uh, that kind of things I googled and uh, for example the ah, top B2B strategies uh and that are the links where I took this. So that's how it works. It's the case number one. Uh, what else?

Speaker B: It helps you with all the questions that you may have or your team may have and you don't spend time on explaining everything. You just can say you're new, you just can tell your new employees please go and ask cursor.

Speaker A: Right, Right. That's right. I'm trying to. I was uh, experimenting yesterday with the other chat and uh, there was a good answer. So I don't want to spend a lot of time on that. But the idea is like that uh, the other case it was uh, that was the case I was started with because the thing is to start using AI agent, um, it's not about okay, I need to learn how it works. It's about try the first use case you have in your real life after you're trying to solve your problem. It's starting to be so native, so natural for you to continue uh using it. And for me it was very nice thing uh when we just started working on our new product and positioning uh let me stop sharing. I will tell you the story and then I can show you how it worked. So when we just started working on our new product, yes AI agent, uh we didn't have any visualization of how it will work. We didn't have any interface. We had just ideas and vision from our founders. We had a couple of conversations with our uh, top clients that were using LE and uh, we came to that uh clients and talked with them during I don't know, maybe six hours about how they're managing their Ads, what is uh, what they're doing from day to day, how their job looks like. We had some uh internal conversations about uh, what it will be, how it will be, how it will work, what use cases we want to implement first. At that time we decided to make um, a landing page about that product and uh, with some details with the interface. Right? Yes, that already exists without anything in real life. But we already started to work on that uh agent but we still didn't have anything. And uh, uh we wanted to create it to make a wait list because we already uh heard from our clients, from the audience that they want to try. Okay, let's check the market. How it will work, what I used. I uh uploaded all that calls to my courser. I had access to the Savva's strategy folder with the pitching, with the vision, with everything. And uh, I uploaded our internal conversations and I asked my cursor first to analyze all the competitors that already started to tell about AI workflows, agents out there, uh, landing pages or main pages they were looking uh then I asked to analyze all the tools like lovable, like other ones, uh, how they are showcasing their product, uh, AI product because it was just the starting point at the time for all of us. And uh, so I put all the context I usually need to understand how my product, how my wording, how my learning might look like. Then I asked him to generate the um, step by step uh workflow on how to create that landing page for us, uh, about the positioning, what I need to think about before I will start and so on. So to generate some questions for my next researchers. And then I put all the information from our calls, from strategy, uh to that workflow, ah with all the competitors, analysis, uh, space analysis and so on. And it generated me a landing page. I will show you. It's already um, the landing page that we are not using. But it was the first already updated. Yeah, uh, it will update it I think in a couple of weeks. Um, but it was the first version without anything I spent I think around. We made three versions because our uh, vision was changing a bit. Uh, but maybe one and a half day per each. But what we did, uh actually everything here. So uh, we made the interface with all the details and actually I did all of that with my cursor. So I was talking with him about okay, in that case we're focusing on the B2C subscription businesses e.g. m. What metrics are important to uh, showcase what details how must look like the uh SQL request, um and uh Based on the information from the calls, for example. Uh, and so we did all the things with cursor in the video as well. Um, the video was made by uh, our designer. But. No, no, no. Yeah, yeah, but you know, I was trying to make it with. Not without cursor, but without the tool. Uh, it was also nice at that moment. But yes, this one. Thank you, Kalian. Uh, he made, uh, was really great. But I mean that all the details on that, what we need to show because that, that's the thing that was made by corsor. Uh, I just checked. So everything looks good.

Speaker B: Yeah.

Speaker A: So, yeah, that was my warm moment at that moment, at that moment. So I was so impressed and I like it so much. Yeah. So now we're using corsor day to day. So my team is generating some documents, some researchers, anything and, or uh, positioning or something else. And they are bringing to me just with the link on um, GitHub and I'm going. Or I can review my course and I can get it inside and then I can work with these documents. And for example, now we will have, uh, we are building the machine, the content machine. Uh, and to build it we need to know the newest updates from our pilot projects to get the newest use cases approved by clients, for example. And I don't need to go to my team to ask them, uh, please share with me something. I'm just using the code transcripts and the team it. The same thing is about the benchmarks from our clients, for example. Yes. Uh, to tell the story about the B2B and our successes, uh, we need to showcase some benchmarks or reports or something. It's. It works.

Speaker B: I want to borrow this, you know, from you because I also need that

Speaker A: I will share with you. So. And now before I needed to go to my sales project team and ask them to um, bring me some, I don't know, uh, some numbers or to go and check, uh, all the call transcripts or hear the conversations to get this information or to put each of them to for example chatgpt and tell him, okay, tell me what was about. Can you find some numbers or something if our customer is happy or not. Right now it's in the one place. So I'm just asking Microsoft and telling them he must name it the prompt. I'm trying to say and to name him also him. Okay, he's his. So I'm asking uh, just the questions like okay, tell me what clients during the last week, um, showed the, that they're happy to uh, find these patterns Find these, uh, things, show, uh, me the thesis, why you do things like that. Uh, show me the, uh, one they told, for example, in the pilots. Okay. This case, I like it. Yes. Uh, it works. I approve it, and he will find it for me. And then I just need to collect that information, make a report, for example, or make a use case on my website or do something like that. And it's like magic. Uh, and my team can take it as well to make, uh, the LinkedIn post, to make the reports, to make, um, blog posts, uh, to add this information as a, um, use case, approved use case on the main page, and so on.

