What most businesses understand as personalization is based on single-model approaches. However, the solution to truly contextualized interactions lies in the model-per-customer approach.
To predict the behavior of an individual in a specific group or population, most businesses use a single model, trained off the data of the entire group.
For example, a bank might create a model per account type, meaning that every customer with a specific account type gets scored by the same model, according to specified features such as age, spend, or time online. The output that is generated - whether it be a recommendation or a message - is considered personalized because it uses an individual's data and scores it against a model.
Additionally, single-model approaches usually utilize batch learning, meaning the model is retrained only after a certain amount of time has passed.
While this method creates the illusion of personalization, it is in reality only making an assumption based on an aggregate, which today can be detrimental to a business's competitive edge.
The Dream: One Model per Customer
The model-per-customer approach involves training models on an individual's interaction data rather than a population's.
Rather than comparing individuals to the patterns identified within a population - "John, who has a savings account and is 25 years old, will behave like other 25-year-old savings account holders" - the model-per-customer approach involves training a model on an individual's data, comparing them only to themselves.
The question then becomes: "What would John do in this context, based on how John behaved in similar contexts?"
This allows models to capture idiosyncratic patterns, leading to better predictions in environments where behavior is highly individual.
Complexities of the Model-Per-Customer Approach
The model-per-customer approach poses several challenges, which is why many businesses do not implement it:
- Production complexity: Training and optimizing a model per customer requires extensive automation in model training, deployment, and monitoring to ensure that your system can scale.
- Smaller data: Some customers will not provide enough interaction data, impacting model accuracy.
- Cold start: New customers lack data, making early predictions unreliable. Building useful history can take time.
- Scale: Supporting millions of models simultaneously is a major engineering challenge.
For many companies, achieving this level of personalization remains a fantasy. Legacy systems often lack the resilience and flexibility to handle the increased load of this approach.
The ecosystem.Ai Model-Per-Customer Solution
For a model-per-customer approach to achieve true contextualization, you require a system that has:
- Automated model lifecycle management: The system must automatically handle model creation, retraining, versioning, rollback, and monitoring to manage millions of models effectively.
- Real-time data pipelines: The system must ingest interaction events in real time, update feature stores instantly, and trigger dynamic retraining.
- Low latency inference layer: Predictions must be generated instantly, requiring millisecond-level inference and efficient model execution.
- Robust monitoring and observability: Aggregate monitoring and automated anomaly detection are essential for managing performance at scale.
- Horizontally scalable architecture: The system must support parallel training and inference, fault tolerance, and dynamic resource allocation.
- Intelligent cold-start handling: The platform must support strategies that learn from first interactions and adapt in real time.
Conclusion
True contextual predictions require a system that can support the model-per-customer approach. This entails advanced automated machine learning capabilities, as well as robust real-time data and inference infrastructure.
The ecosystem.Ai Prediction Platform provides the architecture needed to achieve this - enabling millions of models to operate in real time, at scale, with zero downtime.
