 Hey everybody, Cyrus Shepherd here. I want to introduce our next speaker because she is one of my favorite people in the SEO industry and also one of the most brilliant, Alexis Sanders. You may have heard of Alexis if you've ever played an online SEO game. She has two brilliant games that she's developed, Technical SEO Expert and Technical SEO Guru. So that's technicalseo.expert and technicalseo.guru. Check them out, they're a lot of fun. So even though Alexis is a brilliant Technical SEO, she's going to talk today about something we don't always consider when speaking about Technical SEO, which is the audience and how important finding that audience is to your SEO propositions. So we're going to kick it over to Alexis. I know you're going to enjoy this session. Hi, Mozcon. Customers are the center of quality content user experience and digital marketing. And have you ever thought to yourself, how do I learn about my customers with the resources that are already currently available at my company? And that's what we're going to be getting into today. If you are interested in a copy of this presentation before we get started, it's available at this bit.ly link, Seeking Science, because we'll be going into the science of seeking your customers today. As I mentioned, we're going to be going into the what, why, when, where, and how of finding your specific audience. So getting started with the what. Finding an audience is both an art and a science. We have both the intuition as well as the data that goes behind it. It's about getting to know people and quality data, but it's also about quantitative data as well. It is both micro and macro in that we're looking at large audiences, but we're also looking at individuals as well. It is multi-faceted because the people that we're looking at are not just one dimensional. They have lives, they have depth to them and that makes finding an audience multi-dimensional. And it's something that's constantly evolving. As people evolve, products also evolve and their audience evolve with them. So as a product gets more mature in the cycle, we're going to see shifts in the audience and that's something that is going to constantly change. And finally, it's something that can relate to everyone in your company and something that everyone is interested in. The user is generally considered the North Star for many products, for brands, for the media, for IT, as well as ultimately executives. So something that everyone really cares about. And it really comes down to understanding these questions. Who are they? Why do they buy? What do they ultimately need in the product or service that you're offering? What type of search queries did they use and how are they getting to you? What is that journey? What does that look like? And what matters to them as people? And so that comes down to a few different key questions to answer. So there's first off, in terms of categories, there's key information, there's things that are core to their personality, there's product information, as well as their relationship with technology. So in terms of key information, that often looks like things like demographics. So what is their age? What is their gender, their income, their location, their number of children, and that type of information is very general. And can be really relevant to who buys your product because if you're a local business, then your location is something that's really important to find your customers. Another top question that you can ask in terms of key information is what are the top five adjectives that describe your audience in particular? Personal core relates to, instead of looking at it from who's the person on the outside, looking at the question from who's the person on the inside. So what do they want in life? What are some of their key motivators? What do they value and see as a self-concept? What general attitudes do they have? Like, for instance, I like to learn a lot. That's a general attitude that some people have and some people do not. And what are their cited interests? What are things that they've noted that they are interested in outside of your product and service? It's just all about getting to know them as a full person. Product related. So this gets into the nitty gritty of the information that you're going to want to look for. And at the core of this is, how do you win over this group? What do they really want from that product? What do they look for in the product? And one easy question to ask in terms of your top competition is I am most likely to buy my product or service from X, Y, or Z. And finally, for the last core question, what is their relationship with technology? What is their relationship with the web, with your website? What is their annual spend on that product or service? Where are they engaging online already? And how do we find them and engage with them on digital forms that are meaningful to them? And really the whole point of finding user data and analyzing it and finding insights from it is because it changes the conversation. Really, if you have no data, you have no context and you going into that conversation blind can be much more challenging, which affects many different streams of digital marketing. Context helps us to ask the right questions, to target what drives decisions, to identify where to find online and offline and determine how to ultimately engage. It also helps us connect in a meaningful capacity, identify what actually matters to people and understand ultimately their values and the ultimate goal is to create better experiences overall. So, when would you do this type of analysis? When would you go out and seek information specifically about your users? And really audience profiling and finding data extends the entire journey of a product from its initial development to its introduction to market, to huge growth, maturity, and decline because our audience at each stage is going to be very, very different. And it expands across of course branding as well as media strategy. So every channel within media cares about this and it's something that expands and it's really relevant at any point in time. It also affects deliverables across different VAUs. So, here's just like a simple visual of a very small sample of deliverables that use user information and audience profiling to inform that particular deliverable. And you can see just in this very small micro example that your user information affects your content channel, your