Description:FIELD OF INVENTION
[0001] The embodiments of the present disclosure generally relate to strategy planning and conversion systems. More particularly, the present disclosure relates to a method and a system for positioning long-tail verticals corresponding to one or more products in an electronic commerce (e-commerce) environment.

BACKGROUND
[0002] The following description of the 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 the prior art.
[0003] Generally, some of an e-commerce platform may include customer base primarily indexed towards a middle to a high-income group. The selection and price of products may be always pivoted around that cohort of the middle to high income group. If, the e-commerce platform had to grow organically beyond limitations of the present customers, then the e-commerce platform may need to acquire customers with a very low price point with variety of tail selection of the products. Some of the e-commerce platforms may provide a service using a sub-platform to position the e-commerce platform as a low Average Selling Price (ASP) long tail destination for a “value and variety seeker customer” cohort. However, even after multiple selection visibility design applications used in the e-commerce platform, the selling rate of the products may be less to drive the long tail positioning of the products.
[0004] Conventional methods may provide a trial-and-error method of a vertical identification for visibility and iterate based on a search Click Through Rate (CTR) and conversion. Vertical markets, or verticals may be a business niches where vendors serve a specific audience and their set of needs. Vertical markets are increasingly being served via the e-commerce businesses because of the minimal overhead and ability to reach a worldwide audience. Another conventional methods may provide a method for re-using top-selling verticals, merchants, and selections on other platform. However, the conventional methods may lead to discoverability of low ASP, and high discount deals. As a result, 2% of verticals may be getting approximately 50% of visibility and approximately 27% of verticals had zero impressions and no awareness. Further, the conventional methods may not solve selection visibility gap of the long-tail verticals and may not increase variety and visibility of selection of the long-tail verticals in an optimized manner.
[0005] Therefore, there may be a need for a method and a system for solving the shortcomings of the conventional methods, by providing a method and a system for positioning long-tail verticals corresponding to one or more products in an electronic commerce (e-commerce) environment.

SUMMARY
[0006] This section is provided to introduce certain objects and aspects of the present invention 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. In order to overcome at least a few problems associated with the known solutions as provided in the previous section, an object of the present disclosure is to provide a technique for recommending beauty product(s) for a subject.
[0007] It is an object of the present disclosure to provide a method and a system for positioning long-tail verticals corresponding to one or more products in an electronic commerce (e-commerce) environment.
[0008] It is another object of the present disclosure to provide an assessment framework of long tail position” and “eligibility criteria of product/vertical or category being selected for visibility”.
[0009] It is another object of the present disclosure to provide a method and a system for determining selection variety availability in master data, visibility through impression share, user scroll depth, Customer Intelligence (CI), customer ratings, customer reviews, listing count, merchant listing count, target price, affinity score, and the like.
[0010] It is another object of the present disclosure to provide a method and a system for positioning long-tail verticals for value and variety seeker customer cohort.
[0011] It is another object of the present disclosure to provide a method and a system for analyzing precise verticals and selections list which lacks visibility, and analyzing precise verticals and selections list which are eligible for more visibility.
[0012] It is another object of the present disclosure to provide a method and a system for calculating the listing to conversion ratio and recommend the minimum number of listing required to be there in master data. For the rest of eligible listings verticals recommending the minimum numbers of listings to be merchandised based on conversion and sales of the products.
[0013] In an aspect, the present disclosure provides a method for positioning long-tail verticals corresponding to one or more products in an electronic commerce (e-commerce) environment. The method includes creating one or more first visibility buckets for a pre-selected long-tail verticals corresponding to one or more products, based on a vertical share as compared with a visibility distribution corresponding to an impression share. Further, the method includes segmenting the pre-selected long-tail verticals, and assigning the created one or more first visibility buckets to the segmented long-tail verticals, based on a statistical distribution of data corresponding to the segmented long-tail verticals. Furthermore, the method includes analyzing one or more hypothesis comprising guardrail metrics of each of the long-tail verticals in each of the assigned one or more first visibility buckets, to identify one or more driving factors of difference in a visibility of the long-tail verticals, when the long-tail verticals is assigned to a first low visibility bucket of the one or more first visibility buckets. Additionally, the method includes positioning the impression share of the long-tail verticals, when the one or more hypothesis is maintaining the guardrail metrics in the first low visibility bucket of the one or more first visibility buckets. Further, the method includes creating one or more second visibility buckets for the segmented long-tail verticals, based on the vertical share as compared with a Purchase Price Variance (PPV) distribution, when the long-tail verticals is assigned to a first medium visibility bucket of the one or more first visibility buckets. Furthermore, the method includes creating a funnel within a second low visibility bucket and a second medium visibility bucket of the one or more second visibility buckets, based on analyzing at least one of a listing count, a listing count for merchants, and a target price. Additionally, the method includes positioning an overall visibility of the long-tail verticals, by determining at least one of an affinity in long-tail verticals, high affinity in long-tail verticals, a cross selling to a customer, and a merchant combination.
[0014] In an embodiment, the first low visibility bucket includes no impression share, the first medium visibility bucket is assigned based on a first pre-defined threshold for a selection of the long-tail verticals comprising a first pre-defined threshold of the impression share, and the first high visibility bucket is assigned based on a second pre-defined threshold for a selection of verticals comprising a second pre-defined threshold of the impression share.
[0015] In an embodiment, the one or more hypothesis comprises at least one of a selection that is below average scroll depth for a vertical or a category of the one or more products, and a selection that is above average scroll depth for a vertical or a category of the one or more products.
[0016] In an embodiment, when the one or more hypothesis comprising below average scroll depth for the vertical or the category of the one or more products is selected, the method further includes recommending to move up the position of the no impression share, when the one or more hypothesis passes the guardrail metrics comprising at least one of a Customer Intelligence (CI) data, customer ratings data, customer review data, and content.
[0017] In an embodiment, when the one or more hypothesis comprising above average scroll depth for the vertical or the category of the one or more products is selected, the method further includes determining issues corresponding to at least one of a pricing, a Customer Intelligence (CI) data, customer ratings data, in a top positioned products of the one or more products. The method includes recommending to move up the position of the no impression share, when the one or more hypothesis passes the guardrail metrics comprising at least one of a popularity in other e-commerce environment, the CI, the customer ratings data, the customer review data, and the content. Furthermore, the method includes upgrading the pricing, a product quality, the content, customer intent of scroll depth, when the when the one or more hypothesis comprising highest scroll depth.
[0018] In an embodiment, analyzing the listing count further includes identifying the long-tail verticals comprising at least one of a minimum of pre-defined listings or a minimum of pre-defined Business Unit (BU) standard listings. Further, the method includes calculating a listing to conversion ratio and recommending a minimum required number of listings, for a low-selection long-tail verticals in at least one of the minimum of pre-defined listings or the minimum of pre-defined Business Unit (BU) standard listings. Furthermore, the method includes recommending a minimum numbers of listings to be merchandise, based on the conversion ratio and sales, for remaining eligible listings.
[0019] In an embodiment, determining the affinity in long-tail verticals further includes calculating a cosine similarity score of at least two long-tail verticals.
[0020] In an embodiment, the cosine similarity score is calculated based on at least one of a number of times the one or more products in at least two long-tail verticals is purchased by a same customer in a pre-defined period, a number of times a first product is purchased by a customer in the pre-defined period, and a number of times a second is purchased by the customer in the pre-defined period.
[0021] In an embodiment, when the calculated cosine similarity score is high, at least two long-tail verticals are purchased by the same customer in the pre-defined period.
[0022] In another aspect, the present disclosure provides a system for positioning long-tail verticals corresponding to one or more products in an electronic commerce (e-commerce) environment. The system creates one or more first visibility buckets for a pre-selected long-tail verticals corresponding to one or more products, based on a vertical share as compared with a visibility distribution corresponding to an impression share. Further, the system segments the pre-selected long-tail verticals, and assigning the created one or more first visibility buckets to the segmented long-tail verticals, based on a statistical distribution of data corresponding to the segmented long-tail verticals. Furthermore, the system analyzes one or more hypothesis comprising guardrail metrics of each of the long-tail verticals in each of the assigned one or more first visibility buckets, to identify one or more driving factors of difference in a visibility of the long-tail verticals, when the long-tail verticals is assigned to a first low visibility bucket of the one or more first visibility buckets. Additionally, the system positions the impression share of the long-tail verticals, when the one or more hypothesis is maintaining the guardrail metrics in the first low visibility bucket of the one or more first visibility buckets. Further, the system creates one or more second visibility buckets for the segmented long-tail verticals, based on the vertical share as compared with a Purchase Price Variance (PPV) distribution, when the long-tail verticals is assigned to a first medium visibility bucket of the one or more first visibility buckets. Furthermore, the system creates a funnel within a second low visibility bucket and a second medium visibility bucket of the one or more second visibility buckets, based on analyzing at least one of a listing count, a listing count for merchants, and a target price. Further, the system positions an overall visibility of the long-tail verticals, by determining at least one of an affinity in long-tail verticals, high affinity in long-tail verticals, a cross selling to a customer, and a merchant combination.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS
[0023] 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/sub-components of each component. It will be appreciated by those skilled in the art that the invention of such drawings includes the invention of electrical components, electronic components, or circuitry commonly used to implement such components.
[0024] FIG. 1 illustrates an exemplary block diagram representation of a network architecture implementing a proposed system for positioning long-tail verticals corresponding to one or more products in an electronic commerce (e-commerce) environment, according to embodiments of the present disclosure.
[0025] FIG. 2 illustrates an exemplary detailed block diagram representation of the proposed system, according to embodiments of the present disclosure.
[0026] FIG. 3 illustrates an exemplary graph representation of affine verticals, according to embodiments of the present disclosure.
[0027] FIG. 4 illustrates a flow chart depicting a method of positioning long-tail verticals corresponding to one or more products in an electronic commerce (e-commerce) environment, according to embodiments of the present disclosure.
[0028] FIG. 5 illustrates a hardware platform for the implementation of the disclosed system according to embodiments of the present disclosure.
[0029] The foregoing shall be more apparent from the following more detailed description of the invention.

