Patent Publication Number: US-2023162120-A1

Title: Managing sales opportunities within an organization

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to U.S. patent application Ser. No. 17/409,004, filed Aug. 23, 2021, now U.S. Pat. No. [Claims Allowed], entitled “Managing Sales Opportunities Within An Organization”, which claims priority to a continuation in part of a continuation in part of U.S. patent application Ser. No. 16/885,004, filed on May 27, 2020, now U.S. Pat. No. 11,100,447, entitled “Managing Sales Opportunities Within An Organization” which claims priority to a continuation in part of U.S. patent application Ser. No. 15/381,790, now U.S. Pat. No. 10,970,723, filed on Dec. 16, 2016, entitled “Computer-Implemented System And Methods For Providing Sales Information To Sales Professionals” which claims the benefit of U.S. Provisional Application No. 62/310,686 filed on Mar. 19, 2016, entitled “Systems And Methods For Assisting Sales Professionals in Optimizing Their Sales Results”, the teaching of each of these patent applications is hereby incorporated by reference in their entirety. 
    
    
     FIELD OF THE DISCLOSURE 
     The present invention generally relates to the field of sales information providing sales information to sale professionals and more specifically managing sales between organizations. 
     BACKGROUND 
     Business-to-business (B2B) Sales Professionals are facing unprecedented pressure to perform—they are expected to sell more—and faster. But to be successful in ‘today&#39;s environment, they must overcome many obstacles. A first obstacle needed to be overcome by B2B Sales Professionals is that they are underserved by technology. Tools like Customer Relationship Management (CRM) systems can result in adding an administrative burden more than they help the sales team. They are often instituted for the benefit of the company, not necessarily for that of the sales professional using them. 
     Another obstacle needed to be overcome by B2B Sales Professionals is that they lack the skills and resources to navigate an increasingly complex buyer landscape. With Access to a wealth of information from search engines and social media, ‘today&#39;s buyers no longer rely on salespeople for the educational component of their analysis. This means that salespeople have far less time to nurture leads through the sales process. 
     A further obstacle needed to be overcome by B2B Sales Professionals is that research now shows that, on average, at least 5 people are required to formally sign off on a B2B purchase. The authority to make a purchase now rests within a larger group, most of who are at different stages of the buying journey. 
     Still, another obstacle needed to be overcome by B2B Sales Professionals is that the classroom sales training they typically receive is ineffective, expensive, and outdated. Traditional sales education techniques that were a mainstay for decades now fall short in preparing B2B sales professionals to better align themselves with ‘today&#39;s savvy buyers. 
     The confluence of these factors and obstacles faced by B2B Sales Professionals has created a longer and more complex sales cycle. To solve these problems, there are a number of patents directed at sales methodologies, sales training, and sales optimization. While there have been a number of technologies and systems for facilitating sales, none have provided a simple methodology for overcoming the above-mentioned obstacles. 
     Therefore, a long-standing need exists for simple, yet modern, sales methodologies to help Sales Professionals, such as B2B Sales Professionals, optimize their chances of winning more deals. A further need exists for novel computer-implemented systems and methods that are configured to provide sales information to sales professionals. Finally, a need exists for novel computer-implemented systems and methods that are configured to provide sales information, such as sales methodology, sales training, and sales optimization, to one or more sales professionals. 
     SUMMARY OF THE INVENTION 
     The present invention provides a novel system and method for a consistent scoring system that illustrates the likelihood of successfully closing a sales deal or sales agreement. This allows sales professionals to allocate more time on strategy where it&#39;s needed most. It guides sales professionals on eliminating vulnerabilities or weaknesses in closing a sales deal and leveraging strengths to improve the likelihood to close more sales deals successfully. 
     The present invention is computationally efficient in solving a combinatorial optimization algorithm. Being computationally efficient enables the present invention to be scalable in order to handle more data and transactions. The results of the present invention provide insights in which a sales organization can act upon by re-using knowledge of previous interactions with a buyer or potential buyer. 
     In one example, the present invention provides and system and method for managing sales within organizations. The method begins with receiving input from a user through a client device, the input identifies a set of source key members. Next, a database of a set of target key members is accessed. Stored in this database is a set of source key members and the set of target key members that include a data record with values for a plurality of additional sales attributes, such as RIPAA (Role, Impact score, Priority score, Advocacy score, and Access score) values, in a sales decision process. In one example, the process further includes a first additional sales attribute representing a number of source key players to use. Next, for each source key member in the set of source key members and each target key member in the set of target key members, performing:
         1. normalizing a similarity measurement by calculating a distance between corresponding additional sales attributes in the plurality of additional sales attributes for each of the source key members with each of the target key members to form an array of similarity measurement costs; and   2. applying a combinatorial optimization algorithm for solving an assignment problem using the array of similarity measurement costs, such that one corresponding additional sales attribute of each target key member is assigned to one corresponding additional sales attribute of each source key member, whereby a total similarity measurement cost of assignment is minimized.       

