AU2014101381A4 - Method and system for identifying profitable products- profitable products database - online profitability calculator - Google Patents

Abstract

A method and system for identifying products to sell which may be profitable. In some embodiments, the method may comprise the steps of, with respect to a given product: (a) receiving raw data (e.g. sales-price per unit and cost-to-acquire-goods) from at least one data source and/or from at least one product supplier; (b) pre-processing of the raw data received into seller and sales outputs (e.g. average sales-price) and into product cost information outputs (e.g. average cost-to-acquire-goods); (c) primary processing of the seller and sales outputs and the product cost information outputs into profitability characteristics (e.g. profit margin) and/or ease of trading metrics (e.g. minimum order quantity); and (d) presenting and/or displaying at least one of: the profitability characteristics, the ease of trading metrics, the product cost information outputs, and the seller and sales outputs on at least one graphical user interface made available to a user.

Description

METHOD AND SYSTEM FOR IDENTIFYING PROFITABLE PRODUCTS PRIORITY NOTICE [0001] The present application is an innovation patent application and as such makes claims of priority Provisional Patent Application of IP Right number: 2014904554. CROSS REFERENCE TO RELATED PATENT APPLICATIONS [0002] The present application makes no reference to any other related filed patent applica tions. STATEMENT REGARDING FEDERAL SPONSORSHIP [0003] No part of this invention was a result of any federally sponsored research. TECHNICAL FIELD OF THE INVENTION [0004] The present invention relates in general to software methods and systems employing such software and more specifically to methods and systems for identifying profitable products by generating a variety of profitability characteristics that may be associated with a given product and may aid a user in determining which products to sell.

