DESC:RESERVATION OF RIGHTS
[001] A portion of the disclosure of this patent document contains material, which is subject to intellectual property rights such as, but are not limited to, copyright, design, trademark, Integrated Circuit (IC) layout design, and/or trade dress protection, belonging to Jio Platforms Limited (JPL) or its affiliates (hereinafter referred as owner). The owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights whatsoever. All rights to such intellectual property are fully reserved by the owner.

FIELD OF DISCLOSURE
[002] The present disclosure relates to a computer integrated telecommunication system, and specifically to a system and a method for determining ideal launch price for one or more products.

BACKGROUND OF DISCLOSURE
[003] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.
[004] Launch of a new product is a strategic exercise for an organization as it involves not just selling the product but a chance for the organization to build a brand, gather feedback, disrupt a market, and the like. During the launch of the product, determining right launch parameters may be a challenge for the organizations. Launch price being one of the prominent launch parameters may have far reaching strategic implications. Right launch price for right market is a key factor in determining success of the product. However, conventional methods and systems for pricing new products are unidimensional and qualitative, and do not cover sectors factoring in competition, cannibalization, and revenue/margin maximization, which may lead to a drop in a success rate of the product.
[005] There is, therefore, a need in the art to provide an improved system and a method to determine an ideal launch price for a product by overcoming the deficiencies of the prior art(s).

OBJECTS OF THE PRESENT DISCLOSURE
[006] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[007] It is an object of the present disclosure to provide a one-stop robust and comprehensive solution for determining an ideal launch price of a product by considering multiple factors.
[008] It is an object of the present disclosure to provide a system and a method that factors in price correction based on various factors such as festivals, calendar features, etc.
[009] It is an object of the present disclosure to provide a system and a method that streamlines a process of pricing and launching new products.
[0010] It is an object of the present disclosure to provide a system and a method that determines the ideal launch price of the product effectively.
[0011] It is an object of the present disclosure to provide a system and a method that embeds quantitative approach to determine the ideal launch price of the product.

