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Artificial Intelligence

Who Benefits Most from Content Intelligence?

Content intelligence benefits anyone involved in strategizing, creating, activating, or understanding content and its performance. Here’s why.

Marketers have a lot of information to keep tabs on. Whether you’re a small-ish team that regularly puts out a respectable amount of content, or a global marketing organization — spread across multiple divisions, geographies, and languages — creating and updating thousands of assets each month, there’s just too much content and too many important attributes for one person (or even an entire team) to track and use to deliver the kinds of personalized journeys your buyers and customers expect. In real time. While understanding what’s working most effectively and efficiently for the business.

Why can’t I just track this in a spreadsheet? Can’t I use my CMS to tag my content, like I’ve always done? Why do I need so much data anyway?

These types of questions are a common symptom of “we don’t yet know what we don’t know.” The marketing analytics solutions commonly in use at most companies simply aren’t equipped to do this without a superhuman amount of manual intervention. They’re not designed to understand and analyze all the complexities of content and how it’s used across the buyer’s journey, either.

Content intelligence integrates deep data and insights across the entire content value chain, enabling marketers to make decisions based on real engagement and performance, rather than guesswork and presumptions.

  • Ideation: Marketers can build and execute a unified content strategy across all channels and touchpoints. Because they already know what’s working (and what isn’t), marketers can easily determine if and when new assets are needed.
  • Production and management: Workflows are enhanced through automation and streamlined, centralized content engines, rather than relying on siloed, disjointed, and manual processes.
  • Activation and utilization: Buyers can self-direct relevant, contextualized content experiences to meet their evolving informational needs on the path to purchase. AI-powered content recommendations are informed by individual consumption and engagement behavior, rather than generalized identifiers pre-determined by marketers, such as industry or job title.
  • Analytics and reporting: Marketers have the data they need to truly form a complete picture of every content asset at their disposal.

But who is responsible for content intelligence? And who stands to benefit from it? The short answer is: anyone involved in strategizing, creating, activating, or understanding the performance of content, the associated data, and the resulting personalization capabilities in their organization. When you think about it, that’s a lot of people!

  • CMO: The chief marketer needs content intelligence to understand the lay of the land from day one and quickly make decisions about what to stop, start, and continue doing. CMOs play a key role in leading their companies’ customer experience, digital transformation, and data strategies, and have to work fast to turn brand awareness into revenue. They also need to bring real-time go-to-market data back to the executive team to influence overall business strategy decisions. This content intelligence can unlock valuable insights they don’t have access to today and fuel the digital experiences they need their teams to create at scale.
  • Digital marketer: It’s impossible to create all the possible paths visitors might take through a corporate website to begin or continue their buying journey. When applied to the website, content intelligence can help digital marketers gain a clearer understanding of the content they have and how it’s performing, while automatically optimizing visitor journeys through personalized content recommendations and engagement-based form strategies that convert unknown visitors into known buyers and customers.
  • Content marketer: Marketers involved in content strategy and creation can reduce the manual effort involved in analyzing their existing content across a number of important dimensions, including topic, persona, funnel stage, age, accessibility, etc. They can also gain much-needed access to insights around content engagement and performance, so they can understand their team’s impact on the business and eliminate redundant content production.
  • Demand generation: Instead of guessing which content might resonate with which audiences on which channels, demand gen marketers can use content intelligence to inform and even automate their content activation choices, so prospects self-nurture as quickly as possible through AI-powered content recommendations. They can also use content engagement data to reinforce lead scoring models, improve alignment with sales, optimize channel performance, and tweak nurture programs.
  • Account-based marketer: As Forrester said, “there’s no personalization without content intelligence.” And that goes for account-level personalization as well. Account-based marketers need to know what content is trending in various industries or segments to fuel one-to-many and one-to-few campaigns, and what’s resonating with individual accounts to ensure their one-to-one campaigns have the most impact. Content intelligence also allows marketers to show visitors what content is trending at their own account, ensuring that relevant content is socialized at an account level, even before they begin engaging with a sales rep. Content engagement data can also be strong, early signals that an ABM strategy is working, as engaged accounts will spend more time consuming content and view more assets each session.
  • Marketing operations: Content intelligence data can be integrated by marketing operations teams across many elements of the martech stack to power more relevant targeting, personalization, and scoring. Marketing ops managers need to understand all the different dimensions of content engagement data and make recommendations for where they can improve existing or establish new data-driven automation and insights.
  • Sales: Sales reps from business development reps to account executives need to send a huge volume of content to prospects — one-to-one via email, through LinkedIn, and even by SMS. It’s not always easy for them to determine what to send to whom, when, or even which URL to use. Content intelligence can recommend the most relevant content to send to specific buyers or accounts and automatically generate a trackable link they can send to measure content engagement. Sales reps can also benefit from content intelligence when it is effectively visualized for them in the CRM, helping them to identify which people and accounts to prioritize based on engagement.

