Welcome to the Marketing Millennials, the NO-BS Marketing Podcast. I'm Daniel Murray. Enjoy me for unfiltered conversations with the brains behind marketing's coolest companies. The one request I tell our guests, stories or it didn't happen. Get ready to turn the fuck up. Welcome back to another episode of the Marketing Millennials. Today we have Molly, director of Brand Growth at AYTM. We're going to talk a little bit about market research. It's an interesting topic, especially in the age of AI. So welcome to the podcast, Molly. Hi, Daniel. It's great to be here. Thanks for having me. I want to keep this off. And I know you spend years talking to researchers and marketers. So what does the biggest thing marketers think they know about the customer that's actually wrong? Ooh, I feel like there's a lot of things. Marketers tend to, especially experienced marketers, especially CMOs. People who have been in their industry or in their company for a very long time. They tend to know just at a gut instinct what works and what doesn't. But the thing that I think is very interesting about humans is who would have guessed that worldwide pandemic hits. And people hoard toilet paper or buy bags of beans. There's just nothing that can actively predict what humans are going to do. So that experience is important. But it's also important to test those things, to ensure that as your customer changes, as your company changes, as the times change, as everything changes around us, that you have an understanding of that person. And I think that that's what a lot of marketers could potentially get wrong, is just get a little bit ahead of their assumptions and potentially leave things that are untested and spend a lot of time and money just for something to not be as effective as they would potentially like. The thing that I think is so funny is you see this online, the statement all the time, that we are customer obsessed or I'm in a customer's case marketer. In your definition, what does it actually take to become a customer obsessed marketer? What is the, from the research side of it? It means that you have an understanding of not just the way that your company helps them, but what their daily lives look like, what problems they encounter, what things that they find challenging in their lives, and the reason that they do things. I think there are things that when we put on our marketer hat, we sort of take a, we disconnect ourselves a little bit from the choices that we make as a human being. And it's important to maintain both of those things. I can give you an example. When it comes to qualitative research, this was an example of a guest that I had on BipodCast Securitycurrent. She was speaking about how she did a shop along with a mom and was trying to understand her purchases in the grocery store when she was shopping along with her. And she purchased organic strawberries, but not organic broccoli. And so that was a weird thing because you know, you would think as a mom in this area, like she would potentially only choose organic, but why did she make that distinction? So when she was asked about that, she said, "Oh, well, the organic strawberries are for my kids." And the broccoli that's not organic is for my husband. The guy doesn't need organic. He's already cooked. He's already the way that he is, but my kids, I have a bit more of a blank slate, so I care more about what's going into their body, and he won't eat the strawberries, and they won't eat the broccoli. And it was this moment of, "Okay, as a marketer, especially, that's something about their behavior that I should potentially know, but not something that I would have ever thought made rational sense until that human being tells you why that makes rational sense." So when I hear that marketers are customer obsessed, but they don't do any research, or you get actually behind the curtain, and they don't do a constant tracking or understanding, or any sort of type of quantitative or qualitative research, it's sort of like a pet peeve to me, because if you're saying that your customer obsessed, you're not just obsessed with delivering them a cool experience, you're obsessed with meeting them where they are, and having a full picture of them in order to actually deliver more of what they need in that moment. On that data point, so to say they collected that data point, because a lot of marketers, I think, that I know, or I'm guilty of this too, like we go deep in research, we collect a lot of data, and that data just sits there, we don't action on it. So how do you recommend someone set up a framework that what is the next step? How do I make sure that this action sort of actually influences business decisions, whether it's a product or a new marketing message, or how we put whatever, how do we make sure it's action? That's a great question, because it's not something that only marketers are guilty of. Research companies are guilty of this also, especially as research organizations within companies are guilty of this also, because I'm going to butcher this. It was our CRO who said he used to work at a previous large B to C enterprise company, and he said, "If only my company knew what they already knew." And what he meant by that was that there was so many disparate systems of research that was occurring. Marketing was doing a brand tracker. This product team was doing a package test. This other team was doing a message test over here, and it was gathering all this data and information that didn't talk to each other. They were siloed together. So if only there was a way to put all of that data together for it to be more of a usable warehouse for everybody, this company would be unstoppable. And there's a lot, I'd say, you mentioned in the age of AI, and I'm sure we're going to get into this. But those are what those large language models are especially good at, is data warehousing, data packaging, and presenting those things, and having those things queryable across an organization. And so there's market research tools that are coming out or will be coming out, specifically aimed at marketers that help to warehouse