SUMMARY
[0012] This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
[0013] In an aspect, the present disclosure relates to a system for determining a launch price for a product. The system includes one or more processors, and a memory operatively coupled to the one or more processors. The memory includes processor-executable instructions, which on execution, cause the one or more processors to receive data associated with one or more products from one or more data sources, convert the received data into model consumable data, create a cannibalization cluster and a competitive cluster for the one or more products based on the model consumable data, map at least one product to be launched with the cannibalization cluster and the competitive cluster of the one or more products, quantify cannibalization effect, competitive effect, and revenue effect of the at least one product to be launched based on the mapping, and determine the launch price for the at least one product to be launched based on the cannibalization effect, the competitive effect, and the revenue effect of the at least one product to be launched.
[0014] In an embodiment, the data associated with the one or more products may include at least one of sales data, product attribute data, environmental factors, and calendar factors.
[0015] In an embodiment, the one or more processors may create the cannibalization cluster and the competitive cluster using at least one of a K-means clustering method, a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method, a Clustering in Quest (CLIQUE) method, and a genetic method.
[0016] In an embodiment, the cannibalization cluster may be a cluster grouping with one or more similar products from a same brand, and the competitive cluster may be a cluster grouping including competitors of the at least one product to be launched.
[0017] In an embodiment, the one or more processors may quantify the cannibalization effect, the competitive effect, and the revenue effect of the at least one product to be launched using at least one of an Extreme Gradient Boosting (XGBoost) model, a causality model, a distance relationship, and an elasticity model.
[0018] In an embodiment, the memory includes processor-executable instructions, which on execution, may cause the one or more processors to determine an extent of relationship between the one or more products, and prevent negative unexpected cross-product impact on sales of the at least one product to be launched based on the cannibalization effect, the competitive effect, and the revenue effect of the at least one product to be launched.
[0019] In an embodiment, the one or more processors may determine the launch price by being configured to aggregate the cannibalization effect, the competitive effect, and the revenue effect of the at least one product to be launched based on the quantification, and determine the launch price for the at least one product to be launched based on the aggregation.
[0020] In an embodiment, the memory includes processor-executable instructions, which on execution, may cause the one or more processors to update the launch price of the at least one product to be launched based on occurrence of one or more events.
[0021] In another aspect, the present disclosure relates to a method for determining a launch price for a product. The method includes receiving, by a processor associated with a system, data associated with one or more products from one or more data sources, converting, by the processor, the received data into model consumable data, creating, by the processor, a cannibalization cluster and a competitive cluster for the one or more products based on the model consumable data, mapping, by the processor, at least one product to be launched with the cannibalization cluster and the competitive cluster of the one or more products, quantifying, by the processor, cannibalization effect, competitive effect, and revenue effect of the at least one product to be launched based on the mapping, and determining, by the processor, the launch price for the at least one product to be launched based on the cannibalization effect, the competitive effect, and the revenue effect of the at least one product to be launched.
[0022] In an embodiment, the data associated with the one or more products may include at least one of sales data, product attribute data, environmental factors, and calendar factors.
[0023] In an embodiment, creating, by the processor, the cannibalization cluster and the competitive cluster may include using at least one of a K-means clustering method, a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method, a Clustering in Quest (CLIQUE) method, and a genetic method.
[0024] In an embodiment, quantifying, by the processor, the cannibalization effect, the competitive effect, and the revenue effect of the at least one product to be launched from the cannibalization cluster and the competitive cluster may include using at least one of an Extreme Gradient Boosting (XGBoost) model, a causality model, a distance relationship, and an elasticity model.
[0025] In an embodiment, the method may include determining, by the processor, an extent of relationship between the one or more products, and preventing, by the processor, negative unexpected cross-product impact on sales of the at least one product to be launched based on the cannibalization effect, the competitive effect, and the revenue effect of the at least one product to be launched.
[0026] In an embodiment, determining, by the processor, the launch price may include aggregating, by the processor, the cannibalization effect, the competitive effect, and the revenue effect of the at least one product to be launched based on the quantification, and determining, by the processor, the launch price for the at least one product to be launched based on the aggregation.
[0027] In an embodiment, the method may include updating, by the processor, the launch price of the at least one product to be launched based on at least one of festivals and calendar factors.
[0028] In another aspect, the present disclosure relates to a user equipment. The user equipment includes one or more processors, and a memory operatively coupled to the one or more processors, wherein the memory includes processor-executable instructions, which on execution, cause the one or more processors to receive recommendations associated with a launch price of at least one product to be launched from a system. The one or more processors are communicatively coupled with the system, and the system is configured to receive data associated with one or more products from one or more data sources, convert the received data into model consumable data, create a cannibalization cluster and a competitive cluster for the one or more products based on the model consumable data, map the at least one product to be launched with the cannibalization cluster and the competitive cluster of the one or more products, quantify cannibalization effect, competitive effect, and revenue effect of the at least one product to be launched based on the mapping, determine the launch price for the at least one product to be launched based on the cannibalization effect, the competitive effect, and the revenue effect of the at least one product to be launched, and provide recommendations associated with the launch price of the at least one product to be launched to the user equipment.

BRIEF DESCRIPTION OF THE DRAWINGS
[0029] The accompanying drawings, which are incorporated herein, and constitute a part of this invention, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present invention. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that invention of such drawings includes the invention of electrical components, electronic components or circuitry commonly used to implement such components.
[0030] The diagrams are for illustration only, which thus is not a limitation of the present disclosure, and wherein:
[0031] FIG. 1 illustrates an exemplary network architecture (100) in which or with which a system of the present disclosure may be implemented, in accordance with an embodiment of the present disclosure.
[0032] FIG. 2 illustrates an exemplary block diagram (200) of the proposed system, in accordance with an embodiment of the present disclosure.
[0033] FIG. 3 illustrates an exemplary block diagram (300) for determining an ideal launch price of a product, in accordance with an embodiment of the present disclosure.
[0034] FIGs. 4A-4E illustrate exemplary flow diagrams (400A-400E) for implementing an ideal launch price determination method, in accordance with embodiments of the present disclosure.
[0035] FIG. 5 illustrates an exemplary computer system (500) in which or with which embodiments of the present disclosure may be utilized in accordance with embodiments of the present disclosure.
[0036] The foregoing shall be more apparent from the following more detailed description of the disclosure.

