Content repurposing is an important part of any content strategy, especially one that primarily deals with technical content like Cybersecurity. With the enormous effort required to create technical marketing assets like white papers, ebooks, case studies, webinars, and blog posts, it’s important to get the most value out of every piece of content.
This might look like spinning out several blog posts from a single white paper or scheduling a series of LinkedIn posts based on a video interview with a subject matter expert. Whatever the inputs and outputs are, it’s important to have a plan in place to properly distribute and repurpose every piece of content your team publishes.
It’s tempting to keep content siloed within the area of its initial use case. For example, a white paper as a lead magnet might not seem like a natural fit for generating a social post. After all, we’re trying to capture email addresses in exchange for this asset. Why would we give up the information for free on Social? The purpose of the original white paper is not to drive traffic on its own or to be widely shared on social media, but you can generate content with these goals by repurposing it in a smart way.
Why would you want to transfer the value of the white paper into social media content?
The answer is pretty simple: because your audience is there and it won’t diminish the value of having a gated asset. In many cases, it may promote the existence of the gated asset to your target buyers and more people will raise their hand and want to download the full piece of content.
More so than with less technical industries, content repurposing has its challenges in Cybersecurity. But it also has its advantages for the same reasons if you know how to turn the challenges into opportunities. We’re going to discuss a few of the specific challenges with repurposing and distribution technical content, some of the tools and techniques to get around them, and how competitive content analysis and customized AI language models can help fire up a repeatable repurposing and distribution pipeline to un-silo your content and get more out every type of marketing asset.
Challenges of repurposing technical marketing content
Repurposing and distributing technical marketing content like the kind we typically see from Cybersecurity companies like Fireeye, Palo Alto Networks, and Cybereason can be a daunting task. Some of the core challenges are due to the complex nature of the topics involved, the variety of formats of original content from PDFs to videos to webpages, translating the content for a slightly broader audience on Social, and the fact that a lot of technical content tends to be siloed due to a very specific goal or reason it was produced.
Let’s go through some of the challenges and the potential ways to turn them into advantages.
It’s no secret that Cybersecurity content is complex, technical, and oftentimes is focused on a niche within a niche.
Automata has analyzed tens of thousands of pieces of Cybersecurity content and the breadth of topics is enormous. There are so many areas of expertise, even within the offerings of a single enterprise Cybersecurity company that marketing departments require writers and content strategists that can learn quickly and have the intellectual curiosity to learn new technical topics and acquire subject matter expertise in a relatively short amount of time.
It can be difficult to outsource knowledgeable freelancers, so a premium is put on talented in-house marketers with technical expertise.
Some of the common topics we’ve uncovered are shown below in Automata’s Competitive Content Monitoring Dashboard:
The wide variety of complex topics isn’t inherently a problem. After all, if you’re creating B2B content, your target audience is likely made up of individuals familiar with the majority of your topics. However, there are certainly cases when you are defining a new category that is unfamiliar to many professionals, or you’re offering a solution to a target market that might have expertise in another area of Cybersecurity.
Matching the format of each channel
Cybersecurity content can be dry. There’s no doubt about it.
Reading a white paper on how to implement endpoint and mobile device security isn’t the most interesting way for your average consumer to spend an afternoon. However, that’s not really the point of the white paper. It’s to provide information to the seeker of that information, however few and far between that may be.
That said, there are ways to translate the content and topics of that white paper into other formats that can be more readily consumed by your direct and indirect target audience. There are members of your audience on social media and readers of your blog that may not be ready to buy a mobile endpoint security solution, but it’s important that they are aware of your expertise for the day that they become active buyers.
Creating relatable and digestible content for Social
There are many industries where publishing helpful content on social media is a must. For B2C companies, this is an obvious necessity since the majority of humans spend some portion of their day on social media. This includes Cybersecurity companies that offer consumer products such as McAfee and Kaspersky. However, B2B companies like Check Point Software and Fireeye have a less obvious reason to make social media a primary content channel.
While the majority of B2B Cybersecurity content may be on the company blog or through podcasts or webinars, the drive towards social for B2B marketers across all industries is relentless and the results of demand and lead generation efforts with social media as a pillar component is not something that’s easily ignored.
Marketing content is often siloed
Your white paper that you produced with a partner company often has a single purpose: act as a lead magnet and collect bottom of the funnel leads so your sales team can reach out to them or put them in a drip campaign. That is usually where the story ends for your white paper. However, there’s a whole lot more value to be extracted from that piece of content.
Although you might be in charge of lead generation with assets like white papers and webinars, you undoubtedly have team members focused on social, organic SEO, and promotional videos that would benefit from the research and content development that went into creating the assets you publish. Tearing down the silos and letting content be repurposed across marketing disciplines opens up new lines of creativity and opportunity for every content role.
This works in the reverse as well. A series of blog posts can be repurposed into an ebook to be used as a lead magnet or to be distributed in an email marketing campaign. Just because a piece of content was created for a specific purpose and channel doesn’t mean there is not cross function utility.
