improve your bottom line with a catalog of predictive models deployed on a repeatable, closed-loop process.
Predictive modeling is no longer focused on building the one very best model of its kind. In today’s enterprise, a portfolio of models will most effectively predict the behavior of customers. A proven approach, the predictive model factory process, produces a far larger contribution to revenue than the cost to build and deploy models.
how does an enterprise deploy 10 or more models in a calendar year without losing time and money?
The predictive model factory’s repeatable, explainable, closed-loop process creates a catalog, even a portfolio of models. It mitigates efficiency lost in data collection and aligns the model engineering team with the required level of accuracy for each model. By deploying more models faster, organizations are reducing development and release costs, accelerating release cycles and minimizing losses due to less effective decisions based on degrading models.
The predictive model factory process will change an organization’s ability to provide accurate and responsive strategies and treatments for its customers.
In this RedPaper, Corios President Robin Way leads an in-depth discussion around:
- The model factory process – Learn how the model factory process outperforms the traditional predictive model building process with functional assets that influence customer relationships daily with up-to-date information.
- A formal and standardized process for model performance monitoring – Establish ongoing model maintenance to ensure champion models are leveraged to ensure high business value is attributed back to these model assets.
- Model factory principles – Implement these five key elements—closed loop, play nice, stop translating, single platform and web services—to pivot your predictive modeling approach.
- The benefits of the model factory strategy – Discover where you can save money in the model build and release cycles.
- Building the model factory – Discover how the three key constituencies of model development and deployment: Business and Analytics, Data and Systems, Development and Operations speak the same language and from the same charter to build multiple models in less time.