Beyond the Blog: B2B “research” is costing you your credibility with Tanaaz Khan

Speaker A: Foreign.

Speaker B: Alex here. This episode is brought to you once again by our friends at Beehive. You might have seen it on LinkedIn, but in a few weeks Beehive is hosting their summer release event and the language coming out of their team is pretty loud. They are saying things like largest updates in company history, the project I'm most proud of in my entire career, and updates that will reshape this industry. Now this is notable because I as I've talked about a lot lately, the Beehive team has launched a ton of huge products already over the past several months and they've done those without too much fanfare. Just kind of a casual midweek. Oh yeah, we launched podcasts, right? So obviously my ears perked up when they started promoting this big secret event two months before its launch. The catch is I don't actually know what they're announcing. I'm not in the inside scoop here, but I know it is going to be big. So you will catch me tuning in on July 16th. And if you run a newsletter or, or create content online, you should consider RSVPing as well. I will drop that link in the show notes alongside our Superpath Pro member discount. That's Superpass 30 for 30% off. Beehive that's available for just a little bit longer, so I'll have those in the notes. Enjoy the episode.

Speaker C: Tanas Khan thinks B2B content needs deeper, more rigorous research if we want to stand a chance at shaping our customers perspectives, opinions and yes, buying decisions. She's on the podcast today to discuss her latest piece for the Art of content blog. Most B2B research wouldn't survive a peer review and it's costing us our credibility. Tinaz, thank you so much for joining us today. I'm super excited to chat about this with you. I feel like research in all of its various forms is just super top of mind in the content world and I feel like you have a really unique perspective on it coming from an academic background. But I don't want to get ahead of myself. So let's start with some rapid fire questions about who you are. Are you in house, Agency? Freelance?

Speaker A: Yeah, I'm a consultant. So on the freelance side.

Speaker C: Okay, awesome. And what's your favorite part of your job?

Speaker A: Unsurprisingly, research.

Speaker C: I did think that you would say that. What would you be doing if you weren't in content?

Speaker A: I'd, uh, be an infection biologist.

Speaker C: Okay. Anyways, let's talk about your article that you wrote for the Art of Content. Tell us, like describe the thesis of your article in one sentence, especially for someone who maybe hasn't read it.

Speaker A: So the main idea is that in B2B or SAS, I mean, I'm in the B2B SaaS space, but in B2B marketing, jointly, what you know, qualifies as research would not even make the minimum in any other field, especially in academia, you know, industry R and D, because we use research at a very loosey goosey term, as you say. And I think we need more rigor, more credibility, more proof points to actually win the trust of our audience. Um, there are many ways to do that, but yeah, in one sentence that's pretty much what it is.

Speaker C: Yeah. I loved your article and I think in general I'm always a fan of anyone saying like, hey, we should do something better as opposed to just doing it or doing more of it or doing a lot of things. So that's kind of my inclination. But I keep thinking about, uh, a bit in your piece where you had written. I'll just read it. You had said the content marketing space is built on an operational model that prioritizes speed and volume. But it's also forced us to redefine research by focusing only on its most efficient form. Like find two reports, pick the most agreeable stat, build your argument around it, and then loosely tie that thesis to your product. I just keep thinking about this because this desire to do everything faster and more efficiently, we think about it in terms of outputs like, you know, making more Instagram videos or making more case studies. But it actually, it is bleeding down into every part of the process, even things like research. So yeah, I'm curious your thoughts on one, what we lose when we do that, when we just focus on efficiency over maybe like rigor, as you say. And also I kind of want to play the devil's advocate a little bit and ask like, but if everybody in this industry, if everyone in B2B or B2B SaaS is doing this kind of loosey goosey quote research, then does it even matter? Like if everyone's doing it poorly or if everybody's doing like a watered down version of it, then isn't that good enough? So I'll let you go. I'll let you take either of those and take them where you will.

Speaker A: Yeah, those are really great questions. Coming to the first part of your question, you know, what do we lose in the process? We lose credibility. M. That is by far the most important thing today, especially with AI. Now you have these apps that can create UGC videos and you can create AI influencers. You can make up data, uh, you can use synthetic data to create your own research reports and you can pretend it's all fine and dandy, but ultimately your bias can sniff it out. Mhm. Especially what happens is that, you know, when they may not pay attention, you know, where they're just getting more aware of your brand, your product, but in the sales process, they keep seeing the same stat, the same, you know, messaging that you're using that's built off of whatever research you've done. And then over time they're like, but this doesn't really make sense, like how does this work?