Repurposing Cybersecurity content
Let’s take a look at some examples of how to repurpose Cybersecurity content. A common characteristic across the board for most Cybersecurity companies is that they have a wide variety of content types and formats. If you check out the resource pages of SentinelOne, Crowdstrike, and Cyberark you’ll find multiple webinars, white papers, blogs, and case studies. This is all in addition to social activity and ads across search and social.
Repurposing a white paper as a blog post
A white paper is an authoritative description of a solution to a specific industry problem. It’s common that the intended readership of any one white paper will be quite low. This is generally acceptable because if it’s used as a bottom of the funnel lead magnet, the intent of the user to solve that problem is extremely high. You don’t need that many readers to raise their hand as having this problem to make the document worth creating.
Although white papers are great at capturing bottom of the funnel leads, there is still valuable information inside that is relevant for a broader audience.
The first thing to do whenever repurposing this kind of asset is to understand how to break up the material into smaller pieces by identifying natural sections. In this case, the sections are
- Key Brand Use Cases
- Additional Intelligence Use Cases
- Source Coverage
- Analysis and Delivery Capabilities
- Remediation and Takedown Capabilities
These sections look a lot like the headers of a blog post, and it’s tempting to slap them between a couple of <h2> tags, fill in the rest by copy and pasting from the PDF and call it a day. However, if you look at this white paper critically, you’ll see if it is much too dense and has way too much detail for a blog post. The objective of most blog posts is to generate qualified inbound traffic through search engines and through other distribution channels. We have to repurpose the content of each section with SEO in mind to successfully transfer the insights into this new format.
Some parameters to keep in mind to optimize for SEO are the optimal readability, number of images, inbound and outbound links, and ideal word count. Although Google’s 2022 “helpful content update” is more focused on the quality of the content and the value it adds to the reader, basic SEO guidelines should still be followed when repurposing something into a blog for SEO.
Repurposing a webinar as a Twitter thread
Webinars come in a variety of formats such as a roundtable discussion about an industry topic, a one-on-one interview with a subject matter expert, or even a solo presenter going through a set of slides. Whatever it is, it can be repurposed into multiple formats including a Twitter thread.
A sizable portion of your audience is likely on Twitter. Most of them will never watch your webinar. The most likely people to view your webinar are people looking for a solution and actively in the discovery or buying process.
Unlike the white paper in the previous section, webinars usually don’t have clean and obvious sections we can exploit. If they did, we would put together a series of six tweets, each one based on one section, and we would be done. However, with webinars and most videos that don’t have an accompanying visual with clear sections, we have to work harder to find natural segments in the conversation or discussion.
If the webinar consists of a presenter with a slide deck, the strategy is simple. We can write a single Tweet based on each slide (or group of slides) to put together an informative Twitter thread in the correct format.
Identifying a natural break in a conversation between two or more people can be difficult, but performing a topical analysis to understand which segments of the conversation are focused on various topics is a great way to quickly identify naturally occurring sections.
If this can’t be done, repurposing a single exchange of 4 or more question-answer pairs that sum up the majority of the conversation in a Twitter thread is a great strategy.
Repurposing a case study as an email newsletter
The goal of most email newsletters is to communicate a single message. Unlike a blog post and Twitter thread, an email newsletter doesn’t benefit from a well defined segmented structure of the input document. We don’t have enough room in an email to summarize each one of six sections of a white paper. Similarly, we probably don’t have space to summarize every section of a case study.
The challenge is to distill the main findings of the case study in a single section of the email. A basic global summary is the best approach for this kind of repurposing task.
Repurposing a blog post as a LinkedIn post
Repurposing blogs for social and video tends to be straightforward due to the predictable structure. With clear headings and a single topic, we can repurpose most blog posts into “zero-click” LinkedIn content that goes beyond promoting the existence of the blog with a link.
By creating native social content based on blog content, you are adding value directly into your audience’s feed without requiring them to click to your blog. The basic structure of the blog can be borrowed for the LinkedIn post with the following structure:
*1-2 sentence summary*
*1-2 sentence summary*
*1-2 sentence summary*
Notice that the reference to the blog post, or a link to the article is not necessary and not included in the objective. The goal is to use the blog post to inspire a native LinkedIn post.
Here’s what Amanada Natividad from Sparktoro has to say about it:
Repurpose content to distribute it.— Amanda Natividad (@amandanat) December 22, 2021
Speaking on a podcast? Turn a talking point into a blog post and link to your episode.
Got a blog post? Summarize as a LinkedIn post.
Promoting an event? Write a thread that gives away a few insights. (Like this 👇)https://t.co/BPzog7I0nS
AI writing platforms for technical marketing content
With the release of GPT-3 came an onslaught of AI-powered writing tools. Many offered the same core features and it ended up being a race to see who could gobble up the largest market share with the most effective marketing tactics and the best UI. There were some clear winners in the pure AI writing space, but there are also a number of tools and platforms that were able to implement new features thanks to GPT-3 that complemented their existing offering. These tools did not get the same amount of press as the flashy new GPT-3 writing tools because they added an incremental improvement onto a product that was already in the market.
