Patent Application
20190164202
The present invention relates generally to pricing products and services, and more particularly to methods and systems for dynamically making pricing recommendations for the products and services based on customer preferences.
With the advent of internet and smartphones, e-commerce has developed rapidly. E-commerce is one of the easiest and most convenient channels for vendors and service providers to conduct their businesses. Typically, the vendors may sell their products to customers at a brick and mortar location/shop, through a website, or a combination of both aforementioned channels. E-commerce makes the process of buying the products and services, available in the market, smooth and hassle-free. Further, e-commerce offers access to wide variety information about the products and services to the customers almost immediately. Additionally, e-commerce websites and mobile applications provide wide variety of options and alternatives from which the customers may choose their desired product.
To increase sales for a particular product, vendors and service providers typically introduce discounts or offers for buyers. Using discounts and offers can be advantageous for a vendor to overcome competition from other vendors. The customers rely on discounts and offers to reduce expenditure on purchased products and services. Generally, vendors notify the customers regarding discounts and offers beforehand by way of newspapers, internet advertisements, mobile applications, and media outlets and the like. Further, customers are also notified about the discounts and offers through emails and short message services (SMSs). The customers usually dislike constant receipt of emails and notifications, as they are distracting and disturbing.
Further, the vendors do not take into account preferences and interests of their customers while generating the discounts and offers. The vendors typically send discount and offer notifications to the customers for all the products and services that are up for sale. Further, the vendors generate offers for all the customers irrespective of the location of the customers. Hence, all the customers get the notification of the offers and discounts. The aforementioned method is thus inefficient and leads to wastage of cellular and Internet bandwidth. The vendors send notifications of offers and discounts to the customers to increase their reputation and customer count. Sending notifications of the offers and discounts to all the customers does not ensure that all the customers will be interested in the offers and also degrades sales of the vendors.
Typically, the offers generated for the customers have a fixed discount rate. The offers do not take into account the sales of the product corresponding to the offer, and the number of customers near a vendor business location at the time of the offer. This is disadvantageous as the sale of the product based on the offer may be sub-optimal, and may cause losses to the vendor.
In light of the foregoing, there is a need for a method for generating offers and discounts for the products and services that overcome the aforementioned shortcomings.
In one embodiment, the present invention provides a method for generating an optimal offer price recommendation for an item, such as, for example a product or a service. A first set of customer locations corresponding to a first set of customer mobile devices is determined. Each customer mobile device of the first set of customer mobile devices includes a mobile application. A set of offer coverage areas corresponding to a first set of vendor business locations is determined. The set of offer coverage areas is determined based on an offer coverage perimeter value and a set of offer notification perimeter values. The offer coverage perimeter value corresponds to a vendor and the set of offer notification perimeter values corresponds to the first set of customer mobile devices. A first subset of customer mobile devices is identified from the first set of customer mobile devices based on at least one of the set of offer coverage areas, a vendor business location, and the first set of customer locations. Further, a second subset of customer mobile devices is identified from the first subset of customer mobile devices based on the set of offer notification perimeter values. An optimal sales price for the item is determined based on at least one of customer profiles, vendor profiles, and the second subset of customer mobile devices. The customer and vendor profiles are stored in customer and vendor databases, respectively. An offer accessible through the mobile application for the item is generated for the second subset of customer mobile devices.
In another embodiment, the present invention provides a system for optimizing a sales price for an item, such as, for example a product or a service. The system includes a memory for storing customer profile, customer location, and vendor profile databases. The system further includes a server communicatively coupled with the memory. The server determines a first set of customer locations corresponding to a first set of customer mobile devices. Each customer mobile device of the first set of customer mobile devices includes a mobile application. A set of offer coverage areas corresponding to a first set of vendor business locations is determined. The set of offer coverage areas is determined based on an offer coverage perimeter value and a set of offer notification perimeter values. The offer coverage perimeter value corresponds to a vendor and the set of offer notification perimeter values corresponds to the first set of customer mobile devices. A first subset of customer mobile devices is identified from the first set of customer mobile devices based on at least one of the set of offer coverage areas, a vendor business location, and the first set of customer locations. Further, a second subset of customer mobile devices is identified from the first subset of customer mobile devices based on the set of offer notification perimeter values. An optimal sales price for the item is determined based on at least one of customer profiles, vendor profiles, and the second subset of customer mobile devices. The customer and vendor profiles are stored in customer and vendor databases, respectively. An offer accessible through the mobile application for the item is generated for the second subset of customer mobile devices.