Speaker C: Yeah.

Speaker A: And when you don't have an answer to that because proper research would tie it back concretely to the messaging and the final features or whatever benefits of the product.

Speaker C: Yep.

Speaker A: And that's where you lose the potential customer. Right. Especially in B2B where sales cycles are like six to nine months in a tough economy. You really want to do that? Why not just do it right. And compress that cycle? Um, why do you want to throw spaghetti on the wall and then be like, oh, you know, this is not working or that's not working. Yeah. So just do. And I'm not saying research reports are the only thing like of course, research reports on my mind. But there are many ways to find out what's actually going on in the market. Then your brand relative to other competitors being attention, indirect and even like your direct competitors, then your customers, then your audience. There's a lot going on. You have to compile all that data, which is very hard to do. Mhm. And have like a regular research cycle. When you do that, you can bring credibility into the process. You can compress the sales cycle, you can, you know, become that brand. It's very hard to do these things. So I think most of all we lose credibility. Mhm. Coming to the second part of your question, when you know everybody is doing loosey goosey research, what even matters? Think about. So I come mostly from the SEO world, of course. Now my service is like much broader. But look at the top 10 results on Google for example. For years we've been complaining, you know, they're just one upping each other, skyscraper technique or whatever. It's not working out. Now with AI, you can make up as a way to fight that. We said, uh, okay, let's start interviewing people or let's start, you know, doing small scale research and putting that in. But now you can make up quotes with AI.

Speaker C: Yeah, right.

Speaker A: And of course they're going to fight that. So people will add a random quote and be like, yeah, we interviewed people. I know you didn't. I can tell because it's anonymous. Nobody will let you, you know, cite them like that because you'll get a legal notice. But yeah, so think about the first 10 results. And primarily at least in SaaS like SEO used to be one way to, you know, acquire more people onto your site. But even now with like LinkedIn AI, slots are becoming a problem. Uh, ultimately, where do we go? Like it's a dead Internet theory, right? What happens if everybody's using AI, everybody's making stuff up, where do we go from here? Because if I can't have even an inkling of trust in all the brands that I'm currently evaluating, then I'll default to using the solutions I already have. In most cases you're fighting a spreadsheet, right?

Speaker C: Mhm. Yep.

Speaker A: That's your biggest competitor. Some way or the other, you're fighting a spreadsheet. So they'll be like, you know what, I can just spin up top code and be that. To show that you're the better option, you need to do something really out there. Mhm. And um, proprietary data is one way to do that, but there are other ways. There are many brand plays you can do, the many category plays you can do.

Speaker C: Yep.

Speaker A: It comes down to just thinking about your audience first because all of that's a very self serving. You're like, I don't have the budget, I don't have the people to do this, so I'm going to default to very, you know, gray hat tactics and just do whatever I want and people will come because you know, I said they would do and this is right. So why won't they come and talk to us? That's not how it works.

Speaker C: Yeah, right.

Speaker A: Like you see AI influences on Instagram, you're like, this is, you know, not very nice words come to mind. But yeah, it's not right. It's at the end of the day and you uh, want to do something that's substantially at least 10% better than everybody else out the bar is so low that if you do just 10% better, you can do so much better. And the ripple effect is very, very obvious. Mhm. Like in, within a year you'll notice that difference. So just do it right. And um, think about your audience because they're the ones opening the wallets, giving you their money. Hard on money in a tough economy, it's the least you can do, let's say.

Speaker C: Yeah, I really like what you said about credibility. And I think there is kind of this idea that even if you're not, you know, using AI in kind of the lowest common denominator ways, even if you're not like making up stats or using like a fake AI, something in your research or your content, I think there is a little bit of an idea of like, well, I googled it, I checked my sources, I cited the original source, I didn't cite like a listicle of stats. So that's research. Like that's enough. And I think you get at two really good points. One is that like that might be passable, right? It might be enough, so to speak. But what is it really doing for you that nobody else is already doing? Like, is it really building your credibility? Is it really setting you apart from anybody else in the market? Is it really building trust with your audience? And is it really getting your data and your company talked about and uh, we might want to say like the answer is like, well maybe, but really like we know that it's not. You know what I mean? I am curious too to talk about AI for research because you talk a bit in your article about how like Perplexity's deep research button isn't actually doing what it says. And I think you kind of talk about AI, uh, for research as like a sophisticated Google essentially. But is there a way that AI actually could help us do better research with less resources? Or is it more of like, uh, it makes us feel like we're doing better research but it actually hinders us or I don't know. I'm curious about how you see that.