COPYRIGHT AND TRADEMARK NOTICE [0005] A portion of the disclosure of this patent application may contain material that is sub ject to copyright protection. The owner has no objection to the facsimile reproduction by any one of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyrights whatsoever. [0006] Certain marks referenced herein may be common law or registered trademarks of third parties affiliated or unaffiliated with the applicant or the assignee. Use of these marks is by way of example and should not be construed as descriptive or to limit the scope of this in vention to material associated only with such marks. BACKGROUND OF THE INVENTION [0007] Currently there does not exist a single software platform that automates and/or semi automates a traditionally labor intensive process of researching what products to sell online based upon a product's profitability characteristics and/or various ease of trading considera tions. Currently there is not a single software platform that allows user to research various products with respect to various profitability characteristics and ease of trading consideration, such that the user may determine very quickly and with minimal labor and/cost of what prod ucts to sell online, e.g. to consumers and/or businesses. [0008] For example, profitability characteristics might include profit per unit, profit margin, demand-supply ratios, inventory turnover ratios, ROI (return on investment) ratios, breakeven analysis, and the like for a given product. Ease of trading considerations might include: time 2 of production, transit delivery days to destination, importation risks, size of the product, return merchandise authorization (RMA) risk, after sales support severity (post-sales support), num ber of product suppliers offering a given product, inventory turnover ratios, number of sold products (goods), value of the product (i.e. retail sales price), breakeven analysis, and the like. Note there may be some overlap as to what constitutes a profitability characteristic and what constitutes an ease of trading consideration. [0009] There is a need in the art for a single software platform that automates and/or semi automates the process of researching what products to sell online based upon a product's prof itability characteristics and/or various ease of trading considerations. [0010] It is to these ends that the present invention has been developed. BRIEF SUMMARY OF THE INVENTION [0011] To minimize the limitations in the prior art, and to minimize other limitations that will be apparent upon reading and understanding the present specification, the present invention describes a method and system for identifying products to sell which may be profitable. [0012] In some embodiments, the method may comprise the steps of, with respect to a given product: (a) receiving raw data (e.g. sales-price per unit) from at least one data source and/or receiving raw data (e.g. cost-to-acquire-goods) from at least one product supplier; (b) pre processing of the raw data (e.g. truncating irrelevant information from the received raw data) received into seller and sales outputs (e.g. average sales-price) and into product cost infor mation outputs (e.g. average cost-to-acquire-goods); (c) primary processing of the seller and sales outputs and the product cost information outputs into profitability characteristics (e.g. 3 profit margin) and/or ease of trading metrics (e.g. minimum order quantity); and (d) presenting and/or displaying at least one of: the profitability characteristics, the ease of trading metrics, the product cost information outputs, and the seller and sales outputs on at least one graphical user interface (GUI). In some embodiments, the GUI may be made available to a user. [0013] A method for identifying a more profitable product from at least two different prod ucts may also be taught. [0014] It is an objective of the present invention to provide a method and/or a system for au tomatically and/or semi-automatically generating and/or calculating various profitability char acteristics and/or various ease of trading considerations for a variety of products. [0015] It is another objective of the present invention to provide the method and/or the sys tem for automatically and/or semi-automatically generating and/or calculating various profita bility characteristics and/or various ease of trading considerations for a variety of products in a manner which significantly reduces the traditional labor and/or costs associated with trying to obtain product profitability characteristics. [0016] It is another objective of the present invention to provide a method and/or a system employing a searchable database, wherein a user of the method and/or the system may search for profitable products by searching (e.g. by keyword), sorting, and/or filtering various profita bility characteristics, various ease of trading considerations, product names, product categories, and the like to find profitable products. [0017] It is another objective of the present invention to provide a method and/or a system wherein the profitability characteristics may comprise one or more of: profit per unit, profit margin, demand-supply ratios, inventory turnover ratios, ROI (return on investment) ratio, breakeven analysis, and the like for a given product. 4 [0018] It is another objective of the present invention to provide a method and/or a system wherein ease of trading considerations may comprise one or more of: minimum order quantity, time of production, transit delivery days to destination, importation risks, size of the product, return merchandise authorization (RMA) risk, after sales support severity (post-sales support), number of product suppliers offering a given product, inventory turnover ratios, number of sold products (goods), value of the product (i.e. retail sales price), breakeven analysis, and the like. [0019] It is another objective of the present invention to provide a method and/or a system that measures and makes available inventory turnover times for various products in order min imize overstocking risks and depreciation losses. [0020] It is yet another objective of the present invention to provide a method and/or a sys tem allowing users to purchase, via an ecommerce mechanism, a given product once the user has determined the product fits the user's own profitable product considerations. [0021] These and other advantages and features of the present invention are described herein with specificity so as to make the present invention understandable to one of ordinary skill in the art, both with respect to how to practice the present invention and how to make the present invention. BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS [0022] Elements in the figures have not necessarily been drawn to scale in order to enhance their clarity and improve understanding of these various elements and embodiments of the in vention. Furthermore, elements that are known to be common and well understood to those in 5 the industry are not depicted in order to provide a clear view of the various embodiments of the invention. [0023] FIG. 1(a) may depict an exemplary embodiment of a flow diagram showing overall (global) steps 100, 200, and 300 involved in a method 1 for identifying a profitable product. [0024] FIG. 1(b) may depict an exemplary embodiment of a portion of step 100, for deter mining seller and sales outputs, depicted in a flow diagram. [0025] FIG. 1(c) may depict an exemplary embodiment of a portion of step 100, for deter mining product cost information outputs, depicted in a flow diagram. [0026] FIG. 2 may depict an exemplary embodiment of step 200, of primary processing to yield profitability characteristics and/or ease of trading metrics. [0027] FIG. 3 may depict an exemplary embodiment of step 300, of displaying at least one of the profitability characteristics, the ease of trading metrics, the product cost information outputs, and the seller and sales outputs on at least one graphical user interface (GUI). [0028] FIG. 3(a) may depict an exemplary embodiment of a screenshot showing a research table as the research table may be displayed on the GUI. [0029] FIG. 3(b) may depict an exemplary embodiment of a screenshot showing a feasibility table as the feasibility table may be displayed on the GUI. [0030] FIG. 3(c) may depict an exemplary embodiment of a screenshot showing a purchased data table as the purchased data table may be displayed on the GUI. [0031] FIG. 3(d) may depict an exemplary embodiment of a screenshot showing a product details page as the product details page may be displayed on the GUI. [0032] FIG. 4 may depict an exemplary embodiment of components of a system for identify ing a profitable product, shown as a block diagram. 6 REFERENCE NUMERAL SCHEDULE 1 Method 1 100 Receiving & Pre-Processing of Data 100 101 Searching 101 102 Data Source 102 103 Third Parties 103 104 Publicly Available Data Mining 104 105 Receiving Data 105 106 Pre Processing 106 107 Seller & Sales Outputs 107 108 Sales-Price per Unit 108 109 Average Sales-Price 109 110 Number of Sellers 110 111 Number of Products Sold 111 112 Product-Name 112 113 Product-Category 113 114 Product-Description 114 115 Request-for-Quote 115 (RFQ 115) 116 Product Supplier 116 117 RFQ-Response 117 118 More-Pre-Processing 118 120 Product Cost Information Outputs 120 7 121 Cost-Information 121 122 Minimum Order Quantity 122 (MOQ 122) 123 Cost-to-Acquire-Goods 123 124 Average Cost-to-Acquire-Goods 124 125 Product-Details-Information 125 112 Product-Name 112 126 Size of Product 126 127 Weight of Product 127 200 Primary Processing of Data 200 210 Profitability Characteristics 210 211 Profit per Unit 211 212 Profit Margin 212 213 Hotness Level 213 214 Sellability Score 214 215 Demand-Supply-Ratio 215 216 Competitive-Quantity 216 250 Ease of Trading Metrics 250 110 Number of Sellers 110 126 Size of Product 126 122 Minimum Order Quantity 122 251 Time of Production 251 252 Transit Delivery Days to Destination 252 253 Importation Risk 253 8 254 Return Merchandise Authorization 254 255 After Sales Support Severity 255 300 Presenting and Displaying Processed Data and Other Inputs 300 301 Inputs 301 107 Seller & Sales Outputs 107 120 Product Cost Information Outputs 120 210 Profitability Characteristics 210 250 Ease of Trading Metrics 250 302 Keyword Search by User 302 305 Display Outputs 305 310 Graphical User Interface 310 (GUI 310) 320 Research Table 320 330 Feasibility Table 330 331 Purchase Data Means 331 340 Purchased Data Table 340 350 Product Details Page 350 400 System 400 401 at least one Server 401 402 Memory 402 403 Software 403 404 Database 404 405 Processor 405 406 Network Adapter 406 9 501 Communication Network 501 801 User-Computing-Device 801 802 User-Graphical-User-Interface 802 (User-GUI 802) 850 User 850 901 Staff-Computing-Device 901 902 Staff-Graphical-User-Interface 902 (Staff-GUI 902) 950 Staff 950 10 DETAILED DESCRIPTION OF THE INVENTION [0033] A method and system for identifying products to sell which may be profitable. In some embodiments, the method may comprise the steps of, with respect to a given product: (a) receiving raw data (e.g. sales-price per unit) from at least one data source and/or receiving raw data (i.e. additional raw data) (e.g. cost-to-acquire-goods) from at least one product supplier; (b) pre-processing (and more-pre-processing) of the raw data (e.g. truncating irrelevant infor mation from the received raw data) received into seller and sales outputs (e.g. average sales price) and into product cost information outputs (e.g. average cost-to-acquire-goods); (c) pri mary processing of the seller and sales outputs and the product cost information outputs into profitability characteristics (e.g. profit margin) and/or ease of trading metrics (e.g. minimum order quantity); and (d) presenting and/or displaying at least one of: the profitability character istics, the ease of trading metrics, the product cost information outputs, and the seller and sales outputs on at least one graphical user interface (GUI). In some embodiments, the GUI may be made available to a user. [0034] A method for identifying a more profitable product from at least two different prod ucts may also be taught. [0035] The various embodiments of the methods and/or systems of the prevent invention may be operated by an "Operational-Entity." The "users" of the various embodiments of the methods and/or systems of the prevent invention may be various entities who may desire (wish) to sell (e.g. retail sale, including online retails sales) products that may have an in creased likelihood of being profitable products. That is, the users may be existing sellers of products, or those who may wish to become sellers of profitable products. For example, and 11 without limiting the scope of the present invention, the user may be an entity who wishes to sell profitable products. For example, and without limiting the scope of the present invention, the entity may be an individual (e.g. a sole proprietorship) or some other business entity (e.g. a corporation). Whereas, a product-supplier may be an entity who supplies the products to the user/seller. For example, and without limiting the scope of the present invention, product suppliers may be manufacturers, wholesalers, distributors, and/or importers of the product in question, who provides the product to the user/seller (e.g. retail seller). [0036] In the following discussion that addresses a number of embodiments and applications of the present invention, reference is made to the accompanying drawings that form a part thereof, where depictions are made, by way of illustration, of specific embodiments in which the invention may be practiced. It is to be understood that other embodiments may be utilized and changes may be made without departing from the scope of the invention. [0037] FIG. 1(a) may depict an exemplary embodiment of a flow diagram showing overall (global) Steps 100, 200, and 300 involved in a Method 1 for identifying a profitable product. Step 100 may comprise receiving raw data from at least one Data Source 102 and/or receiving raw data from at least one Product-Supplier 116. Step 100 may also comprise performing some Pre Processing 106 and/or More-Pre-Processing 118 of the received raw data. Outputs of Step 100 may be Seller & Sales Outputs 107 and Product Cost Information Outputs 120. Step 200 may comprise taking as inputs Seller & Sales Outputs 107 and Product Cost Infor mation Outputs 120, and then performing primary processing upon some of Seller & Sales Outputs 107 and Product Cost Information Outputs 120 to yield Profitability Characteristics 210 and/or Ease of Trading Metrics 250. Lastly, Step 300 may comprise displaying and/or presenting at least one of Profitability Characteristics 210, Ease of Trading Metrics 250, Prod 12 uct Cost Information Outputs 120, and Seller & Sales Outputs 107 on at least one graphical us er interface (GUI). The GUI may be assessable by a User 850. [0038] In some embodiments, Method 1 may comprise the steps of, with respect to a given product: (a) receiving raw data (e.g. Sales-Price per Unit 108) from at least one Data Source 102 and/or receiving raw data (e.g. Cost-to-Acquire-Goods 123) from at least one Product Supplier 116; (b) Pre Processing 106 of the received raw data 105 (e.g. truncating irrelevant in formation from the received raw data) received into Seller & Sales Outputs 107 (e.g. Avg. Sales-Price 109) and into Product Cost Information Outputs 120 (e.g. Avg. Cost-to-Acquire Goods 124); (c) primary processing of Seller & Sales Outputs 107 and Product Cost Infor mation Outputs 120 