TECHNICAL FIELD

The present invention relates to systems and methods for optimizing capital allocation for advertising campaigns in the online-based commercial environment to achieve predefined sales goals while respecting inventory, advertising cost of sales, and advertising budget constraints in an autonomous manner.

BACKGROUND OF THE INVENTION

Selling goods through online marketplaces is becoming an increasingly important activity for retailers. The share of Internet sales in the total retail commerce space is continuously growing. E-commerce retailers, i.e., retailers that sell mainly through the Internet, face numerous challenges in managing their businesses, with one such challenge being obtaining a balanced ratio for every Universal Product Code (UPC) between advertising expenditure of an advertising campaign and the actual sales achieved through this campaign.

There are many aspects to consider regarding this balancing ratio, as the process of advertising is composed of at least some of these four basic steps:1. Launching a campaign for a UPC through a search engine based on a list of words automatically generated by the marketplace, i.e., an automatic campaign. While a word is the most commonly-used target, a product target may also be utilized in connection with the present system and method disclosed herein. This list of words in the automatic campaign is proposed by the marketplace using its metrics and algorithms and are intended to catch the attention of potential customers looking for that particular UPC. The retailer bids a predefined maximum value to pay per click—the CPC or cost per click—and stipulates a maximum daily value to spend for the duration of the campaign.2. Analyzing the results of that campaign regarding the number of impressions and clicks for each word, the CPC, and the number of clicks per sale, if any. The actual CPC, which almost always is smaller than the bid, is also informed daily by the marketplace during the advertising campaign.3. Refining the campaign by bidding a maximum value to pay for each click of selected words (e.g., keywords) from the aforementioned list of words.4. Balancing the campaign by adjusting the bid value for each word towards the actual CPC to reduce the Advertising Cost of Sales (ACoS), which is the ratio in percentage points of advertising expenditure to the revenue from actual total sales value, for that UPC while maintaining an acceptable number of sales.

One of the main problems of this process is that it is unknown upfront which words, phrases, or expressions will effectively produce a sale. Additionally, this process of selecting words, phrases, or expressions, i.e., refining, is typically performed manually by individuals analyzing word lists informed by the online marketplace, which is expensive, time-consuming, and demands a heavy amount of skilled manpower.

Search engines usually charge per click, so that its goal is to launch an automatic campaign with the broadest set of words related to the UPC, as it does not know in advance which word will attract the attention of the customer. For instance, for the UPC “Pet Stairs,” a click can be obtained, among others, through the words “Pet,” “Stairs,” “Pet Stairs,” “Dog stairs,” “Cat stairs,” “Stairs for pets,” etc. The retailer is charged for every click, receiving information from the search engine on the word or phrase that generated the charged click.

The broadness of automatic campaigns makes them expensive, so it is desirable, for the sake of economy, to keep them as simple as viable, continuously selecting from the list of clicked words those most promising regarding sale generation, e.g., keywords.

In sequence, the seller launches a specific advertising campaign in the marketplace using that selected list of words, placing a bid (maximum CPC value acceptable to pay) on each of the selected keywords. The marketplace proceeds with a word auction, selecting from the bids (and other metrics related to the seller) the one that will appear in the first place for the customers and returns to the seller with the actual charged value per click for the bid keyword. Appearing before competitors increases the chance of achieving a sale.

The other metrics being equivalent per marketplace criteria, it is obvious that the higher the bid, the higher the chance for the bidder to appear in first place. However, one point to keep in mind is that the higher the bid, the higher the ACoS, such that the difficulty of the decision-making process is to find the fair bidding—the one click cost that balances the extra advertising spending with an adequate increase in sales revenue.

Bidding higher can increase sales, but can also lead to a smaller profit, as the ACoS will be higher and there is no linear relationship between increase in ACoS and increase in sales revenue. It is possible that the net income and the gross margin may become even smaller.

On the other hand, saving on advertising spending by reducing the bid on the marketplace auction can have a great impact on the sales speed and revenue, leaving a smaller profit, reducing the number of sales, and ultimately reflecting adversely on ratings and reviews.