DETAILED DESCRIPTION
[0037] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.
[0038] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.
[0039] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
[0040] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[0041] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.
[0042] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0043] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.
[0044] The present disclosure provides a system and a method for determining an ideal launch price for a product. The system may perform an ideal and comprehensive ideal launch pricing based on factors of competition and cannibalization, and revenue/margin maximization. The system may involve creation of competition and cannibalization clusters to quantify competitive and cannibalization effects among one or more products. The system may also estimate an inherent demand of products based on external factors. User input of new product specifications, exogenous variables, and pricing may be required to arrive at an optimal launch price. Exogenous variables may include, but not limited to, location specifications, festivals, calendar features, etc. The effects of inherent demand, competition, cannibalization, and clustering may be aggregated using a genetic method optimizer to determine an ideal launch price of the new product.
[0045] Various embodiments of the present disclosure will be explained in detail with reference to FIGs. 1-5.
[0046] FIG. 1 illustrates an exemplary network architecture (100) in which or with which a proposed system may be implemented, in accordance with an embodiment of the present disclosure.
[0047] As illustrated in FIG. 1, by way of an example and not by limitation, the exemplary network architecture (100) may include a plurality of computing devices (104-1, 104-2…104-N), which may be individually referred as the computing device (104) and collectively referred as the computing devices (104). The computing device (104) may be interchangeably referred as a user equipment (UE) (104). The plurality of computing devices (104) may include, but not be limited to, scanners such as cameras, webcams, scanning units, and the like.
[0048] In an embodiment, the computing device (104) may include smart devices operating in a smart environment, for example, an Internet of Things (IoT) system. In such an embodiment, the computing device (104) may include, but is not limited to, smart phones, smart watches, smart sensors (e.g., mechanical, thermal, electrical, magnetic, etc.), networked appliances, networked peripheral devices, networked lighting system, communication devices, networked vehicle accessories, networked vehicular devices, smart accessories, tablets, smart television (TV), computers, smart security system, smart home system, other devices for monitoring or interacting with or for the users and/or entities, or any combination thereof.
[0049] A person of ordinary skill in the art will appreciate that the computing device or the user equipment (104) may include, but is not limited to, intelligent, multi-sensing, network-connected devices, that may integrate seamlessly with each other and/or with a central server or a cloud-computing system or any other device that is network-connected.
[0050] In an embodiment, the computing device (104) may include, but is not limited to, a handheld wireless communication device (e.g., a mobile phone, a smart phone, a phablet device, and so on), a wearable computer device (e.g., a head-mounted display computer device, a head-mounted camera device, a wristwatch computer device, and so on), a Global Positioning System (GPS) device, a laptop computer, a tablet computer, or another type of portable computer, a media playing device, a portable gaming system, and/or any other type of computer device with wireless communication capabilities, and the like. In an embodiment, the computing device (104) may include, but is not limited to, any electrical, electronic, electro-mechanical, or an equipment, or a combination of one or more of the above devices such as virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other computing device, wherein the computing device (104) may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as a camera, an audio aid, a microphone, a keyboard, and input devices for receiving input from the user or the entity such as touch pad, touch enabled screen, electronic pen, and the like. A person of ordinary skill in the art may appreciate that the computing device (104) may not be restricted to the mentioned devices and various other devices may be used.
[0051] In an exemplary embodiment, the computing device/user equipment (104) may communicate with a system (108) through a network (106). The network (106) may include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. The network (106) may include, by way of example but not limitation, one or more of: a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a public-switched telephone network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, some combination thereof.
[0052] In an embodiment, the system (108) may be associated with one or more entities (110). The one or more entities (110) may be one or more data sources that send data associated with one or more products to the system (108).
[0053] In an embodiment, the system (108) may receive the data associated with the one or more products from the data sources. The data associated with the one or more products may include, but not limited to, sales data, product attribute data, environmental factors, and calendar factors. The sales data may be transactions data showcasing the products and sales of the products on a time series basis. The product attribute data may be data corresponding to specifications of the one or more products.
[0054] In an embodiment, the system (108) may convert the received data into model consumable data. The model consumable data may be comprehensive data including calendar factors and location specifications of the one or more products.
[0055] In an embodiment, the system (108) may create a cannibalization cluster and a competitive cluster for the one or more products based on the model consumable data. The cannibalization cluster may be a cluster grouping with one or more products similar to a product to be launched from a same brand. The competitive cluster may be a cluster grouping including competitors of the product to be launched from other brands. The cannibalization cluster and the competitive cluster may be created using, but not limited to, a K-means clustering method, a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method, a Clustering in Quest (CLIQUE) method, and a genetic method.
[0056] In an embodiment, the system (108) may map the at least one product to be launched from the cannibalization cluster and the competitive cluster of the one or more products.
[0057] In an embodiment, the system (108) may quantify cannibalization effect, competitive effect, and revenue effect of the product to be launched based on the mapping. The cannibalization effect, the competitive effect, and the revenue effect of the product may be quantified using, but not limited to, an Extreme Gradient Boosting (XGBoost) model, a causality model, a distance relationship, and an elasticity model.
