You Ask, I Answer: Content Marketing Topic Research?

In today's episode, Erica asks, what are your tips and best practices for c topic and keyword research and content marketing? Um this is an interesting question because the answer is dependent upon a couple things on the size of the content, but more importantly on uh the domain expertise and how much risk there is in the content. Remember that while we are writing to be found, to be seen, uh, we are also writing to have our information be used by people, and that means that uh there is an inherent level of risk in content. The level of risk is proportional to the amount of domain expertise we need to have. So if I'm I've I've been asked to write a piece of content on, I don't know, uh number of characters in a tweet or you know, how to emoji influence uh tweets.

That's a relatively low risk piece of content, right? It doesn't require a ton of research, and identifying topics and keywords and things for it is pretty straightforward. And probably not going to screw that up. And even if I do, it's gonna be very low impact, right? If somebody uses the poop emoji instead of the heart emoji, it's not gonna be probably the end of the world.

On the other hand, if I'm being asked to create uh a white paper or a video series about uh important steps to take uh for protecting yourself against uh a pandemic, that piece of content could literally be life or death for somebody. And so I would need to have much greater domain expertise. I would need to invest a lot more time in understanding the topic overall first before even trying to to cobble together keywords and things to understand all the pieces that are related to it and I would want to take a whole bunch of time to get background you know academic papers uh books videos uh studies research all that stuff that will tell me what is the shape of this thing what is the what are the implications and most importantly what is the lexicon and what is it that experts in the field think who are those experts what else do they talk about what are the related topics so that's the first step is to assess your level of risk and what level of domain expertise you're going to need. Then you look at the size of the content how much are we talking about we're talking about like five tweets are we talking about uh a 1500 word blog post a 10 minute video uh 45 minute class a four-hour workshop um or uh a white paper something that you intend to be in an academic journal a book on amazon what is the size of the content the bigger the size the more research you're gonna need the more data you're gonna need um and then you can look at things like you know keywords one of the best sources for keywords and for topics and understanding the topic is actually speech uh people talking because in things like podcasts and videos and interviews and stuff you will get a lot more extraneous words but you will get you also get more of the seemingly unrelated terms. So let's talk, for example um about SARS COVI2, the virus that causes COVID 19 uh in listening to epidemiologists and virologists talk about this thing.

Yes, there are the the commonplace topics, like you know, wearing masks, for example, would be something that would be associated with this topic. Washing your hands would be something you would be associated with this topic, keeping a certain distance away from people. But you would also see things like uh CO2 measurement, uh, how uh how much CO2 is in the air around you, because it's a proxy for how well ventilated a space is. The better a space is ventilated, the less CO2 will be in compared to say the outdoor air. And so you'll see measurements like you know, 350 parts per million, 450 parts per million.

Um these are not topics, these are not keywords that you would initially see if you're just narrowly researching the topic of COVID-19. Um these are important, right? These are things that you would want to include in a in an in-depth piece of research. You might want to talk about antigens and T cells and B cells and how the immune system works. Um those would equally be uh things.

So again, this is a case where you have a very complex topic, which requires a lot of domain expertise, and mapping out the the concepts will be an exhaustive exercise, as it should be, because again, you're creating content that is if if you get it wrong and you uh recommend the wrong things, you could literally kill people with it. Um so that would be the initial assessment. Domain expertise, how much content you're gonna need, what are the risks? After that, uh you need a a solid content plan. How much content, what's the cadence, uh, what are the formats it's going to be distributed in?

Uh a topic and keyword research list is less important, still important, but less important for something like a podcast, right? Unless you're producing a transcript, in which case it's you're back to creating uh making sure that you're mentioning certain specific terms. And you'd want to make sure that you you do that in the context of of the show. One of the things that uh Katie Robert and I do before e every episode of Trust Insights uh podcast is um look at the key associated keywords for a given topic and see other things that uh from a domain expertise perspective we are lacking that we'd want to augment and ver and validate that we're gonna mention in the show because we also publish it as a video. Though that means those keywords and those topics make it into the closed captions file, which means that YouTube can then index it better and show our video more.

Um in terms of the tools that you would do this, use this for this, it depends on the content type. So some things like PDFs are not natively searchable in a text format. Uh you have to use a tool like Acrobat or Preview or something. So there are tools that will export a PDF to a plain text file, and then you can do your normal text mining on it. Text mining tools will be essential for digesting a body of content in order to understand the the keywords and topics.

Um there are uh there's a library I use in the programming language R called uh Quantata that does an excellent job of extracting out here the keywords in context uh and the keywords that are within this large group of of uh documents. So you would take, for example, blog posts, Reddit posts, academic papers, convert them all into plain text, load them into this piece of software, and as a piece of software would digest them all down and say, here are the here's the map of of words that exist in this universe and how they're connected, which is really important because a lot of tools can do, you know, a word cloud. That's easy, but you don't understand necessarily the connections between terms. So for example, you know, T cell and B cell would be connected terms within the immune system in a paper about COVID 19. You'd want to know that to see how those c topics relate to each other.

Social media posts, um transcripts from YouTube videos, transcripts from podcasts, all those things, that level of text mining will give you greater insights into the universe around a topic, uh, in addition to the core keywords themselves. One of the problems with a lot of keyword software is that it's very narrowly restricted. Like you can use, you know, all s mayo contains these terms or you know contains this phrase. But again, something about COVID 19 is not necessarily going to uh have a keyword like uh antigen um or a keyword like uh dexamethasone right very important concept but not necessarily going to be immediately related which is what a lot of more primitive keyword tools do. So I would use some text binding tools to extract out and map the universe of language around a topic then you can start creating your content from it and lining up you know if you're gonna be doing a top uh a piece of content about espresso what are all the terms that go with espresso and then you can see the how they cluster together and that creates your anchor content to cover each of the major concepts so a lot in there a lot to think about but do that risk assessment and that domain expertise assessment first that will govern the size of your project and how much research you need to do.

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