Optimization of the process of designing an advertising campaign, with all its subtleties and mutual influences between intervening factors, is a challenging process. It demands a great deal of attention from the seller, as the purpose is not only to simply guess the fair value for the bid, but also to consider all the involved factors and to come to a fair bidding price—the one that increases the net revenue with as little influence on the gross margin as possible. This optimization for one product in the seller's portfolio on a daily basis demands a lot of work, attention, and skill, and manually performing this optimization daily for hundreds or thousands of products in the seller's portfolio is almost impossible.

SUMMARY OF THE INVENTION

The tool being presented is an automated, or autonomous, optimization process aiming to reduce both the time and cost of achieving a balanced advertising campaign for a product in which the bid for any particular word is automatically calculated such that the ACoS does not exceed a defined value, in percentage points, while achieving predefined sales goals and respecting inventory and budget constraints with no need for human intervention.

The pre-defined ACoS may be either the ACoS for the campaign, which may be defined as the ratio of advertising spending to the advertisement-generated sales volume, or the overall ACoS, i.e., the ACoS for the seller as a whole. The overall ACoS is calculated as the ratio as a percentage between the overall advertising spending of the seller and the total revenue from sales, i.e., both organic sales and advertisement-generated sales.

The present invention optimizes ad spending, autonomously choosing how much to bid on the marketplace for each keyword to generate an amount of sales based on the bid according to CPC/bid behavioral curves. It automatically integrates the purchase and sales process, splitting the sales goals in organic sales goal and ad-generated sales goal.

The proposed tool is capable of accessing and utilizing application programming interface (API) data from the online marketplaces that constitutes a report or reports in the form of, for example, a table, or a continuous streaming of data with all the words used in the automatic campaign, e.g., keywords, their click-through (if any), and the number of sales generated by those clicks (if any). The marketplace may update such report or table in real time, such that the tool is capable of accessing such report, or streamed data, in real-time as well. In other words, the report, streamed data, or table may be sent continuously in real time to the tool. In one exemplary embodiment, the tool may receive a report summary every time the online marketplace performs an update. Such report summary may include the latest information regarding the current words used in the automatic campaign, their current click-through (if any) and the current number of sales generated by those clicks (if any). As such, it is possible that the tool may access or receive multiple report summaries in a given time period. In another exemplary embodiment, the tool maintains its own table that includes at least some of the same information that is in the table maintained by the online marketplace, such that when the online marketplace updates its own table in real time, the tool also updates its own table simultaneously by accessing or receiving the updates made by the online-marketplace in real time.

Based on the data in this report, e.g., table, or streamed data, the tool may perform one or more of the steps in the process of word screening (described in-detail later), in which the tool, for example, selects which words to forego as not promising, and which words to promote and use in a Single Keyword Campaign (SKwC). It also selects which words to watch, keeping them in standby and not foregoing or promoting them in an SKwC until a further increase in the statistical relevance of the word makes a decision possible on whether to negativize or promote it. This process of word selection is preferably fully automated, making it possible to manage the advertising campaign of many UPCs. If desired by the seller, the process can be used in a non-automated manner, in which there is an additional step of manually deciding the action to be taken regarding each word: negativize, promote, or hold in standby.

It should be noted that an advantage of having access to the API data in real time is that the tool is not required to wait until the advertising campaign ends to make a decision regarding which words to forego as not promising and which words to promote for use in a SKwC. For example, it may be clear after the first few hours, in the automatic campaign that is expected to last a week, from the report or table that is accessed or received in real time by the tool, that at least one of the words should be foregone and at least one of the words should be promoted for use in an SKwC. As such, the tool may prematurely end the automated campaign thereby preventing any further unnecessary costs and saving the user time and increasing the chance of achieving more sales through the SKwC of the promoted keywords.

The results of the automatic campaign generated by the online marketplace are continuously analyzed by comparing the value of the bid, the price paid for each click (CPC), the click-through defined as the ratio of the number of clicks to the number of impressions, the number of sales (NOS) during the campaign, and the ACoS (Advertising Cost of Sale) obtained. If both values, the bid and the actual CPC, are close to each other, no change is taken in the advertising campaign and the campaign is resumed. If there is a significant gap between those values, the system algorithm defines a new value for the bid, smaller than the actual CPC. A new campaign is launched, using the new bid value. The new CPC is recorded, the new actual NOS is retrieved and stored, and the new ACoS is calculated. This cycle continues until there is no significant change in ACoS between two cycles of the campaign.