information architecture, user experience, your design as well as all of your analytics deliverables as well as many others as well, which makes it something that's very relevant and useful. So getting into the meat of today's presentation, where? So where do we actually ultimately find this data? And generally comes from one of two places. There's really three out there, but for the most part, most times it's related to first party or third party data. So the third one is second party data. First party data of course is your own data that you collect and assimilate. Third party data is data that you get from someone else. And second party data is kind of that weird ground where maybe you have a partnership with someone and it's kind of an in-between mix of your data and their data and cross-sharing. But that's more of a rare opportunity, although it can be a very fruitful one. So getting it today, we're gonna start with first party data. There are many different places that you can collect first party data. The first and probably generally the most accessible one is web performance analytics. So that's something like Google Analytics or Adobe Analytics, your organic search performance, which is also free through Google Search Console and Big Webmaster Tools. You also have ranking data tools so you can find out what information people are getting to your site from for organic. There's surveying tools that you can use that can be free like Google Surveys and Survey Monkey. And finally, if you're lucky enough to have a CRM team, which we'll get into a little bit later in terms of third party data, some of the cool things about CRM. These are really generally the types of tools. And as you can see, they range from free to very, very expensive. So a lot of the times finding user information and user data is about identifying what's available to you and what's most cost-effective and time-effective. And here's some resources. So I wanted to go into some of the different things, different reports that I really like about within a few of these tools so that if you're looking for user information, you can more easily access that. So the first is GA, mostly because it's very accessible to most brands and most small businesses. There's a report in GA called Audience and Interests. And if you look in there, there's really two groups that you're mainly primarily looking at, the in-market and the affinity. The in-market is going to relate to your user's interest in relation to your current product. So a lot of times this is going to end up being a little bit more relevant, whereas affinity is more about people's general interest and hobby. And that can be interesting because it can tell you maybe a little bit more about like the sighted interests of people and things that they're doing in their free time outside of your product. But generally the in-market, to me, it seems like it's a little bit more useful generally. Other stuff that just kind of didn't fit in other places. So if you really want to get into the weeds, GA has a user explorer report that you can go into. You can filter by organic search. You can also filter by conversions. And you can go in and see the different steps that a user and I guess it's kind of like a user's ID, and that particular journey that has been tagged by GA. So you can literally follow them through a journey. Now, this is of course very in the leads, but if you're trying to get very specific or maybe you're trying to look to do some illustrative user journey mappings, this can be really, really useful. Okay, the last thing about GA that I think is pretty cool is you can actually ask questions in the search bar. I'm sure a lot of the audience has already explored this, but there's a bunch of different basic questions that you can ask relating to content, and Google actually breaks these out in their documentation by understanding trends, content analysis, and understanding user behavior. And a lot of times the user behavior relates to user engagement metrics, like time on site, bounce rate, but having these questions allows you to just ask in the search bar questions like, what are my top landing pages in terms of sessions? So you can get a little bit more, a better understanding of your content analysis, which is pretty cool. All right, of course, GSC, we all know it, we all love it. Yeah, there's a lot of stories to tell, and a lot of stories to tell in click behavior. What are people clicking onto your site? Categorization is obviously a very easy mechanism to identify patterns. There's a bunch of different ways to do this that a lot of people have been over. I have some links in this presentation on ideas for how to do it efficiently in Google Sheets, Jupyter Notebooks, there's a co-lab as well. You could also do it using machine learning. There's been a lot of great suggestions by like bringing more in everyone on how to sort keywords by using machine learning and with Batista as well. So definitely check out some of the stuff that they have. But you can also do it manually. So, but there's a lot of stories to tell about what users are doing and how they're interacting with your site. Surveys. Surveys can be a lot of times the only way to get very specific information that you want from a specific subset of people. Many times in third-party data structures, when they're asking questions on psychographic, they're doing it through a survey, but to a larger audience and with professionals that of course have written surveys for natives. Now, surveys for you can be a great way to get quick information, to get qualitative information, to make sure that you are getting exactly what you need. And I've included some tips here. The primary ones are to make sure you test the survey, to stay consistent as much as possible, to use plain clear lingo, to reference an expert style, keep it short, keep it punchy, and as much as possible, avoid leading questions. And if you have someone who's an expert in the field, definitely have them read it over and check because there's always some weird insights with surveys where it's like, oh, okay, well, I didn't expect that, but it's cool. All right, another thing that you can do if you're really close to some of your top customers and top fans and you wanna get an understanding of their general interests, you can ask them questions of course, but you can also ask them what their Discover interests are in Google. So anybody can access their Discover