DETAILED DESCRIPTION OF INVENTION
[0030] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of the 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.
[0031] 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 invention as set forth.
[0032] 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.
[0033] 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.
[0034] 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.
[0035] As used herein, "connect", "configure", "couple" and its cognate terms, such as "connects", "connected", "configured", and "coupled" may include a physical connection (such as a wired/wireless connection), a logical connection (such as through logical gates of semiconducting device), other suitable connections, or a combination of such connections, as may be obvious to a skilled person.
[0036] As used herein, "send", "transfer", "transmit", and their cognate terms like "sending", "sent", "transferring", "transmitting", "transferred", "transmitted", etc. include sending or transporting data or information from one unit or component to another unit or component, wherein the content may or may not be modified before or after sending, transferring, transmitting.
[0037] 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 invention. 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.
[0038] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. 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 products.
[0039] Various embodiments of the present disclosure provide a method and a system for positioning long-tail verticals corresponding to one or more products in an electronic commerce (e-commerce) environment. The present disclosure provides an assessment framework of long tail position” and “eligibility criteria of product/vertical or category being selected for visibility”. The present disclosure provides a method and a system for determining selection variety availability in master data, visibility through impression share, user scroll depth, Customer Intelligence (CI), customer ratings, customer reviews, listing count, merchant listing count, target price, affinity score, and the like. The present disclosure provides a method and a system for positioning long-tail verticals for value and variety seeker customer cohort. The present disclosure provides a method and a system for analyzing precise verticals and selections list which lacks visibility, and analyzing precise verticals and selections list which are eligible for more visibility. The present disclosure provides a method and a system for calculating the listing to conversion ratio and recommend the minimum number of listing required to be there in master data. For the rest of eligible listings verticals recommending the minimum numbers of listings to be merchandised based on conversion and sales of the products.
[0040] FIG. 1 illustrates an exemplary block diagram representation of a network architecture 100 implementing a proposed system 110 for positioning long-tail verticals corresponding to one or more products in an electronic commerce (e-commerce) environment, according to embodiments of the present disclosure. The network architecture 100 may include an electronic device 108, the system 110, and a centralized server 118. The system 110 may be connected to the centralized server 118 via a communication network 106. The centralized server 118 may include, but is not limited to, a stand-alone server, a remote server, a cloud computing server, a dedicated server, a rack server, a server blade, a server rack, a bank of servers, a server farm, hardware supporting a part of a cloud service or system, a home server, hardware running a virtualized server, one or more processors executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof, and the like. The centralized server 118 may be associated with an entity corresponding to an electronic commerce (e-commerce) environment. The communication network 106 may be a wired communication network or a wireless communication network. The wireless communication network may be any wireless communication network capable of transferring data between entities of that network such as, but are not limited to, a Bluetooth, a Zigbee, a Near Field Communication (NFC), a Wireless-Fidelity (Wi-Fi) network, a Light Fidelity (Li-FI) network, a carrier network including a circuit-switched network, a packet switched network, a Public Switched Telephone Network (PSTN), a Content Delivery Network (CDN) network, an Internet, intranets, Local Area Networks (LANs), Wide Area Networks (WANs), mobile communication networks including a Second Generation (2G), a Third Generation (3G), a Fourth Generation (4G), a Fifth Generation (5G), a Sixth Generation (6G), a Long-Term Evolution (LTE) network, a New Radio (NR), a Narrow-Band (NB), an Internet of Things (IoT) network, a Global System for Mobile Communications (GSM) network and a Universal Mobile Telecommunications System (UMTS) network, combinations thereof, and the like.
[0041] The system 110 may be implemented by way of a single device or a combination of multiple devices that may be operatively connected or networked together. For example, the system 110 may be implemented by way of a standalone device such as the centralized server 118, and the like, and may be communicatively coupled to the electronic device 108. In another example, the system 110 may be implemented in/ associated with the electronic device 108. In yet another example, the system 110 may be implemented in/ associated with respective computing device 104-1, 104-2, …..., 104-N (individually referred to as the computing device 104, and collectively referred to as the computing devices 104), associated with one or more user 102-1, 102-2, …..., 102-N (individually referred to as the user 102, and collectively referred to as the users 102). In such a scenario, the system 110 may be replicated in each of the computing devices 104. The users 102 may be a user of, but are not limited to, an electronic commerce (e-commerce) platform, a marketplace, a merchant platform, a hyperlocal platform, a super-mart platform, a media platform, a service providing platform, a social networking platform, a travel/services booking platform, a messaging platform, a bot processing platform, a virtual assistance platform, an Artificial Intelligence (AI) based platform, a blockchain platform, a blockchain marketplace, and the like. In some instances, the user 102 may correspond to an entity/administrator of platforms/services.
[0042] The electronic device 108 may be at least one of, an electrical, an electronic, an electromechanical, and a computing device. The electronic device 108 may include, but is not limited to, a mobile device, a smart-phone, a Personal Digital Assistant (PDA), a tablet computer, a phablet computer, a wearable computing device, a Virtual Reality/Augmented Reality (VR/AR) device, a laptop, a desktop, a server, and the like. The system 110 may be implemented in hardware or a suitable combination of hardware and software. The system 110 or the centralized server 118 may be associated with entities (not shown). The entities may include, but are not limited to, an e-commerce company, a merchant organization, a travel company, an airline company, a hotel booking company, a company, an outlet, a manufacturing unit, an enterprise, a facility, an organization, an educational institution, a secured facility, a warehouse facility, a supply chain facility, and the like.