Moving forward, the system begins calculating both the sales amount obtained through organic sales, i.e., sales that did not happen through paid clicking on the marketplace, and the sales amount obtained through the advertising effort, i.e., advertisement-generated sales.

One of the greatest issues in planning and implementing advertising campaigns in the marketplace is how to increase the efficiency of overall sales respecting an advertising expenditure ceiling and how to consider the influence of this evolution of the sales volume on the inventory, such that the seller will not be overstocked or sell out. This consideration of mutual influences further points to the use of a robust tool to forecast actual sales numbers.

The present invention accomplishes this through a sophisticated statistical analysis of the efficiency of each word in the advertising campaign such that it is possible to forecast, with an acceptable confidence level, how to better assign the desired advertising resources to each word to guarantee the intended sales volume for the relevant UPC.

In general, in one aspect, the invention features a method of producing an advertising campaign of a product for an online marketplace seller including, under control of one or more processors configured with executable instructions, generating at least one automated advertising campaign of a product having an online marketplace listing with an online marketplace; collecting data on one or more keywords used in relation to the at least one automated advertising campaign, the data including an initial set of daily clicks or sales on the online marketplace listing; generating a single keyword advertising campaign of the product, where a single keyword of the single keyword advertising campaign is selected from the one or more keywords at least in part on the basis of the initial set of daily clicks or sales on the online marketplace listing; collecting data on the single keyword used in relation to the single keyword advertising campaign, the data including a second set of daily clicks or sales on the online marketplace listing; executing a machine learning component of an adaptive machine learning platform to generate a machine learning component output, where the machine learning component output is generated based at least in part on the data on the single keyword used in relation to the single keyword advertising campaign; generating a behavioral curve or table relating to an advertisement bid value of the online marketplace listing and a cost per click value associated with the online marketplace listing based at least in part on the machine learning component output of the machine learning component; generating one or more sales goals of the product for the online marketplace seller on the online marketplace; generating an optimized advertisement bid value based at least in part on the one or more sales goals and the behavioral curve or table; and generating an optimized advertising campaign of the product based at least in part on the optimized advertisement bid value.

Implementations of the invention may include one or more of the following features. The at least one automated advertising campaign may be generated for use in the online marketplace. The data on one or more keywords used in relation to the at least one automated advertising campaign may further include one or more of a number of impressions relating to the online marketplace listing, a total number of clicks on the online marketplace listing, a click-through rate, a conversion rate, a total number of advertisement-generated sales of the product, a total number of organic sales of the product, and a total number of sales of the product through the online marketplace listing. The single keyword of the single keyword advertising campaign may be selected from the one or more keywords at least in part on the basis of a conversion rate and an advertising unit cost of sale. The one or more keywords may be one or more of a word, an exact phrase, an exact expression, and combinations thereof. The machine learning component output may be generated at least in part on one or more of a number of impressions relating to the online marketplace listing, a cost per click for the online marketplace listing, a total number of clicks on the online marketplace listing, a conversion rate, a total number of advertisement-generated sales of the product, a total number of organic sales of the product, a total number of sales of the product through the online marketplace listing, and an advertising unit cost of sale.

The method may further include collecting additional data on the single keyword used in relation to the single keyword advertising campaign, the data including a third set of daily clicks or sales on the online marketplace listing; executing a machine learning component of an adaptive machine learning platform to generate a second machine learning component output, where the second machine learning component output is generated based at least in part on the additional data on the single keyword used in relation to the single keyword advertising campaign; generating a second behavioral curve or table relating to an advertisement bid value of the online marketplace listing and a cost per click value associated with the online marketplace listing based at least in part on the second machine learning component output of the machine learning component; generating a second optimized advertisement bid value based at least in part on the one or more sales goals and the second behavioral curve or table; and generating a second optimized advertising campaign of the product based at least in part on the second optimized advertisement bid value. The one or more sales goals may be generated at least in part on the basis of an actual inventory level of the product, a forecasted inventory level of the product, or both. The optimized advertisement bid value may be generated based at least in part on a simulation of a number of sales of the product and an advertising unit cost of sale of the product performed on all available advertisement bid values for all available keywords relating to the product. The single keyword advertising campaign of the product may be generated as including one or more automated safeguards, said one or more automated safeguards including limiting a maximum advertisement bid value in relation to a set value or a value of a sales price of the product. The optimized advertisement bid value may be generated based at least in part on one or more of a target advertising cost of sale, a total number of organic sales of the product, a total number of sales of the product through the online marketplace listing, a total number of organic sales of the product through the online marketplace listing, and a conversion rate of the product through the online marketplace listing.