interests on Google by going through the mobile experience, clicking on the three little dots and checking out to see what your interests are. And if you're close enough with your users and can ask them, it may be a good area to explore commonalities. And if you can get a bunch of different users to agree to do this and to help you out, you can then take that, copy all that information down and count frequencies and see if anything interesting appears. So it can be kinda cool. All right, so third-party tools. There are a lot of different third-party tools and a lot of different types of third-party tools. First, of course, there is keyword search data. Many people in here are already familiar with that. I only have a short, short tidbit on that. Social data, audience tools, publicly available data sets, like the census or really anything like research, which is pretty cool, research publications, social listening, competitive tools, as well as CRM, which kind of makes finding third-party data this endless stream of information. All right, going into the specifics here, search data. So as many of you know, and this is my only slide on search data here because I wanted to keep it short because a lot of Mulscon attendees are experts, catarising that data by intent, by search features, so search intent proxy, basically, by brand and by different questions can be really useful to extrapolating information about how people are clicking through to the site. As well as using location data where available. Sometimes I've found in the past, I guess this is a more of a competitive type thing, but that certain brands were performing better because they were concentrating in specific markets more, which is just kind of interesting to see when you layer in location onto different competitive data. Yeah, can provide some additional context. Google Find My Audience has a free audience tool that's available. So if your audience aligns really well with the audience is that Google has outlined, you can use these tools and basically go through and input general information. It's very, very high level, but at the same time, you can get these nice PDFs back and you can go through and maybe get some additional insights or places to start more research. A little bit more useful is Facebook audience insights. There are two forms of Facebook audience insights to flavors, if you will. The first one is using your own Facebook profile, basically, or Facebook company information, which you can then load into the tool and look at that specific information. So it's really like looking at your own data on Facebook audience insights. The second is using Facebook's audiences. Overall, they're basically their base audiences and you can identify different themes and different targets and topics and pull out different demographics from that. So that can be another way to get more general, a general foothold into what you are interested in. So for example, if you know that you need to do research into cloud software, you can see what does the cloud software audience on Facebook look like? What are some of their job titles? What are some of their top categories and favorite things on Facebook? What location are they primarily in and what device usages do they have? So it's a great way if you are unfamiliar or maybe don't have a super high budget, can go through and pick out the information and identify some things about user audiences that Facebook has. Or of course, if you have your Facebook audience data, you can use that too. So you can get insights about your own audience that way, which is pretty cool. Of course, I would be a failure to not mention SparkToro, of course, which I'm sure many of you guys have already seen. So I just had some quick little things that I really loved about SparkToro here. And I think it's really useful if you're approaching audience insights from a content or influencer perspective, which of course was their target. It allows you to break data down in a variety of ways. It is pretty straightforward overview of the social accounts and the top accounts that are out there. If you're looking for the high influencers in the field, you'll be able to find those. It shows a lot of online consumption data as well as top-act network. So where's your audience actually engaging online, which of course is a core question. If you're doing work primarily on your website and everyone else is on Reddit, then they're not gonna find you. But yeah. It also includes some useful social listening to bits which can be really fun and if you love keyword research, then you probably also really like this part as well because it just kind of tries to create a broader person which is super interesting. Okay. Next, one of the things, I'm not sure how many other people in the industry do this, but I love looking at research publications. Some of my favorite include Pew Research for just general information. They have a lot of really good stuff. If you have a health client, the CDC has a lot of really great stuff. The US Census is great. There's also a plethora of other data sets that are published by companies like PWC as well as researchers. And this can be a great place to find general high-level audience information that allows you to make assumptions about things, things that are going on in the world. I also highly suggest the Mary Meeker Report every year, the Internet Trends Report. It's very, very good. And it kind of shows the idea of how do you take all this high-level data, compile it into something that's useful and pick out the trends that are going to affect businesses. I think she does a really good job of that, but that's the idea here is basically there's endless rooms of research out there that you can use and you can tap into to help understand what's going on in terms of your audience. All right. The last section I have here is a little bit expanded. And of course CRM fits into both the first party and third-party data world, but I wanted to talk a little bit more about that because I think that CRM isn't something that typically people talk about in SEO. And I think it's something that's super fascinating and a lot of times teams already have a CRM team that's built in another part of the organization and it's a matter of knowing what to ask, what data to ask them for. So one of the things that's really interesting about CRM is generally media of teams, think of CRM as something that's more useful on digital for