[0058] In an embodiment, the system (108) may quantify the cannibalization effect, the competitive effect, and the revenue effect of the product to be launched to determine an extent of relationship between the one or more products, and prevent negative unexpected cross-product impact on sales of the product to be launched.
[0059] In an embodiment, the system (108) may determine an ideal launch price for the product to be launched based on the cannibalization effect, the competitive effect, and the revenue effect of the products.
[0060] In an embodiment, the system (108) may update price of the at least one product to be launched based on one or more events such as, but not limited to, festivals, calendar factors, and the like.
[0061] In an embodiment, the system (108) may provide recommendations associated with the ideal launch price of the product to be launched to the user equipment (104).
[0062] Although FIG. 1 shows exemplary components of the network architecture (100), in other embodiments, the network architecture (100) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 1. Additionally, or alternatively, one or more components of the network architecture (100) may perform functions described as being performed by one or more other components of the network architecture (100).
[0063] FIG. 2 illustrates an exemplary block diagram (200) of the proposed system (108), in accordance with embodiments of the present disclosure.
[0064] With respect to FIG. 2, the system (108) may include one or more processors (202). The one or more processors (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more processor(s) (202) may be configured to fetch and execute computer-readable instructions stored in a memory (204) of the system (108). The memory (204) may store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory (204) may include any non-transitory storage device including, for example, volatile memory such as Random-Access Memory (RAM), or non-volatile memory such as Erasable Programmable Read-Only Memory (EPROM), flash memory, and the like.
[0065] In an embodiment, the system (108) may also include an interface(s) (206). The interface(s) (206) may comprise a variety of interfaces, for example, a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) (206) may facilitate communication of the system (108) with various devices coupled to it. The interface(s) (206) may also provide a communication pathway for one or more components of the system (108). Examples of such components include, but are not limited to, processing engine(s) (208) and a database (210).
[0066] In an embodiment, the processing engine(s) (208) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) (208). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) (208) may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the one or more processors (202) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) (208). In such examples, the system (108) may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system (108) and the processing resource. In other examples, the processing engine(s) (208) may be implemented by an electronic circuitry.
[0067] In an exemplary embodiment, the processing engine(s) (208) may include one or more engines selected from any of a pre-processing engine (212), a conversion engine (214), an Artificial Intelligence/Machine Learning (AI/ML) engine (216), and other engines (218). The other engines (218) may include, but not limited to, an Input/Output (I/O) engine, a prediction engine, an event impact engine, an optimization engine, and the like.
[0068] In an embodiment, the pre-processing engine (212) may receive data associated with one or more products from various data sources. The conversion engine (214) may convert the received data into model consumable data. The AI/ML engine (216) may train an AI/ML model based on the model consumable data. The AI/ML model may include, but not limited to, a XGBoost model, a causality model, a distance relationship, and an elasticity model. The AI/ML model may create a cannibalization cluster and a competitive cluster for the one or more products based on the model consumable data. The AI/ML model may map a product to be launched with the cannibalization cluster and the competitive cluster of the one or more products. The AI/ML model may quantify cannibalization effect, competitive effect, and revenue effect of the product to be launched based on the mapping. Further, the AI/ML model may aggregate the cannibalization effect, the competitive effect, and the revenue effect of the product, and determine an ideal launch price for the product to be launched based on the aggregation of the cannibalization effect, the competitive effect, and the revenue effect of the product to be launched.
[0069] In an embodiment, the database (210) may comprise data that may be either stored or generated as a result of functionalities implemented by any of the components of the processor(s) (202) or the processing engine(s) (208) or the system (108).
[0070] Although FIG. 2 show exemplary components of the system (108), in other embodiments, the system (108) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 2. Additionally, or alternatively, one or more components of the system (108) may perform functions described as being performed by one or more other components of the system (108).
[0071] FIG. 3 illustrates an exemplary block diagram (300) for determining ideal launch price of a product, in accordance with an embodiment of the present disclosure.
[0072] Referring to FIG. 3, the system (108) may receive user input data (302), product specifications data (304), environmental factors (306), and calendar factors (308) associated with one or more products. The system (108) may create a cannibalization cluster and a competitive cluster for the one or more products based on the received data. The system (108) may map a product to be launched with the cannibalization cluster and the competitive cluster of the one or more products. The system (108) may determine cannibalization effect (314), competitive advantage (312), and revenue maximization (310) of the product to be launched based on the mapping. Further, the system (108) may aggregate the cannibalization effect (314), competitive advantage (312), and revenue maximization (310) of the product to be launched, and determine an ideal launch price (316) for the product to be launched based on the aggregation of the cannibalization effect (314), competitive advantage (312), and revenue maximization (310) of the product to be launched.
[0073] FIGs. 4A-4E illustrate exemplary flow diagrams (400A-400E) for implementing an ideal launch price determination method, in accordance with embodiments of the present disclosure.
[0074] With respect to FIG. 4A, the system (108) may include a pre-processing engine (212) to pre-process data received from various data sources to obtain raw data (402). The data received from various data sources may include, but not limited to, sales data and product attribute data. The sales data may be transactions data showcasing the products and their sales on a time series basis. The product attributes data may be data that dwells on the specifications of the product.