In general, in another aspect, the invention features a system configured to produce an advertising campaign of a product for an online marketplace seller, including one or more processors, one or more computer-readable media, and one or more modules maintained on the one or more computer-readable media that, when executed by the one or more processors, cause the one or more processors to perform operations including generating at least one automated advertising campaign of a product having an online marketplace listing with an online marketplace; collecting data on one or more keywords used in relation to the at least one automated advertising campaign, the data including an initial set of daily clicks or sales on the online marketplace listing; generating a single keyword advertising campaign of the product, where a single keyword of the single keyword advertising campaign is selected from the one or more keywords at least in part on the basis of the initial set of daily clicks or sales on the online marketplace listing; collecting data on the single keyword used in relation to the single keyword advertising campaign, the data including a second set of daily clicks or sales on the online marketplace listing; executing a machine learning component of an adaptive machine learning platform to generate a machine learning component output, where the machine learning component output is generated based at least in part on the data on the single keyword used in relation to the single keyword advertising campaign; generating a behavioral curve or table relating to an advertisement bid value of the online marketplace listing and a cost per click value associated with the online marketplace listing based at least in part on the machine learning component output of the machine learning component; generating one or more sales goals of the product for the online marketplace seller on the online marketplace; generating an optimized advertisement bid value based at least in part on the one or more sales goals and the behavioral curve or table; and generating an optimized advertising campaign of the product based at least in part on the optimized advertisement bid value.

Implementations of the invention may include one or more of the following features. The at least one automated advertising campaign may be generated for use in the online marketplace. The data on one or more keywords used in relation to the at least one automated advertising campaign may further include one or more of a number of impressions relating to the online marketplace listing, a total number of clicks on the online marketplace listing, a click-through rate, a conversion rate, a total number of advertisement-generated sales of the product, a total number of organic sales of the product, and a total number of sales of the product through the online marketplace listing. The single keyword of the single keyword advertising campaign may be selected from the one or more keywords at least in part on the basis of a conversion rate and an advertising unit cost of sale. The one or more keywords may be one or more of a word, an exact phrase, an exact expression, and combinations thereof. The machine learning component output may be generated at least in part on one or more of a number of impressions relating to the online marketplace listing, a cost per click for the online marketplace listing, a total number of clicks on the online marketplace listing, a conversion rate, a total number of advertisement-generated sales of the product, a total number of organic sales of the product, a total number of sales of the product through the online marketplace listing, and an advertising unit cost of sale.

The system may further include additional operations including collecting additional data on the single keyword used in relation to the single keyword advertising campaign, the data including a third set of daily clicks or sales on the online marketplace listing; executing a machine learning component of an adaptive machine learning platform to generate a second machine learning component output, where the second machine learning component output is generated based at least in part on the additional data on the single keyword used in relation to the single keyword advertising campaign; generating a second behavioral curve or table relating to an advertisement bid value of the online marketplace listing and a cost per click value associated with the online marketplace listing based at least in part on the second machine learning component output of the machine learning component; generating a second optimized advertisement bid value based at least in part on the one or more sales goals and the second behavioral curve or table; and generating a second optimized advertising campaign of the product based at least in part on the second optimized advertisement bid value. The one or more sales goals may be generated at least in part on the basis of an actual inventory level of the product, a forecasted inventory level of the product, or both. The optimized advertisement bid value may be generated based at least in part on a simulation of a number of sales of the product and an advertising unit cost of sale of the product performed on all available advertisement bid values for all available keywords relating to the product. The single keyword advertising campaign of the product may be generated as including one or more automated safeguards, said one or more automated safeguards including limiting a maximum advertisement bid value in relation to a set value or a value of a sales price of the product. The optimized advertisement bid value may be generated based at least in part on one or more of a target advertising cost of sale, a total number of organic sales of the product, a total number of sales of the product through the online marketplace listing, a total number of organic sales of the product through the online marketplace listing, and a conversion rate of the product through the online marketplace listing.