targeted campaigns. And in truth, it kind of is in that what you're looking for with targeted campaigns is less discerning than what you're looking for if you're looking for a general audience like content. Now there are really two primary metrics that you're going to see in CRM data. The first is frequency. So the percentage of respondents who met a specific criteria and the index, which is the comparison to the base audience. So regardless of whether you're doing work for content or targeted campaigns, you always want to have a high index because that's what makes your audience unique. For example, if you are in Canada and you're Disney or something like that, they may over index on French speakers and that's what in Quebec and that's what makes them in Quebec unique, which is what you're ultimately looking for. The difference really between targeted campaigns and general content and publishing type initiatives is that when you're looking for content, you don't really want to have a low frequency. You really want to have high frequency that you look for. And that's because you don't want to just target those niche populations that maybe you over index on. You want to target the majority of people and you want to target what makes them unique. So, yeah. Okay, and the other thing that I wanted to introduce is this idea that there is a hierarchy of data and that a lot of times what we do in SEO sits somewhere in the middle of the bottom to the bottom of the hierarchy. And that's in my opinion, okay, because we really don't generally do one-to-one personal targeting, but it's something that can be done. So, panel data at the bottom is things like surveys, personas, sample customer data, segment level analysis, audiences, and that's kind of a lot of the comfort area for SEO. I think ultimately, probably in the future, as websites evolve, maybe, I don't know, five, 10 years or something like that, we'll get into something that can address more personal level computer experiences for non-logged-in users or non-authenticated users. As of now, I think a lot of that split is between new users and existing users. If you're, for instance, cookie does one of those. There's cookies, which basically is what that is. So, if you're cookie does an existing user, then you may get an existing user experience. And if you're not cookieed as an existing user, maybe you'll get a new user, or maybe if you, for instance, cleared your cookies or something, you'll get the new user experience. But there is this hierarchy that at the top of it sits personal level data. And it's not really, you know, Ted to Bob or something like that or specific people. And it's more about your IDs that they assign you on one, which is a very, I guess, interesting concept to that it's not personally identifiable information if it's not you, kind of interesting. And this data expands very far. There are many companies out there and pretty much, I guess, every company that you could name probably collects data about you and all this interconnects and you can associate all that data back with your ID if you're lucky enough. And there's something that comes into CRM, which is called the closed loop. And what that means, and that's what they really strive to do to get personal one-to-one data. And what that means is that if on platform one, we have cute bow tie guy, and on platform three, you have cute bow tie guy. And you know that both of those are the same. And the same for platform two and platform four, you know that each of these are the same. But for whatever reason, we find out that platform one guy is also the same as platform four guy. We can actually close this loop and determine that this person is the same person that we're all talking about. Even though we don't know this person's name, we don't know any of their personal information, obviously, we know that when we're talking about him on platform one, we're also talking about him on platform two as well as three and four. And that's what closing the loop is. Now, you can't always have a perfect data set and any database cleanser will tell you that. But there's just something known as match rate, which is basically like how many of these were we able to match and determine that we have data on this person and that matches. And that's pretty much how that number calculates out. And at Merkle, we have an internal system called M1, which is something that mostly are media teams use to for remarketing purposes and to get more specific data on audiences and they have their own thing going on. But if our client has purchased it, it's something that we can also use for SEO. And I just wanted to share with you some of the things that I thought were interesting here. So, and the importance, of course, of having an analyst. So data, of course, is not just, as I mentioned before, a science. It's also ended up in art and there's a lot of intuition that goes behind it. So I just wanna share with you some examples from that particular system. So, all right, minute made. According to data, 35 through 49 year olds crush minute made. And as you can see here, the gray, of course, is the base audience. The blue is, of course, index. So we're looking for how much the data overlaps here in this visual. So you can see for the 35 through 49 year olds, the data just explodes. Like people who are 35 through 49 love minute made. And of course, you look at like their net worth and like, whoa, these people are worth 150,000 to $375,000. Like what is in that choose? Pretty cool. But if you're, of course, an analyst and digging through the data, you may also discover, okay, well, maybe it's not that these guys love minute made. Maybe they're not super pumped about getting some OJ in the morning, but more about the fact that they all have children. So it's kind of one of the more fun things. When people have kids, it kind of messes up the data set, but it's use it in a way that makes it kind of interesting. You know, data looking at this type of stuff and serum data can also make you introspective in such that things like, why do 76 year olds index so low on eating and going out to Chipotle? Even more interesting, why is it that people don't get Chipotle as much when there's a grandparent in the house? I don't know. Maybe make that 2020 initiative, get your grandparent to a Chipotle. Anyways, okay. So diving into a little bit of the how, how do we format this data? How do we compile it? Generally, this ends up being a lot of