into Profitability Characteristics 210 (e.g. Profit Margin 212) and/or Ease of Trading Metrics 250 (e.g. Minimum Order Quantity 122); and (d) presenting and/or display ing at least one of: Profitability Characteristics 210, Ease of Trading Metrics 250, Product Cost Information Outputs 120, and Seller & Sales Outputs 107 on at least one GUI. The GUI may be one that may be assessable by User 850. User 850 may be an entity who wishes to sell profitable products. The entity may be an individual (e.g. a sole proprietorship) or some other business entity (e.g. a corporation). Step (a) and (b) above may comprise Step 100. Step (c) may comprise Step 200. And Step (d) may comprise Step 300. [0039] Note, as used herein "Avg" and "Avg." may be interpreted as "average" as in the sta tistical calculation of average. [0040] In some embodiments, User 850 may have to create an account to gain access to Method 1 and/or System 400. Creation of such an account may require input of User 850 in formation such as one or more of: name (legal name and/or first name and last name), email address, password, phone number, physical address, mailing address, and the like. Such inputs 13 may be non-transitorily stored within Database 404. User 850 email address and password may be User 850 login credentials. Once an account may be created User 850 may access var ious User-GUI 802 webpages. [0041] In some embodiments, categorizing User 850 by postal codes and/or area code may be important, some product data once sold to a given User 850 in a certain postal code and/or area code may remove availability of reselling such data to another User 850 in a same or simi lar postal code and/or area code. [0042] In some embodiments, categorizing User 850 may also be categorized by type of membership level, for example as, free, paid subscription, or free but having paid to access certain product data. Paid subscription User 850 may pay a monetary fee on some recurring time interval period, such as monthly, quarterly, or annually. And the paid subscription User 850 may also be further sub-divided into different levels of paid subscriptions, such as Plati num Members, Gold Members, Silver Members, Bronze Members, and the like. [0043] Note in some exemplary embodiments, the methods (including Method 1) may con tinually run through the steps of any given method embodiment in a cyclic manner per a given time period, such that the at least one Profitability-Characteristic 210 (and/or Ease of Trading Metrics 250) may be continually refreshed (i.e. calculated and/or generated) per the given time period. For example, and without limiting the scope of the present invention, the given time pe riod may be selected from on the order of seconds, minutes, hours, days, weeks, months, quar ters, and the like. [0044] FIG. 1(b) may depict an exemplary embodiment of a portion of step 100, for deter mining Seller & Sales Outputs 107, depicted in a flow diagram. 14 [0045] In some embodiments, the step of receiving the raw data 105 may comprise a step of Searching 101 for at least one Data Source 102. In some embodiments, the step of Searching 101 for the at least one data source may comprise manual entry of at least one Data Source 102 to be targeted for receiving the raw data 105. Thus, in some embodiments, when Staff 950 learns of and/or may be made aware of a particular Data Source 102, Staff 950 may manually enter various details associated with the particular Data Source 102, into Database 404. Staff 950 may be an agent (e.g. an employee) the Operational-Entity. The various details may be comprise contact information of the particular Data Source 102. The contact information may comprise for the particular Data Source 102: entity name, physical address, mailing address, phone number(s), email address(es), website URL(s) (universal resource locator), instructions for contacting the particular Data Source 102, and the like. [0046] In some embodiments, the step of Searching 101 for at least one Data Source 102 may comprise the step of receiving the raw data 105 from Third Parties 103. In some embodiments, Third Parties 103 may be selected from the group comprising of one or more of at least one Data Source 102 and/or from a third party application program interface (API). That is, in some embodiments, Data Source 102 may be Third Party 103. Various Third Parties 103 may have access to the raw data. Various Third Parties 103 may provide access to the raw data for a fee and/or under various contractual provisions with the Operational-Entity. In some embod iments, at least some of the raw data may be provided by API's and/or received from the API's. [0047] In some embodiments, the step of step of Searching 101 for at least one Data Source 102 may comprise a step of data mining publicly available information to locate Data Source 102. In some embodiments, the step of step of receiving the raw data 105 from at least one 15 Data Source 102 may comprise a step of data mining publicly available information associated with at least one Data Source 102. [0048] In some embodiments, the step of receiving the raw data 105 from at least one Data Source 102 may comprise direct receiving and/or indirect receiving of the raw data from at least one Data Source 102. In some embodiments, the step of direct receiving the raw data from at least one Data Source 102 may occur when at least one Data Source 102 may actively transmit the raw data received (e.g. transmitting to at least one Server 401 associated with the Operational-Entity). In some embodiments, active transmission of the raw data by the at least one data source may occur when at least one Data Source 102 may be Third Party 103, a third party API, and/or when at least one Data Source 102 may be under contract with an entity im plementing the method, e.g. the Operational-Entity. [0049] In some embodiments, the step of indirect receiving the raw data from at least one Data Source 102 may occur when at least one Data Source 102, may be data mined of public information available from at least one Data Source 102. [0050] For example, and without limiting the scope of the present invention, in some exem plary embodiments, data mining may be by use of various data mining software. In some em bodiments, data mining may be handled by various APIs and/or Third Parties 103. Such data mining software may be selected from the group comprising: spyders, web-crawlers, other scripts, and the like. However, in all cases, any data mining, when employed, may be of pub licly available information of a given Data-Source 102. For example, such data mining may involve tracking and monitoring traffic characteristics of a given Data-Source 102, in an ag gregate manner. In some embodiments, some or all of the data mining software may be pro 16 vided by a Third Party 103 provider, wherein results of such data mining may be received by Method 1 and/or System 400. [0051] In some embodiments, Data Source 102 may be a seller of a given product. In some embodiments, Data Source 102 may be a retail seller of a given product. In some embodiments, Data Source 102 may be an online retail seller of a given product. Such online retailers may be referred to as sellers within this disclosure. Such online retailers may include traditional brick and mortar retailers who may have an online presence. For example, and without limiting the scope of the present invention, Data Source 102 may be one or more of: Google, Yahoo, Apple, Amazon, eBay, Dell, Staples, Walmart, Kohls, Office Depot, Sears, Macy, Overstock, Home Depot, Costco, BestBuy, Target, and the like. [0052] For example, and without limiting the scope of the present invention, in some embod iments, Data Sources 102 may be selected from price comparison websites like www.pricegrabber.com, www.pricewatch.com, and similar price comparison websites. In some embodiments, Data Sources 102 may be selected from one or more online Product-Suppliers 116, such as manufacturers and/or wholesalers. In some embodiments, Data Sources 102 may be selected from one or more online Product-Suppliers and/or online retailers (Sellers). [0053] In some embodiments, different types of the raw-data may be sourced from (i.e. re ceived from) different Data Sources 102. [0054] In some embodiments, the raw data received 105 from at least one Data Source 102 may be with respect to at least one specific product. In some embodiments, the raw data re ceived 105 from at least one Data Source 102 may selected from the group comprising one or more of: a Product-Name 112, a Product-Category 113, a Product-Description 114, a Sales-Price per Unit 108, an Avg (average) Sales-Price 109, a Number of Sellers 110, a Number of Products 17 Sold 111, and the like. Some Data Sources 102 may provide each of these types of raw data for a given product of interest; whereas, other Data Sources 102 may only provide one of these types of raw data for a given product of interest. [0055] In some embodiments, the step of Pre-Processing 106 of the raw data received 105 in to Seller & Sales Outputs 107 may comprise the step of organizing the raw data received into raw data entries. Each raw data entry received may be organized by each different specific product, e.g. by Product-Name 112 to comprise one or more of: Product-Category 113, Product Description 114, Sales-Price per Unit 108, Avg (average) Sales-Price 109, Number of Sellers 110, Number of Products Sold 111, and the like. [0056] In some embodiments, the step of Pre-Processing 106 of the raw data received 105 in to Seller & Sales Outputs 107 may comprise one or more of the following steps: (a) ranking, from high to low, the raw data entries by Number of Products Sold 111; (b) assigning Product Category 113 to the raw-data-entry received; and/or (c) truncating a given type of received raw data to eliminate any portion of that received raw-data that may be not relevant to the type of re ceived raw-data. For example, and without limiting the scope of the present invention, with re spect to Step (a) of this paragraph, Number of Products Sold 111 may correspond to a measure of demand. For example, and without limiting the scope of the present invention, with respect to Step (b) of this paragraph, available Product-Category 113 may be predetermined by Method 1, or the assigned Product-Category 113 may be selected from a plurality of predetermined Prod uct-Category 113. Some of this Pre Processing 106 may be automatic by Software 403. Where as, other steps of this Pre Processing 106 may require some Staff 950 interaction, oversight (re view), and/or input. 18 [0057] For example, and without limiting the scope of the present invention, with respect to Step (c) of this paragraph, if a type of raw data may be Product-Name 112, and the received Product-Name 112 might be "super cheap 24 Hz Bluetooth headset with extras *** Local Ex press Deliver" then Method 1 may truncate this into "24 Hz Bluetooth headset" as the other words received in the raw data were not relevant to Product-Name 112. [0058] In some embodiments, outputs of the Pre Processed 106 raw data 105 may be Seller & Sales Outputs 107. In some embodiments, Seller & Sales Outputs 107 may be selected from the group comprising one or more of: a cleansed Product-Name 112, a cleansed Product Category 113, a cleansed Product-Description 114, a cleansed Sales-Price per Unit 108, an Avg (average) cleansed Sales-Price 109, a cleansed Number of Sellers 110, and a cleansed Number of Products sold 111, and the like. That is, Pre Processing 106 may transition the raw data received 105 into cleansed data. In some embodiments, raw data received 105 may not comprise raw Avg Sales-Price 109, as cleansed Avg Sales-Price 109 may be calculated from either raw Sales Price per Unit 108 or from cleansed Sales-Price per Unit 108 by the step of Pre Processing 106. [0059] In some embodiments, Product-Name 112 may a brief descriptive name for various products that method 1 and/or system 400 may maintain data on. For example, and without lim iting the scope of the present invention, some example Product-Names 112 may be: 65W laptop AC charger for HP Compaq Presario CQ61 CQ60, 10 x 5 mm drill bit HSS, 2.5 km solar electric fence kit Australian made charger energizer Thunderbird, Escape 100 mL EDP spray for women by Calvin Klein, Samsung Galaxy Tab 10.1 Tablet leather flip stand case cover P7500 P7510, SanDisk Mobile ULTRA 16GB Class 10 Micro SD 16G MicroSDHC Memory Card SDHC, full head clip in hair extensions ponytail bangs synthetic women lady hair H008, lemon 100% purse essential oil 100 mL, photo studio in a box tent cube with lights Steve Kaeser Photographic 19 Lighting, first aid non adherent dressing 7.5 cm x 10 cm (x5), DD computer system upgrade from 4 GB 1.333MHz to 8 GB DDR3 1333 MHz RAM memory, blade antenna for Telstra next G AirCard 320 U 4G, Samsung SE-208DB External Slim USB Powered DVD Burner Drive and NERO, sandalwood pure essential oil therapeutic grade 10 mL, and the like. Note, these listed product-names are merely examples and may change as more profitable products may be added to the database and possibly less profitable products removed from the database. [0060] In some embodiments, Product-Category 113 may a brief descriptive name for vari ous categories of products that method 1 and/or system 400 may maintain data on. For example, and without limiting the scope of the present invention, some Product-Categories 113 may be: laptop & desktop accessories, hardware parts, fencing, fragrances, iPad/Tablet/ebook accesso ries, memory cards, hair extensions, natural & homeopathetic, lighting studio equipment, medi cal, desktop PCs, home networking & connectivity, drives storage & blank media, massage, and the like. In some embodiments, Product-Categories 113 may comprise sub-product-categories. In some embodiments, the product, identifiable by Product-Name 112 may be associated within one more Product-Category 113 (or sub-product-category). Note, these listed Product Categories 113 are merely examples and may change as more profitable products may be added to the database and possibly less profitable products removed from the database. [0061] For example, and without limiting the scope of the present invention, in some embod iments there may be about 35 Product-Categories 113, which may be: Antiques; Appliances Home; Art; Automotive-Marine Parts & Accessories; Automotive-Powersports; Baby Products; Beverage-Food; Books; Business-Industrial; Camera-Photo; Ceramics-Porcelain; Cinema; Clothing, Shoes, Accessories; Coins; Collectables; Commercial; Computers; Consumer Elec tronics & Accessories; Crafts; Dolls, Bears; Entertainment; Games Console-Video Games; Gift 20 Cards; Health-Personal Care-Beauty; Hobby-Tool-Home Improvement; Home-Garden; Mobile Telephones & Accessories; Music; Musical Instruments; Services, Sport-Health Products; Stamps; This and That (miscellaneous); Travel-Fun-Tickets; Watches-Jewelry; and the like. [0062] In some embodiments, new Product-Categories 113 may be added manually by Staff 950, wherein once created the new Product-Category 113 may be populated automatically by Method 1. [0063] In some embodiments, for a given product, a plurality of Sales-Prices per Unit 108 may be received as raw data from the various Data Sources 102 or even from a single Data Sources 102 over different time intervals (such as hourly, daily, monthly, quarterly, and the like). In some embodiments, this plurality of Sales-Prices per Unit 108 received may then be used to calculate Avg (average) Sales-Price 109 during the Pre Processing 106 step. [0064] In some embodiments, Number of Sellers 110 may be a quantity measurement. In some embodiments, Number of Sellers 110 may correspond to a quantity of different Data Sources 102 (e.g. Amazon, eBay, Overstock.com, and the like) providing Sales-Prices per Unit 108 information and/or providing Number of Products Sold 111 information. Note, in some embodiments, Number of Sellers 110 may be a measure of supply, as in supply from the field of economics. [0065] In some embodiments, Number of Products Sold 111 may be a quantity measurement. In some embodiments, Number of Products Sold 111 may correspond to a quantity of the given product in question sold by a given Seller (e.g. a given Data Source 102) or sold by a plurality of Sellers (e.g. a plurality of Data Source 102) all selling the same given product. Note, in some embodiments, Number of Products Sold 111 may be a measure of demand, as in demand from the field of economics. 