     Next, a sub-set of target key members in the set of target key members with a lowest total similarity measurement cost of assignment is identified. The process ends by presenting the sub-set of target key members that has been identified with the lowest total similarity measurement cost of assignment to the user. 
     In one example, the process further includes further comprises normalizing the values for the plurality of additional sale attributes to within a numerical range for each of the source key members. 
     In another example, filters for and combination of
         each additional sales attribute,   a sales status of a sales transaction, e.g., open sales transaction, a successful transaction, and an unsuccessful transaction,   sales competitor,   sales stage, e.g., pre-qualified, qualified, pricing, and proposal, and/or   other text string
 
are included as a user selection before presenting the sub-set of results.
       

     In another embodiment, disclosed is a computer implemented method and system for managing a sales process. The process begins with receiving a role of a key member in a set of key members in a customer&#39;s organization in a sales decision process. Next, input is received from a user through a client device for the set of key members, receiving input by a user through a client device, the input comprises data for populating a data record with values within a given numeric range for one or more attributes for each key member in the set of key members. The input includes each of i) an impact score representing an influence of the key member in completing a sales transaction; ii) a priority score representing a level of lessor or greater importance being allocated by the key member to the sales transaction; iii) an advocacy score representing an amount of support by the key member for completion of the sale transaction; and iv) an access score representing an amount of contact, with the key member. Next, for a given key member in the set of the key members, a set of rule each with a set of conditions per given rule is retrieved. For a given rule in the set of rules, the process for a given condition in the set of conditions performs i) identifying whether the given condition in the set of conditions per given rule in the set of rules applies to the attributes for the given key member in the set of key members; and ii) in response to the given condition applying to the given key member for the given rule in the set of rules, adding a given condition score to a given rule score. Next, the given condition score for each given rule score to a total deal score is added. Next, the process automatically displays to the user, via a display screen of the client device, the set of one or more recommendations as positions in a graph, for a key member in the set of the key members at least one of i) a deal legitimacy graph with one axis representing the impact score and another representing the priority score, wherein a deal legitimacy position on the deal legitimacy graph for one key member in the set of the plurality of key members are based on the impact score and the priority score, and the deal legitimacy graph is presented with three visually distinct regions indicating a value of the impact score and a value of the priority score; ii) a deal position graph with one axis representing the advocacy score and another axis representing the access score, wherein a deal position on the deal position graph for one key member in the set of the plurality of key members are based on the advocacy score and the access score, and the deal position graph is presented with three visually distinct regions indicating a value of the advocacy score and a value of the access score; or both the deal legitimacy graph and the deal position graph. 
     In one embodiment, the visually distinct regions of the deal legitimacy graph is one of shape, color, gradient, pattern, shading, or a combination thereof. In another embodiment, the visually distinct regions of the deal position graph is a shape, a color, a pattern, a shading or a combination thereof. In yet, another embodiment, the visually distinct regions of the deal legitimacy graph and the visually distinct regions of the deal position graph is a combination of three or more overlapping arcuate colored shapes. 
     In still a further embodiment, the deal health over time is presented. More specifically, a user-interface widget for presenting a graph of total deal score over time. In response to receiving a user selection of the user-interface widget, presenting the graph of the total deal score over time. A graph of an average of the positions on the graph that changed below a settable threshold. 