all of that data and make it queryable for your specific thing that you're trying to ask that you're trying to answer. So when you ask about how to potentially make those things actionable, I think that's the first thing is making sure that you have all those things available in that one place, potentially with an AI tool, that you can reach at any point. The other thing is it actually starts with the research itself, and the research question you're trying to ask, and to be very clear and precise about of what that research question is. So if you're saying my goal at the end of this is to decide blue package or red package, that's your question. It can be very, very easy to have project scope and bloat on this project. And suddenly what was a red or red or blue package is now, well, what's this font? What's this? What's the timeline? What's this? When that's the question that you needed answered, and that can sometimes obscure your action ability. So you now have a bunch of teams that are trying to ask the same questions that are trying to ask different things. Let me get my questions in here. When in reality, you don't need those things. And as a marketer, it's a research skill that even researchers need to know, because marketers are curious. Researchers are curious. They want to know every answer to every question. You can't in that type of environment. You need to be very poignant about what you're trying to answer. Get that answer. Okay, definitively in this package test, we simulated a shelf. We had people go in in a shopping, a virtual shopping environment. I set up Amazon. I set up a wall green. I set up all these different virtual shelves. The red package is the winner. Red package is the winner. Okay, we're going to go with the red package. We're going to take that one to market. Answer that question. And don't try to answer anything else. Because then you have data that to your point is just sitting there. Because you just asked a bunch of questions because you were curious. But it wasn't actually what you were trying to answer. I feel like now that I'm going back in my head, a lot of reports you see out there are answering so many questions, especially that people who put out. And it's just even like foundationly and marketing when you're running a test or anything. It should always be one question you're trying to answer. And so there's a problem a lot of research where one research is not set up correctly. So the results actually biased or skewed or wrong. So sometimes in that case, it's better to not even do research if you're not going to set up the research properly, which is crazy to think about. That could be hard to, especially for people who aren't trained in research. I myself did not get a degree. I don't have a fancy master's degree in psychology. I have a common degree. And so what I have learned from researchers is that there are things that you don't even think about that could bias of result. So if you have, let's just say you have a question set and you're trying to do a multi-select of what things have you purchased at a grocery store. Like the last time you went shopping, please select the categories of all the things you purchased. Let's say for example, if you don't have those answer sets randomized, if you don't click that little randomized button, you will get an overreport of people that just select the first thing. Because there is a lot of quality metrics that sit in place of people who are just unattentive or people that, you know, are just clicking the top thing. or when things are seemingly pretty good.
presented in a more favorable light. So if you ask a question, especially a guess or no question, or you ask a question where it seems like you want to get that specific answer. So if you're like trying to think of like an example of like, do you enjoy barbecuing, for example? Obviously, the people are going to say, oh, well, maybe they're asking questions about a barbecue. Maybe they're asking questions about a barbecue sauce. I'm going to say yes. And because that's what-- people just want to please the researcher. They want to please that server. They want to seem like a better version of themselves. And these are all very basic psychology things, but things that marketers might not have a fundamental understanding of. I think that's where also AI tools can come in. Because before you would just go in and say, you'd go to Google, Forms, or whatever, and try to answer your questions. And to your point, if you get a really biased result because it's what the marketer thinks is right, and it subconsciously ends up in that language, that's really hard to manage. And you could have skewed and biased results that lead you to the wrong place. But there are AI tools now that are backed by research knowledge and research language that you can go in in a more for sure not going to be biased environment, and put in those questions and have it create a survey and a sampling plan for you. There was a really interesting thing I read recently. I'm totally blanking on the name. But it was this idea about clawed outputs and other types of language model outputs that said, you read a newspaper article about something you're deeply familiar with, something you've studied, something you've worked in, something that you're very familiar with. There's generalizations, there's over simplifications, there's lack of concrete data. There's behind the times data. And you poke all these holes in it. On the flip side, you read a different article about something you don't have any concept or any sort of ground in at all, no understanding of. And all of a sudden, this article makes so much sense. You're learning a ton, and you're going to take everything it says verbatim. They're both generated by AI, but one has your experience and one doesn't. So if this article has these crazy oversimplifications and things that you disagree with, the likelihood that that article also contains it is very, very high, except you won't be able to catch that. So a lot of times when marketers are going in and saying, clawed, create me a survey, catch a BT, create me