You’re sitting down with your team—or more tragically—facing the blank screen of your Mac, and you’re struggling to try to figure out the answer to, “how do I write about (fill in your topic) one more time, in a way my prospects care about and is interesting so it will drive more leads into my sales funnel?”
We’ve all been there.
Some of the biggest challenges we face as B2B content marketers are the time it takes to create content, the need to create a high volume of content, and the need to personalize the content for each audience segment. Data tells us it’s just going to get worse. Forty-five percent of companies are increasing their content marketing efforts within the coming year alone, but adults in the U.S. consume almost 11 hours of media per day. How much more could they possibly consume?
Having to generate more and more high-quality content to keep up with this pace will become exponentially challenging for you and your marketing team. But there’s a silver lining. Computers may be the answer to the growth explosion. Algorithmic marketing and content creation can help us address the challenges posed by content shock.
Before we dig into the role algorithmic content creation will play in the future of business, let’s define the space a little bit.
What is Content Marketing?
According to the Content Marketing Institute, “Content marketing is the marketing and business process for creating and distributing relevant and valuable content to attract, acquire, and engage a clearly defined and understood target audience – with the objective of driving profitable customer action.”
While B2B content marketing in 2016 has changed quite a bit, content is still understood as anything from social media posts, blogs, online articles, videos, ebooks, infographics, reports, to white papers and case studies.
With all of this content to be created, content manager face a number of challenges.
Content Marketers Face a Number of Challenges, Including Time and Volume Constraints
The B2B Content Marketing Spotlight Report lists the top challenges of content marketing as follows:
- Lack of Time/Bandwidth to Create Content (51%)
- Producing Enough Content Variety/Volume (50%)
- Producing Truly Engaging Content (42%)
- Measuring Content Effectiveness (38%)
- Developing Consistent Content Strategy (34%)
As a point of comparison, B2C marketers, face similar challenges:
- Producing Engaging Content (56%)
- Measuring Content Effectiveness (50%)
- Measuring the ROI of Content Marketing Program (46%)
- Producing Content Consistently (46%)
- Producing a Variety of Content (39%)
- Lack of Budget (35%)
It’s obvious that a large concern for marketers revolves around content creation, specifically in terms of volume, consistency, variety, and engagement.
If you’re struggling with any of these, is it possible that algorithmic content creation might be your solution to these challenges?
Algorithmic Content Creation is Driven by Machines, and Driven by Humans (Like You and Me)
Algorithmic content creation is the creation of content by computers. Computers utilize proprietary artificial intelligence to translate and analyze complex and abundant data points into natural language syntax. Data is both mined and analyzed by executing extremely thorough analytics algorithms to identify patterns, correlations, causalities, trends, and general meaning.
All of this means that an algorithmic content creating computer software program can take a set of golf scores, analyze them, and output a “story” based on the data. This story reads much like anything you may find in the sports pages providing game summaries. As a matter of fact, one big user of this technology is the Big Ten Network. Take a look at their content for a sample of computer-generated content.
How Algorithmic Content Creation Might Help You Meet Your B2B Content Marketing Challenges
Algorithmic content creation presents numerous opportunities for you to address some of the top content marketing challenges.
Volume and Consistency
Algorithmic content creation can handle creating massive content volume by generating streams of content without repose. Some systems can generate a story every 30 seconds while others can write hundreds of pages within the same amount of time. Compare this to the speed of a human writer and it’s a clear given who’s winning the efficiency war. Plus, if you create a workflow whereby data is automatically fed into the software, you can ensure consistent content production—even while you sleep.
Diversity, Engagement, and Segmentation
Algorithmic content creation can also serve your content generation strategy by allowing content marketers to focus on meatier conceptual topics. This in effect enhances and extends your content creation capabilities, driving diverse content quickly through the pipeline.
It can also allow you to remain focused on high priority formats, such as video, which increases conversion rates by at least 17% on average. In this way, it can free you up to direct your creative energies towards creating highly engaging content.
Speaking of engagement, although algorithmic content creation systems focus on data-based content, they intrinsically increase engagement. For example, if you’re a big tech vendor asking your website visitors to download data sheets and sell sheets, how high is your engagement when you’re asking readers to decipher and analyze rows of dry data?
