This invention relates generally to the field of online shopping and customer discounts, such as through the use of promotion codes.
Today, internet shoppers have no way of knowing if an online coupon code or promotion code is valid. There are, for instance, various coupons, discount numbers, promotion codes, and the like that online shoppers can enter in a website to attempt to receive a discount when making an online purchase. These coupons, discount numbers, or promotion codes will generally be referred to as “promotion codes.” Typically, shoppers have to test the validity of promotion codes at checkout to see if a retailer will accept them. Reasons for a retailer not accepting a promotion code include, but are not limited to, the following:
(1) the promotion code has expired (its applicable date has passed);
(2) the promotion code cannot be applied to the current shopping basket (e.g. price of items in basket is insufficient for the discount, promotion code applies to a different product than that selected in basket);
(3) the promotion code cannot be redeemed due to the user's profile (location, etc. . . . ); or
(4) the promotion code has been deactivated by retailer.
The typical way to validate a promotion code, therefore, involves the manual validation of the promotion code by the online shopper during the shopping process. This requires every internet shopper to perform the following steps:
(1) visit the retailer they want to make a purchase from;
(2) add one of the products they want to buy to the shopping cart;
(3) get to the checkout page;
(4) open a new browser tab, and find a promotion code for that retailer;
(5) go back to the retailer's website and apply the promotion code to test whether it is valid; and
(6) repeat from step 4, until the online shopper purchases the item after finding (or not finding) a promotion code he/she is satisfied with or does not make a purchase and abandons the shopping cart.
Put simply, online shoppers have no way of knowing a promotion code's validity without painstakingly performing the manual steps outline above.
According to one embodiment, a method of the invention includes interacting with a promotion code provider programmatically, providing a promotion code to a validation function of the promotion code provider, and evaluating a response from the promotion code provider. The act of interacting with a promotion code provider can include, for example, controlling an interface that simulates user actions, such as but not limited to controlling an in-memory web browser through an API, where the API provides a set of functions for simulating user actions. The act of interacting with a promotion code provider can include navigating to a website of the promotion code provider to a location where a promotion code can be tested. Further, the step of evaluating a response from the promotion code provider can include, for example, determining whether the promotion code is valid. Accordingly, the method can be used to determine whether a promotion code, such as an online coupon, is valid.
Another embodiment of the invention is a computer-readable storage media embodying logic that is operable when executed to perform a series of steps. These steps include interacting with a promotion code provider programmatically, providing a promotion code to a validation function of the promotion code provider, and evaluating a response from the promotion code provider.
Yet another embodiment of the invention is an apparatus that includes at least one driver module, wherein the driver module is operable to control an interface that simulates user actions. The apparatus also includes a monitor to manage the at least one driver. In addition, the driver module contains logic that is operable when executed to: (a) provide a promotion code to a validation function of the promotion code provider and (b) evaluate a response from the promotion code provider. In use, the apparatus can be used to determine whether a promotion code, such as an online coupon, is valid.
The invention is illustrated in the figures of the accompanying drawings which are meant to be exemplary and not limiting, in which like references are intended to refer to like or corresponding part, and in which:
To address the need set forth above, according to one aspect, the invention includes a method and apparatus that can automatically validate one or more promotion codes.
As used herein, the term “promotion code provider” can be one or both of a merchant or a source of a promotion code. The merchant, for example, could be a merchant or reseller that accepts promotion codes from third party sources. The source of a promotion code, for example, could be a third party that provides or accepts promotion codes for products and could include, for example, a product manufacturer or seller.
The present invention solves the problem of online shoppers spending time searching for and testing the validity of promotion codes. The invention can, for example, automatically test the validity of promotion codes and only deliver online shoppers promotion codes that a retailer will accept. This saves the user a significant amount of time because they no longer need to manually validate promotion codes.
The invention also solves problems for online merchants, which collectively experience high shopping cart abandonment rates, which decrease sales. Shopping cart abandonment refers to the loss of a customer who is going through the check-out process of an online transaction.
Because much of online shopping is impulse driven, there is a higher probability that the online shopper will abandon the shopping cart with every additional second that the online shopper does not make a purchase. Over half of all online shoppers use promotion codes, and the searching and testing of these promotion codes increases the time they spend going through the purchase process and thus results in higher shopping cart abandonment. The invention saves the online shopper a significant amount of time by automatically testing the validity of promotion codes, which helps to decrease the time online shoppers spend going through the purchase process, this decreasing abandonment rates.
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The overall exploration process is a set of Bayesian networks, where each node in the macro network is an individual page on a merchant website. Each network consists of one to many nodes with each node representing a web page on the site. The start node is the current page being explored and each outbound node represents a page destination accessible by an HTML element capable of navigation, such as an anchor or link. Over time, the driver 50 builds up a set of paths that describes a relationship between nodes where the conclusion of each path has been a successful promotion code page. When the driver 50 reaches a promotion code validation page, it takes each node in its travelled path and increments its score by the total distance travelled by the number of nodes travelled. Over time, nodes that prove to lead the driver 50 to a promotion code validation page will have the highest scores, helping the driver 50 establish a fast path to the promotion code validation page.
The scoring is based on historical data built up over time by the system. Each evaluation involves doing a depth first traversal using a shortest path algorithm such as Dijkstra or Bellman-Ford. The performance of this approach increases after each consecutive exploration by the magnitude of changed deltas in the site map since the last exploration. Each score represents the likelihood that the algorithm will and a page where a coupon validation function exists. This score is recorded at each edge between nodes, where each node is a web page in a site being explored, and each edge is a relationship between two pages formed by an element, such as a hyperlink. The score is determined by the algorithm during its traversal process, based on a set of rules. These rules measure the divergence potential of a page. For example, a page with four hyperlinks to pages within the site domain has a higher likelihood of leading the driver to a successful state than a page with two hyperlinks that link to pages outside of the sites domain. The basic principal of the scoring function is to find convergence within site pages towards a page that provides a validation function. In the instance where the provider publishes a programmatic interface, such as an application programming interface or API, then the scoring function will rank this highest because pages that provide some form of interface receive a higher score than non-programmatic interfaces, such as web pages.
