Personalization in the Age of Generative AI

Generative AI (GenAI) will be the missing piece in the digital ecosystem needed to bring true 1 to 1 conversations between brands and customers/clients/patients – when it is ready and we understand how to deliver the content with safety and security. To get there, you need to start getting comfortable with the technology, understand what it can and can’t do and build out the governance of your organization to build the pipes and guardrails around the usage. This is not a “sprinkle some AI on the process and it will magically be better” situation. AI is a tool that you have to understand how to use – safely and productively.

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For years, digital marketers have been chasing the Holy Grail: delivering the most relevant message to the right person at the exact moment when they are actively engaged. Personalization has always been the objective, and it’s become increasingly critical in a crowded digital landscape where consumers expect brands to know who they are, anticipate their needs, and deliver meaningful, contextual experiences. Yet, despite advances in data-driven marketing, brands still face challenges in creating personalized content at scale.

Now, with the rise of Large Language Models (LLM’s) that drive GenAI, we’re entering a new era in a similar way that social media and mobile devices changes the landscape of digital communications. This technology has the potential to fill the gaps in traditional personalization strategies by providing scalable content generation that matches the customer’s moment of need. But to fully understand how GenAI is revolutionizing personalization, we first need to explore the essential elements of personalized marketing and how this new technology fits into the mix.

The Evolution of Personalization

Personalization is not a new concept. From the days of early email campaigns to the sophisticated segmentation techniques we see today, digital marketers have always wanted to tailor their messages to individual customers. The objective has been to understand customers, their motivations, and their behaviors, then deliver messages that feel personal, timely, and relevant so that the level of trust and response is increased.

Personalization, at its core, is about relevance—delivering messages that resonate with the customer’s immediate needs and desires. But achieving this level of relevance requires understanding not just the individual customer but also the broader context in which they make decisions. This brings us to one of the key challenges: understanding the customer and their influences.

ID Resolution and Customer Understanding in a Changing Landscape

One of the pillars of personalization is knowing who the customer is. If you don’t get identity right, nothing else matters! This goes beyond just their name, email, or phone number. In today’s digital world, a customer’s journey is increasingly complex, often involving multiple touchpoints, devices, and interactions before a decision is made. Understanding a customer’s influences is especially crucial in Business to Business (B2B) contexts, where the buying process often involves a group of decision-makers rather than a single individual which is more Business to Consumer (B2C).

ID Resolution and Management play a critical role in this process. ID resolution refers to the ability to identify and connect a customer’s actions across multiple touchpoints (both online and offline), providing a unified view of who they are and what influences their decisions. However, this capability is becoming more challenging due to increasing data privacy regulations and the phase-out of third-party cookies. Marketers can no longer rely as heavily on third-party data to track user behaviors across the web, meaning that first-party data—the data a brand collects directly from its interactions with the customer—has become even more important.

ID resolution, therefore, is evolving. Companies are increasingly investing in first-party data strategies and looking at ways to unify customer data across platforms while respecting privacy concerns. For marketers, this shift presents both a challenge and an opportunity. On the one hand, they must adapt to a world where tracking and targeting are more restricted. On the other hand, by focusing on first-party data, marketers can build deeper, more authentic relationships with customers.

Listening to Consumer Signals: The Foundation of Personalization

Another essential aspect of personalization is the ability to listen to consumer signals. These signals come in many forms, from the keywords customers use in search engines to the things they say on social media, the content they consume, and the behavior they exhibit across your digital properties.

Keywords and search behavior provide direct insights into what a customer is looking for at a specific moment. For example, a customer searching for “best fitness equipment for small spaces” is signaling not only an interest in fitness equipment but also a specific need for compact solutions. A personalized experience would deliver content that addresses this specific requirement.

Social media conversations are another rich source of insights. Customers are constantly sharing their thoughts, preferences, and feedback on platforms like Twitter, Instagram, and LinkedIn. Listening to these conversations can help marketers understand the sentiments, trends, and cultural influences that shape customer behavior.

Additionally, content consumption patterns—whether on your website, blog, or social media pages—reveal a lot about a customer’s interests and needs. If a customer frequently reads articles about sustainable fashion, they’re likely interested in brands that align with those values.

Finally, data analytics helps tie all these signals together by providing a comprehensive view of customer behavior over time. Marketers can analyze how users move through the website, engage with emails, interact with ads, or drop off from the purchasing process. These insights form the foundation for personalization efforts.

What’s important to note is that these capabilities—ID resolution, listening to consumer signals, and analyzing behavior—are known capabilities. We’ve had these tools in our marketing toolbox for years, and they have been the foundation of modern personalization strategies. So what’s been missing?

The Content Challenge: The Missing Piece in Personalization

While data and signals provide a foundation for personalization, content is where the true challenge lies. Marketers can have all the data in the world, but without the right content to deliver at the right time, personalization efforts fall flat. Traditionally, creating enough content to serve every potential customer’s unique needs has been nearly impossible.

Here’s the problem: marketers often have only a finite amount of content, designed for specific segments, personas, or customer journeys. When a customer engages with a brand in a way that doesn’t fit neatly into those predefined boxes, the brand may struggle to deliver a relevant message. This becomes even more challenging when scaling personalization efforts. The more granular and personalized you want your messaging to be, the more content you need.