The various features, aspects, and advantages of the present system and method are set forth with particularity in the appended claims. Embodiments of the present system and method will hereinafter be described in conjunction with the appended drawings provided to illustrate, and not to limit, the scope of the claims, wherein like designations denote like elements, and in which:
As used in the specification and claims, the singular forms “a”, “an” and “the” include plural references unless the context clearly dictates otherwise. For example, the term “an article” may include a plurality of articles unless the context clearly dictates otherwise.
Those with ordinary skill in the art will appreciate that the elements in the figures are illustrated for simplicity and clarity and are not necessarily drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated, relative to other elements, in order to improve the understanding of the present invention.
There may be additional components described in the foregoing application that are not depicted on one of the described drawings. In the event such a component is described, but not depicted in a drawing, the absence of such a drawing should not be considered as an omission of such design from the specification.
The detailed description of the appended drawings is intended as a description of the currently preferred embodiments of the present invention, and is not intended to represent the only form in which the present invention may be practiced. It is to be understood that the same or equivalent functions may be accomplished by different embodiments that are intended to be encompassed within the spirit and scope of the present invention.
It may be noted that the components and the method steps have been described in the present specification to show specific details that are pertinent for an understanding of the embodiments described herein. Furthermore, the various components and the method steps have been represented so as not to obscure the disclosure with details that will be readily apparent to those with ordinary skill in the art having the benefit of the description herein.
The exemplary method is illustrated as a collection of blocks in a logical flow chart, which represents operations that may be implemented in hardware, software, or combinations thereof. The various operations are depicted in the blocks to illustrate the functions that are performed during various phases of the exemplary method. In the context of software, the blocks represent computer instructions that, when executed by one or more processors, perform the recited operations. The order in which the exemplary method is described is not intended to be construed as a limitation, and any number of the described blocks may be combined in any order to implement the exemplary method disclosed herein, or an equivalent alternative method. Additionally, certain blocks may be deleted from the exemplary method or augmented by additional blocks with added functionality without departing from the spirit and scope of the subject matter described herein.
At step 102, locations of the customer mobile devices (Clat and Clong) are sent to a server. The server determines the location of the customer mobile devices by receiving a periodic message sent by each customer mobile device through a mobile application running thereon. At step 104, the server saves and updates the locations of the customer mobile devices (Clat and Clong) in a customer location database. At step 106, the server calculates offer coverage areas corresponding to a vendor business location, for each vendor that has the mobile application running on the corresponding vendor mobile device. The server calculates the offer coverage areas based on an offer coverage perimeter (OCP) value corresponding to the vendor, and a maximum allowed value for offer notification perimeter (ONP) value corresponding to the customer mobile devices. An exemplary methodology for calculating the offer coverage areas is described in detail with reference to
At step 108, the server scans through the customer location database, and generates a potential customer list. The customers that lie within the vendor's corresponding offer coverage areas are added to the potential customer list. At step 110, the potential customer list is further refined to generate a target customer list. The target list is further refined based on the customer preferences other than price preference that are stored in the customer profile database. An exemplary methodology for generating potential and target customer lists is described in detail with reference to
At step 112, the server scans through the vendor database, and determines a number of competing offers made by other vendors to the customers in the vendor's offer coverage area. At step 114, an initial optimum sales price is determined for the item on sale. The server configures a machine learning algorithm for determining the initial optimum sales price. The machine learning algorithm determines the initial optimum price based on at least the available number of items for sale and historical averages for number of items sold for different offer prices in the vendor's coverage area during that day of the week and time of the day. The machine learning algorithm also maintains the historical averages for number of competing offers in the vendor's offer coverage area, number of total customers in vendor's offer coverage area, and number of targeted customers in the vendor's coverage area. These historical averages are maintained for each hour of the day and day of the week. At step 116, the server updates the initial optimum sales price based on three multiplication factors. The three multiplication factors are determined as follows:
Multiplication Factor 1=(Average number of competing offers from historical data/current number of competing offers)
Multiplication Factor 2=(Current number of all customers/Average number of all customers from historical data)
Multiplication Factor 3=(Current number of targeted customers/Average number of targeted customers from historical data)
The optimum sales price is determined as follows:
Updated optimum sales price=Initial optimum sales price*Multiplication Factor 1*Multiplication Factor 2*Multiplication Factor 3
Further, at step 118, the target customer list is refined based on the price preferences of the customers stored in the customer profile database. The customer profile database provides the price preferences of the customer that include an offer price range corresponding to a customer. The offer price offer price range is indicative of a price of the item that a particular customer is willing to pay.