[0075] In an embodiment, the system (108) may include a feature engineering module which may be a conversion engine (214). The conversion engine (214) may gather the raw data (402) and perform feature engineering to convert the raw data (402) into model consumable data (404). The model consumable data (404) may include comprehensive data which may include, but not limited to, calendar factors and location specifications in addition to derived features. Table 1 below shows an example of how the conversion engine (214) operate on the raw data (402) to obtain the model consumable data (404).
Raw Data
ID of item Brand Feature-1 Feature-2 Feature-3
xyz1 b1 f11 f21 f31
xyz3 b2 f12 f22 f32
xyz3 b3 f13 f23 f33
Table 1
[0076] In an embodiment, the system (108) may include a clustering module (406). The clustering module (406) may be a part of the AI/ML engine (216). The clustering module (406) may include a repository of various clustering and other unsupervised techniques such as, but not limited to, a K-means clustering method, a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method, a Clustering in Quest (CLIQUE) method, a genetic method and the like, to pool similar products based on a set of features. The clustering methods may be used to create competitive clusters (408) and cannibalization clusters (410). The cannibalization clusters (410) may be clusters grouping with products similar to the product to be launched from the same brand. The competitive clusters (408) may be cluster grouping including competitors of the product to be launched from other brands. The products that fall within a group may affect each other, but may not affect the products outside the group. Table 2 below shows an example of how the clustering module (406) operate on the model consumable data (404) to result into competitive and cannibalization clusters (408, 410).
Model Consumable Data
ID of item Brand Feature-1 Feature-2 Feature-3 Derived-1 Derived-2
xyz1 b1 f11 f21 f31 d11 d21
xyz3 b2 f12 f22 f32 d12 d22
xyz3 b3 f13 f23 f33 d13 d23
xyz4 b1 f14 f24 f34 d14 d24
Table 2
[0077] With respect to FIG. 4B, an example of clustering results obtained by the clustering module (406) is shown. A new product or the product to be launched may be grouped as shown in FIG. 4B into the competitive clusters (408) and the cannibalization clusters (410).
[0078] With respect to FIG. 4C, the system (108) may include a cross-product effect module. The new product (421) or the product to be launched may be mapped with the cannibalization clusters (410) and the competitive clusters (408). The cross-product effect module may capture revenue estimates (422), competition effects (424) or matrices, and cannibalization effects (426) or matrices based on the mapping using a suite of methodologies. The methodologies may include, but not limited to, non-linear elasticity models, for example, causality models (432), distance relationships (430), elasticity models (428), and the like. The revenue estimates (422), the competition effects (424), and the cannibalization effects (426) may be quantified to determine an extent of relationship between the one or more products, and prevent any negative unexpected cross-product impact on the sales of the new product (421). Tables 3 and 4 below show the examples of how the competition effects (424) and the cannibalization effects (426) are created based on the new product (421).
Competition effects
xyz2 xyz3 New Prod
xyz2 0 comp23 comp2new
xyz3 comp32 0 comp3new
New Prod compnew2 compnew3 0
Table 3
Cannibalization effects
xyz1 xyz4 New Prod
xyz1 0 comp14 comp1new
xyz4 comp41 0 comp4new
New Prod compnew1 compnew4 0
Table 4
[0079] With respect to FIG. 4D, the system (108) may include an optimizer module. The optimizer module may be used to iterate on pricing and arrive at a final optimal price. The optimizer module may receive the pricing of the new product (442). The optimizer module may determine the revenue of the new product (444), competition effect with each of its competitors (446), and cannibalization effect with each of the cannibalized products (448). The revenue of the new product (444), the competition effect with each of its competitors (446), and the cannibalization effect with each of the cannibalized products (448) may be used to obtain an objective function (440) of the new product. The objective function (440) of the new product may be provided to a genetic method optimizer (450) to determine an ideal price for the new product. The ideal price may minimize the cannibalization effect (448), maximize the competitive effect (446), and maximize the revenue (444) from the new product.
[0080] With respect to FIG. 4E, the genetic method optimizer (450) may receive the objective function (440) of the new product as an input. The genetic method optimizer (450) may determine an ideal price for the new product by calculating fitness, selecting a price, and performing crossover and mutation of one or more products. Further, the genetic method optimizer (450) may determine if termination criteria are met to determine the ideal price for the new product.
[0081] FIG. 5 illustrates an exemplary computer system (500) in which or with which embodiments of the present disclosure may be utilized, in accordance with embodiments of the present disclosure.
[0082] As shown in FIG. 5, the computer system (500) may include an external storage device (510), a bus (520), a main memory (530), a read only memory (540), a mass storage device (550), a communication port (560), and a processor (570).
[0083] A person skilled in the art will appreciate that the computer system (500) may include more than one processor and communication ports. The processor (570) may include various modules associated with embodiments of the present disclosure.
[0084] In an embodiment, the communication port (560) may be any of an RS-232 port for use with a modem-based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication port (560) may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system (500) connects.
[0085] In an embodiment, the memory (530) may be a Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (540) may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or Basic Input/Output system (BIOS) instructions for the processor (570).
[0086] In an embodiment, the mass storage (550) may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g., an array of disks (e.g., SATA arrays).
[0087] In an embodiment, the bus (520) communicatively couples the processor(s) (570) with the other memory, storage, and communication blocks. The bus (520) may be, e.g., a Peripheral Component Interconnect (PCI)/PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), USB or the like, for connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (570) to computer system (500).
[0088] Optionally, operator and administrative interfaces, e.g., a display, keyboard, joystick, and a cursor control device, may also be coupled to the bus (520) to support direct operator interaction with the computer system (500). Other operator and administrative interfaces may be provided through network connections connected through the communication port (560). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system (500) limit the scope of the present disclosure.
[0089] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.