a survey, even those are still going to have those holes because it doesn't foundationally understand a lot of these research best practices. And also, the prompts can be biased. And what you're actually asking, how you're asking it, it's also the AI tools also trying to please you. We're still at that point as well in how these language models are learning. So it's very important for marketers to also consider that when thinking about going out and doing research and to potentially try and find a different tool that's backed by research organizations and researchers to help you get those more unbiased results. I think also one thing that you said a little bit earlier about the example of the person in the shop buying organic strawberries and then non-organic broccoli. But if you would ask that person in a survey, like a different question, like a question about organic or not organic or something, her bias might come out and pick X versus Y. I always buy organic because I believe strongly in organic. Like she would probably never say, I'm buying not organic from my husband because he is a ticking time bomb. Like that's something that doesn't come out in surveys. I wanted to ask you the question because people at family said that you had to like follow the wallet versus follow the what people say. Or set up or think about doing research by following actual decisions people make versus just putting out a survey and people are just saying, like, I'm sustainable and then they buy something that's not sustainable. Or I like, I care about the environment and then they buy something that's pretty much destroys the environment. So how do you follow that in a survey to make sure you are getting the right data back? - There's a lot of things in there that we talk about with researchers and the biggest thing is what's called the say do gap, which we say a lot of in a lot of different industries. But essentially means the chasm between what you say that you do and then what you actually do. So an example would be how many times do you go to the gym? That's a question of, okay, well I'm a little insecure that I don't go to the gym that often. So I'm gonna make myself feel better and say, I go to the gym four times a week, even though I haven't been to the gym in two months. So it's humans want to inherently look better or that's something consciously. I know that I haven't been to the gym in two months. I'm intentionally trying to make myself look better on the survey, but then to your point about the organic, the shopper may not actually have any concept of what they're doing at all. They might have a psychology, like a psychology block or like a bias of what I actually do. I don't even, I'm not even consciously aware. So she thinks, yes, I buy organic. Of course, I buy organic all the time. And that buckets her into she's an organic shopper, she's a not organic shopper. When in reality, people are way more nuanced than that. And also to your point, I think a great example is I can use myself as an example. I'm getting my masters degree in environmental science. I really care about the environment. I care about my impact on the environment. But right now I have a young son who's 10 months old. I need my groceries delivered, which means plastic bags are being used, which means I don't care in which way you have to get this to me, just that you have to get this to me. You're gonna use single use produce bags, which I don't typically use if I go and do my own shopping. I'm gonna be buying pre-packaged foods that I wouldn't normally be buying, but it's because I don't have time to cook right now. So there's also seasons of life that affect how people function, where they are, what they're doing. Don't even get me started on work travel. I take, I have my, you know, water bottle that I take with me everywhere. I don't use any single use water bottles. But what I'm traveling, I absolutely do because this thing weighs a billion pounds and I travel for work often. So there's a lot of things that make people very, very complicated. So when you ask, how do you actually get to that? It's almost an art as much as it's a science behind research. You have to ask things and utilize language in certain ways that get people to actually say the truth. So instead of saying, how many times have you gone to the gym this week and have it be something where people then have to say, "Oh, well, I went to the gym three times this week." You can say, how, ask the first question, how many times a week do you wish you went to the gym? I wish I went to the gym three times a week. Okay, great. How many times in reality can you get to the gym? Making it seem like maybe there's some external factors that are preventing you from going to the gym. It's not that you wouldn't choose to go to the gym. It's just that, you know, maybe you have to work late, maybe you have to go pick up the kids. Maybe you're just like really tired at the end of the day because you just work the full time job. Maybe there's a barrier that's preventing you from doing this. So it's not necessarily it's your fault for being, you know, a lazy POS. You are actually, you're trying, how many times do you try to get to the gym? How many times do you actually get to the gym? Breaking it down, how people would feel more comfortable answering and giving them the opportunity to say, I wish that I did this more, but this is my current situation because then that can help you figure out in that, say, do gap, what people are saying they do versus what people wish that they had more of the ability to do. You know what's also, what's running through my head right now is how many to say marketers in general try to confine a buyer journey or a buyer process into stages or into something that's very logical. When right now you're buying decisions are way, you said, you have a kid and this, like you have so many things going on and you travel. There's so many different things in your life that could make your buying decisions different or make what you see during your buying decisions. Different like a kid and I do this. I see that. I see that. I'm