21 [0066] FIG. 1(c) may depict an exemplary embodiment of a portion of step 100, for deter mining Product Cost Information Outputs 120, depicted in a flow diagram. [0067] In some embodiments, the step of receiving the raw data from at least one Product Supplier 116 may be preceded by first generating a Request-for-Quote 115 (RFQ 115) with re spect to a given product and secondly by transmitting RFQ 115 to at least one Product Supplier 116. [0068] In some embodiments, the generated RFQ 115 may be submitted (transmitted) elec tronically to at least one Product-Supplier 116. In some embodiments, electronic means of such submission (transmission) may comprise one or more of email, fax (facsimile) message, web form submission, and the like. For example, and without limiting the scope of the present invention, generated RFQs 115 may be submitted by automatically generated email to at least one email address of a given Product-Supplier 116. For example, and without limiting the scope of the present invention, generated RFQs 115 may be submitted by automatically gener ated fax message to at least one email address of a given Product-Supplier 116. Other submis sion (transmission) means may also be used, such as phone calls and/or physical mail. [0069] In some embodiments, RFQs 115 may be both generated and submitted automatically by Method 1. In some embodiments, RFQs 115 may be generated manually by Staff 950. In some embodiments, generated RFQs 115 may be manually submitted by Staff 950. [0070] In some embodiments, Product-Suppliers 116 who may receive the generated RFQ 115 may be selected from a plurality of Product-suppliers 116. The plurality of Product suppliers 116 may be non-transitorily stored within a memory as a plurality of unique product supplier-details. Each product-supplier-details may comprise information (e.g. contact infor 22 mation) of that particular Product-supplier 116. The plurality of unique product-supplier details may be non-transitorily stored within Database 404. [0071] In some embodiments, product-supplier-details may comprise information of that Product-Supplier 116. For example, and without limiting the scope of the present invention, such information may comprise the business entity name and/or dba of that Product-Supplier 116, contact information of that Product-Supplier 116, and reliability indicators for Product Supplier 116. For example, and without limiting the scope of the present invention, such contact information may comprise phone numbers, email addresses, fax numbers, mailing addresses, physical addresses, websites, names of personnel, and the like. For example, and without limit ing the scope of the present invention, reliability indicators may comprise the number of years a given Product-Supplier 116 may have been in business, customer reviews, and the like. [0072] In some embodiments, RFQ 115 may comprise including in RFQ 115 at least some of Seller & Sales Outputs 107 with respect to a given product. In some embodiments, at least some of Seller & Sales Outputs 107 with respect to a given product may comprise the cleansed Product-Name 112, and may optionally comprise the cleansed Product-Category 113 and/or the cleansed Product-Description 114. [0073] In some embodiments, RFQ 115 may request raw data from at least one Product Sup plier 116. This requested raw data may be associated with: Product-Name 112, Product Category 113, and/or Product-Description 114 for a given product. Such received raw data may be raw Product Cost Information Outputs 120. In some embodiments, the raw Product Cost Information Outputs 120 may be selected from the group comprising one or more of: cleansed Product-Name 112, cleansed Product-Category 113, cleansed Product-Description 114, and raw Cost-Information 121. For example, and without limiting the scope of the pre 23 sent invention, each RFQ 115 may request that each Product-Supplier 116 provide raw Product Cost Information Outputs 120 with respect to the products identified in RFQ 115 (e.g. identi fied by Product-Name 112, Product-Category 113, and/or Product-Description 114 for a given product). [0074] In some embodiments, the raw Cost-Information 121 may be selected from the group comprising one or more of: raw Cost-to-Acquire-Goods 123, raw Minimum Order Quantity 122 (MOQ 122), raw Product-Details-Information 125, and the like. [0075] In some embodiments, the Cost-to-Acquire-Goods 123 may be selected from the group comprising one or more of: raw freight on board cost (raw FOB price), raw cost insur ance freight (raw CIF price), raw FOB plus shipment cost, raw ex-works contract for sale (EXW) cost, raw air shipment cost, raw sea shipment cost, raw custom duty cost, raw import tax, raw goods and service tax (GST) cost, raw value added tax (VAT), and the like. [0076] In some embodiments, the raw Product-Details-Information 125 may be selected from the group consisting of one or more of: a raw Size of Product 126, a raw Weight of Product 127, and the like. [0077] In some embodiments, the step of receiving the raw data from at least one Product Supplier 116 may comprise receiving a RFQ-response 117 from at least one Product Supplier 116, in response to RFQ 115 transmitted (submitted) to the at least one product supplier. In some embodiments, RFQ-response 117 may comprise a raw Product Cost Information Outputs 120 with respect to a given product. For example, and without limiting the scope of the pre sent invention, one purpose for receiving the raw-data (e.g. Product-Name 112) from at least one Data Source 102 may be so the raw-data received may be used to automatically generate RFQs 115 for transmission to various Product-Suppliers 116. And one purpose for receiving 24 RFQ-Responses 117 back from Product-Suppliers 116 may be so the raw Product Cost Infor mation Outputs 120, e.g. raw Cost-to-Acquire-Goods 123, for a given product, may be re ceived. And one purpose for receiving Product Cost Information Outputs 120, such as Cost-to Acquire-Goods 123, for the given product may be for calculating various Profitability Characteristics 210 of the given product. [0078] In some embodiments, the step of pre-processing of the raw data received from least one Product Supplier 116 into Product Cost Information Outputs 120 may comprise the step processing the raw Product Cost Information Outputs 120 into cleansed Product Cost Infor mation Outputs 120. This step may correspond to More-Pre-Processing 118 in FIG. 1(c). In some embodiments, the step of pre-processing (e.g. More-Pre-Processing 118) of the raw Product Cost Information Outputs 120 received from least one Product Supplier via RFQ responses 117, may comprise one or more of the following steps: (a) truncating a given type of received raw Product Cost Information Outputs 120 to eliminate any portion of that received raw Product Cost Information Outputs 120 that may not be relevant to the type of received raw Product Cost Information Outputs 120; (b) performing currency conversions so all Product Cost Information Outputs 120 may be in a same currency; (c) deleting the highest and the low est raw Cost-to-Acquire-Goods 123 for a given product; and/or (d) calculating an Avg. (aver age) Cost-to-Acquire-Goods 124 for a given product. [0079] In some embodiments, the cleansed (i.e. after More-Pre-Processing 118) Product Cost Information Outputs 120 may be selected from one or of the group comprising: cleansed Prod uct-Name 112, cleansed product-category 113, cleansed product-description 114, cleansed cost-information 121, and the like. 25 [0080] In some embodiments, Cost-Information 121 may comprise: Cost-to-Acquire-Goods 123, Average-Cost-to-Acquire-Goods 124, Minimum Order Quantity 122 (MOQ 122), Prod uct-Details-Information 125, and the like. Each received and processed data set of Cost Information 121 may have been processed (e.g. via More-Pre-Processing 118) from its corre sponding and complimentary raw-data counterpart. [0081] In some embodiments, Cost-to-Acquire-Goods 123 may comprise one or more of: FOB cost, CIF cost, FOB plus shipment cost, EXW cost, air shipment cost, sea shipment cost, custom duty cost, import tax, GST cost, VAT costs, and the like. [0082] In some embodiments, Product-Details-Information 125 may comprise Size of Prod uct 126 (e.g. packaging dimensions) and/or Weight of Product 127. [0083] In some exemplary embodiments, Cost-to-Acquire-Goods 123 may be with respect to the Seller's costs to obtain a given product that the seller intends to resell. Such costs may in clude not only the sales price for purchasing (and acquiring) the given product from a given Product-Supplier 116 (e.g. factory, manufacturer, wholesaler, etc.), but also may include trans portation (shipping) costs, insurance costs, importation costs (e.g. duty), and other similar costs as noted above. These costs may be added to arrive at a given Cost-to-Acquire-Goods 123 figure for a give Product-Name 112 from a given Product-Supplier 116. For example, and without limiting the scope of the invention, Cost-to-Acquire-Goods 123 may be the FOB cost obtained from Product-Suppliers 116 in RFQ-Responses 117. [0084] Whereas, the Seller's costs associated with reselling the given product (i.e. costs not associated with acquisition of the product) may be categorized as Cost-of-Sales. For example, some example Cost-of-Sales may be the fees a Seller must pay if selling via eBay and/or via Amazon, and/or by accepting payment via Paypal or other similar payment platform. In some 26 embodiments, User 850 may set the Cost-of-Sales, as Cost-of-Sales may be one factor that ide ally should be accounted for in determining profitability of a target product to sell. [0085] In some embodiments, Avg. Cost-to-Acquire-Goods 124 may be calculated by deter mining at least three best Costs-to-Acquire-Goods 123, and then averaging those three best Costs-to-Acquire-Goods 123. Determining the three best Costs-to-Acquire-Goods 123 may be done by ranking (i.e. sorting) Costs-to-Acquire-Goods 123 received via received RFQ responses 117 from least costly to most costly. Such sorting (ranking) may be done via More Pre-Processing 118. The three least costly received Costs-to-Acquire-Goods 123 may be deemed the three best and serve as a basis for calculating Avg. Cost-to-Acquire-Goods 124. [0086] In some embodiments, prior to determining the three best Costs-to-Acquire-Goods 123 received via RFQ-responses 117 from least costly to most costly, a lowest received Cost to-Acquire-Goods 123 and a highest received Cost-to-Acquire-Goods 123 may be not utilized in the ranking nor the average calculation, i.e. such lowest and highest data points may be dis carded for ranking and/or average calculation purposes. Not utilizing the lowest received Cost-to-Acquire-Goods 123 nor the highest received Cost-to-Acquire-Goods 123 may serve as a quality control (QC) function. [0087] In some exemplary embodiments, Method 1 may wait until at least five RFQ Responses 117 may be received before conducting More-Pre-Processing 118, including calcu lating Avg. -Co st-to-Acquire-Goods 124. [0088] FIG. 2 may depict an exemplary embodiment of step 200, of primary processing to yield Profitability Characteristics 210 and/or Ease of Trading Metrics 250 for a given product. [0089] In some embodiments, Profitability Characteristics 210 may be selected from the group comprising one or more of: a Profit per Unit 211, Profit Margin 212, a Hotness Level 27 213, a Sellability Score 214, a Demand-Supply-Ratio 215 (D/S215), a Competitive-Quantity 216, and the like. [0090] In some embodiments, Profit per Unit 211 may be calculated by subtracting Avg. Cost-to-Acquire-Goods 124 from Avg Sales-Price 109 per a given product. Avg. Cost-to Acquire-Goods 124 may be a component of Product Cost Information Outputs 120. Avg Sales-Price 109 may be a component of Seller & Sales Outputs 107. That is, in some embod iments, Profit per Unit 211 may be calculated per the following formula: (Avg Sales Price 109) - (Avg. Cost-to-Acquire-Goods 124) = Profit per Unit 211. [0091] In some embodiments, Profit per Unit 211 may be calculated by subtracting Avg. Cost-to-Acquire-Goods 124 and an average Cost of Sales 109 from Avg Sales-Price 109 per a given product. Avg. Cost-to-Acquire-Goods 124 may be a component of Product Cost Infor mation Outputs 120. Avg Sales-Price 109 may be a component of Seller & Sales Outputs 107. That is, in some embodiments, Profit per Unit 211 may be calculated per the following formu la: (Avg Sales Price 109) - ([average Cost of Sales] + [Avg. Cost-to-Acquire-Goods 124]) = Profit per Unit 211. [0092] In some embodiments, Profit margin 212 may be a percentage of Profit per Unit 211 with respect to Avg Sales Price 109 for a given product. Profit margin 212 may be calculated by taking Profit per Unit 211 and dividing Profit per Unit 211 by Avg Sales Price 109, and then multiplying that result by 100. That is, in some embodiments, Profit Margin 212 may be calculated per the following formula: [(Profit per Unit 211) / (Avg. Sales-Price 109)] * 100 = Profit Margin 212. [0093] In some embodiments, hotness level 213 may be a normalized metric varying in posi tive whole numbers. This normalized metric may be an indicator of product demand for a giv 28 en product sold over a certain frame within a particular market. , wherein the hotness level may be calculated from Number of Products Sold 111. Number of Products Sold 111 may be a component of the Seller & Sales Outputs 107. Number of Products Sold 111 may be a meas ure or indicator of product demand. [0094] For example, and without limiting the scope of the present invention, in some embod iments, hotness level 213 may be a normalized metric varying in positive whole numbers from one to five, or in other embodiments from one to ten. In some embodiments, a smaller hotness level 213 may indicate greater demand. In some embodiments, hotness level 213 of one may indicate more than 1,000 particular products were sold in a certain time frame. In some em bodiments, hotness level 213 of two may indicate that 500 to 1,000 particular products were sold in the certain time frame. In some embodiments, hotness level 213 of three may indicate that 250 to 499 particular products were sold in the certain time frame. In some embodiments, hotness level 213 of four may indicate that 100 to 249 particular products were sold in the cer tain time frame. In some embodiments, hotness level 213 of five may indicate that less than 100 particular products were sold in the certain time frame. [0095] Whereas, in some other embodiments, a higher hotness level may indicate greater demand. [0096] The particular market may be a national market, such as the United States, Australia, and the like. [0097] In some embodiments, the certain time frame may be 30 days. In other