At step 120, the server generates the offer for the customers of the target customer list based on the updated optimum sales price. At step 122, the offer is either presented to the customers of the target customer list for accessing by way of the mobile application installed in their respective customer mobile devices, or an offer recommendation can be made to the vendor for updating the offer by the vendor. The vendor may decide to change the sales price based on the recommendation made by the server of the present invention.
For a customer in the potential customer list, it is determined at step 312 if the distance between the location of the corresponding customer mobile device and the location of the vendor's business is less than the offer notification perimeter value corresponding to the customer. If the distance between the location of the customer mobile device and the location of the vendor's business is less than the offer notification perimeter value corresponding to the customer, step 314 is executed. At step 314, the customer is added to the target customer list. If the distance between the location of the customer mobile device and the location of the vendor's business is greater than the offer notification perimeter value corresponding to the customer, step 316 is executed. At step 316, it is determined that the customer is not a target customer, and is not added to the target customer list.
The vendor business location (VLat, VLong) is stored in the vendor profile database 404b. The vendor profile database 404b is stored in the memory 404. An offer coverage perimeter value OCP is associated with the vendor 406. Offer notification perimeter values ONP1-ONPn are associated with the customer mobile devices 408a-408n. The server 402 determines offer notification areas for each of the customer mobile devices 408a-408n. The server 402 determines centers of the offer coverage areas at the vendor business location (VLat, VLong). Further, based on the offer coverage perimeter value OCP and the offer notification perimeter values ONP1-ONPn, the server 402 retrieves the customer locations (C1Lat, C1Long)−(CnLat, CnLong) from the customer location database 404b, and generates a potential customer list based on the customer locations (C1Lat, C1Long)−(CnLat, CnLong). Further, the server 402 generates the target customer list based on the offer notification values ONP1-ONPn. The server 402 subsequently retrieves the customer profile database 404a from the memory.
Further, based on the customer profile database 404a, the server 402 refines the target customer list to generate a refined target customer list. The server 402 then implements an artificial intelligence algorithm for determining the optimal sales price for the item. The server 402 determines competing offers for the item from other vendors for the customers in the potential customers list. The server 402 subsequently determines the initial optimal sales price for the item based on historical data. The server 402 then updates the optimal sales price based on the multiplication factors. The server 402 then generates an offer OFFER for the customer mobile devices from the refined target list. The server 402 subsequently sends the offer OFFER to customers in the refined target customer list based on the updated optimal sales price. The offer OFFER can be accessed by the customers in the refined target customer list by way of the mobile application.
The sales price optimization system 400 facilitates the implementation of the method for optimization of the sales price of the item and further reduces the cost involved. Since the sales price optimization system 400 is implemented through a mobile application, the need for a brick and mortar location is eliminated. Further, since the server 402 implements the artificial intelligence algorithm, the offer OFFER is sent only to customers included in the refined target customer list. This is advantageous as it saves the customers from the hassle of receiving constant emails and notifications regarding the offer OFFER. Sending the offer OFFER to all the customers causes unnecessary wastage of internet bandwidth. This further increases the cost of operations for the vendor. The sales price optimization system 400 overcomes the aforementioned wastage of internet bandwidth as well as saves the cost of operations for the vendor by sending the offer OFFER only to the customers included in the refined target customer list, as these are the customers that are most likely to avail the offer.