watching kids' YouTube here and I'm watching this here. And this is my son has screwed up my Spotify algorithm that I've worked real hard for. Exactly. I mean, I used to try it so fine because I, until Spotify, I like try to game it a little bit. Like I'm going to listen to this a little more just to make sure that I and Spotify probably knows that now. But I think at first they probably didn't know that people are trying to game the behavior that they want to see it end of. Right. But it's actually funny because at the end of the day it always comes back to what you did actually listen to subconsciously, which is funny. I want to also go into, we talked about this upfront, the AI conversation and research. Every marker has the ability to. have access to these AI tools, Cloud, Gemini, ChatGBT, soda researchers have access to these AI tools. Do you think these tools are helping us get closer to our customer, or is it also helping us-- or is it creating more noise in the research process? I think that it's both. I think it depends on how you use it, and if you use it judiciously. I think it's very easy to panic and think, what is this going to do to humans? What is it going to do to the world? But I sort of take comfort in the fact that it's happened before. People were having very similar conversations when the internet came to be. And we were like, what are these email things? Is this going to replace mail? Is this going to replace faxing? I was like, I'm going to get an email account. And it was a very similar conversation. But obviously, the internet didn't take away our ability to critical thing. It didn't take our way, our ability to do work and to be good marketer. Social media didn't change that. So it's just going to mean that we have to adapt. And we have to-- everybody's using all of their tokens and figuring out what works and what doesn't for their organizations. But it's going to just depend on how you use it. So it is very easy to get overwhelmed with the information. You can ask any AI anything that you want. And it will give you an answer. Whether it's a true answer or a hallucination or that's debatable, but it will give you an answer. There's a-- what's being talked about a lot in research right now is this idea around a real respondent, which is a human being sitting in front of their computer taking a survey or a human being being watched going to the grocery store and going about their daily life. That is a real respondent versus what we're calling synthetic respondents. So synthetic respondents, if you went to chat GPT and you said, OK, I'm trying to determine red package or blue package. Here's my audience type. It's women under the age of 30 who don't have children. Could be married or not married, who live in Southern California, who have purchased a beauty product from Sephora or an Ulta within the last six months. Tell me what you think red or blue package. I think that there's a really good way to use AI as sort of a BS layer detector. So let's just say you have 50 colors that you want to potentially test. Every color of the rainbow. You could use AI in a pretty easy way to help that narrow down that list. So it can tell you out of these 50 colors that it thinks orange, green, and blue are going to be your top three contenders for this particular product. Then that is what you can ask real customers about, if they what they think between the three. So instead of asking customers what they think about the 50, you now have a test somewhat tested more narrow set of those three colors that you could potentially test among those real people. So I would say yes, it can create a lot of noise, but so can a Google search, so can the internet, so can Reddit forums. There's things that have blurred the lines in what's made marketing challenge. We've been a data overload for many, many years. It's nothing new in that sense. It's more how you utilize it as a tool to get to your end goal. I like that answer because I think what you said at the top of that episode that AI has a great ability and probably we can ask about the research side of it too because it's trained on research. But I do think it has a great ability to take data points that would take a human weeks to surf through and connect them for you, find similarities, find things that are stand out, find things that aren't standing out, validates that maybe it's validated a question you have based on things that you data you've collected maybe through watching thousands of customers walk through a store or because or thousands sales reps talking to customers or yeah, it's exceptionally good at that. Yeah, but it's actually bad at another exceptionally bad, but it will give it, if you don't give it any of that context, it's going to find whatever context it needs to try come up with and answer, but I want to ask you on the other side of it is, so it's really good at synthesizing data, maybe to come up with-- Exceptionally good. Exceptionally good. If you have a ton of previous studies and I will say, if you have clearance from your company to put your research into AI and you have that security clearance to do that because I know that that's a whole other thing, it is exceptionally good at trend analysis. So I think the analysis of previous data is one of its strongest use cases because to your point, for a human being to read all of that data, to comprehend all that data, to put it into Excel, to find all the charts, to do all the pivot tables, that was a whole role, that was research analyst, that's a whole function. It's very, very good at doing that. I will add though, and I think this is where you're going with this, everything that's in AI is historic. It doesn't know about the future. It can make a prediction. It can say that based on the way that human beings behave in ex-synario that I've seen before, people will react in why scenario in the future. But it is not a perfect science. So when it comes to trends research or finding the next big thing or predicting the next big thing on social algorithms or what the next trend is going to be, that as a marketer, you want to capitalize