[0043] Further, the system 110 may include a processor 112, an Input/Output (I/O) interface 114, and a memory 116. The Input/Output (I/O) interface 114 of the system 110 may be used to receive user inputs, from the computing devices 104 associated with the users 102. Further, the system 110 may also include other units such as a display unit, an input unit, an output unit, and the like, however the same are not shown in FIG. 1, for the purpose of clarity. Also, in FIG. 1 only a few units are shown, however, the system 110 or the network architecture 100 may include multiple such units or the system 110/ network architecture 100 may include any such numbers of the units, obvious to a person skilled in the art or as required to implement the features of the present disclosure. The system 110 may be a hardware device including the processor 112 executing machine-readable program instructions to position long-tail verticals corresponding to one or more products in an electronic commerce (e-commerce) environment.
[0044] Execution of the machine-readable program instructions by the processor 112 may enable the proposed system 110 to position long-tail verticals corresponding to one or more products in an electronic commerce (e-commerce) environment. The “hardware” may comprise a combination of discrete components, an integrated circuit, an application-specific integrated circuit, a field-programmable gate array, a digital signal processor, or other suitable hardware. The “software” may comprise one or more objects, agents, threads, lines of code, subroutines, separate software applications, two or more lines of code, or other suitable software structures operating in one or more software applications or on one or more processors. The processor 112 may include, for example, but is not limited to, microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuits, and any devices that manipulate data or signals based on operational instructions, and the like. Among other capabilities, the processor 112 may fetch and execute computer-readable instructions in the memory 116 operationally coupled with the system 110 for performing tasks such as data processing, input/output processing, feature extraction, and/or any other functions. Any reference to a task in the present disclosure may refer to an operation being or that may be performed on data.
[0045] In the example that follows, assume that a user 102 of the system 110 desires to improve/add additional features to position long-tail verticals corresponding to one or more products in an electronic commerce (e-commerce) environment. In this instance, the user 102 may include an administrator of a website, an administrator of an e-commerce site, an administrator of a social media site, an administrator of an e-commerce application/ social media application/other applications, an administrator of media content (e.g., television content, video-on-demand content, online video content, graphical content, image content, augmented/virtual reality content, metaverse content), an administrator of supply chain platform, an administrator of blockchain marketplace, an administrator of a travel/services booking platform, an administrator of merchant platform, among other examples, and the like. The system 110 when associated with the electronic device 108 or the centralized server 118 may include, but is not limited to, a touch panel, a soft keypad, a hard keypad (including buttons), and the like.
[0046] In an embodiment, the system 110 may create one or more first visibility buckets for a pre-selected long-tail verticals corresponding to one or more products, based on a vertical share as compared with a visibility distribution corresponding to an impression share.
[0047] In an embodiment, the system 110 may segment the pre-selected long-tail verticals, and assigning the created one or more first visibility buckets to the segmented long-tail verticals, based on a statistical distribution of data corresponding to the segmented long-tail verticals.
[0048] In an embodiment, the system 110 may analyze one or more hypothesis comprising guardrail metrics of each of the long-tail verticals in each of the assigned one or more first visibility buckets, to identify one or more driving factors of difference in a visibility of the long-tail verticals, when the long-tail verticals are assigned to a first low visibility bucket of the one or more first visibility buckets. In an embodiment, the one or more hypothesis comprises at least one of a selection that is below average scroll depth for a vertical or a category of the one or more products, and a selection that is above average scroll depth for a vertical or a category of the one or more products. In an embodiment, the first low visibility bucket includes no impression share, the first medium visibility bucket is assigned based on a first pre-defined threshold for a selection of the long-tail verticals comprising a first pre-defined threshold of the impression share, and the first high visibility bucket is assigned based on a second pre-defined threshold for a selection of verticals comprising a second pre-defined threshold of the impression share.
[0049] In an embodiment, when the one or more hypothesis comprising below average scroll depth for the vertical or the category of the one or more products is selected, the system 110 may recommend to move up the position of the no impression share, when the one or more hypothesis passes the guardrails, metrics comprising at least one of a Customer Intelligence (CI) data, customer ratings data, customer review data, and content. In an embodiment, when the one or more hypothesis comprising above average scroll depth for the vertical or the category of the one or more products is selected, the system 110 may determine issues corresponding to at least one of a pricing, a Customer Intelligence (CI) data, customer ratings data, in a top positioned products of the one or more products. In an embodiment, the system 110 may recommend, to move up the position of the no impression share, when the one or more hypothesis passes the guardrail metrics comprising at least one of a popularity in other e-commerce environment, the CI, the customer ratings data, the customer review data, and the content. In an embodiment, the system 110 may upgrade the pricing, a product quality, the content, customer intent of scroll depth, when the when the one or more hypothesis comprising highest scroll depth.
[0050] In an embodiment, the system 110 may position the impression share of the long-tail verticals, when the one or more hypothesis is maintaining the guardrail metrics in the first low visibility bucket of the one or more first visibility buckets.
[0051] In an embodiment, the system 110 may create one or more second visibility buckets for the segmented long-tail verticals, based on the vertical share as compared with a Purchase Price Variance (PPV) distribution, when the long-tail verticals are assigned to a first medium visibility bucket of the one or more first visibility buckets.
[0052] In an embodiment, the system 110 may create a funnel within a second low visibility bucket and a second medium visibility bucket of the one or more second visibility buckets, based on analyzing at least one of a listing count, a listing count for merchants, and a target price. In an embodiment, for analyzing the listing count, the system 110 may identify the long-tail verticals comprising at least one of a minimum of pre-defined listings or a minimum of pre-defined Business Unit (BU) standard listings. In an embodiment, the system 110 may calculate a listing to conversion ratio and recommending a minimum required number of listings, for a low-selection long-tail verticals in at least one of the minimum of pre-defined listings or the minimum of pre-defined Business Unit (BU) standard listings. In an embodiment, the system 110 may recommend a minimum numbers of listings to be merchandise, based on the conversion ratio and sales, for remaining eligible listings.