Speaker A: Yeah, that is a multi layered question. I'd answer the first part saying that you can do research with AI. Mhm. Provided you know what you're doing. At least 75 to 80% don't know what they're doing. They feel like they know what they're doing because, oh, I can ask ChatGPT, you know, one sentence question and I'll get my answer. But that's not research, right? That's pretty much just a Q A format and you're just getting a pretty average answer compared to what you actually need to know. If you take this academically. Mhm. You have your goal, you have your objectives, then you have your hypothesis, your null hypothesis, which is like the negative version of the question, then your alternative which you're trying to prove. Mhm. So when you consider that you can build a prompt which you can feed your AI and say like, you know, you don't have to say things like you're an SEO professional of 30 years. You don't have to be just say that. See, I'm doing research for the reebook. This is the topic. Mhm. So these are the questions I'm trying to answer. But this is what I already know about the industry. In my company we believe XYZ and our product solves for this. But tell me what the. Are there, you know, data sources supporting this? Are there data sources, you know, uh, arguing against it? Why is that the case? I mean like a market report as such. So it might give you like a support that is your starting point. So you can use complexity or even chatgpt deep research for that. But again that's like your bare minimum starting point.

Speaker C: Mhm.

Speaker A: But other ways to use AI would be like I've built. Now plot has skills, right? So I've built specific skills just translating my actual manual process into an AI workflow type of thing. So for example, for GoFu research I have specific spaces I go to and you know, specific date filters I use. And I'm trying to find out certain things that can depend on the type of piece I'm writing. If it's just an X versus Y piece, then I know what I want. If it's like an alternative space, then of course you have to do it for multiple competitors. You have to do category mapping.

Speaker C: Mhm.

Speaker A: And you have to do like the current situation of the market, what's really happening there, what's happening with the audience. And some of that data your clients should be, I mean for me it's clients because I'm a service provider. But you're working in house. Then your company should be able to give you that. If they're not, then I would recommend just asking them, hey, can I, you know, talk to the, our senior sales rep for like 30, 45 minutes per month. I just want to understand what our customers or prospects are looking for. And you start feeding that data back into your assets. Right. If you're using AI there's and you have access to the internal tools then of course you can pull that data using flawed or nowadays you have these knowledge bases that you know, have some AI search filter. So you can do that. But ultimately it comes down to, you know, getting first party data, asking really good questions. Mhm. And asking multi layered questions which look for like the positive arguments, the negative arguments, the grayish areas, the nuances. Mhm. And then going from there, which is very antithetical to how the typical SEO pieces where you just go find the most Agreeable stat. And yeah, okay, this is my intro. I proved this point and now I can just get into the rest of it. So there are ways, but uh, only if you know what you're doing. And I think there are a lot of resources out there that can help you improve that skill. But if you're already like from a journalism background, you have a habit of interviewing people, then you already know how to do all of this.

Speaker C: Yeah, no, I think this is so good because I think there is kind of a lack of information or skill in even just determining like what is good research. And I think there's a lot of misconception that good research means fact checking. It means that you are making sure that what you're saying is accurate or you're finding more information. And I think that's where the stat dropping kind of comes from is like, well, if I'm making a claim then I need to back it up. Right. I need to make sure that that's an accurate claim. And so I'm gonna go find a stat that kind of proves that what I'm saying is accurate. And we've kind of gotten the idea that is research when really that's like fact checking.

Speaker A: Yeah.

Speaker C: So I love what you're saying, talking about, and I think it's a great kind of breakdown to say like when you're doing this research process, there's all of these different pieces, but a lot of it comes down to being curious, asking really good questions, having data that you're working with and asking multi layered nuanced questions of that data and then actually looking at ah, what the data is saying. Right. And not just kind of cherry picking data to prove the point that you already wanted to make. Right. Like that's not research.

Speaker A: Yeah, exactly.

Speaker C: And I love what you said in your article. You had a piece where you had written like the real job of research is actually the synthesis piece. I think you had defined it as like the ability to contextualize a problem within your audience's reality and to see what the data implies, but doesn't outright say. Which felt a little bit to me like you were actually arguing a bit that the real work isn't even the process of doing the research, but more about like what you pull out of it. I know that sounds kind of cheesy.

Speaker A: Yeah.

Speaker C: But do you know what I mean is like that you were kind of saying something like that. Right?

