Opinion

Understanding how human brains make decisions can help us use tech better

Ben Combe - senior director, data optimisation and personalisation, APAC at Monks - explains how an understanding of how the brain makes decisions can help you better guide machines to do likewise.

BY BEN COMBE

November 7, 2024 6:00

Understanding how human brains make decisions can help us use tech better

In today’s digital landscape, hyper-personalisation at scale is not just a competitive advantage but a necessity. As businesses strive to deliver tailored experiences, the need for AI-driven decisioning becomes paramount. However, the challenge lies in determining where this decisioning should reside across various platforms, including Cloud Data Warehouses, Customer Data Platforms (CDPs), and Marketing Automation Platforms.

As technology evolves, the capabilities of these platforms expand and often blur the boundaries between them. For instance, cloud platforms increasingly integrate AI and machine learning (ML) capabilities, while activation platforms enhance their orchestration and data unification features. This convergence complicates the decision-making landscape, making it difficult for organisations to establish a clear framework for customer decisioning.

Understanding decisioning through the lens of the human brain

To navigate this complexity, we can draw parallels between customer decisioning and the human brain. Nobel Prize winner Daniel Kahneman identified two systems through which we process information and make decisions: System 1 and System 2. The former operates quickly and automatically, making reflexive decisions based on intuition and experience. In contrast, the latter engages in slower, more analytical thinking, allowing for complex decision-making.

By leveraging System 1 and System 2 thinking, we can create a more effective ‘decisioning’ model that enhances customer experiences.

Introducing ‘Decisioning, Fast and Slow’

My recommended model, which I call “Decisioning, Fast and Slow,” combines the strengths of both systems for optimal outcomes.

Here, System 2 is responsible for slower modelling, determining the ideal customer experience by answering critical questions: WHO is the ideal customer? WHAT should they see? WHY should they engage? Tools like Pega and Databricks exemplify this approach, focusing on accuracy and governance.

Meanwhile, System 1 handles orchestration and activation in real-time, deciding HOW messages are served, WHERE they appear, and WHEN they are sent. Platforms such as Adobe Journey Optimiser (AJO) and CDPs excel in this area, emphasizing speed and efficiency.

The synergy between these systems is crucial. System 2 should pass segments and decisions to System 1, while System 1 should provide feedback signals to refine the models in System 2.

“The integration of System 1 and System 2 creates a decisioning brain that is both agile and precise, enhancing the customer journey.”

The role of AI in accelerating decisioning

Regardless of the platform, AI has the potential to accelerate both systems. System 2 can use AI to source and leverage new data sources to make more accurate customer decisions at scale, achieving better results, at greater scale. Meanwhile, System 1 can use AI to implement faster adjustments and automatic optimisations, delivering faster decisions at cheaper costs.

However, while AI has the potential to revolutionise customer decisioning, it is important to recognize that the constraints and challenges will arise as much from organisational structures as from the technical capabilities themselves (as is typical for extracting value from any new technology).

The organisational challenge: Bridging the divide

In many organisations, System 1 and System 2 are treated as separate entities, owned by different teams. Typically, System 2 is managed by data, customer, or technology teams, focusing on accuracy, ownership, governance, intellectual property, and traceability. Conversely, System 1 is overseen by Marketing or Digital teams, prioritising speed, efficiency, performance, and measurement.

This division leads to friction stemming from misaligned priorities, processes, and people, ultimately undermining the customer experience.

“When decisioning systems operate in silos, the customer journey suffers. Alignment is key.”

A call to action: Aligning teams for better decisioning

To overcome these challenges, organisations must foster collaboration between teams managing System 1 and System 2. This alignment can be achieved through:

Shared Goals: Establish common objectives that prioritise customer experience above departmental interests.

Integrated Processes: Develop workflows that facilitate communication and data sharing between teams.

Cross-Functional Training: Equip team members with knowledge of both systems to enhance understanding and cooperation.

By bridging the gap between these two decisioning systems, organisations can create a cohesive strategy that enhances customer experiences and drives business success.

The future of customer decisioning

As we move forward in an increasingly complex digital landscape, the importance of effective customer decisioning cannot be overstated. By embracing a model that integrates System 1 and System 2 decisioning, organisations can harness the power of AI to deliver hyper-personalised experiences at scale.

“The future of customer decisioning lies in our ability to integrate speed and accuracy, creating a seamless experience that resonates with customers.”

In conclusion, the path to successful customer decisioning is clear: align your teams, leverage AI, and embrace a holistic approach that prioritises the customer journey. The time to act is now.