embodiments, other time intervals may be used, such as hourly, daily, weekly, quarterly, and the like. 29 [0098] In some embodiments, Sellability Score 214 may be measure of demand (product demand). Sellability Score 214 may be derived in part from average inventory turnover for a given product. [0099] In some embodiments, inventory turnover and/or average inventory turnover may not be calculated per traditional inventory turnover calculations in the field of economics. In some embodiments, inventory turnover and/or average inventory turnover may be a normalized val ue of positive whole numbers from one to five. Where low inventory turnover values may in dicate slow inventory turnover, which may be less desirable to User 850. And where high in ventory turnover values may indicate fast inventory turnover, which may be more desirable to User 850. Data to generate such inventor turnover calculations may derive for how long (e.g. months) it may take a given User 850 or Seller to sell all of inventory of a given purchased product. Thus, in some embodiments, inventory turnover, average inventory turnover, and Sellability Score 214 may depend upon receiving inventory data from User 850. In some em bodiments, the average inventory turnover may a component of Seller & Sales Outputs 107. [00100] In some embodiments, Sellability Score 214 may a positive whole number, whose value may predict, indicate, or suggest inventory turnover trends. Sellability Score 214 may be similar but different than inventory turnover, in that Sellability Score 214 may include prod ucts for which there may currently be no or limited sales inventory data on, e.g. new products to a market. [00101] In some embodiments, Sellability Score 214 may a whole number from one to ten. wherein a higher Sellability Score 214 for a first product compared to a different and lower Sellability Score 214 for a different second product may indicate a higher demand for the first 30 product compared to the second product. In some embodiments, a higher Sellability Score 214, like a higher inventory turnover, may be desirable to User 850. [00102] In some embodiments, Sellability Score 214 may be assigned to each product where average inventory information may be available. Sellability Scores 214 may be stored non transitorily within the memory. [00103] In some embodiments, Demand-Supply-Ratio 215 (D/S 215) may be calculated by dividing a demand measure (demand indicator) by a supply measure (supply indicator) at a particular moment in time. [00104] In some embodiments, a supply measure (supply indicator) may be provided by the active number of sellers selling a given product within a given market, i.e. by Number of Sellers 110. In some embodiments, a demand measure (demand indicator) may be provided by the Number of Products Sold 111, across all Sellers, for a given product. In some embodi ments, a measure of demand may be provided by the inventory-turnover for a given product. The particular moment in time may be that moment when the demand and the supply may be measured. In some embodiments, this measurement may be done on a daily, weekly, monthly, and on a like basis for a given product. [00105] For example, and without limiting the scope of the present invention, D/S 215 may be calculated as follows in the below example: 31 Product-Name 112 Particular Moment in Supply: Number of Demand: Number of Time Sellers 110 Products Sold 111 Kids motocross gog 01/09/2014 4 20 gles Kids motocross gog 02/09/2014 4 25 gles Kids motocross gog 03/09/2014 4 30 gles Kids motocross gog 04/09/2014 5 30 gles Kids motocross gog 05/09/2014 1 30 gles [00106] Then D/S 215 may be calculated as follows: 01/09/2014: D/S 215 = 20/4 = 5 02/09/2014: D/S 215 = 25/4 = 6.25 03/09/2014: D/S 215 = 30/4 = 7.5 04/09/2014: D/S 215 = 30/5 = 6 05/09/2014: D/S 215 = 30/1 = 30 [00107] A higher the number for a given D/S 215 calculation the better for User 850, as this may indicate higher demand in conjunction with less supply, i.e. less competitors currently serving that demand, which may signal opportunities for User 850. In some embodiments, a raw calculated D/S 215 may be normalized on a scale, such as of positive whole number from one to ten. Additionally, in some embodiments, a trending of how D/S 215 may be changing 32 over time may be displayed. Such display may be done graphically by plotting D/S 215 against time and/or first derivatives may be reported to demonstrate changes in trending of D/S 215. [00108] In some embodiments, Competitive-Quantity 216 may be a point where supply equals demand. In some embodiments, Competitive-Quantity 216 may be determined by plotting supply and demand on a same chart, and wherein the point of intersection between the supply curve and the demand curve may yield Competitive-Quantity 216. Competitive-Quantity 216 may be important, because the value may provide User 850 with an optimal amount of inven tory to carry for that particular moment in time that the supply and demand values were ob tained from. For example, in the above table example, the demand and the supply were meas ured on a monthly basis, however, that measurement basis may also be daily, weekly, and the like. [00109] In some embodiments, Ease of Trading Metrics 250 may be selected from the group comprising one or more of: Number of Sellers 110, MOQ 122, Size of Product 126, Weight of Product 127, a Time of Production 251, a Transit Delivery Days to Destination 252, an Impor tation Risk 253, a Return Merchandise Authorization 254, an After Sales Support Severity 255, and the like. [00110] In some embodiments, Number of Sellers 110 may be a component of Seller &Sales Outputs 107. In some embodiments, Number of Sellers 110 may be a supply measure (supply indicator). [00111] In some embodiments, MOQ 122 may be a component of Product Cost Information Outputs 120. MOQ 122 may be the minimum number of a given product that a Seller must 33 order from Product-Supplier 116. The larger MOQ 122, the greater that such a factor may act as a barrier to entry to other Sellers. [00112] In some embodiments, Size of Product 126may be a component of Product Cost In formation Outputs 120. In some embodiments, Size of Product 126, may be the per unit pack aging dimensions of a single packaged product. In some embodiments, size of product 126 may be displayed as net length, height, width in cm (centimeters), without packing material ex ternal to the single product package. [00113] In some embodiments, Weight of Product 127 may be a component of Product Cost Information Outputs 120. In some embodiments, Weight of Product 127, may be the per unit packaging weight of a single packaged product as shipped by Product-Supplier 116. In some embodiments, Weight of Product 127 may be displayed as net kilograms, grams, pounds, and the like. [00114] In some embodiments, Time of Production 251may be the time it may take to manu facture a given product in a factory. [00115] In some embodiments, Transit Delivery Days to Destination 252 may be a number of days it takes to deliver an order of given products from Product-Supplier's 116 location to the Seller's location (or end Buyer's location in the case of drop shipping arrangements). [00116] In some embodiments, Importation Risk 253 may be a normalized metric presented in positive whole numbers, with a higher Importation Risk 253 indicating greater risks associated with importing a given product. Importation Risk 253 may be a measure noting that some products may have a higher risk of various importation controls, that may increase the Seller's costs to acquire the given product and/or increase the time it may take to obtain the given product. For example, some products may require inspections and/or other product may re 34 quire quarantines. Food, plants, and agricultural products often have such importation risks. In some embodiments, Importation Risk 253 may be presented (displayed) as a normalized positive whole number. For example, a range from one to ten may be assigned for Importation Risk 253, with ten being products with the greatest such risks. [00117] In some embodiments, Return Merchandise Authorization 254 (RMA 254) may be a normalized positive whole number indicating a risk of a given product not functioning as in tended. For example, and without limiting the scope of the present invention, electronic devic es may have a larger RMA 254 than clothing. Products with higher RMA 254 may be returned more frequently and thus lower a Seller's profitability. [00118] In some embodiments, After Sales Support Severity 255 may be a normalized posi tive whole number indicating a risk of a given product requiring after sales support which may be an added cost that detracts from profitability. For example, and without limiting the scope of the present invention, After Sales Support Severity 255 (i.e. post-sales support), may be a customer service representative being available to explain products and/or to assist in trouble shooting problems with products. Generally, the more expensive and/or the more complicated a product, the higher After Sales Support Severity 255. Such customer service may be provid ed email and/or phone call. [00119] In some embodiments, Profitability Characteristics 210 and/or Ease of Trading Met rics 250 may also include a breakeven analysis. In some embodiments, a breakeven analysis may require knowledge of fixed and/or variable costs of User 850 and of revenue of User 850. A breakeven point may be a point of revenue per a number of sold units, at which received revenue from the sale of such units equals the total costs associated with obtaining that reve nue, wherein revenue received is plotted against units sold for each product. 35 [00120] In some embodiments, breakeven analysis may determine the point at which revenue received equals the costs associated with receiving the revenue. Breakeven analysis may de termine what is known as a margin of safety, i.e. which may an amount of revenue received exceeding the breakeven point. This may be an amount that revenue may fall while still stay ing above the breakeven point. Thus if User 850 may sell less units than the breakeven point User 850 may not be making a profit from selling that given product. Conversely, if User 850 may sell more units than the breakeven point, then User 850 may be making a profit from sell ing that given product. Thus, in order to make breakeven point calculations and perform breakeven analysis Method 1 and/or System 400 may receive revenue and cost data (fixed and/or variable costs) from User 850. [00121] For example and without limiting the scope of the present invention, if it costs $50 to produce a widget, and there are fixed costs of $1,000, the breakeven point for selling the widg ets may be, depending upon the sale price point: If selling the widgets for $100, then the breakeven point is 20 widgets. This breakeven point may be calculated as follows: ($1,000)/($100 - $50). That is, the formula may be: (total fixed costs)/(sales price - cost of goods). If selling the widgets for $200, then the breakeven point is 7 widgets. This breakeven point may be calculated as follows: ($1,000)/($200 - $50), which actually yields 6.7, but the value is rounded up the nearest whole widget. In this example, if someone sells the widget for a higher price, the breakeven point will come faster. However, breakeven analysis does not show is that it may be easier to sell 20 widgets at $100 each than to sell 7 widgets at $200 each, i.e. as the sales price is increased the demand decreases. A demand-side analysis may tend to give User 850 such information. The above example only included fixed costs, in reali ty, variable costs may be also be a factor and may be accounted for in the formula by adding 36 on to the fixed costs. In some embodiments, all of the breakeven point formula inputs may be received by Method 1 and/or System 400 from User 850. [00122] In some embodiments, Profitability Characteristics 210 and/or Ease of Trading Met rics 250 may also include a return on investment ratio (ROI) ratio analysis. In some embodi ments, the ROI ratio may be Profit per Unit 211 divided by Sales-Price per Unit 108. General ly, Sales-Price per Unit 108 may be greater than Profit per Unit 211 and so the ROI ratio may generally be a value less than one. A high ROI ratio may be good for User 850. In some em bodiments, a ROI ratio of 0.25 may be acceptable, with ROI ratios higher than 0.25 desirable for User 850 and with ROI ratios less than 0.25 less desirable for User 850. ROI ratio may help User 850 to allocate investment towards products that may exhibit a better return on the investment. Three examples: Example 1): Given a Sales-Price per Unit 108 of $10 and a Prof it per Unit 211 of $5, the ROI ratio is: 5/10 = 0.5. Such a ROI ratio may be good for User 850. Example 2): Given a Sales-Price per Unit 108 of $20 and a Profit per Unit 211 of $5, the ROI ratio is: 5/10 = 0.25. Such a ROI ratio may be acceptable for User 850. Example 3): Given a Sales-Price per Unit 108 of $40 and a Profit per Unit 211 of $5, the ROI ratio is: 5/10 = 0.13. Such a ROI ratio may be too low for User 850. [00123] FIG. 3 may depict an exemplary embodiment of step 300, of displaying and/or pre senting at least one of Profitability Characteristics 210, Ease of Trading Metrics 250, Product Cost Information Outputs 120, and Seller & Sales Outputs 107 on at least one GUI. [00124] In some embodiments, the at least one GUI may present and/or display the at least one of Profitability Characteristics 210, Ease of Trading Metrics 250, Product Cost Infor mation Outputs 120, and Seller & Sales Outputs 107 in one or more interactive tables and/or one or more webpages. In some embodiments, the one or more interactive tables may be inter 37 acted with by receiving a command to sort a table column from high to low or low to high; and/or by receiving a command to search for a keyword. In some embodiments, the one or more interactive tables may be selected from the group comprising one or more of: a Research Table 320, a Feasibility Table 330, a Purchased Data Table 350, and the like. The one or more webpages may display the one or more interactive tables. In some embodiments, the one or more webpages may display at least one Product Details Page 350. [00125] FIG. 3(a) may depict an exemplary embodiment of a screenshot showing Research Table 320 as Research Table 320 may be displayed on/in the GUI. In some embodiments, up on Method 1 receiving login credentials of User 850, the GUI may display Research Table 320. [00126] In some embodiments, Research Table 320 may display for a given product one or more of: Hotness Levels 213, Product-Names 111, Product-Categories 113, Number of Prod ucts Sold 111, and the like. In some embodiments, Research Table 320 may display for the given product one or more of: Hotness Levels 213, Product-Names 111, Product-Categories 113, Number of Products Sold 111, and the like; wherein each category may display under complimentary column headers of: Hotness Level 213, Product-Name 111, Product-Category 113, Number of Products Sold 111, and the like. Such information may be displayed in a table format. Each display column header may be sortable. Each display column header may be sortable by clicking on a given column header. [00127] In some embodiments, Research Table 320 may display a field for entering keywords, i.e. Keyword Search 302. Upon receiving a keyword search query Research Table 320 may re display with updated data fields pursuant to the keyword search query or wherein a no results found message may be displayed. 