[0053] In an embodiment, the system 110 may position an overall visibility of the long-tail verticals, by determining at least one of an affinity in long-tail verticals, high affinity in long-tail verticals, a cross selling to a customer, and a merchant combination. In an embodiment, for determining the affinity in long-tail verticals, the system 110 may calculate a cosine similarity score of at least two long-tail verticals. In an embodiment, the cosine similarity score is calculated based on at least one of a number of times the one or more products in at least two long-tail verticals is purchased by a same customer in a pre-defined period, a number of times a first product is purchased by a customer in the pre-defined period, and a number of times a second is purchased by the customer in the pre-defined period. In an embodiment, when the calculated cosine similarity score is high, at least two long-tail verticals are purchased by the same customer in the pre-defined period.
[0054] FIG. 2 illustrates an exemplary detailed block diagram representation of the proposed system 110, according to embodiments of the present disclosure. The system 110 may include the processor 112, the Input/Output (I/O) interface 114, and the memory 116. In some implementations, the system 110 may include data 202, and modules 204. As an example, the data 202 may be stored in the memory 116 configured in the system 110 as shown in FIG. 2.
[0055] In an embodiment, the data 202 may include visibility bucket data 206, long-tail verticals data 208, vertical share data 210, visibility distribution data 212, statistical distribution data 214, hypothesis data 216, guardrail metrics data 218, driving factor data 220, Purchase Price Variance (PPV) distribution data 222, listing count data 224, affinity data 226, and other data 228. In an embodiment, the data 202 may be stored in the memory 116 in the form of various data structures. Additionally, the data 202 can be organized using data models, such as relational or hierarchical data models. The other data 218 may store data, including temporary data and temporary files, generated by the modules 204 for performing the various functions of the system 110.
[0056] In an embodiment, the modules 204, may include a creating module 232, a segmenting module 234, an analyzing module 236, a positioning module 238, and other modules 240.
[0057] In an embodiment, the data 202 stored in the memory 116 may be processed by the modules 204 of the system 110. The modules 204 may be stored within the memory 116. In an example, the modules 204 communicatively coupled to the processor 112 configured in the system 110, may also be present outside the memory 116, as shown in FIG. 2, and implemented as hardware. As used herein, the term modules refer to an Application-Specific Integrated Circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality.
[0058] In an embodiment, the creating module 232 may create one or more first visibility buckets for a pre-selected long-tail verticals corresponding to one or more products, based on a vertical share as compared with a visibility distribution corresponding to an impression share. The created one or more first visibility buckets may be stored as the visibility bucket data 206. The pre-selected long-tail verticals may be stored as the long-tail verticals data 208. The vertical share may be stored as the vertical share data 210. The visibility distribution corresponding to an impression share may be stored as the visibility distribution data 212.
[0059] In an embodiment, the segmenting module 234 may segment the pre-selected long-tail verticals, and assigning the created one or more first visibility buckets to the segmented long-tail verticals, based on a statistical distribution of data corresponding to the segmented long-tail verticals. The statistical distribution of data corresponding to the segmented long-tail verticals may be stored as the statistical distribution data 214.
[0060] In an embodiment, the analyzing module 236 may analyze one or more hypothesis comprising guardrail metrics of each of the long-tail verticals in each of the assigned one or more first visibility buckets, to identify one or more driving factors of difference in a visibility of the long-tail verticals, when the long-tail verticals are assigned to a first low visibility bucket of the one or more first visibility buckets. The analyzed one or more hypothesis may be stored as the hypothesis data 216. The guardrail metrics of each of the long-tail verticals may be stored as the guardrail metrics data 218. The one or more driving factors of difference in a visibility of the long-tail verticals may be stored as the driving factor data 220. In an embodiment, the one or more hypothesis comprises at least one of a selection that is below average scroll depth for a vertical or a category of the one or more products, and a selection that is above average scroll depth for a vertical or a category of the one or more products. In an embodiment, the first low visibility bucket includes no impression share, the first medium visibility bucket is assigned based on a first pre-defined threshold for a selection of the long-tail verticals comprising a first pre-defined threshold of the impression share, and the first high visibility bucket is assigned based on a second pre-defined threshold for a selection of verticals comprising a second pre-defined threshold of the impression share.
[0061] In an embodiment, when the one or more hypothesis comprising below average scroll depth for the vertical or the category of the one or more products is selected, the system 110 may recommend to move up the position of the no impression share, when the one or more hypothesis passes the guardrails, metrics comprising at least one of a Customer Intelligence (CI) data, customer ratings data, customer review data, and content. In an embodiment, when the one or more hypothesis comprising above average scroll depth for the vertical or the category of the one or more products is selected, the system 110 may determine issues corresponding to at least one of a pricing, a Customer Intelligence (CI) data, customer ratings data, in a top positioned products of the one or more products. In an embodiment, the system 110 may recommend, to move up the position of the no impression share, when the one or more hypothesis passes the guardrail metrics comprising at least one of a popularity in other e-commerce environment, the CI, the customer ratings data, the customer review data, and the content. In an embodiment, the system 110 may upgrade the pricing, a product quality, the content, customer intent of scroll depth, when the when the one or more hypothesis comprising highest scroll depth.
[0062] In an embodiment, the positioning module 238 may position the impression share of the long-tail verticals, when the one or more hypothesis is maintaining the guardrail metrics in the first low visibility bucket of the one or more first visibility buckets.
[0063] In an embodiment, the creating module 232 may create one or more second visibility buckets for the segmented long-tail verticals, based on the vertical share as compared with a Purchase Price Variance (PPV) distribution, when the long-tail verticals are assigned to a first medium visibility bucket of the one or more first visibility buckets. The Purchase Price Variance (PPV) distribution may be stored as the PPV distribution data 222.
