Some examples are:
- Developing a credit model to predict borrower delinquency and default.
- Estimating the impact of interventions using causal inference methods.
- Developing a reinforcement learning1 system for automated and adaptive decisioning.
- Designing and building A/B testing infrastructure to assist go/no-go product change decisions.
- Forecasting unit demand for improved inventory management.
- Developing a dynamic pricing model where prices respond in real time to changes in supply and demand.
- Estimating customer lifetime value (‘LTV’) to determine spending on marketing channels.
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Separately, you may be interested in our educational content (see left side, or below if you’re on mobile). We’ve written about a variety of topics to spread an understanding of how these techniques work and foster a discussion of their application.
Reinforcement learning is a nascent technology for commercial applications. Clients should be aware that there is a long trial-and-error period for developing RL systems materially better than simpler operations research alternatives. ↩