When the driver 50 starts its exploration process, it begins at a root node, such as, for example, the start page 162 of a site as shown in
Each time the driver 50 recurses and traverses to an end state, it places a score against the graph edges that led to the end state, with higher scores being given to edges that led to a promotion code validation page, lower scores being assigned to pages that led to dead ends (no exit paths), and the lowest score being given to cyclic edges.
As an example, if the driver 50 starts at the home page, there may be four possible ways to exit the page—-there might be 3 links and one input element. The driver 50 assembles these in a list, and then selects the first path, such as the first of the 3 links. It navigates that link and evaluates the destination. If the destination caused the driver 50 to leave the domain of the site, for example, from domainx.com to domainy.com, then the recursion terminates. If the destination is a page within the parent domain (including sub-domains), then the page is evaluated as potentially having a promotion code input box, an input control, or exit paths. If there are no exit paths and no promotion code input boxes or input controls, then the recursion terminates. If the page has exit paths, the algorithm recluses. If the destination has a promotion code control, the driver 50 records this and still exits, but does so by setting a flag to indicate success. Each root path is evaluated until a success is found or until all root paths have been explored.
Using the recursive traversal process set forth above allows the driver 50 to learn about various merchant websites and use that learning in the future. For instance, if a driver 50 has evaluated a particular merchant website in the past, the next time the driver 50 arrives at the same merchant site, it will look up the learning corpus from its path traversals, and if a prior successful path exists, it will validate this path. If the path succeeds, the driver 50 navigates it. If not, the driver will evaluate a new path in the manner set forth above.
For each node in the macro network, the invention can establish a micro network for all possible exit points. In exploring a web site and looking for a particular goal, it is important to have some basis for moving forward. Web pages have a finite set of possible exit points, because the published specification for HTML describes the possible controls that enable site navigation. Being able to determine how to move forward from a page helps the driver explore the site, making it possible to find the promotion code validation page and control. Without an exit point, it would be very difficult to understand how to discover the promotion code validation page in a methodical, efficient, programmatic manner. Each node in the micro graph represents an element on the page, where the edges represent meaningful relationships and scores.
There are two graphs stored within the system. The graphs can be stored either as adjacency matrices or lists, depending on the size of the site. The primary basis for the graphs is to describe relationships between web pages, or within a web page, relationships between elements. The data structure is laid out to provide efficient application of the search algorithm, where the union of two nodes provides information on scoring, proximity, etc. First, the site-level graph lays out the relationship between Web pages. For example, the home page links to the contact page. Second, the page-level graph shows the relationship of elements on the page. For example, on the home page, there are two hyperlinks and two images. Like the site-level graph, the page-level graphs are scored using historical metrics based on prior explorations, but are also weighed on change. The page DOM can be calculated based on elements, but not necessarily based on layout. Any change in the DOM score will require the page to be explored again.
Each element on the page is assessed on two attributes. The first attribute is its exit-ability, which is based on a finite set of known HTML elements. The second attribute is proximity to an exit-able element. The edges on the graph are based on actions, with implicit decision criteria, based on a forward-flow model where the system is always moving forward.
The primary benefit of using a combination of machine learning, graph theory, and automation, as explained above, is that any website that offers a user the ability to validate a promotion code can be explored by the system. Machine learning can help to optimize this process to run at scale, especially in relation to using learned routes and just-in-time evaluation of page changes. Graph theory can allow the system to simulate multiple explorations using a dynamic set of traversal and scoring algorithms, such as Dijkstra, Bellman-Ford, and a range of models such as generalized Voronoi graphs or frontier-based methods.
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As one example of the evaluation of a response 30 from the promotion code provider 60, the response from the promotion code provider 60 may read, “The promotional code you entered is not valid.” The validation driver must parse this response and interpret what it means—in this case, the proper interpretation is simply that the promotion code is not valid. In another example, the response from the promotion code provider 60 may read, “Error 555.” The validation driver may interpret this response to mean, for example, that the promotion code is not valid. In yet another example, the response from the promotion code provider 60 may read, “The promotional code you entered has expired.” The validation driver should interpret this response to mean that the promotion code was once valid, but has now expired. The validation algorithm uses natural language processing to score and weight a response from a provider. There are trigger words such as invalid, or not applicable, within the responses, that will indicate the validity of a promotion code. However, some of these mappings between response and validity may result in a new workflow. For example, if the validation algorithm detects that a coupon might be valid, given the right shopping cart contents, it will search for product descriptions that might lead to the coupon becoming valid. The goal of the system is to validate the coupon or offer, so attempting to find ways, such as selecting appropriate shopping cart contents is a valid course of action for the algorithm.
Thus, improved techniques for validating a promotion code have been described. The use of the method and apparatus can, for example, allow a large number of promotion codes to be validated in an efficient and timely manner that saves shoppers from having to go through a series of manual steps to determine whether a promotion code is valid.
Although the invention has been described and illustrated in the foregoing illustrative embodiments, it is understood that the present disclosure has been made only by way of example, and that numerous changes in the details of implementation of the invention can he made without departing from the spirit and scope of the invention. Features of the disclosed embodiments can be combined and rearranged in various ways.
Number | Date | Country | |
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Parent | 15592807 | May 2017 | US |
Child | 16245021 | US | |
Parent | 13307586 | Nov 2011 | US |
Child | 15592807 | US |