in that trend lifespan of like two weeks on TikTok. If that's your goal, AI is not going to be it for you because it doesn't know and it can't predict humans that well yet. I don't think there's ever going to be something that can predict humans that. No, if they were-- Not even humans. No, if there was, there would be tons of trillion dollar companies out there if you can predict what humans can do. And that's why there isn't. Because like you've been saying up top, its humans are naturally illogical creatures. Or weird. And we decide on emotional, environmental, like the toilet paper example thing is you see a craze on TikTok and then you start having fear that you're not going to have toilet paper and then you go to the store and then you don't want to be the last person there. And then people are freaking out. And it's just the spiral of-- the spiral in a human's mind that I don't want to be the one left without toilet paper for months whole year. So it's just a weird phenomenon that you have. But you can never predict that because you can never predict that TikTok happening. I think we've talked about this before. We ever got on the podcast. But there's so many of these TikTok trends that you never guessed that these ingredients would sell out because someone made a weird dish of-- 10 fish. Or dot cake. Or dot cake. Who would have guessed that dot cake would go viral right now? And everybody would start making dot cake things. It's like nobody would move with that. Yeah. The thing that always weirds me out is that people were doing like 10-thish shark hood reports, like fish shooter reports. I don't know. I'm probably butchering that. I'm like, I sound so old and out of touch. But it was very confusing to me because I was like, I thought Sardines and a can of disgusting. But now it's very trendy. I would have never thought things like that could catch on. Or things that were popular in the past that were uncool are now cool again. I don't know. Some things are very cyclical. Fashion can be very cyclical. So pants that are-- I mean, we're on the marketing millennials' podcast, so you can pride my skinny jeans out of my cold dead hands. But pants have come back. We're back to the '90s-type pants. So sometimes trends can come back, but sometimes trends are just so off the wall. There's no way of knowing. Yeah, there's pendulum in all of society. Like being swing back, but there's also moment in time trends. And those are hard to predict. The moment in time versus what's actually a pendulum swinging trend that will come back. Baggy shorts, skinny, like baggy long shorts, short shorts, all number being-- Oh, my God. --being young and being like, why are people wearing shorts? Why don't people wear short shorts? And then every guy now is wearing like five inch pants. And now let's go back to baggy again. And like, Gen Z's taking it back to baggy. It's crazy. I want to ask you also, besides the AI being really good at synthesizing data, what do you think is AI is actually like, surprisingly good at that people aren't thinking about when it comes to research? I think that AI is really good at simplifying processes of things that you do over and over and over again. So it can synthesize data. But if you're running repetitive type things, and you need it to automate that. I think that it works.
really well to do that. If you're continuously running searches, if you're continually doing trend mapping, if you're continually, I just said that it can't do trends, but like mapping the trends at least, it's very, very good at things that you can just generally automate. I think the other thing that's important to mention as it comes to AI is a lot of people are thinking, "Well, AI's going to take my job. Oh, AI is going to be. AI is going to be so good at marketing that it's going to replace marketers. I don't necessarily think that's true. I don't think that marketers are going anywhere, but I think the function of marketing is changing and will fundamentally change." So people say, "Oh, AI is going to take my job because it's really good at doing marketing." No, no, no. If you know marketing, you know that it's not doing it as well as you think, but you need to be able to utilize AI. So AI is not going to take your job. The marketer who knows how to use AI is going to take your job because I feel like there was this utopia we thought that we were going to live in that AI was going to do all this work and we would have three day work weeks and be able to be sipping your clad as on the beach. I think the reality of the business side of that is that no, that means that the work is consolidating. So whereas before you were a content marketer and that's what she did is you wrote content and you followed SEO and you did all your SEO. Great, but now AI can write your articles for you and plug in those SEO articles. That's a repetitive process that it can do very well. It can continue to write things that you have trained about in your customers' reception voice, your brand voice, you've trained it. It can create articles. You can plug in your primary, secondary, intershary keywords and have it go. And now you're sitting there thinking, "Well, what else can I do with my time now that I have this thing writing in the background?" And that it's like repeatedly doing these things and optimizing these things based on the knowledge that it's then receiving. This article was successful because it netted X. This article was not successful because it netted Y. Let's continue optimizing it as we go. And then this is also where my role as a marketer on a research organization comes in handy because I'm seeing firsthand that research is now becoming a marketing function. So we talk about centers of excellence. Centers of excellence can be anywhere. The research center of excellence is starting to at these large organizations transfer to marketing teams. So while these different things that AI is really good at optimizing is going in the background that's a marketing function that a marketer can oversee, now there's an upskill that's required of, "I need to learn how to run my company's brand