Speaker A: Yeah. Ah, yeah, yeah, that's exactly what I meant. So when you conduct research of any kind, the data collection is just one Part of it, Right. The way you analyze and synthesize the information really matters because see, like, for example, I was working on a research report last month and it was on automation, like document automation. Okay. And there are so many automation providers in the space and there's a larger conversation about automation in the industry, especially, you know, conflating it with AI and really understanding the value of automation. There are basic stats like, okay, you know, it's not working for people. They're still falling back to manual methods for many reasons. But the way we made sense of the data was it two ways. One is for the market. Like, this is what we're seeing in the market. This is what our audience is telling us and this is how, connecting that back to, you know, this is how we are solving the problem. So we understand the constraints you are currently living in. And um, we have, uh, layered in technology in a way that you will be able to solve that problem. Now there are, you know, nuances that you may not be able to set it up quickly or whatever, but you should be able to see value within 30 days, 60 days. So that makes a difference. Now because we are in like the document automation space, we interpreted the data relative to our audience, so finance team, the operations teams. But if I was doing similar research for, let's say, marketers, I would interpret it very differently. Right. Because marketers, we've got like so many, you know, any 10 workflows and make.com workflows and Zapier workflows. And we tend to be more mature because if, you know, all the people are online. Yeah. Even though sometimes we haven't really figured it out. So the way I would frame that data would be very different, as opposed to hydrogen for this audience. So understanding what's going on in your audience's world.

Speaker B: Mhm.

Speaker A: And then using that data to sort of say, like, hey, I get cd. Underlying idea is you're very subtly implying that, hey, I get what you're going through.

Speaker C: Mhm.

Speaker A: And I have data saying, now this is what you're going through and this is a solution to the problem. You should be able to get that in every asset you create. Right. Because ultimately it's a marketing material. You're not really reading it for 10 giggles online. So you have to sort of offer everything you have in like one neatly packaged solution. There are many frameworks for that, but ultimately it really comes down to knowing your audience and you will not be able to interpret the data in any way unless you know what's going on with them. And I say audience, I don't, I mean like prospective customers and even just the general audience, not like trust our customers. Because if you're looking at just one lens, you're going to interpret it differently because your customers have already solved the problem. Right? So there's a way, like when you're talking about audience, define who it actually is. Are you talking with the broader audience? Are you talking about just your customer? Because for like ROI studies it makes sense to just talk about your customers. But um, in market research, like general product research, you need to define them and how you synthesize the information comes down to what you already know. So again, comes back to I need to know my audience well, only then I can do everything else really well. Having all that data documented somewhere is one most marketing teams I've seen miss. Because when you get a brief, you know, you have, this is everything we know about the brand, this is everything we know about the product. But there's not that much, you know, audience information. So I think as the in house person, as uh, the consultant, especially if you're going to be working with a specific client for a longer period of time, it's sort of your job to go and live in the world. Many ways to do that, go to different watering holes, spend time there when automating, research full data from those sources as well. Unless it's like a closed group. But I mean that's how you'll put in the reps, understand more and then do better. Uh, creating something.

Speaker C: Yeah, that's so good. I love what you said too about like, you have to start by understanding your audience and getting into their heads. And until you, you do that, you can't really do any other piece of the process. Super. Well, I feel like that too is an area of research that gets overlooked or kind of like glossed over. And we say like, well, we have these Persona slides so we know our audience and it's like, well that's not. Do you know your audience? Like, was that really enough? Yeah. Even this conversation about how to do research can apply to that very first step of like, how do we research and know our audience in a way that is more than just surface level, you know, primary poly kind of thing.

Speaker A: Yeah, exactly.

Speaker C: I am curious because, you know, I'm hearing you talk about this and you're clearly doing this for clients and brands and yet I would say most teams are not, not incentivized to work this way or to do this kind of research or rigorous content work. And I'm curious your Perspective, especially from the clients that you've worked with, like what does it take to work this way? Or you know, what are kind of the incentives or the metrics or the outcomes that we could be measuring or looking for in order to incentivize this kind of work and make it feel worth it, so to speak.

Speaker A: Yeah, this is a tough one. But I will say that it's slightly easier for me as a consultant because this is what I sell. Mhm. So I talk about it a lot online. I'm not on the in house side where, you know, somebody is like reading down my neck and they're like, I need five blog posts this week. You know, it's not that way. I uh, very, you know, carefully chosen what to talk about online and then which conversations I take part in. So I will say it is slightly easier for me. But there is still this notion in the general B2B space where people conflate research with like just one type of research. Maybe if I say research, for some people it could be like a research report.