ADVANTAGES OF THE PRESENT DISCLOSURE
[0090] The present disclosure provides a one-stop robust and comprehensive solution for determining an ideal launch price of a product by considering multiple factors.
[0091] The present disclosure provides a system and a method that factors in price correction based on various factors such as festivals, calendar features, etc.
[0092] The present disclosure provides a system and a method that streamlines a process of pricing and launching new products.
[0093] The present disclosure provides a system and a method that determines the ideal launch price of the product effectively by determining and aggregating cannibalization effect, competitive effect, and revenue effect of the new product.
[0094] The present disclosure provides a system and a method that embeds quantitative approach to determine the ideal launch price of the product.
,CLAIMS:1. A system (108) for determining a launch price for a product, the system (108) comprising:
one or more processors (202); and
a memory (204) operatively coupled to the one or more processors (202), wherein the memory (204) comprises processor-executable instructions, which on execution, cause the one or more processors (202) to:
receive data associated with one or more products from one or more data sources;
convert the received data into model consumable data;
create a cannibalization cluster and a competitive cluster for the one or more products based on the model consumable data;
map, via an artificial intelligence (AI) engine (216), at least one product to be launched with the cannibalization cluster and the competitive cluster of the one or more products;
quantify cannibalization effect, competitive effect, and revenue effect of the at least one product to be launched based on the mapping; and
determine the launch price for the at least one product to be launched based on the cannibalization effect, the competitive effect, and the revenue effect of the at least one product to be launched.