This list isn’t exhaustive and could include a number of other roles throughout the organization, including customer marketing, partner/channel marketing, data/analytics teams, revenue/sales operations. Content intelligence champions can use the data to break down silos across their business and bring a smarter, more data-driven approach to virtually every function in the revenue organization, leading to greater sales and marketing alignment and a better customer experience.

Perhaps most importantly, however, content intelligence benefits buyers. It enables organizations to offer a seamless, self-directed content experience that supports prospects with the information they need to help make a purchasing decision, rather than simply forcing them through a sales funnel. According to Salesforce, 83% of B2B buyers expect companies to use new technologies to create better experiences, and 77% are open to the use of AI. It’s up to marketers to take the lead and implement content intelligence to truly deliver to their prospects at the next level.

Marketers can use content intelligence data to:

  • Figure out what’s working (and produce more of it)
  • Elevate content that’s already effective
  • Stop producing content that doesn’t move the needle
  • Eliminate redundant content
  • Deliver relevant experiences at scale

Not sure where to begin? Here are three ways you can initiate your organization’s content intelligence journey and prepare to implement a more significant transformation down the road. Remember, the most important thing is that you don’t go it alone. If you are the content intelligence champion at your company, recruit help from your colleagues in the roles and functions mentioned in Part 4 of this guide. Content intelligence is not just a content initiative, or even just a marketing initiative. It requires breaking down silos and bringing the whole team together to discuss content utilization, activation, performance, and data.

Step 1: Stop centering the product and service over the buyer’s experience

Before diving into content intelligence, it’s important to “unlearn” some of the more traditional practices surrounding B2B content, including:

  • Using only rules-based segmentation to deliver content to prospects and customers by account, industry, vertical, company size, geography, product lines, or job title, rather than content consumption, engagement, and topics of interest
  • Hard-gating all content to capture leads, which prioritizes the marketer’s informational needs over the customer’s experience and introduces unnecessary friction into the buyer’s journey
  • Operating in silos, with each business unit responsible for its own content planning, production, activation, and analytics
  • Treating your website as a static publishing platform, rather than a dynamic and unique experience for every prospect or account

These are all symptoms of the same mindset, which centers the product or service over the buyer’s experience.

Step 2: Start asking the right content and data questions

To get the ball rolling, start thinking about the important content and data questions you’ll eventually need to answer, like:

  • What content data do you currently have available?
  • What is the quality of your existing content data?
  • How is your content data being used? What metrics are you using to measure performance of individual content assets and their impact on pipeline and revenue?
  • How much content do you have at your disposal?
  • When was the last time it was updated?
  • How do you currently organize your content? Do you have any taxonomy or metadata in place? If so, what kind?
  • What topics do you cover extensively? What topics aren’t as important? Do you need to ramp up or scale down on certain subjects?
  • What do you know about your prospects, beyond industry or job title?
  • Which content is most effective at each stage of the buyer’s journey?
  • When current customers are highly engaged, which content do they consume the most?
  • Which content do customers consume when they’re about to renew or upsell?
  • Looking at your largest closed opportunities over the past quarter, which content assets did the prospects engage with on their path to purchase? In what order did they view them? Did they spend the same amount of time on each asset, or did some prompt higher engagement times than others? Which asset did they spend the most time on? Were any assets viewed multiple times?
  • Which content asset has led to the highest number of binge sessions among prospects?
  • Which individual content asset has had the greatest revenue influence so far this month? What about over the previous quarter or year? What’s your single most valuable asset of all time?

Even if you can’t answer every question right away, they will help identify the biggest gaps in your existing martech stack and strategic capabilities. From there, it will be easier to build a digital transformation roadmap and begin future-proofing your content engine by integrating content intelligence.

Step 3: Start small… then scale

It’s not realistic for every organization to start from scratch or overhaul your entire content engine right away, and that’s okay. We get it. Start small by identifying your priorities:

  • What data are you currently collecting to realize your goals and objectives?
  • What content do you have in production? What’s the current lay of the land?
  • Are there any short-term wins to help you close the data gap or automate a step in your content analysis, production, or distribution process?
  • Can you make small improvements within a particular business unit, learn best practices, and then implement globally?
  • How can you provide personalized content recommendations to every visitor at scale?

Once you’ve chosen where and what to focus on, you can begin introducing content intelligence in manageable, measurable stages. Treat it as an experiment, create proof, and share your results to gain universal and executive buy-in. From there, you’ll be able to truly implement digital transformation by scaling content intelligence across your entire organization and future-proofing your content engine.

But how does content intelligence actually work?

Enabled by AI, content intelligence captures rich content and engagement data at every touch point, delivering hyper-relevant, personalized content experiences which adapt to visitor behavior in real-time. It’s a huge topic, which is why we’ve created the first-ever content intelligence guide for B2B marketers, showing you exactly how it works and how to use it to drive rapid growth.

Rachel Cravit
Written By
Rachel Cravit
Rachel is the Content Marketing Manager at PathFactory. She has extensive experience as a content writer and marketer, especially in the B2B tech space. Rachel is also a dog mom and design enthusiast.

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