However, if this data were to get fed into an algorithmic content creation system, the content you’d deliver would be meaningful with computer-authored analytics in a human-readable format. Insights between sales, markets, and manufacturers can be gleaned instantly, for example, providing much more value than a set of numbers alone.
These systems can also serve as toolkits for content marketers like you that go beyond content generation for external consumption. Some of the best practices for creating engaging content incur research and analysis of one’s readership. Automated narrative generation can assist with this by creating a “meta-story” to be used internally that provides an analysis of blog comments, product reviews, support emails, and social media activity. Using these, you can gain valuable insights and ultimately create content that directly addresses the needs, desires, and pain points of their audience — all markers of engaging content.
Algorithmic content creation can also drive diverse content creation by providing content in areas that you haven’t previously considered. For example, to create financial content you’d need to identify and hire a writer with specialized experience and knowledge. But algorithmic content creation systems can natively derive meaning from financial data. This allows marketers to explore different topics that may be of relevance to their audience without needing a specialized background.
Which Brings us to…Content Segmentation
Creating content that is diverse can also be done by leveraging the different content types that these systems can generate. Everything you can analyze from data can be transformed into a story. Examples of this include a product description, a real estate listing, or even an analysis of Tweets. You can easily feed customer communication data to these systems and auto-generate testimonials. You can also create content that varies in tone, length, and style to address different audience needs, another way of segmenting and personalizing content.
Strategy and Measurement
Lastly, freeing up the creation process can help you focus on the other highly challenging items you face: strategy and measurement. By dedicating time to designing a solid strategy and implementing effective measurement practices, content marketers can iteratively identify their most effective content, focus on this type of content, amplify it, and ultimately increase ROI. These activities are just as important as content creation itself when it comes to increasing the effectiveness of a content marketing campaign.
Automated narrative systems can also be used to assist in measurement by creating reports brimming with insights, analysis, and advice based on collected data. Everyone wants to “see the data” and having access to data that generated content AND the results of the activity will be a powerful tool for content marketers in the future.
There are algorithmic content products in the space that can help us address our core challenges—let’s take a quick look at some of these.
Algorithmic Content Marketing Tools
We’re early in the stages of leveraging AI, machine learning, and deep data for automating content creation, so there are only a few algorithmic content creation systems on the market and some of them have pretty specialized focuses.
The most prolific of these is Narrative Science’s Quill. Quill can take any data set and run extremely thorough analytics against it to output a story. It can write about weather forecasts, the stock market, and sports scores, among many others. The benefit of Quill above other products is that it isn’t template-driven. It’s significantly more advanced than systems that use static field value replacement. As I mentioned earlier, Quill is one system you can use as a tool for measurement. See a sample solution overview of the software’s utility for a marketing campaign here.
Automated Insight’s Wordsmith performs similarly to Quill. On its website, you can even see samples of its market data converted into stories. It addresses use cases for e-commerce, media, real estate, financial, marketing, and business intelligence. To see content created by Wordsmith take a look at its In Action page.
If you’re like many of our international tech clients, you’ll need a multilingual system like Yseop’s Compose. It offers similar capabilities as Wordsmith and Quill but also promises to deliver content “at the speed of thousands of pages per second.” Yseop also provides a lightweight plug-in to analyze data in Qlik and Excel. Like Quill, you can also utilize Compose as a tool to generate marketing insights. See their use case page for this here.
Will Algorithmic Content Creation Replace Humans in Content Marketing?
If you’re a content marketer with pain points around creating content, consider the benefits of algorithmic content creation. I, like many in industries where tech innovation seems to be threatening, don’t believe that algorithmic content creation will replace us, but help us deliver better quality, work more efficiently, and help smaller teams scale the work they’re already doing.
If cost is a concern, Quill, Wordsmith and Compose all have variable pricing structures with prices starting at around the same cost as a full-time salary for an in-house writer. This represents enormous savings when you consider how efficient algorithmic content creation is.
Using machines to generate your content may seem like a dream of the future. But the reality is that large corporations and content networks have been leveraging them for years. According to Narrative Science’s Kristian Hammond, 90% of all news stories will be generated by computers within 15 years. Why not get ahead of this future trend?
Would you be willing to use these systems for your content needs? What other benefits do you foresee in using algorithmic content creation? Let us know. We’d love to hear from you.