2. The system (108) as claimed in claim 1, wherein the data associated with the one or more products comprises at least one of: sales data, product attribute data, environmental factors, and calendar factors.

3. The system (108) as claimed in claim 1, wherein the one or more processors (202) are to create the cannibalization cluster and the competitive cluster using at least one of: a K-means clustering method, a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method, a Clustering in Quest (CLIQUE) method, and a genetic method.

4. The system (108) as claimed in claim 3, wherein the cannibalization cluster is a cluster grouping with one or more similar products from a same brand, and wherein the competitive cluster is a cluster grouping comprising competitors of the at least one product to be launched.

5. The system (108) as claimed in claim 1, wherein the one or more processors (202) are to quantify the cannibalization effect, the competitive effect, and the revenue effect of the at least one product to be launched using at least one of: an Extreme Gradient Boosting (XGBoost) model, a causality model, a distance relationship, and an elasticity model.

6. The system (108) as claimed in claim 5, wherein the memory (204) comprises processor-executable instructions, which on execution, cause the one or more processors (202) to determine an extent of relationship between the one or more products, and prevent negative unexpected cross-product impact on sales of the at least one product to be launched based on the cannibalization effect, the competitive effect, and the revenue effect of the at least one product to be launched.

7. The system (108) as claimed in claim 1, wherein the one or more processors (202) are to determine the launch price by being configured to:
aggregate the cannibalization effect, the competitive effect, and the revenue effect of the at least one product to be launched based on the quantification; and
determine the launch price for the at least one product to be launched based on the aggregation.

8. The system (108) as claimed in claim 1, wherein the memory (204) comprises processor-executable instructions, which on execution, cause the one or more processors (202) to update the launch price of the at least one product to be launched based on occurrence of one or more events.

9. A method for determining a launch price for a product, the method comprising:
receiving, by a processor (202) associated with a system (108), data associated with one or more products from one or more data sources;
converting, by the processor (202), the received data into model consumable data;
creating, by the processor (202), a cannibalization cluster and a competitive cluster for the one or more products based on the model consumable data;
mapping, by the processor (202) via an artificial intelligence (AI) engine (216), at least one product to be launched with the cannibalization cluster and the competitive cluster of the one or more products;
quantifying, by the processor (202), cannibalization effect, competitive effect, and revenue effect of the at least one product to be launched based on the mapping; and
determining, by the processor (202), the launch price for the at least one product to be launched based on the cannibalization effect, the competitive effect, and the revenue effect of the at least one product to be launched.

10. The method as claimed in claim 9, wherein creating, by the processor (202), the cannibalization cluster and the competitive cluster comprises using at least one of: a K-means clustering method, a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method, a Clustering in Quest (CLIQUE) method, and a genetic method.

11. The method as claimed in claim 9, wherein quantifying, by the processor (202), the cannibalization effect, the competitive effect, and the revenue effect of the at least one product to be launched comprises using at least one of: an Extreme Gradient Boosting (XGBoost) model, a causality model, a distance relationship, and an elasticity model.

12. The method as claimed in claim 11, comprising determining, by the processor (202), an extent of relationship between the one or more products, and preventing, by the processor (202), negative unexpected cross-product impact on sales of the at least one product to be launched based on the cannibalization effect, the competitive effect, and the revenue effect of the at least one product to be launched.

13. The method as claimed in claim 9, wherein determining, by the processor (202), the launch price comprises:
aggregating, by the processor (202), the cannibalization effect, the competitive effect, and the revenue effect of the at least one product to be launched based on the quantification; and
determining, by the processor (202), the launch price for the at least one product to be launched based on the aggregation.

14. The method as claimed in claim 9, comprising updating, by the processor (202), the launch price of the at least one product to be launched based on occurrence of one or more events.

15. A user equipment (104), comprising:
one or more processors; and
a memory operatively coupled to the one or more processors, wherein the memory comprises processor-executable instructions, which on execution, cause the one or more processors to:
receive recommendations associated with a launch price of at least one product to be launched from a system,
wherein the one or more processors are communicatively coupled with the system, and wherein the system is configured to:
receive data associated with one or more products from one or more data sources;
convert the received data into model consumable data;
create a cannibalization cluster and a competitive cluster for the one or more products based on the model consumable data;
map, via an artificial intelligence (AI) engine (216), the at least one product to be launched with the cannibalization cluster and the competitive cluster of the one or more products;
quantify cannibalization effect, competitive effect, and revenue effect of the at least one product to be launched based on the mapping;
determine the launch price for the at least one product to be launched based on the cannibalization effect, the competitive effect, and the revenue effect of the at least one product to be launched; and
provide recommendations associated with the launch price of the at least one product to be launched to the user equipment (104).