You are interested in developing a clear, repetitive and automatize processes for model productionalization, which would include model validation, model assessment and maintenance as well as the continual modification or improvement of the model. You are also interested in the deployment of the predictive model to data warehouse and databases, such that, predictive scores become available for each and every given elements/targeted/response variable in the data warehouse, such that, their predictive scores are available to the Marketing Analytics and Segmentation team for use during segmentation and campaign formulation and launching.

Throughout both processes (models productionalization and deployment), what elements should be included in the development of a comprehensive protocol to guide the processes and anticipated outcomes and usage? Any suggestions?

Emphasis should be focused on the organization technological capacity, data warehouse capabilities, collaboration between all stakeholders that might use the models and how they can use it to enhance the business decision preferences, processes to guide the use of predictive scores in the data warehouse accurately to minimize misuse, processes for annual model improvement/enhance using historical data of the segments of the targeted/response variables used in the initial model development, model meta data, which provides information on each of the variables in the model, etc. Training of all potential stakeholders on the use of the model and what each predictive score per customer stands for?