Content may be king, but it’s proliferating at a pace that makes it extremely difficult to tell the tinpot from the titans. So much so that a 2017 Forrester blog post opened with a riddle: What's the difference between your content and mashed potatoes?
The answer, of course, was Nothing – we continue to churn out content only for it to be buried like more mash under a mountain of mash.
Today, content is being created and deployed at an increasingly exponential scale. However, capabilities required to design, target, optimize and manage content at such a scale have not kept pace. As a result, content performance is flagging and will likely have a knock-on effect on content budgets.
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But content intelligence can change all that, Forrester says, by helping content understand itself in terms of objectives, relevance, engagement and ROI.
Content intelligence is an emergent blend of several advanced technologies – such as machine learning (ML), artificial intelligence (AI), natural language processing (NLP) and big data – that facilitates a data-driven, insight-led approach to content marketing and management.
It represents the next generation of analytics that converts content data into insights about the effectiveness and value of a company’s content. Content intelligence applies advanced predictive and prescriptive analytics to the practice of content management and is designed to address some of the critical capability gaps that exist in our current approach.
As with almost every business function in the digital age, content management has not been immune to the hype of technologies such as AI.
These technologies have already managed to shake up if not completely disrupt content management at its source – content generation.
Several news outlets, including The Associated Press, USA Today and The Washington Post, have been leveraging intelligent technologies for a few years now to automate reporting on financial earnings, election results, sporting events and even produce short form video content.
But, as a Financial Times analysis diagnosed, these algorithms are still incapable of maintaining an overarching narrative even though they are quite good at creating coherent text.
The rest of the content management value chain, however, is ready for some deep and comprehensive intelligent transformation. Content marketing functions, such as content analysis, keyword selection, data-driven content creation, optimization, personalization, and A/B testing, currently dominate seven of the top ten use cases for AI in marketing.
So, content intelligence arrives at an opportune time when businesses are looking for intelligent solutions to their content marketing and management problems.
Content intelligence solutions deliver an accessible, curated and orchestrated set of intelligent services that allow businesses to harness the power of data-driven digital technologies without getting lost in the weeds. These solutions focus on all aspects of content strategy, but, in their essence, all include three key elements:
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1. COLLECT: Content intelligence is powered by a diverse set of data points from consumer inquiries and feedback, sentiment analysis, channel analytics, web analytics and others. So, the focus has to be on deploying an assortment of tools that can aggregate all types of content related data.
2. ANALYZE & INTERPRET: This requires a combination of people and technology in order to be effective. Tough technology can help streamline and automate the process of analysis, it still has to be rooted in the vision, objectives, and expected outcomes defined by a company’s content strategy.
Ideally, every business already has some form of review process to assess the impact of their content. Content intelligence can help further streamline and automate the analysis and review process in content management.
3. INSIGHT & ACTION: This element is about converting analysis into meaning and insights that can drive decision-making across the entire content management value chain. Understanding how audiences engage with content, how content performs across channels, etc. can help companies refocus and fine-tune their entire approach to content management.
Though this three-element classification may seem rather fundamental, the current breed of content intelligence solutions offers an impressive array of transformative applications. Here, then, are a few use cases areas where content intelligence can deliver measurable improvements in a company’s approach to content management.
This is the first step towards building an intelligent content strategy and technologies like AI and ML can help publishers build a more nuanced understanding of what piques audience interests. Moreover, it is no longer about designing content for an aggregated audience profile or even for broad persona subsets.
Rather, it is about leveraging AI and ML to generate “segment of one” insights. Some content intelligence platforms can recognize over a million distinct audience interests and deliver interest graphs at the level of individual users.
This granular approach to audience segmentation allows marketers to deliver more hyper-personalized content – in terms of topic and channel – thereby enhancing customer experience as well as content performance.
Identifying Content that Works
When it comes to topic selection, most content decisions are generally based on gut instincts or informed guesstimates. Content intelligence can take the guesswork out of content strategy by pinpointing content types and topics that connect with the target audience.
Content intelligence solutions can analyze content by topic and rank topics based on a Current Impact score. This makes it easier to focus content strategy on topics that deliver the required engagement.
(Image source: ceralytics.com)
The sophistication of modern content intelligence platforms also enables them to suggest topics with individual Potential Impact scores that could trend in the near future. Companies can even measure content performance by author, topic and audience segment so that publishers can optimize across these three variables to maximize impact.
(Image source: ceralytics.com)
The channels on which content is delivered have a huge influence on how the audience engages with and consumes the content. This is a key metric for content performance that is often overlooked. Content intelligence platforms allow marketers to study the correlation between content performance and channel so that content can be tailored to individual channels to fit the expectations of that specific audience segment.
With the power of ML and AI, content intelligence platforms can now automatically calculate not only the right platform but also the right time to post content on the platform for maximum audience engagement.
Content Engagement eventually has to translate into a predetermined business outcome. And there are two key levers for making this happen.
The first is to understand the stage of the buying journey that each customer is at and then delivering content designed to appeal to the needs and behaviours of the audience in that particular stage.
Content intelligence facilitates this by providing a granular segment of one profile that allows content to be fine-tuned to every stage of the purchase funnel.
The second is to have the proper conversion funnel analysis tools and metrics that marketers can use to determine if the content is successful in transitioning audiences across the purchase funnel to conversion.
Content intelligence platforms can track conversions by topic, by channel, and by business objective. Businesses can now continuously optimize their content strategy by combining the right mix of topic, channel, and preferred business outcome for peak marketing effectiveness.
In a world overrun with content, competitive intelligence can often be a critical tool for marketers to differentiate their content strategy from the market. But competitive intelligence has traditionally been a painstaking, time-consuming and predominantly manual task.
Today, some of the most advanced content intelligence platforms feature built-in tools for competitive intelligence that automate the process of not only tracking activity but the performance of competitors’ content strategy.
In addition, there are specialized competitive intelligence and analysis platforms that can be deployed just as effectively to track the competition’s content-related activities. These platforms automatically track hundreds of data points, including content and campaigns launched, so that marketers can then adapt and fine-tune their campaign for competitive advantage.
The growing demand for market intelligence and audience interest analysis is expected to drive the global content intelligence market from USD 485 million in 2019 to USD 1.9 billion by 2024, a CAGR of just over 32%.
The use cases mentioned here represent just a fraction of the potential represented by the still nascent practice of content intelligence.
Today, there are already several intelligent content solutions, from integrated platforms offering end-to-end content management tools to specialized services that focus on specific functions such as keyword tracking and topic research. But content intelligence is still a large enterprise phenomenon.
With the cloud-based content intelligence segment estimated to grow the fastest, more and more companies will soon be able to tap into the next generation of content analytics, intelligence and automation.
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