Product evaluation system and method of use

A method of providing a competitive assessment of a target product on a marketplace, comprising:

• with a processor, evaluating the target product to determine attributes of the target product, the attributes generated based on purchase information for the target product in a digital marketplace, the attributes associated with at least one of:
• ratings provided by at least one purchaser of the target product;
• reviews provided by at least one purchaser of the target product;
• listed prices of the target product;
• content within the reviews provided by at least one purchaser of the target product;
• and
• ranking of the target product relative to at least one organic competing product;
• with the processor accessing the digital marketplace to determine at least one organic competing product comparative to the target product based on attributes and purchase information for the at least one organic competing product;
• upon execution of a competitivity score generator by the processor, calculating a competitivity score based on the attributes of the target product and the attributes of the at least one organic competing product, the competitivity score related to the ability of the target product to compete with the at least one organic competing product in the digital marketplace;
• and
• upon execution of a recommendation module, generating an actionable report based on the competitivity score and revenue data associated with the at least one organic competing product, the actionable report comprising data descriptive of price points and target advertising cost of sale (ACoS) statistics for the target product to use to increase revenue in the digital marketplace relative to the at least one organic competing product, in response to satisfying a threshold competitivity score;
• augmenting the actionable report with data descriptive of:
• search terms found to be most general and most similar between the target product and organic competing product;
• and
• most relevant and most frequent search terms similar between the target product and organic competing product;
• and
• generating a winnability report descriptive of:
• a probability of winning a winnable search term associated with the target product;
• and
• an estimated cost to win the winnable search term;
• and
• executing, via the processor, a machine learning module to develop the augmented actionable report and winnability report, wherein the machine learning module uses different input node values to finely tune a weight matrix to form a trained machine learning classifier used in generating the winnability report.

A computer program product for evaluating a product, the computer program product comprising:

• a non-transitory storage medium;
• and computer program code, encoded on the non-transitory storage medium, wherein the computer program code is configured to cause at least one processor to perform the steps of:
• causing an assessment module to:
• assess attributes of a target product, the attributes generated based on purchase information for the target product in a computer-networked marketplace, the attributes associated with at least one of:
• ratings provided by at least one purchaser of the target product;
• reviews provided by at least one purchaser of the target product;
• listed prices of the target product;
• content within the reviews provided by at least one purchaser of the target product;
• and
• ranking of the target product relative to at least one organic competing product;
• via a network interface device, accessing the computer-networked marketplace and determining at least one organic competing product based on the attributes of the at least one organic competing product and purchase information for the at least one organic competing product;
• comparing the attributes of the target product to the attributes of the at least one organic competing product to generate a competitivity score that identifies how the target product competes with the at least one organic competing product;
• and
• determine a recommendation using the competitivity score and revenue data associated with the at least one organic competing product, the recommendation descriptive of an ability or inability of the target product to compete with the at least one organic competing product in the computer-networked marketplace, the recommendation including additional economic data descriptive of price points and ACoS statistics to use in order to increase revenue, in response to satisfying a threshold competitivity score;
• augmenting the recommendation with data descriptive of:
• search terms found to be most general and most similar between the target product and organic competing product;
• and
• most relevant and most frequent search terms similar between the target product and organic competing product;
• and
• generating a winnability report descriptive of:
• a probability of winning a winnable search term associated with the target product;
• and
• an estimated cost to win the winnable search term;
• and
• executing, via the least one processor, a machine learning module to develop the augmented recommendation and winnability report, wherein the machine learning module uses different input node values to finely tune a weight matrix to form a trained machine learning classifier used in generating the winnability report.

A system for providing a competitive assessment of a target product on a marketplace, the system comprising:

• a processor;
• a data store;
• a network interface device;
• an assessment module to:
• evaluate the target product to determine attributes of the target product, the attributes generated based on purchase information for the target product in a digital marketplace, the attributes associated with at least one of:
• ratings provided by at least one purchaser of the target product;
• reviews provided by at least one purchaser of the target product;
• listed prices of the target product;
• content within the reviews provided by at least one purchaser of the target product;
• and
• ranking of the target product relative to at least one organic competing product;
• and
• accesses the digital marketplace to determine at least one organic competing product based on comparative attributes to the target product and purchase information the at least one organic competing product;
• a competitivity score generator configured to calculate a competitivity score based on the attributes of the target product and the attributes of the at least one organic competing product, the competitivity score related to the ability of the target product to compete with the at least one organic competing product;
• and
• a recommendation module to generate an actionable report using the competitivity score and revenue data associated with the at least one organic competing product, the actionable report based on the ability of the target product to compete with the at least one organic competing product, the actionable report comprising data descriptive of price points and target advertising cost of sale (ACoS) statistics for the target product to use to increase revenue in the digital marketplace relative to the at least one organic competing product, in response to satisfying a threshold competitivity score;
• augmenting the actionable report with data descriptive of:
• search terms found to be most general and most similar between the target product and organic competing product;
• and
• most relevant and most frequent search terms similar between the target product and organic competing product;
• and
• generating a winnability report descriptive of:
• a probability of winning a winnable search term associated with the target product;
• and
• an estimated cost to win the winnable search term;
• and
• executing, via the processor, a machine learning module to develop the augmented actionable report and winnability report, wherein the machine learning module uses different input node values to finely tune a weight matrix to form a trained machine learning classifier used in generating the winnability report.