Posted by randfish
Have you ever tried to create 10 x material? It’s not easy, is it? Knowing how and where to start can often be the biggest obstacle you’ll face. In this oldie-but-goodie episode of Whiteboard Friday, Rand Fishkin talks about how you can develop your own 10 x material to help your brand stand out.
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Video Transcription
Howdy, Moz fans, and welcome to another copy of Whiteboard Friday. This week we’re chatting about how to create 10 x material.
Now, for those of you who might need a refresher or who haven’t seen previous Whiteboard Fridays where we’ve has spoken about 10 x content, this is the idea that, because of content saturation, material overload, the idea that there’s just so much in our flows and standing out is so hard, we can’t just say, “Hey, I want to be as good as the top 10 parties in the search results for this particular keyword term or phrase.” We “re saying”, “How can I generate something 10 durations better than what any of these tribes are currently doing? ” That’s how we stand out.
What is 10x content?
10x content is content that is 10 durations better than the highest ranking result for a generated keyword( s ). Here are 119 Specimen of 10x Content .
Criteria for 10 x content 😛 TAGEND
It has to have enormous UI and UX on any device. That material is generally a combination of high quality, trustworthy, it’s beneficial, interesting, and impressive. It doesn’t have to be all of those but some combining of them. It’s got to be considerably different in scope and in detail from other employments that are serving the same visitor or user intent. It’s got to create an emotional response. I want to feel awe. I want to feel surprise. I want to feel joy, prospect, or praise for that patch of content in order for it to be considered 10 x. It has to solve a problem or answer a question by providing thorough, accurate, extraordinary information or aids. It’s got to deliver content in a distinct, remarkable, frequently unexpectedly enjoyable wording or medium.
If you hit all of these things, you probably have yourself a piece of 10 x content. It’s precisely very difficult to do. That’s what we’re talking about today. What’s a process by which we can get to checking off all these boxes?
Step 1 – Gain deep penetration.
So let’s start now. First off, when you have an issue, let’s say you’ve got a piece of content that you know you want to create, a topic you know you’re going to address that topic. We can talk about how to get to that topic in a future Whiteboard Friday, and we’ve had some in the past certainly around keyword research and choosing topics and that sort of thing. But if I know the topic, I it is necessary first addition a penetrating, deep revelation into the core of why people are interested in this subject.
So for example, let’s do something simple, something we’re all familiar with.
“I wonder what the most highly-rated brand-new movies are out there.” Essentially this is, “Well, okay, how do we get into this person’s brain and try and answer the core of their question? ” They’re virtually questioning, “Okay, how do I figure out . . . help me decide what to watch.”
That could have a bunch of directions to it. It could be about user ratings, or it could be maybe about honors. Maybe it’s about esteem. What are the most popular movies out there? It could be meta ratings. Maybe this person wants to see an aggregated listing of all the data out there. It could be editorial or critic ratings. There’s a bunch of slants there.
Step 2 – We have to get unique.
We know that uniqueness, being exceptional , not the same as everyone else but different from everyone else out there, is most important.
So as we brainstorm different ways that we might address the core of this user’s problem, we were able to say, “All right, movie ratings, could we do a round-up? ”
Well, that already exists at places like Metacritic. They kind of aggregate everything and then gave it all together and tell us what reviewers versus publics reflect across countless, many different websites. So that’s already been done.
Awards versus notoriety, again, it’s already been done in a number of places that do likeness of here’s the ones that had the highest box office versus here’s the ones that won certain forms of gives. Well, okay, so that’s not particularly unique.
What about critics versus publics? Again, this is done basically on every different website. Everyone indicates me user ratings versus critic ratings.
What about by availability? Well, there’s actually a knot of websites that do this now where they prove you this is on Netflix, this is on Hulu, this is on Amazon, this you can watch on Comcast or on requisition, this you can see on YouTube. All privilege, so that’s not unique either.
What about which ratings can I trust? Hang on a tick. That might not exist hitherto. That’s a great, distinct insight into this problem, because one of the challenges that I have when I want to say, “What should I decide to watch, ” is who should I trust and who is required to I believe. Can I go to Fandango or Amazon or Metacritic or Netflix? Whose ratings are actually trustworthy?
Well , now we’ve had something unique, and now we’ve got that core insight, that unique inclination on it.
Step 3 – Uncover strong methods to provide an answer.
Now we want to uncover a powerful, hard-to-replicate, high-quality method to provide an answer to that question.
In this case, that are likely to, “Well, you know what? We can do a statistical analysis.” We get a sample rectify big enough, enough films, maybe 150 movies or so from the past year. We take a look at the ratings that each work furnishes, and we see if we can find decorations, patterns like: Who’s high and low? Do some have different genre likings? Which one is trustworthy? Does one correlate with awards and critics? Which ones are outliers? All of these are actually was just trying to get to the “which one can I trust” question.
I think we can answer that if we do this statistical analysis. It’s a pain in the butt.
We have to go to all these places. We have to collect all the data. We is therefore necessary to placed it into a statistical pattern. We then have to run our simulation. We is therefore necessary to make sure that we have a big enough sample adjusted. We’ve got to see what our equivalences are. We have to check for outliers and spreads and all this kind of stuff. But once we do that and formerly we prove our approach , now all we have to do is …
Step 4 – Find a distinct, powerful, excellent road to present this material.
In fact, FiveThirtyEight.com did exactly this.
They took this statistical analysis. They looked at all of these various places, Fandango and IMDB useds versus analysts versus Metacritic versus Rotten Tomatoes and a number of other sites. Then they had this one graph that presents basically the starring rating medians across I think it was 146 different films, which was the sample determined that they defined was accurate enough.
Now they’ve created this bit of 10x content, and they’ve answered this unique take on the question, “Which rating service can I trust? ” The answer is, “Don’t trust Fandango, ” mostly. But “youre seeing” more in there. Metacritic is pretty good. A couple of the other ones are reasonable.
Step 5 – Expect that you’re going to do this 5 to 10 periods before you have one smacked.
The only way to get good at this, the only way to get good is experimentation and rule. You do this over and over again, and you start to develop a sixth sense for how you can uncover that distinct ingredient, how you can present it in a unique mode, and how you can make it sing on the Web.
All right, everyone, I look forward to hearing your thoughts on 10 x material. If you have any patterns you’d like to share with us, delight feel free to do so in the comments. No problem associating out. That’s just fine. We will see you again next week for another edition of Whiteboard Friday. Take care.
Video transcription by Speechpad.com
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