[0064] In an embodiment, the creating module 232 may create a funnel within a second low visibility bucket and a second medium visibility bucket of the one or more second visibility buckets, based on analyzing at least one of a listing count, a listing count for merchants, and a target price. The listing count, the listing count for merchants, and the target price may be stored as the listing count data 224. In an embodiment, for analyzing the listing count, the system 110 may identify the long-tail verticals comprising at least one of a minimum of pre-defined listings or a minimum of pre-defined Business Unit (BU) standard listings. In an embodiment, the system 110 may calculate a listing to conversion ratio and recommending a minimum required number of listings, for a low-selection long-tail verticals in at least one of the minimum of pre-defined listings or the minimum of pre-defined Business Unit (BU) standard listings. In an embodiment, the system 110 may recommend a minimum numbers of listings to be merchandise, based on the conversion ratio and sales, for remaining eligible listings.
[0065] In an embodiment, the positioning module 238 may position an overall visibility of the long-tail verticals, by determining at least one of an affinity in long-tail verticals, high affinity in long-tail verticals, a cross selling to a customer, and a merchant combination. The affinity in long-tail verticals, high affinity in long-tail verticals may be stored as the affinity data 226. In an embodiment, for determining the affinity in long-tail verticals, the system 110 may calculate a cosine similarity score of at least two long-tail verticals. In an embodiment, the cosine similarity score is calculated based on at least one of a number of times the one or more products in at least two long-tail verticals is purchased by a same customer in a pre-defined period, a number of times a first product is purchased by a customer in the pre-defined period, and a number of times a second is purchased by the customer in the pre-defined period. In an embodiment, when the calculated cosine similarity score is high, at least two long-tail verticals are purchased by the same customer in the pre-defined period.
Exemplary scenario:
[0066] Consider, a scenario of identifying visibility buckets for overall selection of longtail verticals corresponding to one or more products. For example, identification and quantification of visibility problem may be eliminated by creating, for example 3 buckets, based on vertical share vs visibility distribution (i.e., impression share), keeping a logical threshold as per the target or setting a target to move more variance towards the equitable distribution, and segmenting the long-tail verticals, based on the statistical distribution of data. For example, segmenting the long-tail verticals may include, a high visibility basket (i.e., 2%-5% selection with 30%-50% impression share), a medium visibility basket (i.e., majority of the selection having a moderate visibility of 70%-50% of the impression share), and a low visibility bucket (i.e., selection bucket with ‘0’ impression share).
[0067] For example, the low visibility selection may need to be improved. To improve low visibility selection, the system 110 may identify drivers of visibility anomaly and problem size the actionable areas. This step tests the hypothesis to identify the driving factors of difference in visibility. The hypothesis in the low visible verticals may include selection which is below average scroll depth for that vertical or category, and the selection may be above average scroll depth for that vertical or category. The system 110 may suggest Artificial Intelligence (AI) model for each hypothesis maintaining the guardrail metrics. For example, if hypothesis such as the selection which is below average scroll depth for that vertical or category, is accepted, then the system 110 may recommend to move up the position of the ‘0’ impression selection, if the selection pass the guardrails of, but are not limited to, Customer Intelligence (CI), customer ratings and customer reviews, content, and the like. Further, if hypothesis such as the selection may be above average scroll depth for that vertical or category is accepted, then it implies that top positioned items have issues around pricing, CI, user ratings, and the like. The system 110 may recommend to move up the position of the ‘0’ impression selection, if the selection passes the guardrails of, but are not limited to, popularity in other platform, the CI, the customer ratings and customer reviews, content, and the like.
[0068] For example, for selection already at the top, the system 110 may improve upon, but not limited to pricing, the product quality, the content, customers intent to scroll more, and the like. After suggesting the AI for each hypothesis maintaining the guardrail metrics, a ‘X%’ of the selection may start getting visibility.
[0069] Once, the low visibility is actioned upon, the system 110 may improve the medium visibility selection of the long-tail verticals. The system 110 may suggest Artificial Intelligence (AI) model for each hypothesis maintaining the guardrail metrics. The system 110 may create, for example, 3 buckets based on vertical share vs the PPV distribution as, a low visibility bucket (i.e., all verticals with ‘0’ PPV), a medium visibility bucket and a high visibility bucket. For example, for the low visibility bucket and the medium visibility bucket, the system 110 may create the funnel based on, but are not limited to, a listing count, a merch-able (i.e., merchant) listing count and a target price. Further, the listing count-based analysis and recommendation may include, identifying, by the system 110, the verticals with minimum, for example 100, listing or a Business Unit (BU) standard minimum listings. For low-selection verticals, the system 110 may calculate the listing to conversion ratio and recommend a minimum number of listing required to be there in master data. For the rest of eligible listings verticals, the system 110 may recommend the minimum numbers of listings to be merchandised based on the conversion and sales of the long-tail verticals corresponding to the one or more products.
[0070] Further, the system 110 may perform CI based analysis and recommendation. For example, the system 110 may keep a threshold of for example, 0.8-1.1 as the eligibility criteria.
[0071] For overall vertical visibility improvement, the system 110 may develop a vertical affinity model. With the high affinity verticals, the system 110 may implement, but not limited to, a cross sell, merchant combination, and the like.
[0072] Further, for the high visibility selection of the long-tail verticals, there may be no action to be taken upon.
[0073] The system 110 using the vertical affinity model may include, cosine similarity of two verticals may be taken into consideration to arrive at affine verticals. The cosine similarly may be calculated as shown in table 1 below:
Cosine similarity formula:
Pair Count No of times the pair is bought by the same customer
Individual Count 1 No of times item 1 is bought
Individual Count 2 No of times item 2 is bought
Cosine Score ((pairCount^2)/(individualCount1*individualCount2)) * (10^6)
Table 1
In the table 1, the pair may be referred to a bundle of two verticals which are hypothesized to be bought by the same customer in a short period. Higher the cosine score, the pair of verticals are more likely to be bought by the same customer in the given time.
[0074] The vertical affinity model may output as shown in table 2 below:

Corona virus protection Cookware/ Bakeware Domestic hacks Kitchen essentials Creative corners Indoor Games WFH Essentials Monsoon Essentials Health and
Fitness Lounge wear
coronavirus
mask Cake mold Mops and buckets flask crayons Indore board games ear phone Umbrella gym equipment Innerwear
men women
sanitizer Baking trays
Cleaning gloves (with soft spikes) Cups and Mugs Painting kit/combos Cricket bat Headphone
Raincoat dumbbells Shorts
mask Manual hand
blender Sanitizer spray bottle Glass Glue gun Toys power backup Emergency
light Abb exerciser T shirt
n95 mask Grill tray Floor cleaner Mashers Gum/Glue Bay toys Laptop stand
Mosquito bat Maxi
Hand wash Kebab sticks Alcohol based
cleaner Slicer color sprayer Role play toy Extension cord Rain shoes Slipper
Table 2
[0075] The long-tail verticals including the one or more products may be for example shown in the above table 2. For example, FIG. 3 illustrates a graphical representation of the affine verticals. The impact of positioning long-tail verticals corresponding to one or more products is as shown in FIG. 3.
[0076] FIG. 4 illustrates a flow chart depicting a method 400 of positioning long-tail verticals corresponding to one or more products in an electronic commerce (e-commerce) environment, according to embodiments of the present disclosure.
[0077] At block 402, the method 400 includes creating, by a processor 112 associated with a system 110, one or more first visibility buckets for a pre-selected long-tail verticals corresponding to one or more products, based on a vertical share as compared with a visibility distribution corresponding to an impression share.
[0078] At block 404, the method 400 includes segmenting, by the processor 112, the pre-selected long-tail verticals, and assigning the created one or more first visibility buckets to the segmented long-tail verticals, based on a statistical distribution of data corresponding to the segmented long-tail verticals.
[0079] At block 406, the method 400 includes analyzing, by the processor 112, one or more hypothesis comprising guardrail metrics of each of the long-tail verticals in each of the assigned one or more first visibility buckets, to identify one or more driving factors of difference in a visibility of the long-tail verticals, when the long-tail verticals are assigned to a first low visibility bucket of the one or more first visibility buckets.
[0080] At block 408, the method 400 includes positioning, by the processor 112, the impression share of the long-tail verticals, when the one or more hypothesis is maintaining the guardrail metrics in the first low visibility bucket of the one or more first visibility buckets.
[0081] At block 410, the method 400 includes creating, by the processor 112, one or more second visibility buckets for the segmented long-tail verticals, based on the vertical share as compared with a Purchase Price Variance (PPV) distribution, when the long-tail verticals are assigned to a first medium visibility bucket of the one or more first visibility buckets.
[0082] At block 412, the method 400 includes creating, by the processor 112, a funnel within a second low visibility bucket and a second medium visibility bucket of the one or more second visibility buckets, based on analyzing at least one of a listing count, a listing count for merchants, and a target price.
[0083] At block 414, the method 400 includes positioning, by the processor 112, an overall visibility of the long-tail verticals, by determining at least one of an affinity in long-tail verticals, high affinity in long-tail verticals, a cross selling to a customer, and a merchant combination.
[0084] The order in which the method 400 is described is not intended to be construed as a limitation, and any number of the described method blocks may be combined or otherwise performed in any order to implement the method 400 or an alternate method. Additionally, individual blocks may be deleted from the method 400 without departing from the spirit and scope of the present disclosure described herein. Furthermore, the method 400 may be implemented in any suitable hardware, software, firmware, or a combination thereof, that exists in the related art or that is later developed. The method 400 describes, without limitation, the implementation of the system 110. A person of skill in the art will understand that method 400 may be modified appropriately for implementation in various manners without departing from the scope and spirit of the disclosure.
[0085] FIG. 5 illustrates a hardware platform 500 for implementation of the disclosed system 110, according to an example embodiment of the present disclosure. For the sake of brevity, the construction, and operational features of the system 110 which are explained in detail above are not explained in detail herein. Particularly, computing machines such as but not limited to internal/external server clusters, quantum computers, desktops, laptops, smartphones, tablets, and wearables which may be used to execute the system 110 or may include the structure of the hardware platform 500. As illustrated, the hardware platform 500 may include additional components not shown, and that some of the components described may be removed and/or modified. For example, a computer system with multiple GPUs may be located on external-cloud platforms including Amazon® Web Services, or internal corporate cloud computing clusters, or organizational computing resources, and the like.
[0086] The hardware platform 500 may be a computer system such as the system 110 that may be used with the embodiments described herein. The computer system may represent a computational platform that includes components that may be in a server or another computer system. The computer system may execute, by the processor 505 (e.g., a single or multiple processors) or other hardware processing circuit, the methods, functions, and other processes described herein. These methods, functions, and other processes may be embodied as machine-readable instructions stored on a computer-readable medium, which may be non-transitory, such as hardware storage devices (e.g., RAM (random access memory), ROM (read-only memory), EPROM (erasable, programmable ROM), EEPROM (electrically erasable, programmable ROM), hard drives, and flash memory). The computer system may include the processor 505 that executes software instructions or code stored on a non-transitory computer-readable storage medium 510 to perform methods of the present disclosure. The software code includes, for example, instructions to gather data and documents and analyze documents. In an example, the modules 204, may be software codes or components performing these steps. For example, the modules may include an acquiring module 222, a causing module 224, a receiving module 226, a retrieving module 228, a determining module 230, an analyzing module 232, a mapping module 234, a recommending module 236, and other modules 238.
[0087] The instructions on the computer-readable storage medium 510 are read and stored the instructions in storage 515 or in random access memory (RAM). The storage 515 may provide a space for keeping static data where at least some instructions could be stored for later execution. The stored instructions may be further compiled to generate other representations of the instructions and dynamically stored in the RAM such as RAM 520. The processor 505 may read instructions from the RAM 520 and perform actions as instructed.
[0088] The computer system may further include the output device 525 to provide at least some of the results of the execution as output including, but not limited to, visual information to users, such as external agents. The output device 525 may include a display on computing devices and virtual reality glasses. For example, the display may be a mobile phone screen or a laptop screen. GUIs and/or text may be presented as an output on the display screen. The computer system may further include an input device 530 to provide a user or another device with mechanisms for entering data and/or otherwise interacting with the computer system. The input device 530 may include, for example, a keyboard, a keypad, a mouse, or a touchscreen. Each of these output devices 525 and input device 530 may be joined by one or more additional peripherals. For example, the output device 525 may be used to display the results such as bot responses by the executable chatbot.
[0089] A network communicator 535 may be provided to connect the computer system to a network and in turn to other devices connected to the network including other clients, servers, data stores, and interfaces, for instance. A network communicator 535 may include, for example, a network adapter such as a LAN adapter or a wireless adapter. The computer system may include a data sources interface 540 to access the data source 545. The data source 545 may be an information resource. As an example, a database of exceptions and rules may be provided as the data source 545. Moreover, knowledge repositories and curated data may be other examples of the data source 545.
[0090] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be implemented merely as illustrative of the invention and not as a limitation.

