Systems and methods for omnichannel environment relevance analytics

A system, comprising:

• one or more processors;
• and
• memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to:
• receive a request to obtain an omnichannel relevance index for a product;
• identify a set of domain priorities, wherein the set of domain priorities correspond to one or more platforms through which the product is made available;
• obtain product data sets associated with the one or more platforms through which the product is made available, wherein the product data sets are obtained via navigation pathways across the set of domain priorities, wherein the product data sets include data corresponding to the product and a set of other products, and wherein the data is weighted based on listing positions of the product and the set of other products within the one or more platforms;
• normalize the product data sets to generate normalized product data sets;
• generate one or more machine learning algorithms to provide a score based on the normalized product data sets, wherein the one or more machine learning algorithms are generated by iteratively varying a set of hyperparameters associated with the one or more machine learning algorithms according to a test data set and a predefined specification;
• process the normalized product data sets through the one or more machine learning algorithms to generate the score;
• generate the omnichannel relevance index for the product and a set of recommendations for improving the omnichannel relevance index, wherein the omnichannel relevance index and the set of recommendations are generated based on the score, and wherein the omnichannel relevance index indicates a position of the product in relation to the set of other products;
• receive feedback resulting from implementation of the set of recommendations;
• and
• update the set of hyperparameters associated with the one or more machine learning algorithms based on the feedback.

A non-transitory, computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to:

• receive a request to obtain an omnichannel relevance index for a product;
• identify a set of domain priorities, wherein the set of domain priorities correspond to one or more platforms through which the product is made available;
• obtain product data sets associated with the one or more platforms through which the product is made available, wherein the product data sets are obtained via navigation pathways across the set of domain priorities, wherein the product data sets include data corresponding to the product and a set of other products, and wherein the data is weighted based on listing positions of the product and the set of other products within the one or more platforms;
• normalize the product data sets to generate normalized product data sets;
• generate one or more machine learning algorithms to provide a score based on the normalized product data sets, wherein the one or more machine learning algorithms are generated by iteratively varying a set of hyperparameters associated with the one or more machine learning algorithms according to a test data set and a predefined specification;
• process the normalized product data sets through the one or more machine learning algorithms to generate the score;
• generate the omnichannel relevance index for the product and a set of recommendations for improving the omnichannel relevance index, wherein the omnichannel relevance index and the set of recommendations are generated based on the score, and wherein the omnichannel relevance index indicates a position of the product in relation to the set of other products;
• receive feedback resulting from implementation of the set of recommendations;
• and
• update the set of hyperparameters associated with the one or more machine learning algorithms based on the feedback.