Christopher Pappas (00:14) Welcome back, guys. Today I want to talk about something that sounds incredibly simple. Are we actually solving the right problem? That is the question. Businesses are very good at jumping into solution mode. For example, is down, let's create more content. Sales are down, we need more leads. People aren't using a new system. Let's give them more training. AI isn't giving us the results we expected. Maybe we need another AI tool. But sometimes what we are seeing is only the symptom. Annita, you spend a lot of time looking at exactly these kinds of problems across different parts of the business. So let me start with this. How often do you think companies are actually solving their own problem? Annita (01:11) Probably more often than they would like to admit. Well, the physical problem is usually the easiest one to react to. So if traffic is failing, traffic becomes a problem. If sales aren't converting, sales become the problem. But the harder and usually more valuable question is, why is this happening? So maybe... we don't need more leads or maybe the leads we are attracting are wrong. Maybe sales isn't the problem. Maybe the positioning isn't clear. Or even the employees don't need more training. Or about the processes that always need optimization. Sometimes things don't make sense. So we need to change things a bit. The symptom tells you where something hurts, but it doesn't necessarily tell you what caused it. Christopher Pappas (02:08) It is true to say, Annita, that I have seen many situations, as a founder, where initially the teams thought that the problem was one thing and later discovered something completely different. To give you a practical example, marketing: we need a campaign to solve the problem, sales: we need more leads, tech: we will need a new tool. So the question is how you bring all these departments or different angles which are extremely important to solve the problem. Basically, I mean, you need to think, but at the same time, you need to act. You can investigate forever. So do you want to elaborate a little bit on that? Annita (02:52) Exactly, because when many departments are involved, you need to see different entries of each challenge that comes up. But the answer isn't to spend three months analyzing every decision. So it's simply to spend enough time making sure you're solving the right thing before you commit your resources to find the solution. So sometimes the fastest way... to waste six months is to solve their own problem really well. I think this has happened to every business and you know, we spend time, we say, we'll try this, we try that and it's been six months and nothing happens in the end and the problem might become even bigger. For example, maybe a site has problem with the website traffic and they can't solve it or another website cannot get the leads they need. So everyone has to start looking but do not spend so much time. Christopher Pappas (03:57) And of course to contribute their point of view because you will have a lot of angles at the same problem, probably we will come with a better solution. And let's say that I'm running right now a company, okay, and I have a complex problem. And after seeing all this point of views from different departments, different managers, they say we need to do the X thing. What as a founder or a problem solver is the first question that I should ask. Annita (04:26) The first question is, what are we actually trying to change? Because it's not only the problem, this might have five different things you can change to make it work again. So sometimes, you know, jump straight from here's what's happening to here's what we should do. But if someone tells me, for example, we need more content. Obviously, it has happened before, even in our business and in any publication I've seen, to be honest. So they say, someone says, we need more content. Why? Do we need more traffic? Do we need more qualified traffic? Do we need more brand awareness? Do we need more leads? Because all these, answers to these questions help decide what kind of content we should create. Should the content bring better conversions? Should it help us with the AI visibility? So all these are completely different problems and they might require completely different solutions. Christopher Pappas (05:33) That is extremely interesting what you are saying, Annita, and what tortures my mind is always two pillars. The first one is the data. The second one is experience. Okay, I'm a huge fan of solving problems based on data. The question is when you should trust experience and when you should trust the data. If we keep mind on a problem and we believe that this is the solution, we always focus on this specific data. You know what I mean? I you buy a new car and in every road trip you see the same car. Okay. So the thing is that if we don't have enough data, how we approach that problem? Annita (06:15) I'm not sure that it's because we don't have enough data. Sometimes we might have too much data. For example, maybe you have a CRM like HubSpot and you a lot of data, but it's not structured well, you haven't created the right funnel, you cannot understand the buyer's journey. So this is actually a problem instead of being a tool that could help you, let's say. So you need someone to put the data in the right order. And here is expertise that you mentioned before. So we do need the data, but we also need the expertise to solve the problem. We cannot rely only on data because sometimes if you don't have the expert, and again, it's not just one expert, it might be the performance marketer. It might be the content strategist. It might be the business strategist. It might be the commercial director. Everyone from their side, they can get different insights from the data that is collected. Christopher Pappas (07:26) I think the work of the manager is to put all this data and all these points of view in one place and find, let's say, the ultimate solution. Yeah. So, especially now with AI, because I hear that a lot of time. I mean... the answer is obvious but I have to ask that. To buy a tool, it might feel