ADVANTAGES OF THE PRESENT DISCLOSURE
[0091] The present disclosure provides a method and a system for positioning long-tail verticals corresponding to one or more products in an electronic commerce (e-commerce) environment.
[0092] The present disclosure provides an assessment framework of long tail position” and “eligibility criteria of product/vertical or category being selected for visibility”.
[0093] The present disclosure provides a method and a system for determining selection variety availability in master data, visibility through impression share, user scroll depth, Customer Intelligence (CI), customer ratings, customer reviews, listing count, merchant listing count, target price, affinity score, and the like.
[0094] The present disclosure provides a method and a system for positioning long-tail verticals for value and variety seeker customer cohort.
[0095] The present disclosure provides a method and a system for analyzing precise verticals and selections list which lacks visibility, and analyzing precise verticals and selections list which are eligible for more visibility.
[0096] The present disclosure provides a method and a system for calculating the listing to conversion ratio and recommend the minimum number of listing required to be there in master data. For the rest of eligible listings verticals recommending the minimum numbers of listings to be merchandised based on conversion and sales of the products.
 
, Claims:1. A method for positioning long-tail verticals corresponding to one or more products in an electronic commerce (e-commerce) environment, the method comprising:
creating, by a processor (112) associated with a system (110), one or more first visibility buckets for a pre-selected long-tail verticals corresponding to one or more products, based on a vertical share as compared with a visibility distribution corresponding to an impression share;
segmenting, by the processor (112), the pre-selected long-tail verticals, and assigning the created one or more first visibility buckets to the segmented long-tail verticals, based on a statistical distribution of data corresponding to the segmented long-tail verticals;
analyzing, by the processor (112), one or more hypothesis comprising guardrail metrics of each of the long-tail verticals in each of the assigned one or more first visibility buckets, to identify one or more driving factors of difference in a visibility of the long-tail verticals, when the long-tail verticals is assigned to a first low visibility bucket of the one or more first visibility buckets;
positioning, by the processor (112), the impression share of the long-tail verticals, when the one or more hypothesis is maintaining the guardrail metrics in the first low visibility bucket of the one or more first visibility buckets;
creating, by the processor (112), one or more second visibility buckets for the segmented long-tail verticals, based on the vertical share as compared with a Purchase Price Variance (PPV) distribution, when the long-tail verticals is assigned to a first medium visibility bucket of the one or more first visibility buckets;
creating, by the processor (112), a funnel within a second low visibility bucket and a second medium visibility bucket of the one or more second visibility buckets, based on analyzing at least one of a listing count, a listing count for merchants, and a target price; and
positioning, by the processor (112), an overall visibility of the long-tail verticals, by determining at least one of an affinity in long-tail verticals, high affinity in long-tail verticals, a cross selling to a customer, and a merchant combination.