38 [00128] In some embodiments, Number of Products Sold 111 (i.e. the "Items Sold" in FIG. 3A) may refer to the number of products sold over a period of time, e.g. 30 days, and by one or more Sellers. In some embodiments, Number of Products Sold 111 may be a component of Seller & Sales Outputs 107. [00129] In some embodiments, User 850 searching by Keyword Search 302 and/or sorting by Hotness Level 213, Product-Name 112, Product-Category 113, and/or Number of Products Sold 111, may allow User 850 to perform some initial product research into potential products of interests. Results of such searching and/or sorting may be displayed on the GUI in a tabular form, i.e. with rows and columns, with the data fields of Hotness Level 213, Product-Name 112, Product-Category 113, and Number of Products Sold 111corresponding to column head ers. In some embodiments, such a table may be styled (labeled) as "Product Research" i.e. as Research Table 320. In other embodiments, a different name other than "Product Research" may be used for Research Table 320. [00130] In some embodiments, clicking on any product listed in Research Table 320 (e.g. by clicking any displayed Product-Name 112) may then take User 850 to Feasibility Table 330 for that particular selected product, if User 850 may have an appropriate paid subscription account permitting access to Feasibility Table 330. [00131] In some embodiments, clicking on any product listed in Research Table 320 may then take User 850 to Product Details Page 350, a webpage, for that particular selected product, if User 850 may have purchased data on that particular selected product. [00132] FIG. 3(b) may depict an exemplary embodiment of a screenshot showing Feasibility Table 330 as the Feasibility Table 330 may be displayed on the GUI (e.g. User-GUI 802). In some embodiments, Feasibility Table 330 may display for a given product one or more of: 39 Product-Name 112 column header without displaying actual Product-Names 112, Product categories 113, Profit Margin 212, Profit per Unit 211, an aggregate profit over a time period (e.g. "Profit Last 30 Days"), Number of Products Sold 111 (e.g. "Items Sold"), and Purchase Data Means 331. [00133] In some embodiments, Feasibility Table 330 may only be assessable by User 850 with an appropriate paid subscription account. [00134] In some embodiments, User 850 searching by Keyword Search 302 and/or sorting by Product-Category 113, Profit per Unit 211, Profit Margin 213, Number of Products Sold 111 (i.e. "Items Sold" in FIG. 3B), and the aggregate profit over last 30 days, may allow User 850 to perform some product profitability research into potential profitable products of interest. Results of such searching and/or sorting may be displayed on the GUI in a tabular form, i.e. with rows and columns, with the data fields of Product-Name 112, Product-Category 113, Profit per Unit 211, Profit Margin 212, Number of Products Sold 111, and the aggregate profit over last 30 days corresponding to column headers. In some embodiments, such a table may be styled (la beled/titled) as "Feasibility Product" i.e. as Feasibility Table 330 may reflect that at least some data populating of the table may be derived from feasibility calculations. In other embodiments, a different name other than "Feasibility Product" may be used for Feasibility Table 330. For ex ample, and without limiting the scope of the present invention, Feasibility Table 330 may be styled (labeled/titled) as "MerchMiner Profitable Products Database." [00135] In some embodiments, some Feasibility calculations, the results of which may be dis played in Feasibility Table 330 may provide values for Profit Margin 212, Profit per Unit 211, aggregate profit over last 30 days, and the like. In some embodiments, in Feasibility Table 330, the actual Product-Names 112 below Product-Name 112 column header may not be dis 40 played, i.e. the actual Product-Names 112 may be hidden from User 850, until User 850 may purchase the data for that product. [00136] In some embodiments, such Feasibility Table 330 may comprise an additional column of Purchase Data Means 331, wherein a given product may be selected for purchasing the full set of Profitability-Characteristics 210, Ease of Trading Metrics 250, and/or Products-Details Information 125 associated with that selected product. Completing such a purchase transaction may permit User 850 to access to the given Product Details Page 350. Such a data purchase option in Feasibility Table 330 may be depicted by as a single buy button, a Purchase Data Means 331, for each row with product data displayed in Feasibility Table 330. See e.g., FIG. 3B. In some embodiments, keeping the actual Product-Names 112 (as well as Product Supplier 116 contact information) hidden may be an incentive for User 850 to engage Purchase Data Means 331 and acquire access to this hidden information. [00137] In some embodiments, clicking on any product listed in Feasibility Table 330 may then take User 850 to Product Details Page 350, a webpage, for that particular selected prod uct, if User 850 may have purchased data on that particular selected product.] [00138] In some embodiments, the Profit (over) Last 30 Days data (see e.g., FIG. 3(b) and/or FIG. 3(c)), that may be presented beneath the column header of the name "Profit Last 30 Days," and may present aggregate profit data for a 30 day time window for a given product. In some embodiments, the profit over last 30 days for a given product may be calculated by Method 1 in Step 200 and/or by System 400 by multiplying the items sold (Number of Prod ucts Sold 111) by Profit per Unit 211 for the given product. Aggregate profit may be calculat ed over other time periods as well, such as hourly, weekly, quarterly, and the like. Aggregate profit calculated over a time period may be a component of Profitability Characteristics 210. 41 [00139] FIG. 3(c) may depict an exemplary embodiment of a screenshot showing Purchased Data Table 340 as Purchased Data Table 340 may be displayed on the GUI. In some embodi ments, Purchased Data Table 340 may display for a given product one or more of: Hotness Levels 213, Product-Names 112, Product-categories 113, Profit Margin 212, Profit per Unit 211, an aggregate profit over a time period, and Number of Products sold 111 (e.g. "Items Sold"), and the like. [00140] In some embodiments, User 850 may also have access to a "Your Purchased Data" table, i.e. Purchased Data Table 340. See e.g., FIG. 3C. Purchased Data Table 340 table may display on the GUI column headers of Hotness Level 213, Product-Name 112, Product-Category 113, Profit per Unit 211, Profit Margin 212, aggregate profit over last 30 days, Number of Prod ucts Sold 111 (e.g. "Items Sold" in FIG. 3C), and the like. In some embodiments, clicking on any product listed in Purchased Data Table 340 may then take User 850 to Product Details Page 350 for that particular selected product. [00141] FIG. 3(d) may depict an exemplary embodiment of a screenshot showing Product Details Page 350 as Product Details Page 350 may be displayed on the GUI. In some embod iments, the one or more webpages may be a given Product Details Page 350 for each specific product. In some embodiments, Product Details Page 350 may display for a given product one or more of: Product-Details-Information 125, Size of Product 126, Weight of Product 127, Product Cost Information Outputs 120, Cost-Information 121, and Seller & Sales Outputs 107. [00142] For example, and without limiting the scope of the present invention, Product Details Page 350 as depicted in FIG. 3(d), may display: Avg. Sales-Price 109 (e.g. "Average Sales Price"), Number of Products Sold 111 (e.g. "Items Sold"), Product-Name 112, Product Category 113, Minimum Order Quantity 122 (e.g. "MOQ-Unit" ), Cost-to-Acquire-Goods 123 42 (e.g.: FOB Price, CIF, FOB + Shipment, Shipment Air, Shipment Sea, Custom Duty, GST, and Final Cost), Avg. Cost-to-Acquire-Goods 124 (e.g.; FOB Price, CIF, FOB + Shipment, Ship ment Air, Shipment Sea, Custom Duty, GST, and Final Cost), Size of Product 126, Weight of Product 127, Profit per Unit 211, Profit Margin 212, and Cost of Sales. In some embodiments, the FOB Price, CIF, FOB + Shipment, Shipment Air, Shipment Sea, Custom Duty, GST, and/or Final Cost displayed may be a best (i.e. lowest) Cost-to-Acquire-Goods 123. In some embodiments, the FOB Price, CIF, FOB + Shipment, Shipment Air, Shipment Sea, Custom Duty, GST, and/or Final Cost displayed may be a best Avg. Cost-to-Acquire-Goods 124. [00143] In some embodiments, Product Details Page 350 may display at least one Product Supplier 116 contact information details. [00144] FIG. 4 may depict an exemplary embodiment of components of a System 400 for identifying a profitable product, shown as a block diagram. In some embodiments, System 400 may comprise: at least one Server 401, at least one Data-Source 102, at least one Product Supplier 116, GUI, a Communication Network 501, and the like. [00145] In some embodiments, at least one Server 401 may comprise Memory 402, Processor 405, Network Adapter 406, and the like. Memory 402 may be computer readable media. Memory 404 may non-transitorily store Software 403 and a Database 404. In some embodi ments, Software 403 may code for various instructions to perform the various steps of Method 1. In some embodiments, Database 404 may be a SQL database or equivalent database. In some embodiments, Processor 405 may execute Software 403. Processor 405 and Software 403 may be in electronic communication with each other. In some embodiments, Network Adapter 406 may be controlled by Processor 405. Network Adapter 406 may be configured for electronic communications across Communication Network 501. In some embodiments, 43 Communication Network 501 may be a wide area network (WAN), such as the internet, and/or a local area network (LAN). Network Adapter 406 and Processor 405 may be in electronic communication with each other. Network Adapter 406 may facilitate external communications with at least one Server 401. [00146] In some embodiments, as noted above in the discussion of Method 1, raw data may be received from at least one Data-Source 102 by at least one Server 401 across Communication Network 501. Such received raw data may be non-transitorily stored within Database 404. [00147] In some embodiments, as noted above in the discussion of Method 1, additional raw data may be received from at least one Product-Supplier 116 by at least one Server 401 across Communication Network 501. Such received additional raw data may be non-transitorily stored within Database 404. Note, "additional raw data" as used herein may be to note that the raw data received from at least one Product-Supplier 116 may comprise different information than the raw data received from at least one Data-Source 102. [00148] In some embodiments, Software 403 may comprise instructions for Pre-Processing 106 of the raw data into Seller & Sale Outputs 107. In some embodiments, Seller & Sale Out puts 107 may be non-transitorily stored within Database 404. In some embodiments, Software 403 may comprise instructions for More-Pre-Proces sing 118 of the additional raw data into Product Cost Information Outputs 120. In some embodiments, Product Cost Information Out puts 120 may be non-transitorily stored within Database 404. In some embodiments, Software 403 may comprise instructions for primary processing of Seller & Sale Outputs 107 and Prod uct Cost Information Outputs 120 into Profitability Characteristics 210 and/or Ease of Trading Metrics 250. In some embodiments, Profitability Characteristics 210 and Ease of Trading Metrics 250 may be non-transitorily stored within Database 404. 44 [00149] In some embodiments, Software 403 may comprise instructions for generating graph ical user interfaces (GUIs). Such GUIs may display at least one of Profitability Characteristics 210, Ease of Trading Metrics 250, Product Cost Information Outputs 120, Seller & Sale Out puts 107, and the like. [00150] In some embodiments, System 400 may further comprise: at least one User Computing-Device 801 and at least one Staff-Computing-Device 901. In some embodiments, User-Computing-Device 801 may be a desktop computer or a server. In some embodiments, User-Computing-Device 801 may be selected from various mobile computing devices, such as, but not necessarily limited to, smart phones, laptops, tablet computing devices, smart watches, and the like. In some embodiments, Staff-Computing-Device 901 may be a desktop computer or a server. In some embodiments, Staff-Computing-Device 901 may be selected from various mobile computing devices, such as, but not necessarily limited to, smart phones, laptops, tablet computing devices, smart watches, and the like. [00151] In some embodiments, at least one User-Computing-Device 801 may comprise a Us er-Graphical-User-Interface 802 (User-GUI 802). In some embodiments, at least one Staff Computing-Device 901 may comprise a Staff-Graphical-User-Interface 902 (Staff-GUI 902). In some embodiments, the GUI may comprise User-GUI 802 and Staff-GUI 902. In some em bodiments, Software 403 may comprise instructions for displaying on User-GUI 802 and/or Staff-GUI 902. Note, the interactive tables and webpages displayed on User-GUI 802 and Staff-GUI 902 may not be the same. Staff-GUI 902 may have access to a greater diversity of interactive tables and webpages than may be available to User-GUI 802. For example, and without limiting the scope of the present invention, Staff-GUI 902 may have access to various 45 administrative interactive tables, webpages, an ability to edit various data fields stored within Database 404. [00152] In some embodiments, the various GUIs may be accessible by User 850 and/or Staff 950 navigating to a domain name or URL (universal resource locator) associated with the Op erational-Entity. For example, and without limiting the scope of the present invention, such GUIs may be accessible by User 850 and/or Staff 950 navigating to a domain name or URL of MerchMiner.com. [00153] With respect to Database 404, for example, and without limiting the scope of the pre sent invention, there may be about 75,000 different products included within Database 404 with respect to an Australian market. Each such different product may include data as dis played Product Details Page 350, see e.g., FIG. 3(d). For example, and without limiting the scope of the present invention, there may be about 75,000 different Product-Names 112 within Database 404 with respect to the Australian market. In some embodiments, data displayed in User-GUI 802 may be only be that of profitable products, whereas, Database 404 may also in clude data with respect to non-profitable products. For example, and without limiting the scope of the present invention, there may be over 300,000 raw data entries received (extracted) daily from a given market, e.g. the Australian market. But not all such raw data entries may be for profitable products. [00154] A method and system for identifying products to sell which may be profitable has been described. The foregoing description of the various exemplary embodiments of the in vention has been presented for the purposes of illustration and disclosure. It is not intended to be exhaustive or to limit the invention to the precise form disclosed. Many modifications and 46 variations are possible in light of the above teaching without departing from the spirit of the invention. [00155] While the invention has been described in connection with what is presently consid ered to be the most practical and preferred embodiments, it is to be understood that the inven tion is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. 47

