Transforming Data into Dialogue

Customers expect more than just their first name in an email to show a piece of content is intended exclusively for them—they want brands to understand their needs. That’s where hyper-personalization comes in.

Hyper-personalization goes beyond historical data and customer segmentation to leverage real-time data for tailored messaging at a personal level. Robust software solutions (i.e., behavioral analytics, customer data platform, and AI algorithms) provide you intent insights, enabling engaging conversations.

What is Hyper-personalization?

Deloitte¹ defines “hyper-personalization” as, “Creating custom and targeted experiences through the use of data, analytics, AI, and automation.” In other words, it’s about delivering the right message to the right person at the right time, every time.

Traditional personalization uses historical, static data applied to a customer segment. Hyper-personalization bases communication on historical and real-time data, which powers AI-driven models to generate detailed, dynamic customer profiles at an individual level.

Consider this example: A bank may email all customers in a certain income bracket to promote a premium credit card, which is traditional personalization. However, when the bank uses AI to analyze customers’ real-time spending habits and the intent behind their digital interactions to present a tailored credit card, such as a travel rewards card for a customer who often books flights, it has achieved hyper-personalization.

Why Hyper-personalization Matters Now

Consumers ignore most of the marketing messages flooding them from all directions. Traditional personalization is no longer enough. Customers expect you to understand the intent driving their behaviors, anticipate their needs, and engage meaningfully. Hyper-personalization is a key differentiator for building stronger relationships and driving conversions. Key factors contributing to the urgency for brands to embrace this shift include:

• Rising customer expectations: When customers share their data, they expect you to use it wisely. Nearly three-quarters (71%)² of consumers want a personalized experience from brands, and they spend 37% more³ with brands that meet this expectation.
• Data availability and AI: The explosion of customer data and the rise of AI mean brands must combine the two to extract meaningful intent insights used to create value.
• Competitive pressure: Brands must stand out amidst the digital noise with a more engaging customer experience. Hyper-personalized interactions deliver value, enjoyment, and convenience—all of which are buying experiences rated favorably⁴ by US consumers—can achieve this.

You need a complete picture of each customer to make hyper-personalization work. You can connect and reconcile information from multiple sources to build a comprehensive customer profile. The next step is mastering data sources and identity resolution to ensure every engagement is relevant and integrated across channels.

Data Foundation

Effective hyper-personalization starts with a strong data foundation. To create meaningful, individualized experiences, brands must build robust customer profiles by gathering data from multiple sources, including:

• Zero-party data: Explicitly shared by customers to the brand
• First-party data: Behavioral, transactional, and engagement insights collected by the brand
• Second-party data: Shared from trusted partners
• Third-party data: Gathered from external sources

Each of these data types plays a role in shaping a complete picture of the customer.

However, collecting data isn’t enough—you must unify it to recognize individuals across engagements. Identity resolution, enabled by a customer data platform, connects fragmented data, ensuring a single, accurate view of each customer rather than just broad audience segments. Customer data platforms (CDPs) play a key role in real-time personalization by working with AI and marketing automation tools, such as HCL Unica, to tailor experiences.

Having the correct data is just the beginning. You need to understand their digital body language to engage with customers meaningfully.

Digital Body Language: The Missing Link in Customer Conversations

Digital body language reads between the lines of customer interactions, revealing intent and context in real-time. It offers deep user behavior information to refine real-time communication and improve customer experience. Through digital body language, you can identify moments of struggle or friction in the journey and moments of magic where engagement moves toward conversion.

AI assists brands in interpreting digital body language instantly. When the customer data platform (CDP) receives behavioral signals, AI analyzes real-time and historical data to identify the customer’s intent. The platform uses these insights to provide recommendations for the next best action. For instance, the platform sends a customer an email with tips about documents required for mortgage prequalification while the customer is researching mortgage interest rates.

With so much data to process, AI is essential for real-time personalization at scale. Even with the power of AI, hyper-personalized communication is not without its challenges.

Overcoming Challenges in Hyper-personalization

There are three primary challenges to achieving hyper-personalization resonating with customers. Understanding these challenges can guide you towards formulating a plan to address them before they hinder your outcomes.

• Data privacy & compliance: As privacy regulations, such as GDPR and CCPA, continue to change to keep pace with technology, you must have best practices for personalization efforts to adhere to laws.
• Avoiding over-personalization: There’s a fine line between hyper-personalized and invasive. Being transparent with customers about data collection and use will give back some control over the messaging they receive.
• Ensuring data quality: AI is only as effective as the quality and accuracy of the data it processes. To meet your hyper-personalization objectives, collect only necessary customer data and feed it to your AI and machine learning engine.

Addressing these potential challenges will ensure you stay compliant and delight your customers.

Future of Customer Conversations

As advancements in AI, machine learning, and automation continue, brands will better understand customer intent. Conversational AI and real-time analytics will be the foundation for dynamic, context-aware interactions. The rapid pace of technological change will require you to stay agile and continuously refine your personalization strategies to align with changing consumer expectations.

The future of customer engagement belongs to brands who can balance AI-driven personalization with transparency and trust. Customers are more willing to engage with brands who respect their data and provide clear value in return. You can turn real-time insights into stronger customer relationships with the right platform. That’s where a solution like HCL Unica comes in—it helps you refine your strategies and build lasting loyalty.

References

1. https://www2.deloitte.com/content/dam/Deloitte/ca/Documents/deloitte-analytics/ca-en-omnia-ai-marketing-pov-fin-jun24-aoda.pdf
2. https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/unlocking-the-next-frontier-of-personalized-marketing
3. https://www.deloittedigital.com/us/en/insights/research/personalizing-growth.html
4. https://www.bcg.com/publications/2024/what-consumers-want-from-personalization