Speaker C: Yep.

Speaker A: Some people could be a small type form survey that they analyze, you know, they just publish uh, the questions and answer. So some people it could be really rigorous, you know, like a $30,000 report. Some people it could be 10 interviews. Build that into series based content. Hm. There are many ways to do it. But for, you know, like clients, usually when they come to me, they sort of have a general idea about what they want and um, we work back from their goals. Right. So for example, if they are like, you know, we need to build category awareness, that's uh, a whole other resource conversation compared to I need, you know, talk about how my customers are getting ROI from a product. Because one conversation could be about, I need like survey 500 people or talk to 100, like conduct maybe 50 interviews. And one conversation could be about serving or talking to maybe 50 customers. It's very different. Right. So when it comes to the outcome, um, it really depends on them. They have certain goals so we just map the outcomes accordingly. Similarly, if you're in house, you can do that as well. If you're like, you know, if you want to build category awareness, then you just need to start with a report that talks about your category, go and interview or like survey your customers. But if you're doing something that's, you know, maybe you're a growth stage company and you have specific goals, you're like, I want to, you know, start doing more data driven pieces. Then that's again, you have one anchor assets, you can't just do like a uh, simple brand awareness report. Yeah, you have to ask very specific questions. You have to really map the market, see your current state, see the future state people want to be and ask them what's stopping them from getting there. And the way you phrase the question is very different as opposed to like, as opposed to uh, or somebody doing their, you know, a report for the first time. So it really depends on the client, at least in my experience. So it could be about the product, it could be about the market, it could be about just you know, getting more links like they are doing like PR course for different teams come into play. But yeah, it's always map the outcome to your final goal. So if you have like PR then you know, you need X number of backlinks for that. You need to be asking questions a certain way where a journalist is not like, you know, this is too self serving, we can't publish it. Yeah, it has to be more of an overview of the market. You can't ask questions like, hey, are you happy with XYZ products?

Speaker C: Right.

Speaker A: Publish that research. It's very easy to do that as well. Especially if there are too many cooks in the kitchen. Eventually the question set becomes that and you've done this for so long, like you dragged out a project for three months and then you're like, okay, whatever question set you have, let's just do it, you know, we'll see what to do with the data. Yeah, yeah, but uh, yeah, like just think about what you really want. Take some time to really understand what you want. Then a healthy comes from there.

Speaker C: Yeah, I like that kind of shift from. I think there's a little bit of an idea that like all research kind of leads to a particular end or a particular outcome. And then you kind of choose if you need to do research or not and you're kind of flipping that and saying like, oh no, like research, real research, like is what builds your credibility and what sets you apart and gives people reason to talk about you and why you might need to do do that kind of depends on your goals and the goals that you have. Then shape what kind of research you do or how you package it rather than it just being like a uh, bucket thing. Which I guess leads me to a uh, question I have been thinking about throughout this conversation and you've mentioned a few times how there's like a real like research reports are really popular in content right now. I feel like everybody's talking about original research and trying to do a research report. I'M curious your thoughts on like, the rise of the popularity of this. I tend to see it as a good move overall, but I wonder if you see it with a little bit more nuance, if it feels slightly performative to you or if it feels like a little. A lot of the research reports that are happening aren't real research or if you're happy about it. I'm just curious how you see this

Speaker A: trend as somebody who's, you know, actively talking to people about this. There is a, uh, general interest in research, I will say that. But it is, you know, there's a very fine line between it being a good move and just being performative. Doesn't seem that way, but it really is because it is a good move to do. There are many benefits to it. Like you could do your PR push, you can get cited in AI engines, you can build a lot of assets for like an entire year. That's great. But once people realize how much work it involves, all of a sudden that enthusiasm becomes like reality. Right? You're just like, oh, this is a lot of work. I'm not used to it. And somebody is, you know, telling me right now that I need to be doing this in the next two months. And m. If you have, um, it figured out and if you're used to doing data reports on the reg, then that's fine, you can do it. But for most people that's not the case. And when you're in a larger company, there are many people involved. So it takes typically three months to get the survey date, like figure out the strategy, get the survey data and then analyze it and do all the work, write it, and then have the final asset ready. Mhm. But I will say, I mean, I won't name names, but I have seen companies do a lot of research recently and um, you know, especially in the marketing space itself, you have a lot of people doing like we studied 50,000 prompts and we found this chills fine. But again, it goes down the guardrails of research. Like when you research these prompts, are you absolutely sure your audience is using these prompts?