information. So you have to have a way of processing it and thinking about it and how do we present this so that we can develop something that we can simulate to send out to the rest of the teams. My suggestion is two things. The first, brainwriting. The second being mini shreds. So brainwriting is exactly like brainstorming, but instead of having everybody get in the same room together and talk out ideas initially, you have people go and write their thoughts out and process the information. And oftentimes what that allows people to do is come back to their roots to idea before talking and process information. It also allows for if you have a few extroverted voices or people that are of influence that are more extroverted and more authoritative, it allows people who are maybe more introverted or take a little bit more time to process to think about their thoughts, go through and process them and have them all written out and prepared ahead of time. So you can have a productive meeting. Where the charade comes in is basically a mini meeting where everybody comes together and talks about their ideas and you compile it into something that's useful and kind of pulls out the top insights from all different angles here. And of course, I included some screenshots down here for you guys to see. Some examples where we used Microsoft whiteboard and you can see in the brainwriting side on the blue side is one person, the red overlay is another, the yellow overlay is another. And you can see these can get kind of exciting and it's kind of has like a dynamic feel to it. So if you're pulling data from a bunch of different parts and a bunch of different tools and each person is doing different tool, you can compile it that way as well. How do you present this information? So I have three different ideas of how I personally think are different ways to present this information. But I mean, really it's endless. Like there's many different things you could use data for and really doesn't necessarily need to be something that's presented, it could be bullets in an email, but here's some ideas. So the first is an illustrative user journey for content. And what that looks like is basically picking out either an audience or a specific user, identifying different stages along their journey. And of course you would fill in all these little boxes here, identifying and aligning what website touchpoints exist. And then finally identifying the primary focal point of the user. Are they looking for something like convenience at this stage? Are they looking for information, trying to figure out what is the one specific user audience? And sometimes this can help because instead of trying to think of it as this big problem, you can reduce it into a problem that's more accessible and more relatable. Then later you can take it and expand this out to be something a little bit bigger. But it's something that can, as I mentioned, reduce the problem, which is key in solving complex issues. The next is, I wanted to introduce before I got started into the audience journeys and briefs, the means and model. And there's a ton of different psychological models that you can use. The means and model is just one. But what it relates to is this idea that there are product attributes and product benefits that are things that are on the surface that people see and that are tangible, but then underline our feelings and a sense of self that come with every single thing that we as human beings do. We seek to align. And a simple sentence to kind of describe how this goes is what are the tangible benefits of the product that lead to functional benefits for the person? Basically, what are they getting out of it on the surface? Which affect how that user or customer feels and reflects on their personal values. And that's something that I think fits really well into briefs because you can basically identify those underlying components that go into each person. But that of course requires a lot of data, a lot of analysis, a lot of chunking, a lot of different ideas coming into the room. So yeah, this is one example of what an audience brief may look like. Of course, there's a lot of flexibility there. And finally, there's this idea of mapping user journey. For content, I think there are a few different rows of information that are really important. So making sure that you identify what is the primary goal of the user at that particular stage, the types of content that they would need, the common traits, I think is something that's very useful. Content location, you can keep it or leave it. The next stage is also really important. Like how are we pushing them down the particular funnel? And of course, starting with the different columns, acceptance, I'm sorry, awareness, acceptance, consideration, decision, it makes sense. Sometimes you need to expand and contract that model depending on the different audience. And you can see here we modified it a little bit. And journey mapping is something that can also expand to all channels. It's not something that necessarily is just about content. It also can be about every single channel and every single touch point and how all those points engage. And here's just one small screenshot example of what that looks like at a larger strategic level. So our TLDR for today, our summary, is that in my opinion, for doing user research, use what data you have available. And there is data available to you out there. Communicate to others on teams to get a fuller picture of what's going on with users because other teams may have different data than you and you can use that. The third is that user research is something that's constantly evolving and something that you can consistently do but is something that makes more sense to do at specific intervals that makes sense for you and is efficient for your team and something that you need to almost constantly be doing as you go and constantly learning, which is exciting. And finally, that personal one-to-one campaigns are our thing. And to close out today, I just wanted to mention this kind of closing thought that it's about constant change and that is the new norm. It's not about guessing right and having a project and going back to some limo. It's about learning fast and trying different things. I have a few special things to you, Abby, Chris, Max, Caitlin and Adam. Thank you so much. And it's been honored to spend miles coming with all of you. Once again, virtual. Yay. Thanks everyone.