     A deal legitimacy graph and/or deal positions graph over time can be presented using a user-interface widget for presenting a graph of the deal legitimacy graph over time, the deal position over time, or both and in response to receiving a user selection of the user-interface widget, presenting a series of the deal legitimacy graph over time, a series of the deal position over time, or both. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying figures where like reference numerals refer to identical or functionally similar elements throughout the separate views, and which together with the detailed description below, are incorporated in and form part of the specification, serve to further illustrate various embodiments and to explain various principles and advantages all in accordance with the present disclosure, in which: 
         FIG.  1    illustrates a high-level client-server environment, according to one aspect of the present invention; 
         FIG.  2    illustrates examples of a novel interface for providing input of a Role, an Impact score, a Priority score, an Advocacy score, and an Access score (RIPAA), according to one aspect of the present invention; 
         FIG.  3    illustrates the evolution of sales process from managing structured sales conversations using RIPAA to sales coaching to uncover areas of improvement with sales professionals; 
         FIG.  4    is a graphical illustration of matching source deals and target deals of four (4) key players as step 1, according to one aspect of the present invention; 
         FIG.  5    is another graphical illustration of matching source deals and target deals as step 1 with five (5) key players in comparison to four (4) key players in  FIG.  4   , according to another aspect of the present invention; 
         FIG.  6    is an example of a matrix of the source key players and target key players illustrating RIPAA values of the deals of  FIG.  4    as step 2, according to one aspect of the present invention; 
         FIG.  7    is an example of a matrix of the source and target with a first order normalization of  FIG.  6    as step 3, according to one aspect of the present invention; 
         FIG.  8    is an example of a matrix of the source and target with a second order normalization of  FIG.  7    as step 4 illustrating a similarity measurement costs, according to one aspect of the present invention; 
         FIG.  9    is an example of a matrix of the source and target using a combinatorial optimization algorithm for solving an assignment problem of the similarity measurement costs of  FIG.  8    as step 5, according to one aspect of the present invention; 
         FIG.  10    is an example of the results of the lowest total similarity measurement costs of assignment found in  FIG.  9    as step 6, according to one aspect of the present invention; 
         FIG.  11    is an example of using a combinatorial optimization algorithm for solving an assignment problem of the similarity measurement costs of  FIG.  8   , according to one aspect of the present invention; 
         FIG.  12    is an example of using a combinatorial optimization algorithm for solving an assignment problem of the similarity measurement costs of  FIG.  11   , according to one aspect of the present invention; 
         FIG.  13    illustrates the filtering of matching source deals and target deals, according to an example; and 
         FIG.  14 A  and  FIG.  14 B  is a novel graphical display of matching source deals and target deals of using aspects of  FIG.  4    thru  FIG.  13   , according to an example. 
         FIG.  15    is an overall process flow of managing sales within an organization, according to an example; 
         FIG.  16    is an overall process flow of finding matching deal attributes of  FIG.  15   , according to an example; 
         FIG.  17    is an overall process flow of generating reports of  FIG.  15   , according to an example; 
         FIG.  18   , shown is a graphical display of matching source deals and target deals using aspects of  FIG.  4    thru  FIG.  13   , similar to  FIG.  14 A , according to an example; 
         FIG.  19    is a graph  1900  of the Deal Health status over time, according to one example; 
         FIG.  20    is a graphical display that illustrates the details that on January 18 of  FIG.  19   , the seller added a decision maker Alex W.; and 
         FIG.  21    is a graphical display that illustrates that the seller was able to turn three of the key player decision makers into advocates. 
     