tracker." That's what I do at AYTM. I run the company's brand tracker. I run our customer satisfaction surveys. I run like a bunch of different things and I have my hands in all of these different types of research and reporting that I wasn't ever doing before because that was a function of another team. But now that there's AI tools that are helping me write the survey, come up with the sampling plan, run the survey, communicate, speak the language that I'm adding that to my skill set that's optimized by AI. So AYTM is coming out with a tool later this year. It's not a self-promise that promise, but they're coming out with a tool later this year that's geared towards marketers. And it's specific for helping marketers navigate this new role that they're taking on of running research and having it be built for that audience instead of a sophisticated, "I've done research for 15 years audience." So those trends are already showing that this is where the research role is going to be moving. I mean, I can see why because everything in marketing to be successful, there's two things I think that to be successful is one, having proprietary first-party data in your content. Because if AI could do the basic level, like I can write, like everything, I can write and people are going to use it to write. The way you differentiate in one level is having data that AI can't find, which is like first-party collected research data that you can pull from any time and write thousands of articles that AI could eventually pull from and then you become like the answer in LLM's and all that stuff because you did your own research. I think the second part is where you're seeing the trend and it's been doing a trend forever. But like having thought leadership or leaders with actual point of view or faces of your company becoming something because people want to trust humans now more and more because they don't want to. So the trends are coming. It's not even trendy. You need first-party data to do unique things as a company and have a unique point of view and talk about you because people first, it's human beings, I react with emotion, but then they need logical things to justify that this article's false. They need data to vent, always data and actually quotes from people who are going to say it on the other side of it is they want a face to connect someone behind before we can be faceless and get away with it. But now they need face to be the leaders or a creator or someone who's the face of your brand and they trust that if I'm they're saying something from their point of view and also whoever's talking, they're one thing that they have ahead of everybody. One thing that they why it matters is when you write something with that's faceless, there's no reputation on the line. Wherever humans talking, there's a little bit of a reputation on a line when they're going to tell you something. And that's something that weighs heavily in the trust factors, like reputation, like someone's not going to tell you something. They might if they they're a little bit of craziness, but like that there's a lie to hurt their reputation. So I think those are the two things that I'm seeing why and that's why I think research is one of the things that are rising is because that we need that first-party data, we need that collection of data and marketing needs to own that because we need it everywhere in our content and our website and every every part of a touch point we have in our marketing. Yeah, I think that goes back to your point about is this noise or is this actually something that's a thought leadership perspective? I use the example of like using AI to create SEO articles more for the sake of utility, but if you're using if you're creating like a big flagship piece that's going to be your thought leaders perspective on X, I definitely think that that trust factor is super, super important especially for a YTM is a B2B company. So all of B2B relationships are built on trust and reputation management and you can't just go out and blast the same thing that everybody's blasting and and make that a differentiator. Do you know what I mean? Like you can't just say the same thing that everybody's saying but be the standout of why your customers should continue to pick you. You just blend into this thing into this kind of blow space and it's almost like when you read when you read AI content that is not heavily heavily edited by a human being or heavily skilled. I think there's ways that you can definitely skill an AI tool to make it sound like it comes from a unique voice, but that requires an additional set of skills to understand how to prompt it in the right way. But if you just go to Claude and you say write me an article about whatever marketing topic, there's sort of, I for lack of a better term, I think about it almost like uncanny valley, like people know. You just sort of know that it's written by an AI but you don't really like know why you just have this feeling that it was written by AI and then you're not really connecting with a human being and your point too about that proprietary data, super important when it comes to any sort of like research on research pieces that you're doing. So like we're working right now about on our side about a lot of like different data quality pieces of work that we're doing and we are not going to AI and pulling this in this, we might use it for some preliminary research to maybe ground some of our data to make sure that it's not like outside of the adorable, like this is completely wrong. It's a bit of a gut check, but we're still absolutely running our own studies, running research on our own research, running things across our studies, doing trend analysis and things like that across our own data to say our scrub rates for data sampling is X or our our survey length of time is X and you know and that's the good thing or that's a bad thing you know and this and that. So yes absolutely to your point for a lot of these things you need to come up with something that's new and then also too if it's appropriate and security's okay you can use that to then train your own models to have a better understanding of your business and iterate as you go. I want to ask you about like re-running research because I think there's a little sometimes when you have a point of view as a company or a statement of the company and you go out and try to prove it and it might come back as a like a false like a negative of what like nobody thinks is you might be a little bit early in the game for that point of view to kind of settle and and you might need some marketing behind it to