In another embodiment, the present invention may include a method of automatically producing an advertising campaign of a product for an online marketplace seller, the method performed by an application including one or more programs of instruction embodied in a non-transitory computer readable medium and executable by a processor to configure the application, comprising: generating at least one automated advertising campaign of a product having an online marketplace listing with an online marketplace; obtaining, in real time, a keyword table from the online marketplace, the keyword table including one or more keywords used in relation to the at least one automated advertising campaign and keyword data in connection with each of the one or more keywords, the keyword table being updated in real time by the online marketplace; generating a single keyword advertising campaign of the product, wherein a single keyword of the single keyword advertising campaign is selected from the keyword table at least in part on the basis of the keyword data; collecting data on the single keyword used in relation to the single keyword advertising campaign, the data including a second set of daily clicks or sales on the online marketplace listing; executing a machine learning component of an adaptive machine learning platform to generate a machine learning component output, wherein the machine learning component output is generated based at least in part on the data on the single keyword used in relation to the single keyword advertising campaign; generating a behavioral curve or table relating to an advertisement bid value of the online marketplace listing and a cost per click value associated with the online marketplace listing based at least in part on the machine learning component output of the machine learning component; generating one or more sales goals of the product for the online marketplace seller on the online marketplace; generating a current advertisement bid value based at least in part on the one or more sales goals and the behavioral curve or table; generating a current advertising campaign of the product based at least in part on the optimized advertisement bid value; and generating an optimized advertisement bid value by continuously adjusting the current advertisement bid value until the current advertisement bid value has reached an optimized value, the continuous adjustment being performed by: collecting data on the optimized advertising campaign, the data including a set of sales on the online marketplace listing; generating an adjusted advertisement bid value based at least on one of the data relating to the optimized advertising campaign, the one or more sales goals and the behavioral curve or table; and generating an optimized advertising campaign of the product based at least in part on the adjusted advertisement bid value.

Implementations of the invention may include one or more of the following features. The method further comprises analyzing a keyword in the one or more reports to determine whether to promote the keyword to a single advertising campaign, maintain the keyword in the keyword table, or remove the keyword from the keyword table. The analysis of the keyword is performed based on the keyword data in connection with the keyword. The keyword data for the keyword includes at least one of a click-through rate of the keyword and the number of sales in connection with the click-through rate. In the case that the keyword is neither promoted to a single advertising campaign nor removed from the keyword table, the keyword is maintained in the keyword table until there is sufficient keyword data to perform another analysis of the keyword. In the case that the keyword is neither promoted to a single advertising campaign nor removed from the keyword table, the keyword is maintained in the keyword table while complying with any automated exclusion criteria defined by the seller. The method further comprises modifying the keyword table based on the analysis of the keyword.

In yet another embodiment, the present invention may include a method of automatically producing an advertising campaign of a product for an online marketplace seller, the method performed by an application including one or more programs of instruction embodied in a non-transitory computer readable medium and executable by a processor to configure the application, comprising: obtaining one or more prior advertising campaigns of a product generated by the seller in the online marketplace by connecting with an online marketplace account of the seller; generating at least one automated advertising campaign of the product having an online marketplace listing with an online marketplace using information from the one or more prior advertising campaigns; collecting data on one or more keywords used in relation to the at least one automated advertising campaign, the data including an initial set of daily clicks or sales on the online marketplace listing; generating a single keyword advertising campaign of the product, wherein a single keyword of the single keyword advertising campaign is selected from the one or more keywords at least in part on the basis of the initial set of daily clicks or sales on the online marketplace listing; collecting data on the single keyword used in relation to the single keyword advertising campaign, the data including a second set of daily clicks or sales on the online marketplace listing; executing a machine learning component of an adaptive machine learning platform to generate a machine learning component output, wherein the machine learning component output is generated based at least in part on the data on the single keyword used in relation to the single keyword advertising campaign; generating a behavioral curve or table relating to an advertisement bid value of the online marketplace listing and a cost per click value associated with the online marketplace listing based at least in part on the machine learning component output of the machine learning component; generating one or more sales goals of the product for the online marketplace seller on the online marketplace; generating a current advertisement bid value based at least in part on the one or more sales goals and the behavioral curve or table; generating a current advertising campaign of the product based at least in part on the optimized advertisement bid value; and generating an optimized advertisement bid value by continuously adjusting the current advertisement bid value until the current advertisement bid value has reached an optimized value, the continuous adjustment being performed by: collecting data on the optimized advertising campaign, the data including a set of sales on the online marketplace listing; generating an adjusted advertisement bid value based at least on one of the data relating to the optimized advertising campaign, the one or more sales goals and the behavioral curve or table; and generating an optimized advertising campaign of the product based at least in part on the adjusted advertisement bid value.