2. The method as claimed in claim 1, wherein the first low visibility bucket comprises no impression share, the first medium visibility bucket is assigned based on a first pre-defined threshold for a selection of the long-tail verticals comprising a first pre-defined threshold of the impression share, and the first high visibility bucket is assigned based on a second pre-defined threshold for a selection of verticals comprising a second pre-defined threshold of the impression share.

3. The method as claimed in claim 1, wherein the one or more hypothesis comprises at least one of a selection that is below average scroll depth for a vertical or a category of the one or more products, and a selection that is above average scroll depth for a vertical or a category of the one or more products.

4. The method as claimed in claim 3, wherein, when the one or more hypothesis comprising below average scroll depth for the vertical or the category of the one or more products is selected, the method further comprises:
recommending, by the processor (112), to move up the position of the no impression share, when the one or more hypothesis passes the guardrails metrics comprising at least one of a Customer Intelligence (CI) data, customer ratings data, customer review data, and content.

5. The method as claimed in claim 3, wherein, when the one or more hypothesis comprising above average scroll depth for the vertical or the category of the one or more products is selected, the method further comprises:
determining, by the processor (112), issues corresponding to at least one of a pricing, a Customer Intelligence (CI) data, customer ratings data, in a top positioned products of the one or more products;
recommending, by the processor (112), to move up the position of the no impression share, when the one or more hypothesis passes the guardrails metrics comprising at least one of a popularity in other e-commerce environment, the CI, the customer ratings data, the customer review data, and the content; and
upgrading, by the processor (112), the pricing, a product quality, the content, customer intent of scroll depth, when the when the one or more hypothesis comprising highest scroll depth.

6. The method as claimed in claim 1, wherein analyzing the listing count further comprises:
identifying, by the processor (112), the long-tail verticals comprising at least one of a minimum of pre-defined listings or a minimum of pre-defined Business Unit (BU) standard listings;
calculating, by the processor (112), a listing to conversion ratio and recommending a minimum required number of listings, for a low-selection long-tail verticals in at least one of the minimum of pre-defined listings or the minimum of pre-defined Business Unit (BU) standard listings; and
recommending, by the processor (112), a minimum numbers of listings to be merchandise, based on the conversion ratio and sales, for remaining eligible listings.

7. The method as claimed in claim 1, wherein determining the affinity in long-tail verticals further comprises:
calculating, by the processor (112), a cosine similarity score of at least two long-tail verticals.

8. The method as claimed in claim 7, wherein the cosine similarity score is calculated based on at least one of a number of times the one or more products in at least two long-tail verticals is purchased by a same customer in a pre-defined period, a number of times a first product is purchased by a customer in the pre-defined period, and a number of times a second is purchased by the customer in the pre-defined period.

9. The method as claimed in claim 8, wherein, when the calculated cosine similarity score is high, at least two long-tail verticals are purchased by the same customer in the pre-defined period.

10. A system (110) for positioning long-tail verticals corresponding to one or more products in an electronic commerce (e-commerce) environment, the system (110) comprising:
a processor (112);
a memory (114) coupled to the processor (112), wherein the memory (114) comprises processor-executable instructions, which on execution, cause the processor (112) to:
create one or more first visibility buckets for a pre-selected long-tail verticals corresponding to one or more products, based on a vertical share as compared with a visibility distribution corresponding to an impression share;
segment the pre-selected long-tail verticals, and assigning the created one or more first visibility buckets to the segmented long-tail verticals, based on a statistical distribution of data corresponding to the segmented long-tail verticals;
analyze one or more hypothesis comprising guardrail metrics of each of the long-tail verticals in each of the assigned one or more first visibility buckets, to identify one or more driving factors of difference in a visibility of the long-tail verticals, when the long-tail verticals is assigned to a first low visibility bucket of the one or more first visibility buckets;
position the impression share of the long-tail verticals, when the one or more hypothesis is maintaining the guardrail metrics in the first low visibility bucket of the one or more first visibility buckets;
create one or more second visibility buckets for the segmented long-tail verticals, based on the vertical share as compared with a Purchase Price Variance (PPV) distribution, when the long-tail verticals is assigned to a first medium visibility bucket of the one or more first visibility buckets;
create a funnel within a second low visibility bucket and a second medium visibility bucket of the one or more second visibility buckets, based on analyzing at least one of a listing count, a listing count for merchants, and a target price; and
position an overall visibility of the long-tail verticals, by determining at least one of an affinity in long-tail verticals, high affinity in long-tail verticals, a cross selling to a customer, and a merchant combination.

11. The system (110) as claimed in claim 10, wherein the first low visibility bucket comprises no impression share, the first medium visibility bucket is assigned based on a first pre-defined threshold for a selection of the long-tail verticals comprising a first pre-defined threshold of the impression share, and the first high visibility bucket is assigned based on a second pre-defined threshold for a selection of verticals comprising a second pre-defined threshold of the impression share.

12. The system (110) as claimed in claim 10, wherein the one or more hypothesis comprises at least one of a selection that is below average scroll depth for a vertical or a category of the one or more products, and a selection that is above average scroll depth for a vertical or a category of the one or more products.

13. The system (110) as claimed in claim 12, wherein, when the one or more hypothesis comprising below average scroll depth for the vertical or the category of the one or more products is selected, the processor (112) is further configured to:
recommend to move up the position of the no impression share, when the one or more hypothesis passes the guardrails metrics comprising at least one of a Customer Intelligence (CI) data, customer ratings data, customer review data, and content.

14. The system (110) as claimed in claim 12, wherein, when the one or more hypothesis comprising above average scroll depth for the vertical or the category of the one or more products is selected, the processor (112) is further configured to:
determine issues corresponding to at least one of a pricing, a Customer Intelligence (CI) data, customer ratings data, in a top positioned products of the one or more products;
recommend to move up the position of the no impression share, when the one or more hypothesis passes the guardrails metrics comprising at least one of a popularity in other e-commerce environment, the CI, the customer ratings data, the customer review data, and the content; and
upgrade the pricing, a product quality, the content, customer intent of scroll depth, when the when the one or more hypothesis comprising highest scroll depth.

15. The system (110) as claimed in claim 10, wherein for analyzing the listing count, the processor (112) is further configured to:
identify the long-tail verticals comprising at least one of a minimum of pre-defined listings or a minimum of pre-defined Business Unit (BU) standard listings;
calculate a listing to conversion ratio and recommending a minimum required number of listings, for a low-selection long-tail verticals in at least one of the minimum of pre-defined listings or the minimum of pre-defined Business Unit (BU) standard listings; and
recommend a minimum numbers of listings to be merchandise, based on the conversion ratio and sales, for remaining eligible listings.

16. The system (110) as claimed in claim 10, wherein for determining the affinity in long-tail verticals, the processor (112) is further configured to:
calculate a cosine similarity score of at least two long-tail verticals.

17. The system (110) as claimed in claim 16, wherein the cosine similarity score is calculated based on at least one of a number of times the one or more products in at least two long-tail verticals is purchased by a same customer in a pre-defined period, a number of times a first product is purchased by a customer in the pre-defined period, and a number of times a second is purchased by the customer in the pre-defined period.

18. The system (110) as claimed in claim 17, wherein, when the calculated cosine similarity score is high, at least two long-tail verticals are purchased by the same customer in the pre-defined period.