get it to a certain level. And I think sometimes we talk about humans being illogical. Sometimes people don't know how they feel until it becomes in common knowledge and sometimes research. Even if you do the best research, it can't detect that. Like it can't, it couldn't detect like the toilet. Like you said, it can't detect like the future. And even maybe right now it could detect it, but in the future. So how do you like, how often should you be running research? That's a real question. And how often should you be setting up these surveys to collect data that's fresh and new? Because I think a lot of people say I'm going to run a survey once. I might never look at it for another like eight months and then maybe. And so how often is the right answer? Or I know it probably depends on what that is, but I'm just wondering how often should you be thinking about collecting new data points in tied to your system? It, this, you're going to be the answer. You don't like it totally depends on what you're trying to accomplish, the type of research that you're doing. And the I would, yeah, I would say that the type of research you're trying to do, what questions you're trying to answer and what your audience is like. And also, what your sales cycle is like. If you're going to be to be organization that is selling a six-figure subscription for a year, your sales cycle might be nine months long. So for your marketing to make an impact, you might not see ROI on a campaign you did in January until September. And that's okay if that is the sales cycle that your organization is used to versus like a B2B company or a B2C company that is selling a low-cost skincare option. You might do an advertisement and see an uptake of sales immediately after that. And you're like, "Gay, my advertisement was successful." So it totally depends. It also depends on the type of research that you're doing. If you're doing a brand tracker, let's just say to understand your brand sentiment among your, not just your consumers, but your prospective customer base, total addressable market. And you're looking for what that type of total addressable market thinks about the reputation of your brand. You might run that exact same study month after month after month after month. You might run that exact same study once, do it next quarter, do it the next quarter. You might run that study and do it once a year. It totally depends on what types of answers you're trying to get. If your sales cycles are long, you could potentially run that research beginning of the year, run it at the beginning of the year, the following year, and see over that time because your sales cycles are long, did your reputation improve, did your brand saturation improve, did your unated awareness of your brand improve among your total addressable market? That is if you're doing sort of a long-term management thing. That's if you're, if you sit in a brand function, if you're like a brand marketer, a brand reputation marketer, that's something that you're going to want to trend analysis over time. Is it really useful if you are, you know, big B2B companies to be doing this constantly? No, it doesn't make sense. But if you are more of a quick turn, you sell a cheaper product, you sell a B2C product, it's easier to attain, it might make more sense for you to do that as things change over time and also how saturated you are in trends. Especially if you are a skincare product or you have your specific ingredient for a skincare, you might see that fluctuate hugely depending on your activities. The other thing is the research question you're trying to answer. So that's more brand reputation management. If you're trying to answer, do I go to market with the red package or the blue package? That's a question that's a moment in time as it relates to a product launch. Or if you're doing a new advertisement where you're posting, I don't know, something on TikTok that is a capitalization on a certain trend, and is it best for us to take this angle or this angle? Again, moment in time, it's going to be very quick. You're going to want an answer within 24 hours of which direction should you sort of go with? So if you have, and also have you answered those questions historically before, is going to determine if you have to do it again. So if you've run the red versus the blue package eight times, and the blue package has always won, it's probably a better shot that you continue with that package design unless you really want to make a brand shift because the blue package is bad now and whatever people like really are boycotting blue or you know, whatever the reason is. But unless you want to actually make that pivot or make that change, it also depends on what you've collected historically and what you can analyze historically. And if you take a look at that proprietary data and say, apply everything you know here to this current situation, you can probably predict that without having to do the research again. So that was probably a very long-winded answer. What do I have to say? It depends on what you're trying to answer and the type of company that you work at. Totally makes sense. And even like your boycott, like point of view, I think so many people pivot because of a good moment in time thing and it might not be something that you flip that decision because if they keep choosing blue, even though there's a boycott of blue, it means you probably should stick with the blue, you would have the boycott. Because the boycotts are usually moment in time things. There's very few boycotts that like I'm saying besides there's something that's really