What You Need to Know About AI for Marketing

What You Need to Know About AI for Marketing

In a highly competitive B2B landscape, AI can be the strategic advantage your brand needs. Here’s everything you need to know about AI for marketing.


  • AI enables predictive analysis – the ability to look at a large set of data and predict what steps to take to reach a desired outcome.
  • Social listening powered by AI gives marketers key insights into brand perception and audience reaction.
  • When considering purchasing an AI technology for marketing, consider if it includes its own Big Data source.

When we think about artificial intelligence (AI), it’s often with a twinge of unease. Whether it’s pop culture telling us that robots will take over at their earliest opportunity, or fears of human labor being replaced with machines, AI is a complex, controversial, and even mysterious topic. But when it comes to the applications of AI for marketing, there’s actually a lot to celebrate.

It’s important for marketers not only to have a thorough understanding of the uses of AI for marketing, but to be aware of industry trends, and how to determine investment to maximize ROI.

What is AI for marketing?

While it’s not necessary for marketers to be artificial intelligence and robotics experts, it’s beneficial to have a functional understanding of the technology that enables AI for marketing. In a general sense, the term “AI” refers to the area of computer science that enables the creation of software and machines that possess what we think of as intelligence. That is, they are able to work, react, and learn without being specifically programmed for each task.

AI is enabled by data science, “the practice of organizing and analyzing massive amounts of data.” When it comes to marketing, AI can be thought of as an extension and development of marketing automation. Essentially, AI for marketing is software that collects, analyzes, and reacts to large amounts of data, with increasing levels of sophistication.

According to content intelligence expert Bart Frischknecht, of Vennli, AI for marketing can be categorized in one of two ways.

  • Recommending: This type of marketing software “predicts which action will have the most positive outcome in order to recommend a next step in a series of events.” Frischknecht describes these recommendations as “stepping stones on the way to fully automating a given task.”
  • Automating: Software that automates is a furtherance of software that recommends. For a task to be automated, it needs to be “routine and repeatable, the goal needs to be specific, and the steps to achieve that goal must follow an exact set of rules.”

Think of data as the fuel that powers AI for marketing. As we gather more and more data, and devise increasingly sophisticated analytical methods, the possibilities for intelligent automation in marketing will continue to expand.

5 examples of AI for marketing

1) Data filtering and analysis

At Fronetics, we’ve advocated for a data-driven approach to marketing since our founding. For marketers, data is the most powerful strategic weapon in your arsenal, and AI is sharpening it even further. AI software can consolidate large amounts of data, and analyze it to determine patterns and trends.

2) Social listening

Social listening, also known as social monitoring, is the process of observing and examining social media, to identify and access what is being said about your brand. Social listening gives marketers valuable market intelligence, prospect insight, tone awareness, and competitive advantage.

Current AI software lets marketers not only engage in sophisticated social monitoring, but it also enables “sentiment analysis,” automatically generating a report of the overall attitude of your audience and perception of your brand.

3) Predictive analysis

Beyond simply filtering and analyzing data, AI for marketing goes a crucial step further: predictive analysis, the practice of applying the information extracted from data sets to predict a future outcome or trend.

This revolutionary capability of AI can be used to analyze buyer purchase behavior, for example, and determine when and how to distribute certain types of content. Social media scheduling tools, for instance, use predictive analysis to suggest the optimal times to share content.

4) Audience targeting and segmentation

As B2B buyers increasingly come to expect personalization at all stages of the buyer’s journey, it can be a challenge for marketers to deliver. However, AI makes personalization possible at a large scale, drawing on data to segment and categorize audiences.

The limits of the specificity of this segmentation are determined only by the amount of data available. In other words, the more data, the more the AI software can instantly segment a contact list and deliver personalized correspondence.

5) Chatbots

One of the most ubiquitous examples of AI for marketing, chatbots are computer programs that simulate human conversation using auditory or textual methods. Chatbots communicate with buyers within a messaging app, like Facebook messenger.

3 questions to ask when considering an investment in AI for marketing

While the possibilities of AI for marketing are virtually endless, the reality for most companies is that marketing budgets are not. When considering an investment in any technology, including AI, maximizing ROI should be top of mind. Frischknecht suggests that marketers ask the following three questions when considering an investment in AI for marketing:

  • Which marketing task will this technology automate, and will doing so alleviate a significant burden for marketing staff?
  • Does purchase of the tech include its own Big Data source, or do I need to provide all the data? If the latter, do I have adequate data, and can I connect my data source to the tech?
  • What evidence exists of the tech making good recommendations or automating one of my tasks.

AI is revolutionizing marketing. Investing intelligently in these technologies can provide critical market insights, data processing capabilities, and predictive analysis.

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