like progress. However, what happens if you haven't defined the problem? Annita (07:54) Exactly, that is what most businesses and teams are facing, because there is a vast collection of tools out there, you don't know which kind of tools you need to use in your stack, and AI creates a perfect example. So, plenty in our audience must have had in mind, if they need an AI strategy. So, okay, do you need one? Why? What business problem are you trying to solve? Are you trying to improve something? Is it about revenue, productivity, customer experience? Do you need AI to help you with decision making or to speed up things, you know, to produce more content, let's say? So if we can't answer that, we're starting with the solution and that never ends well. Christopher Pappas (08:53) That is true and the thing is that right now we are living in an era in which everything is changing rapidly. What does that mean? If you're running a business, you rarely have the perfect information. Meaning that you have to make a decision quickly. So the question is, how do you distinguish good solving from simply overthinking everything? I refer to analysis paralysis. Annita (09:25) So, good decision making doesn't really require perfect information. It requires clarity about your assumptions. So, before jumping into a major solution, I'd ask some very simple questions. Number 1. What is happening? Second question, why do we think this is happening? What evidence do we have? What outcome are we trying to change? And after we answer all these, what should we do? So sometimes the answer will still be, we don't know, and that's okay. We can run a small experiment, the assumption, learn something, then make the bigger decision. And I can give you an example here. We've also tried several tools because we've had to solve different challenges. So we might have spent one or two months, let's say, testing with three different tools to find which process is the best. One tool might have been for video editing. Another tool might be... to help with image generation and another, let's say, strategy and decision making. So it's a trial and error. You have to test it out to see what works, even if you know what you want to solve. Christopher Pappas (10:49) Excellent, excellent. And Annita, I don't know about you and also guys, if you are listening to this podcast, I also want your feedback. In my experience, when you see a problem, probably in the first two or three seconds, the first answer that pops in your mind is sometimes the solution. Meaning that you can take action fast without complete information. Okay? Annita (11:13) You've said this to us many times, that you prefer to give it a try without having something perfect, 100%, and take it from there. Because if you don't start, you will never do it and learn what happens. Christopher Pappas (11:32) Annita, what you are saying right now is extremely interesting because there is no perfect solution. Maybe the perfect solution is in my mind, in a company's mind or whatever. I that the perfect solution to a problem is coming from the people that are facing that problem. What I mean by saying that. You have customers. You are expecting from your customers the feedback in order to solve a complicated problem. Okay, you can think all the scenarios, but still you are not the guy that is using that software. There are other guys out there that they are using the software. And as we said before, speed is extremely important. Okay, so let's create, let's say the beta phase. Have people try that beta phase. Okay, and based on their feedback, building the original software or service or whatever. The thing is that we needed to solve real life problems or real life business problems without wasting our time and our customers' time. Annita (12:41) I totally agree. If you have an idea, try to create an MVP and start Christopher Pappas (12:48) Exactly. Annita (12:49) from there. Christopher Pappas (12:50) Yeah, yeah. And Annita, we have seen that a lot of things happen in the States versus Europe. In Europe, we don't create a super fast MVP. Okay. And this is why we have Annita (13:03) We want everything to be perfect. Christopher Pappas (13:05) Perfect. Okay. And perfectionism and all perfect is like success. It's different for each human being. Okay. I will not go back to the ancient times with engine grids, and what they were talking about. But yeah, experience matters. Sometimes after running a company for years, you recognize pattern before you have all the data. Is that correct, Annita? Annita (13:31) Well, since you have the company, I guess you do. So yeah, absolutely. Data doesn't replace judgement, but judgement shouldn't be confused with certainty. Because you can say, based on what we know, I believe this is the problem. Okay, this is an assumption and something that you can derive from your experience, but... you have to test it. So good leaders don't need to be right immediately. They need to recognize quickly when they are wrong as well. Christopher Pappas (14:06) That is absolutely true and yeah, that is absolutely true. And that does not refer to owners of a business. It has to do also with the managers, etc. etc. So perhaps the takeaway isn't that businesses need to spend more time analyzing every problem. It's that we need to create a little more space between something is wrong and here is what we are going to do about it. If someone watching uh this has a problem sitting on their desk right now, okay, what's the one question you would want them to ask before doing anything? Annita (14:49) So I'd ask what are we actually trying to change? Because once you are clear about that, you can start asking whether the solution you're considering will actually get you there. So sometimes businesses don't need better answers, they need better questions. And I love questions. Christopher Pappas (15:12) And I think that is the perfect place to end. Before you solve the next problem, make sure it's actually the problem. Okay. Thanks, Annita. And thanks to everyone listening. And we will see you next time, guys. Bye, guys. Annita (15:27) Thank you, too, Chris. Bye.