Speaker B: Mhm.

Speaker A: Second, how did you pick these prompts? Did you consider variations of the prompts? And is the actual number, you know, really 50,000? How have you really gone about that research? You're saying citation scores. But if I am in finance and you haven't looked at finance related prompts, is that research applicable to me? Because when you're publishing it, you're saying we did research on 50,000 prompts and this is valid research, that's okay. But when you start looking at it, you know, two layers deep, three layers deep, you're like, a lot of this doesn't apply to me. Which is one of the reasons I don't always share these reports online. I've very rarely done it. M There are for example, consultants who they're working with multiple clients so they see GSE data and they know what's happened. That's different because they have access to the data, they've interpreted it using their expertise. It's very different. But yeah, like it can uh, be borderline performative if you're just showing that you've done first party data and approach but you haven't really considered the nuances. Mhm. I think for a research study it really needs to, you know, boil down to again, coming back to the audience but also understanding the nuances of that particular study. Right. Because there will be limitations. Are you being honest about the limitations? Yeah, because there is a certain way to interpret the data. So yeah, it comes down to credibility. Again, how credible are you being in the entire process and are you credibly interpreting the data? Uh, does it actually apply to your audience? Are you considering all of that when you're publishing or is it just I've done this kind of research, you just have to believe me, uh, and here's my product, that's not how it really works. So it can be a good move, but if you're doing it well, and it takes a lot of expertise and work to really do it well, but the return is again multifold. So it's really a matter of are you ready to do better rather than just be in the same baseline of what everybody else is doing.

Speaker C: Wow, that's so good. I think that's a great place to land. I love what you're saying and I think I love this perspective of like this can really work for you if you're really willing to do the work. And I think it's okay for some brands to say like, this isn't a priority for us right now. But I think when you have a brand that's trying to hop on this trend of like, we're doing research, we're data driven, but they don't actually have the resources or time or inclination to do it, then it can just reflect poor poorly if they are doing research that's not really credible. And the reverse is also just as true. Like if you're willing to do research that is super credible and understand your audience and be clear about your methodology and understand and be honest about the limitations of your research, then that is just going to reward you multifold with credibility and substance and it's just only going to work good things for you.

Speaker A: Yeah, uh, that's exactly it.

Speaker C: It's such a good conversation. I want to ask you a couple of rapid fire questions before we wrap up. What is the number one skill or thing that can make you better at researching?

Speaker A: The ability to actually read the material.

Speaker C: M. That's good.

Speaker A: A lot of people are not.

Speaker C: Yeah. Do you think that research matters for every B2B company?

Speaker A: Yes, I do. Because it informs your positioning, your messaging, it informs the way you speak, your narrative, it informs the way you sell. Um, even for your sales team, for your CS teams, the way they communicate. Because if you have a code of speaking and there's a certain way to do it, then it informs. Everything works. So if you do that at first, then you're better.

Speaker C: Yeah. Makes sense. What are your favorite B2B brands that are doing great research right now? If you have any?

Speaker A: I think Carta has very good report. Ramp is coming up. They've hired a couple of, you know, experts in the space. They're on substack.

Speaker C: Okay.

Speaker A: Publish like very small reports, but like they're very good stuff because they're using their own data. Right. So they found a way to, to do it in a very repeatable way. Mhm.

Speaker C: Is it better to do loosey goosey research or none at all?

Speaker A: I'd say none at all. Because Lucy goosey research can be, you know, it can seem like you know what you're doing, but it can harm your brand in the long term. Mhm.

Speaker C: That makes sense. And last question for now is, what's one skill that all content marketing should be building?

Speaker A: Oh, I think right now the ability to just take your process three steps further. Um, it could be anything you do, you know, we're doing like messaging research. How can you make it better if you're writing, how can you make your writing better if you're editing, how can you edit something for it to be better? You know, so that's something I think just being better at what you do, that's so good.

Speaker C: Well, thank you so much for coming on today. Apologies for all the technical issues which our listeners will never know about. But yeah, just thank you so much. This was an awesome conversation. I feel like I need to go back and listen again so that I could just take some notes. I love your perspective and I love chatting about resources research with you. So yeah, thank you so much for being here, and I will personally talk to you soon.

Speaker A: Yeah. I appreciate this so much. Thank you so much for having me.