US-12626273-B2 - Systems and methods for generating a true return on advertisement sales value using short-term and long-term factors to analyze advertising effectiveness

Abstract

A method includes: receiving a first product value; determining a cannibalization value based on at least one of experimental data and non-experimental data; generating a second product value by adjusting the first product value using the cannibalization value; determining a return value based on return data; generating a third product value by adjusting the second product value using the return value; determining a lifetime customer value based on at least the cannibalization value; generating a fourth product value by adjusting the third product value using the lifetime customer value; determining an organic rank value based on organic placement data and value of organic placement data; generating a fifth product value by adjusting the fourth product value using the organic rank value; generating, based at least on the fifth product value, an actionable report descriptive of an efficacy rate of a target product in a computer-networked marketplace.

Inventors

• MITCHELL PARK
• JACOB NEPHI MILLER
• HAMILTON SCOTT NOEL

Assignees

• PATTERN INC.

Dates

Publication Date

20260512

Application Date

20240223

Claims (20)

1. 1 . A system comprising: a processor; and a memory including instructions that, when executed by the processor, cause the processor to: receive, for a target product, a first product value; determine, for the target product, a cannibalization value using at least one of experimental data and non-experimental data to generate a model of probabilities comprising a neural network that includes a plurality of layers, wherein the model of probabilities is configured to apply one or more weight matrices at each layer of the model of probabilities to predict whether each sale of a plurality of sales of the first product would be realized without an identified advertising characteristic associated with the first product, and wherein the model of probabilities provides, at an output layer of the model of probabilities, a cannibalization probability indicating, at least, the cannibalization value; generate a second product value by adjusting the first product value using the cannibalization value; determine, for the target product, a return value based on return data; generate a third product value by adjusting the second product value using the return value; determine, for the target product, a lifetime customer value based on at least the cannibalization value; generate a fourth product value by adjusting the third product value using the lifetime customer value; determine, for the target product, an organic rank value based on organic placement data and value of organic placement data; generate a fifth product value by adjusting the fourth product value using the organic rank value; generate, based at least on the fifth product value, an actionable report descriptive of an efficacy rate of the target product in a computer-networked marketplace.
2. 2 . The system of claim 1 , wherein the first product value is determined based on a comparison between an advertisement sales value and an advertisement spend value for the target product.
3. 3 . The system of claim 1 , wherein the first product value is received from the computer-networked marketplace.
4. 4 . The system of claim 1 , wherein the experimental data is associated with at least one intentional experiment configured to identify advertising data indicating incremental effects of advertisements for products associated with the computer-networked marketplace.
5. 5 . The system of claim 1 , wherein the experimental data is associated with at least one natural experiment configured to discover advertising data indicating naturally occurring advertising scenarios of products associated with the computer-networked marketplace.
6. 6 . The system of claim 1 , wherein the non-experimental data is associated with at least product placement and market share for products associated with the computer-networked marketplace.
7. 7 . The system of claim 1 , wherein adjusting the first product value using the cannibalization value includes subtracting the cannibalization value from the first product value.
8. 8 . The system of claim 1 , wherein the return data includes return data provided by the computer-networked marketplace.
9. 9 . The system of claim 1 , wherein adjusting the second product value using the return value includes subtracting the return value from the second product value.
10. 10 . The system of claim 1 , wherein the lifetime customer value is determined further based on new customer data provided by the computer-networked marketplace and a future lifetime value.
11. 11 . The system of claim 1 , wherein adjusting the third product value using the lifetime customer value includes adding the lifetime customer value to the third product value.
12. 12 . The system of claim 1 , wherein adjusting the fourth product value using the organic rank value includes adding the organic rank value to the fourth product value.
13. 13 . A method comprising: receiving, for a target product, a first product value; determining, for the target product, a cannibalization value using at least one of experimental data and non-experimental data to generate a model of probabilities comprising a neural network that includes a plurality of layers, wherein the model of probabilities is configured to apply one or more weight matrices at each layer of the model of probabilities to predict whether each sale of a plurality of sales of the first product would be realized without an identified advertising characteristic associated with the first product, and wherein the model of probabilities provides, at an output layer of the model of probabilities, a cannibalization probability indicating, at least, the cannibalization value; generating a second product value by adjusting the first product value using the cannibalization value; determining, for the target product, a return value based on return data; generating a third product value by adjusting the second product value using the return value; determining, for the target product, a lifetime customer value based on at least the cannibalization value; generating a fourth product value by adjusting the third product value using the lifetime customer value; determining, for the target product, an organic rank value based on organic placement data and value of organic placement data; generating a fifth product value by adjusting the fourth product value using the organic rank value; generating, based at least on the fifth product value, an actionable report descriptive of an efficacy rate of the target product in a computer-networked marketplace.
14. 14 . The method of claim 13 , wherein the experimental data is associated with at least one of (i) at least one intentional experiment configured to identify advertising data indicating incremental effects of advertisements for products associated with the computer-networked marketplace, and (ii) at least one natural experiment configured to discover advertising data indicating naturally occurring advertising scenarios of products associated with the computer-networked marketplace.
15. 15 . The method of claim 13 , wherein the non-experimental data is associated with at least product placement and market share for products associated with the computer-networked marketplace.
16. 16 . The method of claim 13 , wherein adjusting the first product value using the cannibalization value includes subtracting the cannibalization value from the first product value.
17. 17 . The method of claim 13 , wherein adjusting the second product value using the return value includes subtracting the return value from the second product value.
18. 18 . The method of claim 13 , wherein adjusting the third product value using the lifetime customer value includes adding the lifetime customer value to the third product value.
19. 19 . The method of claim 13 , wherein adjusting the fourth product value using the organic rank value includes adding the organic rank value to the fourth product value.
20. 20 . A computing device comprising: a processor; and a memory including instructions that, when executed by the processor, cause the processor to: receive, for a target product from a computer-networked marketplace, a first product value; determine, for the target product, a cannibalization value using at least one of experimental data and non-experimental data to generate a model of probabilities comprising a neural network that includes a plurality of layers, wherein the model of probabilities is configured to apply one or more weight matrices at each layer of the model of probabilities to predict whether each sale of a plurality of sales of the first product would be realized without an identified advertising characteristic associated with the first product; generate a second product value for the target product based on at least the cannibalization value.

