Building a Consumer Digital Twin: Technology, Regulations, and Ethical Challenges

Building a digital twin of the consumer by blending heterogeneous data from sources such as global analytics databases, social media-derived data, and first-party data (collected directly from the customer) is a complex yet powerful process. This technique creates a dynamic digital representation of the consumer, akin to a "behavioural DNA," to predict future preferences, behaviours, and interactions. However, its implementation must comply with European regulations, particularly GDPR and the AI Act.

How to Build a Consumer Digital Twin

A consumer digital twin is based on the integration of different types of data:

• Demographic and psychographic data: Provided by tools that analyze consumer behaviours, attitudes, and interests on a global scale.
• Behavioural data from social media: Interactions, sentiment analysis, likes, shares, and comments help understand real-time preferences.
• First-party data: Information from the customer (e.g., CRM, purchase history, or customer service interactions).

The process involves:

1. Data collection: Aggregating data from different sources into a centralized repository.
2. Data blending: Using advanced data blending techniques to combine heterogeneous datasets, ensuring consistency and data quality.
3. Building the digital model: Applying machine learning algorithms to create a dynamic model that realistically represents consumer behaviour.
4. Continuous updates: Integrating new data to keep the digital twin updated and relevant.

Regulatory Implications: GDPR and AI Act

Using heterogeneous data to create digital twins requires strict regulatory compliance:

GDPR

• Legal basis for data processing: A valid legal basis (such as explicit consent or legitimate interest) must be identified for collecting and using personal data.
• Data minimization: Only the strictly necessary data should be processed for declared purposes.
• Data anonymization: Digital twins can use synthetic or anonymized data to mitigate privacy risks.
• Transparency: Through detailed notices, users must be clearly informed about how their data is used.

AI Act

• Risk classification: AI systems used in digital twins may be classified as "high risk" if they significantly influence consumer decisions. If so, they must meet specific transparency, accuracy, and human oversight requirements.
• Bias and discrimination: Models must be designed to avoid data biases that could lead to discrimination.
• Audit and traceability: Data collection and processing procedures must be documented to ensure regulatory compliance.

Operational and Ethical Challenges

1. Data quality: Incomplete or inaccurate data can compromise the effectiveness of the digital twin.
2. Re-identification risk: Combining datasets could lead to individual identification even with anonymisation.
3. Ethical use of data: It is crucial to ensure that digital twins are not used to manipulate or unduly influence consumers (e.g., through nudging).

Conclusions

Digital twins represent an advanced frontier in market research, offering hyper-personalized and predictive insights. However, their development requires balancing technological innovation with compliance with personal data protection regulations. With an approach based on "privacy by design" and "ethics by design", it is possible to fully leverage the potential of digital twins while minimizing legal and ethical risks.