Claims (35)

1. A method to identify a profitable product, comprising the steps: (a) receiving raw data from at least one data source and/or from at least one product supplier; (b) pre-processing of the raw data received into seller and sales outputs and into product cost information outputs; (c) primary processing of the seller and sales outputs and the product cost information outputs into profitability characteristics and/or ease of trading metrics; and (d) presenting and/or displaying at least one of the profitability characteristics, the ease of trading metrics, the product cost information outputs, and the seller and sales outputs on at least one graphical user interface GUI made available to a user.

2. The method to identify the profitable product according to claim 1, wherein the step of receiving the raw data further comprises a step of searching for the at least one data source.

3. The method to identify the profitable product according to claim 2, wherein the step of searching for the at least one data source further comprises manual entry of the at least one data source to be targeted for receiving the raw data.

4. The method to identify the profitable product according to claim 2, wherein the step of searching for the at least one data source further comprises a step of receiving the raw data from third parties.

5. The method to identify the profitable product according to claim 4, wherein the third parties are selected from the group consisting of one or more of the at least one data source and from a third party application program interface.

6. The method to identify the profitable product according to claim 2, wherein the step of step of searching for the at least one data source further comprises a step of data mining publicly available information.

7. The method to identify the profitable product according to claim 1, wherein the step of receiving the raw data further comprises direct receiving and/or indirect receiving of the raw data from the at least one data source.

8. The method to identify the profitable product according to claim 7, wherein the step of direct receiving the raw data from the at least one data source occurs when the at least one data source actively transmits the raw data received.

9. The method to identify the profitable product according to claim 7, wherein active transmission of the raw data by the at least one data source occurs when the at least one data source is a third party, a third party API, and/or the at least one data source is under contract with an entity implementing the method.

10. The method to identify the profitable product according to claim 7, wherein the step of indirect receiving the raw data from the at least one data source occurs when the at least one data source, is data mined of public information available from the at least one data source.

11. The method to identify the profitable product according to claim 1, wherein the raw data received from the at least one data source is selected from the group consisting of one or more of: a product-name, a product-category, a product-description, a sales-price per unit for each product-name, an average sales-price for each product-name, a number of sellers providing each product-name, and a number of products sold for each product name.

12. The method to identify the profitable product according to claim 11, wherein the step of pre-processing of the raw data received into seller and sales outputs comprises the step of organizing the raw data received into raw data entries, wherein each raw data entry received is organized by each different product-name to comprise one or more of: the product-category, the product-description, the sales-price per unit for each product name, the average sales-price for each product-name, the number of sellers providing each product-name, and the number of products sold for each product-name.

13. The method to identify the profitable product according to claim 12, wherein the step of pre-processing of the raw data received into seller and sales outputs comprises one or more of the following steps: (a) ranking, from high to low, the raw data entries by the number of products sold, wherein the number of products sold corresponds to a measure of demand; (b) assigning the product-category to raw-data-entry received, wherein the product-category is predetermined by the method, wherein the assigned product-category is selected from a plurality of predetermined product-categories; and/or (c) truncating a given type of received raw-data to eliminate any portion of that received raw-data that is not relevant to the type of received raw-data.

14. The method to identify the profitable product according to claim 13, wherein an output of the pre-processed raw data are seller and sales outputs, wherein the seller and sales outputs are selected from the group consisting of one or more of: a cleansed product-name, a cleansed product-category, a cleansed product-description, a cleansed sales-price per unit for each product-name, an average cleansed sales-price for each product-name, a cleansed number of sellers providing each product-name, and a cleansed number of products sold for each product-name.

15. The method to identify the profitable product according to claim 1, wherein the step of receiving the raw data from at least one product supplier is preceded by first generating a request-for-quote with respect to a given product-name and secondly transmitting the request-for-quote to the at least one product supplier.

16. The method to identify the profitable product according to claim 15, wherein the generated request-for-quote is submitted electronically to the at least one product-supplier.

17. The method to identify the profitable product according to claim 15, wherein the request for-quote comprises including in the request-for-quote at least some of the seller and sales outputs with respect to a given product-name.

18. The method to identify the profitable product according to claim 17, wherein the at least some of the seller and sales outputs with respect to a given product-name comprises the cleansed product-name, and optionally includes the cleansed product-category and/or the cleansed product-description.

