Abstract: The present disclosure relates to systems and methods for optimizing benefits plan options offered by an organization through balancing derived population preferences with organizational preferences by analyzing historical selections made by individuals. Census data dividing members of an organization into census divisions may be applied to machine learning algorithm(s) to derive estimated selection preferences of the members. Using selection preferences, costs of various product offering scenarios and overall member satisfaction estimates of the scenarios may be calculated. Product offering scenarios meeting member preference criteria and organizational budget criteria may be presented for review.

Abstract: The present disclosure relates to systems and methods for optimizing benefits plan options offered by an organization through balancing derived population preferences with organizational preferences by analyzing historical selections made by individuals. Census data dividing members of an organization into census divisions may be applied to machine learning algorithm(s) to derive estimated selection preferences of the members. Using selection preferences, costs of various product offering scenarios and overall member satisfaction estimates of the scenarios may be calculated. Product offering scenarios meeting member preference criteria and organizational budget criteria may be presented for review.

Abstract: In an illustrative embodiment, systems and methods for providing subscription product recommendations can identify, from trained data models, cost-driving factors impacting costs of subscription products offered by a provider. The data models can be trained with claims data from a member population with multiple years of claims data. The cost-driving factors can correspond to attributes of the claims data in a first year that predict future costs in a following year. Requests for product recommendations include responses to questions each associated with a cost-driving factor. Based on the responses, the member can be mapped to a cluster grouping associated with a projected cost to the member for a subscription product. Recommendations can be generated based on an economic equivalent score for each subscription product reflecting the respective projected cost and one or more adjustment factors indicating an impact of one or more qualitative factors on subscription product selection choices.

Abstract: The present disclosure relates to systems and methods for optimizing benefits plan options offered by an organization through balancing derived population preferences with organizational preferences by analyzing historical selections made by individuals. Census data dividing members of an organization into census divisions may be applied to machine learning algorithm(s) to derive estimated selection preferences of the members. Using selection preferences, costs of various product offering scenarios and overall member satisfaction estimates of the scenarios may be calculated. Product offering scenarios meeting member preference criteria and organizational budget criteria may be presented for review.

Abstract: The present disclosure relates to systems and methods for optimizing benefits plan options offered by an organization through balancing derived population preferences with organizational preferences by analyzing historical selections made by individuals. Census data dividing members of an organization into census divisions may be applied to machine learning algorithm(s) to derive estimated selection preferences of the members. Using selection preferences, costs of various product offering scenarios and overall member satisfaction estimates of the scenarios may be calculated. Product offering scenarios meeting member preference criteria and organizational budget criteria may be presented for review.

Abstract: The present disclosure relates to systems and methods for optimizing benefits plan options offered by an organization through balancing derived population preferences with organizational preferences by analyzing historical selections made by individuals. Census data dividing members of an organization into census divisions may be applied to machine learning algorithm(s) to derive estimated selection preferences of the members. Using selection preferences, costs of various product offering scenarios and overall member satisfaction estimates of the scenarios may be calculated. Product offering scenarios meeting member preference criteria and organizational budget criteria may be presented for review.

Abstract: In an illustrative embodiment, an automated system provides for generating product recommendations for clients. The system may include computing systems and devices for receiving a client intake information with product preferences, member demographic information, and current product information, and in response, generating product offerings including at least a portion representing variations of the current product. The member demographic information may be consolidated into census divisions that are each associated with a category of the member demographic information, and probabilities of selecting the product offerings may be calculated for each of the census divisions. The product offerings may be presented in a user interface with optimization scores that are a function of the probability of selecting the product offerings as well as provider financial goals, employee perception goals, and market competitiveness goals.

Abstract: In an illustrative embodiment, an automated system provides for generating product recommendations for clients. The system may include computing systems and devices for receiving a client intake information with product preferences, member demographic information, and current product information, and in response, generating product offerings including at least a portion representing variations of the current product. The member demographic information may be consolidated into census divisions that are each associated with a category of the member demographic information, and probabilities of selecting the product offerings may be calculated for each of the census divisions. The product offerings may be presented in a user interface with optimization scores that are a function of the probability of selecting the product offerings as well as provider financial goals, employee perception goals, and market competitiveness goals.