You Ask, I Answer: Identifying Working Topics in Content Marketing

In today's episode, Matt asks, how do you know what topics convert the best? This is a very common question and is a very challenging question because it requires a lot of lifting on your part to know this. First, in all the content you have, how well is it tagged, right? By topic. This is a topic uh content about email marketing.

This is a topic about SEO, this is a topic about social media, and so on and so forth. So you all of your content in your data has to be tagged to say like this is what the topic of this of this thing is. And then you need outcomes. Like how do you know what converts the best? Well, it depends on the conversion type.

Like what kind of conversion? Is it a newsletter subscription? Is it a uh form fill? Is it a purchase? What is the conversion you're trying to get?

And then you have to mix these things together. And you have every blog post you have on your blog, for example, and you have it's by day, and you have your conversions per day. Ideally, you have it at a at a per blog post level using something like uh Markov chain modeling, which we talked about in an earlier episode this week. Ideally, that's in there as well. And then in that table, you will have tagged either manually or with machine assistance, whether a post is about a specific topic.

So you have say a row where there's an your URL, and there's a column says email, and there's a zero or one, whether it's an email post, there's a column for SEO, zero or one. And then what you do is you summarize all that and you run a regression and you say, okay, we know that our response column is leads generated or what have you. Do any of these topics, the presence of any of these topics in all these different columns, have an influence or driving factor that leads to conversion? Because you're still going to have other metrics in there like sessions, time on page, bounce rate, and all that stuff. All that is in there and should be in there.

But those topics that you've identified and coded into your data will become part of the algorithm that you run to do that analysis to say this combination of three things lead seems to have the highest correlation to conversions. So posts on Wednesdays, right? Posts that are 700 characters long and posts about email marketing. All three of those factors in combination lead to this outcome you care about. And that information then allows you to start building a testing plan.

That testing plan gives you the insights you need to say, okay, we've now proven correlation, that posts on Wednesdays, they're 700 words long about email marketing, uh, seem to convert best. Let's do a bunch of those and see if you know, if you double the number of posts on Wednesdays about email marketing, there's 700 words long. Do you see a doubling in your conversions? If the answer is yes, now you've teased out causality. If the answer is no, then what you had was correlative and not causal.

So that's how you you make that determination on topics. It's a lot of work. It's a lot of work because even if you do it like bag of words style, where if a post has the words email marketing in it, that's an email marketing post. It still takes time to tag through. Now, if you've got a really big website, um, you could do use like deep learning to try and read the content and assign it topics.

But again, even that's still a little bit iffy, and that still requires a lot of training data, right? A lot of information to tell the machines here's what this is about based on previous examples. So that part is tricky as well. But I would do that. I would I would tag it to the best of your ability, run the regression analysis, and then say, yep, topics, whatever they are, however many of them you have, either are or are not mathematically suggestive of the outcome you care about.

And then you build your testing plan. Good question.