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We could all be forgiven for briefly believing that artificial intelligence (AI) and machine learning (ML) in marketing are only about generating content. The end of 2022 drowned in the splash from generative AI such as ChatGPT, Jasper.ai and others for text, and DALL-E, Midjourney, and others for images. But it’s a new year. Time to move past the awe of the previous few months to take a level-headed look at the many ways AI shows up in marketing technology to generate broad impact.
Where can you find AI in marketing technology?
Pretty much everywhere.
MarTech platform providers took a bet that marketers would be some of the earliest and most active adopters of AI in the enterprise. As a result, they have been embedding AI and ML capabilities into marketing technology platforms to enhance features and functionality.
That doesn’t mean your organization uses AI broadly and deeply in marketing. It does mean you probably have more AI functionality available than you realized—even in your content marketing technology.
For example, if your company uses an email-serving platform or service provider, chances are the platform has AI-based predictive analytics to identify which recipients will open a message based on its subject, day of send, and time of send, among other factors.
Advertising is another area of marketing where predictive AI has become quietly embedded into the core AdTech solutions that enable companies to optimize ad placement with more effective targeting and bidding.
Personalized product offers, subject line optimization, and customer data preparation and analysis—all of these applications depend on AI in the relevant layer of marketing technology, and they are all becoming mainstream in marketing.
Let’s look a bit deeper into where they are showing up.
AI Helps Predict a Customer’s Next Action
You’re probably familiar with sentiment analysis. This is the practice of understanding consumer attitudes and opinions using technology. It works with natural language processing (NLP) AI that “reads” or “listens” to what consumers say in product reviews, in conversations with customer support personnel, on social media, and in survey responses. It then analyzes those conversations to understand consumer attitudes about the brand.
The next step after understanding what the customer thinks today is to predict where those sentiments will lead them tomorrow.
Using predictive analytics from AI, businesses can use what they learn from sentiment analysis to broadly improve the customer experience. Examples include:
- Enhance customer service processes to solve the customer’s problem before they have it.
- Adapt product innovation roadmaps to pilot functions that AI predicts will be in high demand.
Content marketers, for our part, can use customer sentiment to inform content plans and ad spending in a way that prioritizes the channels, content formats, and subjects that AI predicts will be top-of-mind for customers in the near future.
Understanding customer attitudes and intentions doesn’t just apply to B2C companies, either. B2B companies can also access customer sentiment data for their target market. The information can help you understand your market reputation (or if anyone knows about you at all). It can also analyze the behavior of prospects in your target market to predict when they have started looking for a solution like yours.
Marketers today have options in the MarTech solutions they use to identify leads and understand sentiment. Example marketing technologies include:
- B2B “account-based marketing” platforms like 6sense and Demandbase include predictive intelligence that allows sales and marketing to deliver a focused and personalized sales experience for a selected set of targeted enterprise accounts.
- Marketing automation platforms like Adobe’s Marketo, HubSpot, Oracle Eloqua, or Salesforce also include embedded AI analytics capabilities, including predictive “lead scoring.” Think of this as a quality rating that allows you to predict how likely a given lead will become a customer in your market (so sales can focus more human activities on high-potentials and less on ambivalent prospects).
Personalization Will Reach New Heights With AI
As a content person, I would be remiss if I didn’t mention how the AI embedded in content marketing technologies can also help get the right content in front of the right customer to improve the customer experience—in other words, to personalize it.
The most straightforward application of AI for optimizing the customer experience is content personalization. This capability uses the data and information a company has collected about their customer or prospect to predict what content they would find most valuable—and deliver it.
Content personalization solutions include PathFactory and On24. There is also content personalization functionality embedded in platforms like HubSpot and Demandbase.
The Takeaway: Don’t Limit Your AI Experiments
Research by Deloitte and McKinsey finds that organizations taking the lead in AI value creation adopt a strategic and comprehensive approach. It’s not just a point solution for them. And while content marketers can only control the AI deployed in their area of influence, be aware of AI’s influence across the entire customer journey. It may help you fine-tune your content approach and get key assets in front of the right prospects.
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