19. The method to identify the profitable product according to claim 15, wherein the request for-quote requests raw data from the at least one product supplier, wherein the raw data is raw product cost information outputs.

20. The method to identify the profitable product according to claim 19, wherein the raw product cost information outputs is selected from the group consisting of one or more of: cleansed product-name, cleansed product-category, cleansed product-description, and raw cost-information.

21. The method to identify the profitable product according to claim 20, wherein the raw cost-information is selected from the group consisting of one or more of: raw cost-to acquire-goods, raw minimum order quantity, and raw product-details-information.

22. The method to identify the profitable product according to claim 21, wherein the raw cost-to-acquire-goods is selected from the group consisting of one or more of: raw freight on board cost, raw cost insurance freight, raw FOB plus shipment cost, raw ex-works contract for sale cost, raw air shipment cost, raw sea shipment cost, raw custom duty cost, raw import tax, raw goods and service tax cost, and raw value added tax.

23. The method to identify the profitable product according to claim 21, wherein the raw product-details-information is selected from the group consisting of one or more of: a raw size of the product and a raw weight of the product.

24. The method to identify the profitable product according to claim 1, wherein the step of receiving the raw data from the at least one product supplier comprises receiving a RFQ response from the at least one product supplier, in response to the request-for-quote transmitted to the at least one product supplier.

25. The method to identify the profitable product according to claim 24, wherein the RFQ response comprises a raw product cost information outputs with respect to a given product name.

26. The method to identify the profitable product according to claim 25, wherein the step of pre-processing of the raw data received from the least one product supplier into product cost information outputs comprises the step processing the raw product cost information outputs into the product cost information outputs.

27. The method to identify the profitable product according to claim 26, wherein the step of pre-processing of the raw product cost information outputs received from the least one product supplier via the RFQ-responses, comprises one or more of the following steps: (a) truncating a given type of received raw product cost information outputs to eliminate any portion of that received raw product cost information outputs that is not relevant to the type of received raw product cost information outputs; (b) performing currency conversions to all product cost information outputs are in a same currency; (c) deleting the highest and the lowest raw cost-to-acquire-goods for a given product-name; and/or (d) calculating an average cost-to-acquire-goods for a given product-name.

28. The method to identify the profitable product according to claim 26, wherein the product cost information outputs is selected from one or of the group consisting of: cleansed product name, cleansed product-category, cleansed product-description, and cost-information.

29. The method to identify the profitable product according to claim 26, wherein an average cost-to-acquire-goods is calculated by determining at least three best costs-to-acquire-goods; wherein determining the three best cost-of-goods is done by ranking the costs-to-acquire goods received via the RFQ-responses from least costly to most costly, wherein the three least costly received costs-to-acquire-goods are deemed the three best and serve as a basis for calculating the average-cost-to-acquire-goods.

30. The method to identify the profitable product according to claim 30, wherein prior to determining the three best costs-to-acquire-goods received via the RFQ-responses from least costly to most costly, a lowest received cost-to-acquire-good and a highest received cost-to acquire-good are not utilized in the ranking nor the average calculation.

31. The method to identify the profitable product according to claim 1, wherein the profitability characteristics are selected from the group consisting of one or more of: a profit per unit, a profit margin, a hotness level, a sellability score, a demand-supply-ratio, and a competitive-quantity.

32. The method to identify the profitable product according to claim 31, wherein the profit per unit is calculated by subtracting an average-cost-to-acquire-goods from an average sales price per a given product-name, wherein the average-cost-to-acquire-goods is a component of the product cost information outputs and the average sales-price is a component of the seller and sales outputs.

33. The method to identify the profitable product according to claim 31, wherein the profit per unit is calculated by subtracting an average-cost-to-acquire-goods and an average cost of sales from an average sales-price per a given product-name, wherein the average-cost-to acquire-goods is a component of the product cost information outputs and the average sales price is a component of the seller and sales outputs.

34. The method to identify the profitable product according to claim 31, wherein the profit margin is a percentage of the profit per unit with respect to the average sales-price for a given product-name; wherein the profit margin is calculated by taking the profit per unit and dividing the profit per unit by the average-sales-price, and then multiplying that result by

100. 35. The method to identify the profitable product according to claim 31, wherein the hotness level is a normalized metric varying in positive whole numbers, wherein this normalized metric is an indicator of product demand for a given product sold over a certain frame within a particular market, wherein the hotness level is calculated from a number of products sold, wherein the number of products sold is a component of the seller and sales outputs. 36. The method to identify the profitable product according to claim 31, wherein the sellability score is a measure of demand, wherein the sellability score is derived in part from average inventory turnover for a given product. 37. The method to identify the profitable product according to claim 36, wherein the sellability score is a whole number from one to ten, wherein a higher sellability score for a first product compared to a different and lower sellability score for a different second product indicates a higher demand for the first product compared to the second product. 38. The method to identify the profitable product according to claim 36, wherein the generated sellability score is assigned to each product, wherein sellability scores are stored non-transitorily within a memory, wherein the memory is computer-readable-media, wherein the memory is in communication with a processor. 39. The method to identify the profitable product according to claim 31, wherein the demand-supply-ratio is calculated by dividing the demand by the supply at a particular moment in time. 40. The method to identify the profitable product according to claim 31, wherein the competitive-quantity is a point where supply equals demand. 41. The method to identify the profitable product according to claim 1, wherein the ease of trading metrics are selected from the group consisting of one or more of: a number of sellers, a minimum order quantity, a size of product, a weight of product, a time of production, a transit delivery days to destination, an importation risk, a return merchandise authorization, and an after sales support severity. 42. The method to identify the profitable product according to claim 41, wherein the number of sellers is a component of the seller and sales outputs, wherein the number of sellers is a measure of supply. 43. The method to identify the profitable product according to claim 41, wherein the minimum order quantity is a component of the product cost information outputs. 44. The method to identify the profitable product according to claim 41, wherein the size of product is a component of the product cost information outputs. 45. The method to identify the profitable product according to claim 41, wherein the weight product is a component of the product cost information outputs. 46. The method to identify the profitable product according to claim 41, wherein the time of production is the time it takes to manufacture a given product in a factory. 47. The method to identify the profitable product according to claim 41, wherein the transit delivery days to destination is a number of days it takes to deliver an order of given products from the product-supplier's location to the seller's location. 48. The method to identify the profitable product according to claim 41, wherein the importation risk is a normalized metric presented in positive whole numbers, with a higher importation risk indicating greater risks associated with importing a given product. 49. The method to identify the profitable product according to claim 41, wherein the return merchandise authorization is a normalized positive whole number indicating a risk of a given product not functioning as intended. 50. The method to identify the profitable product according to claim 41, wherein the after sales support severity is a normalized positive whole number indicating a risk of a given product requiring after sales support which is an added cost that detracts from profitability. 51. The method to identify the profitable product according to claim 1, wherein the at least one graphical user interface (GUI) presents and/or displays the at least one of the profitability characteristics, the ease of trading metrics, the product cost information outputs, and the seller and sales outputs in one or more interactive tables and/or one or more webpages. 52. The method to identify the profitable product according to claim 51, wherein the one or more interactive tables are interacted with by receiving a command to sort a table column from high to low or low to high; or by receiving a command to search for a keyword. 53. The method to identify the profitable product according to claim 51, wherein the one or more interactive tables are selected from the group consisting of one or more of: a research table, a feasibility table, and a purchased data table. 54. The method to identify the profitable product according to claim 53, wherein upon receiving login credentials of the user, the GUI displays the research table. 55. The method to identify the profitable product according to claim 53, wherein the research table displays for a given product one or more of: a hotness level, a product-name, a product category, and a number of products sold. 56. The method to identify the profitable product according to claim 53, wherein the research table displays a field for entering keywords, wherein upon receiving a keyword search query the research table is re-displayed with updated data fields pursuant to the keyword search query or wherein a no results found message is displayed. 57. The method to identify the profitable product according to claim 53, wherein the feasability table displays for a given product one or more of: a product-name column header without displaying the product-names, a product-category, a profit margin, a profit per unit, an aggregate profit over a time period, a number of products sold, and a purchase data button. 58. The method to identify the profitable product according to claim 53, wherein the purchased data table displays for a given product one or more of: a hotness level, a product name, a product-category, a profit margin, a profit per unit, an aggregate profit over a time period, and a number of products sold. 59. The method to identify the profitable product according to claim 51, wherein the one or more webpages is a product details page. 60. The method to identify the profitable product according to claim 59, wherein the product details page displays for a given product one or more of: product-details-information, a size of product, a weight of product, the product cost information outputs, cost-information, and the seller and sales outputs. 61. A system for identifying a profitable product, comprising: at least one server; wherein the at least one server comprises: memory, wherein the memory is computer readable media, wherein the memory non transitorily stores software and a database; a processor, wherein the processor executes the software; wherein the processor and the memory are in electronic communication with each other; a network adapter; wherein the network adapter is controlled by the processor, wherein the network adapter is configured for electronic communications across a communication network; wherein the network adapter and the processor are in electronic communication with each other; at least one data-source; wherein raw data is received from the at least one data-source by the at least one server across the communication network; wherein the raw data is non-transitorily stored within the database; at least one product-supplier; wherein additional raw data is received from the at least one product-supplier by the at least one server across the communication network; wherein the additional raw data is non-transitorily stored within the database; wherein the software comprises instructions for pre-processing of the raw data into seller and sale outputs; wherein the seller and sales outputs are non-transitorily stored within the database; wherein the software comprises instructions for more-pre processing of the additional raw data into product cost information outputs; wherein the product cost information outputs are non-transitorily stored within the database; wherein the software comprises instructions for primary processing of the seller and sales outputs and the product cost information outputs into profitability characteristics and/or ease of trading metrics; wherein the profitability characteristics and the ease of trading metrics are non-transitorily stored within the database; wherein the software comprises instructions for generating graphical user interfaces; wherein the graphical user interfaces display at least one of the profitability characteristics, the ease of trading metrics, the product cost information outputs, and the seller and sales outputs. 62. The system for identifying the profitable product according to claim 61, wherein the system further comprises: at least one user-computing-device; wherein the at least one user-computing-device comprises a user-graphical-user-interface; at least one staff-computing-device; wherein the at least one staff-computing-device comprises a staff-graphical-user-interface; wherein the software comprises instructions for displaying on the user-graphical-user interface and/or on the staff-graphical-user-interface. 63. A method to identify a profitable product as described and disclosed herein. 64. A system for identifying a profitable product as described and disclosed herein.