Over the past year, I designed a new go-to-market strategy for Bitdefender and wrote dozens of blog articles and hundreds of LinkedIn posts with the help of AI. As a Product Marketing leader, I am always racing against the clock, juggling with strategic and tactical activities. I’m keen on making sense of market data and customer insights, figuring out how to stand out in a crowded market, creating content to enable sales efforts, and planning various campaigns to announce new releases and generate sales leads.
When generative AI first came into the picture, it intrigued me. I was very familiar with the AI buzz from cybersecurity but this time the frenzy was cross-industry. Would it really make a difference, or was it just another buzzword? So, I started experimenting with AI for various product marketing tasks.
The most obvious place to start was content generation but then I expanded into other areas like research, data insights, and strategic decision-making. In this blog, I will share insights on how product marketing can be done better and more efficiently with AI support.
AI-Powered Content Generation and Personalization
AI is rapidly transforming content generation and is a hot topic from whatever angle you look at it. Efficient? YES! Effective? Yes. Ethical? It depends. If used properly, it reduces the time to deliver content pieces while preserving originality. From my experience, generative AI produces great results if closely directed through outline, writing style examples, and clear audience information. Left on autopilot it will most likely create content with little added value.
If AI is widely used for content creation, content personalization through AI is less common. There are two flavors to be discussed: static and dynamic content personalization. Static content personalization is easier to implement and involves tailoring content with AI support to match specific audience segments. This approach creates a more relevant experience for each segment but lacks the flexibility to adapt in real time to user interactions.
On the other hand, dynamic content personalization takes things a step further. It leverages AI’s ability to analyze data in real-time and adjust content accordingly. This means that the content a user sees on a webpage can change based on their current behavior, context, or even their interaction with previous content. The email that a user gets is adjusted depending on his consumption behavior. This creates a truly customized, ever-evolving conversation with your audience.
Data-Driven Customer Insights for Product Marketers with AI
Understanding customers goes far beyond content personalization. It informs everything a company does from product development to go-to-market execution. AI’s powerful pattern recognition can turn data analysis from a slow, manual process into a swift, automated one. It will also unveil hidden customer segments and preferences we hadn’t noticed before.
One of the most interesting tools I’m looking at is Customer Data Platforms (CDP). CDPs powered by AI consolidate information from all your data sources (website, CRM, social media). This creates a comprehensive 360-degree view of individual customers. Instead of guessing what different segments might like, you’ll know exactly what messaging resonates with them.
CDPs can segment your audience based on purchase history, browsing patterns, or even social media sentiment. This lets you target your next product launch with high precision, increasing your chance of attracting the right people from the very start. AI isn’t just making data analysis more efficient; it’s fundamentally changing how we understand and interact with our customers.
Enhanced GTM Strategy with AI
And finally, let’s take a step back from the tactics and look at the broad picture. Traditionally, crafting a GTM strategy involved a hefty dose of market research and educated guesses. The integration of AI into defining Go-To-Market (GTM) strategies brings precision, timing, and insights into the audience.
AI’s ability to analyze vast datasets and uncover patterns is a game-changer for enhancing GTM strategies. Instead of relying on broad market trends and historical data, we can now get granular insights into customer behavior, preferences, and emerging market opportunities. This level of detail allows for a GTM strategy that’s not just reactive, but proactive and predictive.
For example, in preparing for a new marketing initiative, I used ChatGPT and Google Bard (now Gemini) to dissect market data, trends, and competitor information to uncover potential customer segments. These insights allowed me to tailor the GTM strategy, positioning the product to meet the specific needs and crafting personalized touchpoints for each segment.
Conclusion
While this is not a comprehensive list, these three AI for Product Marketing use cases are the most fruitful and easiest to implement. Product marketing is complex, and harnessing generative AI might feel less obvious than in other marketing areas. But inaction isn’t an option. Start by experimenting, even with small tasks like content generation or market analysis.
Explore AI tools actively and discover their strengths. This hands-on experience is how you’ll unlock real competitive advantages. Find the balance between AI’s insights and your creativity. Balance data with ethical decision-making. The possibilities for those who boldly navigate this new landscape are immense. The time to seize them is now!