really horrible and something. But there's maybe-- Oh yeah, invest in your PR crisis management for sure. Lastly, I asked everybody in this podcast this question, but what is a marketing hill you would die on? A marketing hill I would die on. This is going to be a very unpopular opinion, but I'll tell you why I have it as an opinion that I share. I think that marketing and sales have to work together. I think that that is a really sometimes difficult thing to hear, especially in a B2B world where marketing and sales sometimes but heads or don't get along or like marketing is sending over trash leads and sales is upset about it and like whatever, whatever the thing is, there's always a reason why. I think that as a marketer, into being very intentional about ensuring that the leads you send, the content that you create, the messaging that you share is grounded in things that actually make your sales team's life easier. That's resonating with their customers. That's resonating with their customers because they're the ones getting on the calls in front of them every single day. That is important. And that you can do also your own little internal research that doesn't require you to create a study and ask your customers. But if you have a sales recording tool like a like a goger or a zoom tool or something and you can go in and do some research about, is the thing that we said, so let's just say we did a piece on X, but X is a big problem right now. Like we are saying that we're really great at X, but our customers are like, what are you talking about? You are having a huge gunning of customers because X is awful right now. You're not doing this well. That's making the sales team's life harder. I say this with some bias maybe because my husband works in sales and we met at a couple companies ago. So I think that maybe we're bridging the gap between sales and marketing is what we always say. But especially in a B2B organization, learning what their issues are, learning what leads are converting what's functioning, what's working for them, digging into that conversion reporting. Marketers a lot of times I find can sit in this ivory tower and that their goal is lead conversion. Once the lead has converted or once the customer has purchased something, that's the end. They don't care about the downstream effects. And that's a good marketer should know the entire life cycle because if you're sending over, let's just say, oh my gosh, all these people interacted with our ads and they're all wanting demos and they're all wanting to come over and try out our software. Fantastic, but sales getting on those calls and realizing that 80% of them are not actually our target or 80% of them are flabbergasted because they can't afford our pricing. That's then not a success for a marketer. You know, that means that a marketer is cluttering up the sales team's time and is not actually doing a good job. They should be understanding what the customer life cycle is to do out. They should be looking at the win rates of those people who convert to customers or who buy the product eventually from their different campaigns. They should be managing campaign effectiveness as the whole thing and not just the they turned into a lead. And I know there's a lot of times where marketers and organizations have gold marketing teams to stop caring at that point in time. And I think that that's a detriment to the organization as a whole. Marketers at the top of the funnel should have an understanding of what success is measured at throughout the whole life cycle to be more effective at the top. I mean, I out plus one that I've always been told throughout my career as I'm marking up. So I was always like the gap, like the glue between everything. His revenue is a team sport. It's not an individual sport. It's a I mean, teaming that every sales needs to own it. Marketing needs to own it. Everybody's to get revenue. I mean, the end of goal of like revenue is not going up. Everybody's jobs on the line. I'm not and marketing is probably the first part.
- Marketing is always the first. - Well, I mean, if you don't care about revenue, you probably job and revenue's going down, they're gonna come after you because you the top. You're like the, the, the, the, the, the, the, the spring going. Sales is very formulaic. And if you know sales, it's like, I, I work back from a revenue number to get a pipeline number. I mean, a conversion number, to get a pipeline number, to how many AEs I need for each to get, hit that pipeline to how many S, if I have an S, here's how many S, T, R's I need to fill that those AEs. Pipeline and then work back to how many leads marketing needs to get to get that. And it's a very formulaic thing. It's hard to do, would predict marketing perfectly because channels change, inputs change. And if you only go on that one thing and not a gold on the bottom, especially in B2B, e-commerce owns everything because you have to, that ad needs to convert. Like that's like why e-commerce? It's like, okay. You need it, you need to sell. So yeah, I totally agree. Lastly, where could people find you and what you're doing? So you can follow me on LinkedIn. Feel free to shoot me a message. Can access me, follow me, anything. Molly, Strondon, Karen, you know, on LinkedIn. You could also reach out to me directly.
[email protected]. I would love to hear from you. I'd love to hear you rip my podcast up and so to far. Love you know how it's wrong. Love you know how, you know, it resonated with you. And I'd be happy to have a chat with, with any of the listeners out there. Well, thank you so much. Awesome. Thank you so much for having Daniel. Thanks so much for listening. Keep tuning in to hear more great insights from the coolest marketers from around the world. If you haven't ready, make sure to subscribe and follow the Marketing Milano's podcast on Apple podcasts, Spotify, YouTube, or wherever you get your podcasts. And if you'd like what you hear, I would greatly appreciate you giving us a five star rating. It helps bring more marketers into our community.
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