Implementations of the invention may include one or more of the following features. The information from the one or more prior advertising campaigns includes one or more prior keywords used in the one or more prior advertising campaigns and prior keyword data in connection with the one or more prior keywords. The prior keyword data includes a set of daily clicks or sales in the online marketplace listing in connection with the one or more prior keywords. The prior keyword data further includes one or more of a number of impressions relating to the online marketplace listing, a total number of clicks on the online marketplace listing, a click-through rate, a conversion rate, a total number of advertisement-generated sales of the product, a total number of organic sales of the product, and a total number of sales of the product through the online marketplace listing. The one or more prior keywords are included in the one or more keywords used in the at least one automated advertising campaign. The method further comprises analyzing each keyword of the one or more keywords in the automated advertising campaign to determine whether to promote the keyword to a single advertising campaign, maintain the keyword in the at least one automated advertising campaign, or remove the keyword from the at least one automated advertising campaign. When a prior keyword of the one or more keywords used in the at least one automated campaign is analyzed to determine whether to promote the prior keyword to a single keyword advertising campaign, the analysis is performed based at least in part on the prior keyword data in connection with the prior keyword.

In yet a further embodiment, the present invention may include a method of automatically producing an advertising campaign of a product for an online marketplace seller, the method performed by an application including one or more programs of instruction embodied in a non-transitory computer readable medium and executable by a processor to configure the application, comprising: obtaining information from one or more competitor products listed in the online marketplace by competitors to the seller; generating at least one automated advertising campaign of a product having an online marketplace listing with an online marketplace based on the information obtained from the one or more competitor products; collecting data on one or more keywords used in relation to the at least one automated advertising campaign, the data including an initial set of daily clicks or sales on the online marketplace listing; generating a single keyword advertising campaign of the product, wherein a single keyword of the single keyword advertising campaign is selected from the one or more keywords at least in part on the basis of the initial set of daily clicks or sales on the online marketplace listing; collecting data on the single keyword used in relation to the single keyword advertising campaign, the data including a second set of daily clicks or sales on the online marketplace listing; executing a machine learning component of an adaptive machine learning platform to generate a machine learning component output, wherein the machine learning component output is generated based at least in part on the data on the single keyword used in relation to the single keyword advertising campaign; generating a behavioral curve or table relating to an advertisement bid value of the online marketplace listing and a cost per click value associated with the online marketplace listing based at least in part on the machine learning component output of the machine learning component; generating one or more sales goals of the product for the online marketplace seller on the online marketplace; generating a current advertisement bid value based at least in part on the one or more sales goals and the behavioral curve or table; generating a current advertising campaign of the product based at least in part on the optimized advertisement bid value; and generating an optimized advertisement bid value by continuously adjusting the current advertisement bid value until the current advertisement bid value has reached an optimized value, the continuous adjustment being performed by: collecting data on the optimized advertising campaign, the data including a set of sales on the online marketplace listing; generating an adjusted advertisement bid value based at least on one of the data relating to the optimized advertising campaign, the one or more sales goals and the behavioral curve or table; and generating an optimized advertising campaign of the product based at least in part on the adjusted advertisement bid value.

Implementations of the invention may include one or more of the following features. The one or more competitor products have features or characteristics that are similar to the product. The information obtained from the one or more competitor products includes one or more competitor keywords used in one or more corresponding competitor advertising campaign and competitor keyword data in connection with the one or more competitor keywords. The competitor keyword data includes a set of number of impressions, number of daily clicks, number of sales, or volume of sales in the online marketplace listing. The method according to claim17, wherein the one or more competitor keywords are included in the one or more keywords used in the at least one automated advertising campaign. The method further comprises determining a best-selling or highest-ranked competitor product of the one or more competitor products based on product sales information from the online marketplace; selecting one or more competitor keywords used in one or more corresponding competitor advertising campaign for the best-selling or highest-ranked competitor product; and generating an automated advertising campaign using the one or more competitor keywords.

