Predictive conversion systems and methods
In one embodiment, a system and method of predicting sale transaction conversion rate of an item operates through a search of information in response to a query over a network. The system and method can includes discovering available information of the item of interest, extracting certain of the available information of the item, analyzing the certain information by comparing the information to other item information, weighting the information for the item in comparison to other items of the category, calculating a predictive score for the item of interest, and presenting the information of the item of interest ranked according to the predictive score as compared to other items of the category.
1. A computer system for generating and applying a formula for calculating an expected conversion rate, the computer system comprising:
an electronic memory storage configured to store modules;
a computer processor configured to execute the modules comprising at least:
a data access module configured to access from a data repository stated metadata and derived metadata elements, wherein the stated metadata elements and the derived metadata elements are for a plurality of products that led to prior user conversions, wherein stated metadata comprises attributes of a product that are obtained from a product description provided by advertisers or suppliers of the plurality of products in the inventory database, and wherein derived metadata comprises physical attributes of a product that are identified based on characteristics of the product and that are not stated in a product description provided by advertisers or suppliers of the plurality of products in the inventory database,
a conversions module configured to determine a statistical relevance of each stated metadata element and each derived metadata element in deriving expected conversion rates, wherein the statistical relevance of each stated metadata element and each derived metadata element comprises a likelihood or predictability of at least one of a click-through and a lead generation and wherein the determination of the statistical relevance is based on the use of a linear or logistic regression analysis,
a formula generation module configured to generate on a periodic basis a formula based on the determination, wherein the formula takes into account a plurality of stated metadata elements and derived metadata elements that have been determined to be statistically relevant to expected conversion rates, and
a formula application module configured to apply the formula to a plurality of products in an inventory database to assign a predicted conversion rate for each of the plurality of products in the inventory database, wherein the predicted conversion rate relates to generating higher revenue.
2. The computer system of claim 1 , further comprising an analytics module configured to receive a request for a listing of available products in a specific product category or product identification over a network.
3. The computer system of claim 2 , further comprising a ranking and recommendation module configured to prioritize the listing of available products based on the assigned predicted conversion rates, and configured to transmit the listing of available products over the network.
4. The computer system of claim 2 , wherein the request is received from a computing device from the group comprising of a desktop, a laptop, a personal data assistant, or a data-enabled phone.
5. The computer system of claim 1 , wherein the linear or logistic regression analysis is generated based on an analysis of historical conversion rates for at least one product in the plurality of products.
6. The computer system of claim 5 , wherein the analysis of historical conversion rates comprises a probability that a user clicks on a particular product based on an analysis of historical user behavior.
7. The computer system of claim 1 , wherein derived metadata comprises attributes of a product that are provided by a third party source.
8. The computer system of claim 1 , wherein the predicted conversion rate relates to lead generation.
9. The computer system of claim 1 , wherein the formula takes into account a weighting factor for each of the plurality of stated and derived metadata elements that have been determined to be statistically relevant to expected conversion rates.
10. The computer system of claim 9 , wherein the conversions module is further configured to increase a weighting factor for each of the plurality of stated and derived metadata elements that lead to a greater number of conversions.
11. A computer-implemented method of generating and applying a formula for calculating an expected conversion rate, the method comprising:
accessing, by a server device, from a data repository stated metadata and derived metadata elements, wherein the stated metadata elements and the derived metadata elements are for a plurality of products that led to prior user conversions, wherein stated metadata comprises attributes of a product that are obtained from a product description provided by advertisers or suppliers of the plurality of products in the inventory database, and wherein derived metadata comprises attributes of a product that are identified based on characteristics of the product and that are not stated in a product description provided by advertisers or suppliers of the plurality of products in the inventory database;
determining, by the server device, a statistical relevance of each stated metadata element and each derived metadata element in deriving expected conversion rates, wherein the statistical relevance of each stated metadata element and each derived metadata element comprises a likelihood or predictability of at least one of a click-through and a lead generation, and wherein the determining of the statistical relevance is based on the use of a linear or logistic regression analysis;
generating, by the server device on a periodic basis, a formula based on the determining, wherein the formula takes into account a plurality of stated metadata elements and derived metadata elements that have been determined to be statistically relevant to expected conversion rates; and
applying, by the server device, the formula to a plurality of products in an inventory database to assign a predicted conversion rate for each of the plurality of products in the inventory database, wherein the predicted conversion rate relates to generating higher revenue,
wherein the server device comprises at least a computer processor and an electronic memory device.
12. The method of claim 11 , further comprising receiving, by the server device, a request for a listing of available products in a specific product category or product identification over a network.
13. The method of claim 12 , further comprising:
prioritizing, by the server device, the listing of available products based on the assigned predicted conversion rates; and
transmitting, by the server device, the listing of available products over the network.
14. The method of claim 11 , wherein the formula takes into account a weighting factor for each of the plurality of stated and derived metadata elements that have been determined to be statistically relevant to expected conversion rates.
15. The method of claim 14 , further comprising increasing a weighting factor for each of the plurality of stated and derived metadata elements that lead to a greater number of conversions.
16. A computer-readable, non-transitory storage medium having a computer program stored thereon for causing a suitably programmed computer system to process by one or more computer processors computer-program code by performing the method when the computer program is executed on the suitably programmed computer system, the method comprising:
accessing by the suitably programmed computer system from a data repository stated metadata and derived metadata elements, wherein the stated metadata elements and the derived metadata elements are for a plurality of products that led to prior user conversions, wherein stated metadata comprises attributes of a product that are obtained from a product description provided by advertisers or suppliers of the plurality of products in the inventory database, and wherein derived metadata comprises attributes of a product that are identified based on characteristics of the product and that are not stated in a product description provided by advertisers or suppliers of the plurality of products in the inventory database;
determining by the suitably programmed computer system a statistical relevance of each stated metadata element and each derived metadata element in deriving expected conversion rates, wherein the statistical relevance of each stated metadata element and each derived metadata element comprises a likelihood or predictability of at least one of a click-through and a lead generation, and wherein the determining of the statistical relevance is based on the use of a linear or logistic regression analysis;
generating by the suitably programmed computer system on a periodic basis a formula based on the determining, wherein the formula takes into account a plurality of stated metadata elements and derived metadata elements that have been determined to be statistically relevant to expected conversion rates; and
applying by the suitably programmed computer system the formula to a plurality of products in an inventory database to assign a predicted conversion rate for each of the plurality of products in the inventory database, wherein the predicted conversion rate relates to generating higher revenue.
17. The non-transitory storage medium of claim 16 , further comprising receiving by the suitably programmed computer system a request for a listing of available products in a specific product category or product identification over a network.
18. The non-transitory storage medium of claim 17 , further comprising:
prioritizing by the suitably programmed computer system the listing of available products based on the assigned predicted conversion rates; and
transmitting by the suitably programmed computer system the listing of available products over the network.
19. The non-transitory storage medium of claim 16 , wherein the formula takes into account a weighting factor for each of the plurality of stated and derived metadata elements that have been determined to be statistically relevant to expected conversion rates.
20. The non-transitory storage medium of claim 19 , further comprising increasing by the suitably programmed computer system a weighting factor for each of the plurality of stated and derived metadata elements that lead to a greater number of conversions.