Speaker B: Yeah, that definitely sounds like magic about the thing that you want to, you know, name the courser as him. I heard that some people, they name the AIs, like, you know, this is John. Okay. Uh, he doesn't work very well, so we fired him, and now he's replaced by, I don't know, like, by Tom. So now we have all these AI employees. Like, at least we started to have them. And, yeah, this is fun.

Speaker A: It's a trend. Like, now we want to, uh, talk not with the machine, but, uh, we are trying to humanize, uh, our AIs, and because now we are, uh, working on our naming and rebranding that will happen in Q1. So. And we already started to, uh, to test our new naming. And, uh, we're also, um, trying to humanize it and, uh, to bring the, uh, meanings in the name. And, uh, actually, maybe next time we will tell, because I like my, our new names so much. There is a really great meaning inside it, and, uh, I hope it will work.

Speaker B: So let's wrap it up. You started almost four years ago with a, um, product that was hard to sell, and you went a long way with all the marketing hypothesis, partnerships, sales calls, and now it was successful in a way. But you understood that the thing that you're selling is very hard to sell, and you need to change something, the pivot. And, uh, now you're here. So tell me about your plans. And you, as you have an AI tool, how do you plan to use AI in the future? And maybe what advice would you give to the marketers or to the founders that are doing the same thing right now?

Speaker A: Oh, okay. Um, lots of questions, insights. So about the future plans, uh, for marketing. So we are still not just we will do our marketing, but our clients, our ambassadors, our partners, our community will make it for us as well and with us as well. That's what we're building inside right now. You know we are building the loops. Uh, that's, I think that's what uh Elena Verna was talking a lot during your last webinars. So you need to build a loop loop. That's what we are doing right now. And that's the plan. AI is helping uh, our marketing and our team to build it a lot. Uh and we will definitely continue using it. So we uh. I didn't tell about that. But for example we are automating a lot of things right now. Not just collecting knowledge base but also automating everything. Uh for example the trend watching system will be also inside that. Uh so you take the RSS US lists uh, and analyze it and bring us uh, the uh, insights uh, that we can use in our uh, content uh and so on. So automate uh a lot of things and we will continue. And knowledge base is getting bigger and bigger. The wonderful thing is that we have um, team members that's already in the company like all the time, during all the time. And we have it in you guys. And uh, you know it's always a great thing because you have all that knowledge base and experience with the olders I guess. And uh, that started to be shareable for the whole team and the new ideas from the new members that can uh, make this revolution happen for your business because they have uh, a new view of what you're doing. It's also uh started you know the way to get to the olders is really short right now so you just need to upload it and uh, you can get it. It doesn't mean that we don't have meetings but uh, we have and the human talking and uh, communication and relationships, they're still very much important. I think it's much more important than ever.

Speaker B: It just started to be more meaningful. You know we can talk about the strategy and, and something very important and not about the project management or you know, the client's results.

Speaker A: Yeah, the conversations are different for sure. You finally, especially in the remote world, you finally have time to talk not just about the work, you have time to um, know each other sometimes and to spend some time just to understand what's going on with your colleague. That was about our marketing plans. That's what we're building. Uh, and that was a bit about our, our AI plans. We are definitely transitioning from the uh, learning just from LLMs to uh, learning action machines. So uh, that is the future I think. So uh, now ah, all of us, all the businesses are growing, are going to that space. So uh, the solutions that will have the access to the data and will be possible to act. That is the future because uh, now with all the tools, with all that stuff, we can wipe code anything we want to, but we still don't have that, uh, access to the data. For example. Yes, you can wipe code. The solution. I saw a lot of examples during the last week, like the guys, uh, that already uh, know how to automate the processes they uh, showcased like how they wipe coded the solution, the agent that could add platforms. But all of them told like, okay, we still don't have the data. And to m. Make, to get the data, to analyze it, to make it work, it's. You'd still need a team, you still need people, you still need experience and yes. And on the other hand those guys who has uh, access to the data, they can't automate because it's really, it's uh, we are going this way. It's really huge. There are lots of things that you need to implement to know, to understand, to feel, to build. So yeah, I think that is the future data agent action. I hope this future will be.

Speaker B: Yes. Thank you Evelyn for uh, coming. It was a really insightful talk and I'm sure a lot of founders will find this very insightful and important because you know, every company can pivot. But the um, moment, sometimes you need to feel this moment when you need to do this and yeah, how to get there, what kind of thing you need to understand. So I think you described it in a very detailed way and I wish you a great success. Success with the Plurio. Uh, and uh, actually I also want to try it for ourselves.

Speaker A: We're looking forward to it. Thank you so much. Thank you. It was interesting conversation.

Speaker B: Thanks for listening for rebuilding SaaS marketing. If this episode gave you spark or idea, um, share it with another founder who is in the same stage of growth. Follow Digital hunch on LinkedIn and find Rebuilding SaaS Marketing on YouTube or where wherever you get your podcasts, Apple Podcasts, Spotify, Castbox or Substack. Join us as we keep redefining how SaaS marketing works in the AI era.