The public response to the new AI writing tools was certainly mixed. There seems to be battle lines drawn within the ranks of loud marketers on social media. The entire spectrum of opinions can be observed from “this is the best tool that will allow me to write 10x more content” to “I would never use one of these tools, as I am a sophisticated marketer with self respect.” Of course, the most likely correct opinion is somewhere in the middle. The tools have un-ignorable value when used the right way, but the chasm between expectations and preconceived notions and what is actually experienced using the products seems to be fairly wide due to the grand marketing promises by these new AI writing front runners.
AI writing tools are good at understanding patterns and language.— Andrew Fraine (@AMFraine) August 10, 2022
They can help you explore new ideas and expand on old ones.
With all the advancement in AI-powered writing, it’s generally agreed that these tools have limited value for highly technical, niche writing. The AI language models are trained on billions of web pages from across the internet. If a huge number of pages were focused on cloud access security brokers and hybrid clouds, we would be in good shape. Unfortunately, this isn’t the case and these topics are not where AI writing tools excel.
However, there are still a ton of applications where AI-powered writing tools can help enterprise marketers and writers who are operating in a technical or niche space. Here are some details around why this is the case and what we can do to tackle these problems.
Why do AI writing platforms struggle with technical writing?
AI language platforms are trained on text from across the internet. This means food blogs, Reddit, and Youtube comment sections all play a role in the type of language that it understands. Cybersecurity blogs and websites certainly play a role in its understanding of language, but it goes without saying that this type of content does not make up the majority of the internet.
AI language models understand the fundamentals of how we use language. They don’t provide a resource for retrieving facts like an encyclopedia. They simply answer the question, “based on the words so far, what is the word that most likely comes next?”. By answering this question over and over, it is able to put together coherent phrases with some limitations.
You might be wondering, does it know information specific to my company and the products and solutions that I write about?
I wouldn’t count on that. Not out of the box.
If it does get some information right, count it as your lucky day and don’t expect it to provide that level of insight with any regularity.
The reason AI writing tools struggle with technical writing is because technical writing makes up a relatively small portion of the internet. It’s rare that a super specific term or phrase is going to come up as “the most likely word or phrase to come next” in most circumstances.
How do customized AI models help with technical writing?
AI language models do not perform well with technical and niche topics out of the box.
However, there are ways you can add contextual layers on top of the core technology that can give the AI engine an idea of the type of content and the actual facts that you want to include in your writing.
The two main levers we can pull are fine-tuning and prompt engineering. In both cases, we are providing the baseline AI language model with context around your specific niche so it can modify its answers to “what word or phrase is most likely to come next?” By providing information about your technical area of expertise in the question that you ask it to answer, or by showing it thousands or tens of thousands of examples of relevant technical content examples before you ask it a question, it becomes much smarter along the dimensions that are important to you.
The importance of strict content filtering of AI generated content
AI language models are known to produce some pretty wacky stuff at times. As mentioned before, they are not encyclopedias. If they mention a fact about a company that’s headquartered in a certain place, there’s a good chance that the company doesn’t exist, nevermind it being headquartered in that place. However, this is by design. The references to proper nouns should be viewed as placeholders for the “correct” proper nouns.
The inclusion of proper nouns in AI-generated text should be interpreted as “a proper noun is likely to go here.” This is fine as long as you have proper filtering or a search and replace plan in place.
When Automata repurposes a piece of content, the first step is to analyze the uploaded content and to pull out any proper nouns that exist. For example, in a case study that is featuring Palo Alto Networks, we first understand that Palo Alto Networks is a company mentioned in the piece. When a company name is produced by our AI engine, we only let it through our filters if it belongs to the list of company names that we found in the original document. If it seems like the AI engine came up with a new company name that we didn’t find, we throw that piece of content away and ask it to try again.
We have similar rules for places, dates, and statistics. As long as one of these entities is in the original document, it passes our filters. If the AI engine seems to have made something up, we toss it. This, along with customized fine tuning and prompt engineering, is the main difference between our approach to AI writing for niche topics and AI writing based on the out of the box functionality.
Competitive content analysis and content repurposing
Your marketing team might create a lot of content, but it’s likely that your target audience is consuming content from your competitors and other related industry leaders. By analyzing the competitive topical landscape across blogs, video, and social media you have much more data to use to inform an AI writing platform.
Here’s a snapshot on how Automata keeps track of content published in every industry and scores each piece of content as it is published.
Content repurposing and distribution for technical industries has challenges beyond the simple development of a repeatable and consistent strategy. The variety of formats and use cases of every piece of content offers both a hurdle and an opportunity to work across marketing disciplines to produce more educational content that reaches a broader target audience.
By understanding the key challenges and how tools like AI-powered content repurposing can help solve them with customized AI language models and competitive content monitoring, Cybersecurity marketing and content teams can start to confidently transfer insights in assets like ebooks, white papers, and webinars into multiple formats for more channels.