Artificial Intelligence

Why Your MarTech Stack Needs Content Intelligence

2023 is bringing economic volatility that has most B2B marketers scratching their heads. Sellers are having a hard time meeting their quotas, while B2B go-to-market teams are still depending on marketing tactics that are quickly going out of style—like cold calling and blanket email marketing.

AI, and more specifically content intelligence, is transforming B2B marketing by helping businesses offer highly personalized content experiences that help generate better results in the forms of leads and closed deals. The goal of using AI in marketing should be to give every visitor a highly personalized experience, while providing the ability for markets to quickly activate highly-personalized marketing tactics to an engaged audience.

Not just a buzzword: AI is a necessary part of a martech stack

According to Gartner’s report Hype Cycle for Digital Advertising, there are four emerging technologies that will have a transformational impact on digital advertising and digital channels as a whole.

In an era of hyper-personalization, one technology that resonates with B2B businesses is influence engineering, or influence AI. Gartner defines it as “the production and deployment of algorithms designed to automate elements of digital experience that guide user choices at scale by learning and applying techniques of behavioral science”.  From that, we can describe AI Marketing as a series of processes and algorithms that provide insights into human behavior, help marketers better understand their customers and prospects’ needs and accelerate consumer choices.

Leveraging AI in marketing involves collecting and analyzing large datasets, highlighting engagement patterns, designing effective decision nudges and so much more. Marketers can then leverage this now uncovered knowledge to automate decisions or facilitate decision-making, trigger engagement and activate retention strategies. The best data to use as inputs for this kind of exercise is any behavioral data around what the prospect is doing. In the case of B2B marketing, AI can help with:


Providing a personalized experience isn’t just about adding a customer’s name to an email any longer. It’s about delivering the right content to the right people at the right time. In recent years, personalization extends to providing data-backed content journeys that consider the actions, preferences, and intent. Features like self-optimizing content recommendations provide a highly-personalized content journey for your website visitors, in real time.

Content Generation:

This refers to creating relevant content for specific audiences by analyzing past interactions on a website. Using machine learning algorithms can help marketers develop content for different audiences (or even just for individual segments) more efficiently. Of course, AI-generated content still requires a content marketer’s touch but it’s much easier to optimize a draft than to get started from scratch.


Retargeting visitors with relevant advertising is another way AI can help create targeted messaging, enable dynamic segments and select the best ad placements based on performance and conversion metrics.


AI can help analyze high-volume datasets using different approaches:

  • Descriptive analytics: to understand what has happened
  • Predictive analytics: to hypothesize what could happen
  • Prescriptive analytics: to highlight recommended actions towards an expected outcome.

These insights help marketers understand the levers they can pull to further personalize and optimize their customers’ buyer journeys.

Customer spotlight: Apptio leverages AI marketing to increase content engagement

Apptio, a software company focused on delivering ROI to business customers, had a lot of content but they were having difficulty identifying their audience and maximizing content engagement. By leveraging PathFactory, the team grouped related content into content tracks that were shared with prospects as part of nurture campaigns. Using PathFactory’s AI-powered recommendation engine, buyers now receive next-best content recommendations based on what they consumed previously, and where the buyer is in their journey. The result? Increased time spent consuming content, and improved funnel velocity.

By moving from static content recommendations to AI-powered recommendations, higher-intent visitors surfaced quicker, so the team could focus on buyers with the highest intent to purchase

Looking ahead: buyers expect personalized content experiences

Personalization is consistently named the main priority for the year ahead, and we can see why: prospects expect more from vendors and they want the least amount of friction when they’re searching for the information they need to make a purchasing decision. With traditional B2B sales tactics like cold calls and email cadences losing steam, B2B businesses need to get creative with how they reach their buyers, while ensuring that the buying experience feels tailored specifically to them.