Abstract: When customizing a sales prediction model for a particular client, there exists a cold-start problem when the client's training data is of low quality and/or quantity. Disclosed embodiments solve this cold-start problem by normalizing the data based on weighted or unweighted recency and/or trend. In addition, the data from a wide range of clients may be used to train a base sales prediction model, which can then be fine-tuned using a particular client's data to produce a client-specific sales prediction model. A self-monitoring mechanism may be used to continually retrain the client-specific sales prediction model on new client data, to gradually improve upon the accuracy of the client-specific sales prediction model as more data become available.