Additionally, personalization isn’t just about what the customer needs at a specific moment—it’s about their relationship with the brand. A new customer may need introductory content that explains your product’s value, while a loyal customer may be looking for deeper, more advanced insights or exclusive offers. The content needs to match not only the customer’s needs but also their stage in the customer journey and their past interactions with the brand.

This is where GenAI comes in.

Generative AI: The Missing Piece for Hyper-Personalization

Generative AI represents the missing piece in the puzzle of true, scalable hyper-personalization. While traditional personalization approaches have relied on pre-designed templates and manually crafted content, GenAI can create new, personalized content on the fly, tailored to the individual’s needs, preferences, and context.

Generative AI models, such as OpenAI’s GPT-3 and Google’s Bard, are trained on vast amounts of data, enabling them to generate human-like text, images, and even video. This means that instead of relying on a limited pool of pre-made content, brands can use GenAI to create personalized messages at scale—whether that’s product descriptions, blog posts, emails, social media responses, or even personalized website experiences.

Let’s break down how GenAI can revolutionize personalization:

1. Content Generation at Scale: Imagine a retail brand with thousands of products and millions of customers. Traditionally, creating personalized product descriptions or marketing messages for each individual would be an overwhelming task. With GenAI, the brand can automatically generate unique content based on each customer’s browsing history, preferences, and past purchases. This allows for real-time personalization that feels tailored to each customer.
2. Adapting to the Customer’s Journey: Personalization isn’t just about addressing a customer’s immediate need—it’s about delivering the right content based on where they are in their journey with your brand. A first-time visitor should receive different messaging than a repeat buyer. GenAI can adapt the content dynamically based on real-time customer data, ensuring that the right message is delivered at the right moment.
3. Creating Contextual, Relevant Content: As we mentioned earlier, listening to consumer signals is key to personalization. GenAI can take those signals—such as search keywords, social media conversations, or content consumption—and generate personalized content that speaks directly to the customer’s interests. For example, if a customer has been engaging with content about sustainable living, GenAI can create personalized email campaigns that highlight eco-friendly products or initiatives.
4. Tailoring Content to Demographics and Preferences: Beyond just behavior, GenAI can generate content that takes into account a customer’s demographic information, such as their age, location, or cultural background. This allows brands to create content that resonates on a deeper, more personal level. For example, a fashion brand could use GenAI to generate product recommendations based on local trends in the customer’s region or cultural preferences.
5. Real-Time Adaptation: One of the biggest advantages of GenAI is its ability to adapt in real-time. As customers interact with a brand’s digital properties—whether they’re browsing a website, engaging with a chatbot, or scrolling through a mobile app—GenAI can generate content that responds to their actions in real time. This creates a more dynamic and engaging experience, where the content feels alive and responsive to the user’s needs.

Overcoming the Challenges of AI-Driven Personalization

While generative AI holds immense potential for personalization, it’s not without its challenges. To fully leverage the power of AI-driven personalization, brands need to be mindful of several key considerations:

1. Data Privacy: As with any data-driven strategy, personalization efforts must balance relevance with privacy. With data privacy regulations like GDPR and CCPA, brands must be transparent about how they collect and use customer data. GenAI models rely on vast amounts of data to generate personalized content, so brands need to ensure that they are handling customer information responsibly and in compliance with local laws.
2. Maintaining Brand Consistency: One of the risks of using AI to generate content is the potential loss of brand voice and consistency. While GenAI can create personalized content, marketers need to ensure that the messaging still aligns with the brand’s tone, values, and identity. This may require training the AI models on brand-specific data to ensure that the output matches the desired brand voice.
3. Quality Control: GenAI is powerful, but it’s not perfect. Marketers need to implement quality control measures to ensure that AI-generated content is accurate, relevant, and high-quality. This might involve human review, feedback loops, or real-time adjustments to ensure that the content meets brand standards.
4. Integrating AI with Existing Technologies: Implementing GenAI for personalization requires integration with a brand’s existing technology stack, including customer data platforms (CDPs), content management systems (CMS), and marketing automation tools. This requires a cohesive strategy and investment in the right infrastructure.

Read this post on Operationalizing AI

The Future of Personalization

As generative AI continues to evolve, we’re moving closer to the future of hyper-personalization. The combination of ID resolution, consumer signals, and AI-driven content generation opens up new possibilities for creating dynamic, real-time personalized experiences that go beyond what’s possible with traditional marketing methods.

The age of generative AI is here, and it’s transforming the way brands connect with their customers. By leveraging AI to generate personalized content at scale, marketers can finally deliver on the promise of true personalization—meeting each customer where they are, understanding their needs, and providing content that resonates in real time.

For digital marketers, this represents a significant opportunity. Those who embrace the power of generative AI will be able to create deeper, more meaningful connections with their customers, driving loyalty, engagement, and long-term growth. The future of personalization is no longer just about knowing who your customer is—it’s about anticipating their needs, adapting to their context, and delivering content that feels truly personal.