Further, implementation of the artificial intelligence algorithm facilitates the vendor to modify the offer OFFER in real time based on the customer and vendor profile databases 404a and 404b. Ability to modify the offer OFFER in real time enables the vendor to increase sales, while further increasing the probability of a customer buying the item. Since the offer OFFER is sent to any customer based on the liking of the customer, it aids in increasing the sales, while helping the vendor to maintain his clientele. Further, the implementation of the artificial intelligence algorithm also enables the vendor to apply further analytical tools to determine the choice of the customers, and modify the item accordingly.
Referring now to
The computer system 500 includes a processor 502 that may be a special purpose or a general-purpose processing device. The processor 502 may be a single processor, multiple processors, or combinations thereof. The processor 502 may have one or more processor “cores.” In one example, the processor 502 is an octa-core processor. Further, the processor 502 may be connected to a communication infrastructure 504, such as a bus, message queue, multi-core message-passing scheme, and the like. The computer system 500 further includes a main memory 506 and a secondary memory 508. Examples of the main memory 506 may include RAM, ROM, dynamic RAM (DRAM), and the like. The secondary memory 508 may include a hard disk drive or a removable storage drive, such as a floppy disk drive, a magnetic tape drive, a compact disc, an optical disk drive, a flash memory, and the like. Further, the removable storage drive may read from and/or write to a removable storage device in a manner known in the art. In one example, if the removable storage drive is a compact disc drive, the removable storage device may be a compact disc. In an embodiment, the removable storage unit may be a non-transitory computer readable recording media.
The computer system 500 further includes an input/output (I/O) interface 510 and a communication interface 512. The I/O interface 510 includes various input and output devices that are configured to communicate with the processor 502. Examples of the input devices may include a keyboard, a mouse, a joystick, a touchscreen, a microphone, and the like. Examples, of the output devices may include a display screen, a speaker, headphones, and the like. The communication interface 512 may allow data to be transferred between the computer system 500 and various devices that are communicatively coupled to the computer system 500. Exemplary communication interfaces 512 may include a modem, a network interface, i.e., an Ethernet card, a communications port, and the like. Data transferred via the communication interface 512 corresponds to signals, such as electronic, electromagnetic, optical, or other signals as will be apparent to a person skilled in the art. The signals may travel via a communications channel (not shown) which transmits the signals to devices that are communicatively coupled to the computer system 500. Examples of the communications channel include, but are not limited to, cable, fiber optics, a phone line, a cellular phone link, or a radio frequency link.
Computer program medium and computer usable medium may refer to a non-transitory computer readable medium, such as the main memory 506 and the secondary memory 508, which may be a semiconductor memory such as a DRAM. The computer program medium may provide data that enables the computer system 500 to implement the methods illustrated in
A person having ordinary skill in the art will appreciate that embodiments of the disclosed subject matter can be practiced with various computer system configurations, including multi-core multiprocessor systems, minicomputers, mainframe computers, computers linked or clustered with distributed functions, as well as pervasive or miniature computers that may be embedded into virtually any device. For instance, at least one processor such as the processor 502 and a memory such as the main memory 506 and the secondary memory 508 implements the above described embodiments. Further, the operations may be described as a sequential process, however some of the operations may in fact be performed in parallel, concurrently, and/or in a distributed environment, and with program code stored locally or remotely for access by single or multiprocessor machines. In addition, in some embodiments the order of operations may be rearranged without departing from the spirit of the disclosed subject matter.
Techniques consistent with the present invention provide, among other features, systems and methods for generating optimal price recommendations for products and services using machine learning, artificial intelligence, and data analytics. While various exemplary embodiments of the disclosed system and method have been described above it should be understood that they have been presented for purposes of example only, not limitations. It is not exhaustive and does not limit the invention to the precise form disclosed.
In the claims, the words ‘comprising’, ‘including’ and ‘having’ do not exclude the presence of other elements or steps then those listed in a claim. The terms “a” or “an,” as used herein, are defined as one or more than one. Unless stated otherwise, terms such as “first” and “second” are used to arbitrarily distinguish between the elements such terms describe. Thus, these terms are not necessarily intended to indicate temporal or other prioritization of such elements. The fact that certain measures are recited in mutually different claims does not indicate that a combination of these measures cannot be used to advantage.
While various embodiments of the present invention have been illustrated and described, it will be clear that the present invention is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the spirit and scope of the present invention, as described in the claims.