Description

TECHNICAL FIELD The present disclosure relates generally to commerce systems and methods, and more specifically, to generating a true return on advertisement sales value using short-term and long-term factors to analyze advertising effectiveness. BACKGROUND Commerce systems are well known in the art and are effective means to allow for the transaction of products, commodities, services and the like from one party to another. Commonly, commerce systems are embodied by a market, where many products are offered for sale and people that are customers are able to shop or browse the products and select items for purchase. Such markets may be managed by companies that include Ebay®, Amazon®, Wayfair®, Costco®, Walmart®, and Target®, among others. With the advent of digital marketplaces, sellers are allowed to list products for purchase to anyone with an internet connection. Commonly, many sellers will offer the same or similar products. Shoppers (e.g., users accessing digital marketplaces via the internet) are able to sort through and browse all of these products to find what they are looking for. One of the problems commonly associated with common commerce systems and digital marketplaces is their density of potential products that may be sold. For example, when a shopper wants to purchase a product, the shopper may start with a search at a search engine that provides hundreds or thousands of products. Unlike “brick and mortar” marketplaces (e.g., physical markets), shoppers search at least one designated digital marketplace and potentially multiple digital marketplaces that may provide thousands of results. Any specific product may be lost within the copious amounts of results provided from the search. This may make it difficult for a seller of a product to get that product noticed and purchased. Still further, a seller may have recently created a product or has recently placed that product on the digital marketplace but may not know to what extent the seller should focus on promotion of that product. In this example, a seller may not know what appropriate target advertising cost of sale (ACoS) to meet in order to see long term gains in lieu of short-term profits. When the density of the products within the marketplace is high, spending more money to meet and exceed a minimum return allows for more recognition in these digital marketplaces allowing for more potential sales. In addition, sellers may use various metrics to determine an investment return on, for example, advertising spend. For example, Return on Advertising Spend (ROAS) is one of the most widely used metrics in advertising. ROAS is calculated by dividing advertising sales by advertising spend and measures how many dollars of revenue an ad generated per dollar spent. However, such ROAS calculations may ignore variables that may limit the efficacy and/or accuracy of the ROAS calculation. SUMMARY OF THE DISCLOSURE The various systems and methods of the present disclosure have been developed in response to the present state of the art, and in particular, in response to the problems and needs in the art that have not yet been fully solved by currently available digital marketplaces. An aspect of the disclosed embodiments includes a system that includes a processor, and a memory. The memory includes instructions that, when executed by the processor, cause the processor to: receive, for a target product, a first product value; determine, for the target product, a cannibalization value based on at least one of experimental data and non-experimental data; generate a second product value by adjusting the first product value using the cannibalization value; determine, for the target product, a return value based on return data; generate a third product value by adjusting the second product value using the return value; determine, for the target product, a lifetime customer value based on at least the cannibalization value; generate a fourth product value by adjusting the third product value using the lifetime customer value; determine, for the target product, an organic rank value based on organic placement data and value of organic placement data; generate a fifth product value by adjusting the fourth product value using the organic rank value; generate, based at least on the fifth product value, an actionable report descriptive of an efficacy rate of the target product in a computer-networked marketplace. An aspect of the disclosed embodiments includes a method that includes: receiving, for a target product, a first product value; determining, for the target product, a cannibalization value based on at least one of experimental data and non-experimental data; generating a second product value by adjusting the first product value using the cannibalization value; determining, for the target product, a return value based on return data; generating a third product value by adjusting the second product value using the return value; determining, for the target pr