In this episode, Katie and Chris discuss ways of avoiding running out of content marketing. How do you keep things fresh and relevant once you’ve tackled the major topics that you want to be known for? Follow along as they look at analyzing competitors with predictive SEO forecasts, and even develop a new product idea from the search data.

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher Penn

In this week’s in your insights we are talking about doing competitive keyword research and SEO. Now, one of the things that we’ve been doing for a while here at trusted insights is taking known keyword phrases like predictive analytics or time series forecasting and putting that into an SEO tool, we use the RF tool, but really this will work with RF spy foo, sem rush Mas, the vendor of your choice. And we look at phrase and uses the same words that so to give us a more expanded keyword list of things that people might type in. But last week, we were meeting with one of our clients, we were saying to ourselves, well, what’s our blind spot? When you type things into a keyword tool? Like predictive analytics, you have a good sense of like, this is what we do we know generally about our audience. But in many cases, competitors may because of the especially the high volume content ones maybe ranking for things that we don’t even think about. So one of the things we came up with last week was let’s scan seven or eight different competitors. See which terms they at least two of them rank for. That we don’t. Now we want to look at. So Katie, taking a look at the seven the seven competitors, we have SAS word stream martech, advisor, Chief martech NG data, HubSpot McKinsey size sense, IBM, in your point perspective, do you feel like those are good aspirational competitors for our company?

Katie Robbert

for our company, in particular, I do. Because we’ve always had this challenge of describing ourselves because we strongly have one foot in marketing, and we strongly have the other foot in true data science and finding ways for the two to come together has always been tricky. So I think that where we stand right now, we really need to make sure that we are thinking about competitors on both sides of that fence. So I do I think this is a strong lyst for us,

Christopher Penn

for us. So let’s take a look at the actual results of the terms that people are going to be searching for what we did, of course, we fed this to our predictive analytics software to do a time series forecast, and came up with the terms that people are most likely to search for in the next 52 weeks. And we’ll put a screenshot of this just of the term list of maybe a couple of weeks in the show notes so that you can get a sense of the follow along a little bit about this. But Katie, what do you what do you see when you look at at this lovely mix of over 10,000 terms

Katie Robbert

that the top 30 or so terms have to do with social media, specifically Instagram, which I find really interesting given our mix of IBM and other, more data science, heavy competitors, but what it really says to me is that marketers and other businesses in general are struggling to figure out how to reach consumers how to reach their specific audience. And for right now, social media, specifically Instagram seems to be where everybody is headed. So I do find that to be really interesting. Instagram is such a hot topic at the moment because there’s such limited capabilities with Instagram, but yet people are re