Data is a true cornerstone of any high-performing strategy, whether it is provided directly by customers or inferred from their behavior. Indeed, two main categories can be distinguished: explicit data and implicit data.
Being able to distinguish between these two types of data, understanding their complementary roles, and knowing how to use them in a balanced way enables you to build more relevant campaigns, deliver personalized experiences, and ultimately, optimize loyalty and conversion.
This article provides a complete overview to help you decode both dimensions of data and turn them into true growth levers.
What Is Explicit Data?
As the name suggests, explicit data refers to information that customers voluntarily and consciously share. It comes directly from their willingness to disclose details about themselves, their profile, or their expectations.
Concrete examples of explicit data:
- Information provided in a registration form: name, email address, age, occupation.
- Preferences selected in a customer account (communication channels, interests).
- Responses to surveys, satisfaction questionnaires, or market studies.
- Reviews or ratings left on a product or service.
- Information shared during a direct interaction with customer service.
Strengths of explicit data:
- Clarity: data is expressed without ambiguity.
- Declared reliability: it reflects the customer’s stated perception or intent.
- Immediate usability: can be directly leveraged for segmentation or personalization.
Example: if a customer ticks a box indicating they prefer SMS over email, the company knows exactly which channel to prioritize for future communication.
Limitations of explicit data:
- Sometimes incomplete: customers only share what they find relevant or what is asked.
- Potentially biased: shaped by a desire to look better, by lack of time, or by distrust.
- Requires active effort: filling out forms, updating profiles, responding to surveys—actions many customers will not take.
Explicit data is highly valuable but insufficient if not complemented by other sources.
What Is Implicit Data?
Unlike explicit data, implicit data isn’t directly given by the customer. Instead, it’s observed and inferred from their behavior. These weak signals, when correctly analyzed, reveal a great deal about real preferences.
Examples of implicit data:
- Pages visited on a website.
- Time spent on a page or app.
- Items added to a cart (even without purchase).
- Frequency of logins or service usage.
- Interactions with marketing campaigns: email opens, link clicks, social shares.
- Navigation path before a purchase or cancellation.
Strengths of implicit data:
- Behavioral realism: reflects what customers actually do, not just what they say.
- Dynamic richness: constantly renewed with every interaction.
- Contextual precision: reveals not just who the customer is, but also their mindset at the time of action.
Example: a user who repeatedly visits the same product page without buying sends a strong implicit signal of interest—or hesitation.
Limitations of implicit data:
- Requires fine analysis to interpret correctly.
- Risk of misinterpretation: visiting a page doesn’t always mean genuine interest.
- Privacy and compliance concerns: often collected without users being fully aware (e.g., GDPR in Europe).
Implicit data is a goldmine, but it demands careful analysis, contextualization, and caution to extract meaningful insights.
Explicit and Implicit Data: A Strategic Complementarity
Rather than opposing them, the real challenge is understanding how these two data types complement one another.
- Explicit data gives a declared, conscious view of the customer.
- Implicit data provides a real, dynamic picture of behavior.
One without the other leaves blind spots. Together, they create a fuller, more reliable customer profile.
Concrete example:
A client may declare (explicit) an interest in sports and subscribe to a newsletter. But by observing behavior (implicit), you might notice they mostly click on running content and ignore football or tennis articles.
This cross-analysis enables refined personalization: highlight running-related content first, while keeping diversification options open.
How to Collect and Leverage Both Types of Data
1. Create Engaging Explicit Data Touchpoints
- Use short, interactive surveys rather than long forms.
- Offer benefits in exchange for data (discounts, exclusive content).
- Be transparent: explain why data is collected and how it will be used.
2. Observe and Analyze Implicit Behaviors
- Use analytics tools (customer journey, click-through rates, etc.).
- Implement marketing automation scenarios to react in real-time (abandoned cart reminders, personalized recommendations).
- Segment behaviors to separate genuine interest from casual browsing.
3. Cross-Reference Data to Enrich Customer Profiles
The real power lies in combining both data types.
- A user states (explicit) a preference for video over written content. If analytics confirm (implicit) they watch videos until the end, this reinforces the declared preference.
- Conversely, if behavior doesn’t align with what was declared, this may indicate misreporting, shifting interests, or specific contexts.
4. Respect Ethics and Privacy
Data collection and usage must always come with clear communication on privacy protection. Compliance with GDPR in Europe or Law 25 in Québec isn’t just a legal obligation—it’s also a driver of trust and, ultimately, engagement.
The Benefits of a Balanced Strategy
- Finer personalization: move beyond traditional segmentation to deliver truly tailored experiences.
- Better anticipation of needs: implicit signals often reveal intent before it’s expressed.
- Stronger loyalty: customers who feel understood are more likely to stay loyal.
- Optimized marketing campaigns: improved targeting, reduced ad waste, higher ROI.
- More authentic relationships: balancing listening (explicit) and observation (implicit) creates more respectful and lasting connections.
Conclusion
Explicit and implicit data are two sides of the same coin: one provides the customer’s direct voice, the other reveals their actual behaviors. Using both in a complementary way ensures a richer, more precise, and more human understanding of needs and expectations.
Customer experience has become a key differentiator, and this complementarity is now a strategic asset. Brands capable of effectively orchestrating the collection, analysis, and activation of both types of data will not only build long-term loyalty but also turn every interaction into a lever for growth.