    
    
     DETAILED DESCRIPTION 
     As required, detailed embodiments are disclosed herein; however, it is to be understood that the disclosed embodiments are merely examples and that the systems and methods described below are embodied in various forms. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the disclosed subject matter in virtually any appropriately detailed structure and function. Further, the terms and phrases used herein are not intended to be limiting, but rather, to provide an understandable description. 
     Non-Limiting Definitions 
     Generally, the terms “a” or “an”, as used herein, are defined as one or more than one. The term plurality, as used herein, is defined as two or more than two. The term another, as used herein, is defined as at least a second or more. The terms “including” and “having,” as used herein, are defined as comprising (i.e., open language). The term “coupled,” as used herein, is defined as “connected,” although not necessarily directly, and not necessarily mechanically. The term “configured to” describes hardware, software or a combination of hardware and software that is adapted to, set up, arranged, built, composed, constructed, designed or that has any combination of these characteristics to carry out a given function. The term “adapted to” describes hardware, software or a combination of hardware and software that is capable of, able to accommodate, to make, or that is suitable to carry out a given function. The phrase “at least one of A and B” means either A or B separately or both A and B. 
     The terms “application”, “software”, “software code” or “computer software” refers to any set of instructions operable to cause a computer to perform an operation. Software code may be operated on by a “rules engine” or processor. Thus, the methods and systems of the present invention may be performed by a computer or computing device having a processor based on instructions received by computer applications and software. 
     The phrase “calculating a distance” means a distance of two points on a two dimensional or x-y place. For example, the distance in two dimensions the distance (d) between (x 1 , y 2 ) and (x 2 , y 2 ) is given by: d=√{square root over ((x 2 −x 1 ) 2 +(y 2 −y) 2 )}. The present invention extends this concept for a distance between two groups or vectors of RIPAA values. The distance between two vectors of RIPAA values is given by 
     
       
         
           
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     in which the values are Xrs=Role Source, Xrt=Role Target, Xis=Impact Source, Xit=Impact Target, Xps=Priority Source, Xpt=Priority Target, Xads=Advocacy Source, Xadt=Advocacy Target, Xacs=Accessibility Source, and Xact=Accessibility target. 
     The term “client device” as used herein is a type of computer or computing device comprising circuitry and configured to generally perform functions such as recording audio, photos, and videos; displaying or reproducing audio, photos, and videos; storing, retrieving, or manipulation of electronic data; providing electrical communications and network connectivity; or any other similar function. Non-limiting examples of electronic devices include: personal computers (PCs), workstations, laptops, tablet PCs including the iPad, cell phones including iOS phones made by Apple iOS phones, Android OS phones, digital music players, or any electronic device capable of running computer software and displaying information to a user, memory cards, other memory storage devices, digital cameras, external battery packs, external charging devices, and the like. 
     The term “combinatorial optimization algorithm” is used to find an optimal object from a finite set of objects. In many such problems, an exhaustive search is not tractable. It operates on the domain of those optimization problems in which the set of feasible solutions is discrete or can be reduced to discrete, and in which the goal is to find the best solution. Examples of combinatorial optimization algorithm include the Hungarian algorithm, the Kuhn-Munkres algorithm, the simplex algorithm, and others. 
     The term “computer” refers to a machine, apparatus, or device that is capable of accepting and performing logic operations from software code. 
     The term “computer readable medium” as used herein refers to any medium that participates in providing instructions to the processor for execution. A computer readable medium may take many forms, including but not limited to, non-volatile media, and volatile media. 
     The term “confirmation” is portion of the workflow for maintenance and repair in which a party, typically a requestor or dispatcher must affirmatively respond to a prompt for the workflow to continue down a maintenance path or repair path. In the event that no confirmation is received, the workflow will branch to an alternative path that puts the order in a holding or cancelled state. 
     The term “data network” or “network” shall mean an infrastructure capable of connecting two or more computers, such as client devices, either using wires or wirelessly, allowing them to transmit and receive data. Non-limiting examples of data networks may include the internet or wireless networks or (i.e. a “wireless network”) which may include Wi-Fi and cellular networks, Bluetooth, and near field communications. For example, a network may include a local area network (LAN), a wide area network (WAN) (e.g., the Internet), a mobile relay network, a metropolitan area network (MAN), an ad hoc network, a telephone network (e.g., a Public Switched Telephone Network (PSTN)), a cellular network, or a voice-over-IP (VoIP) network. 
     The term “database” shall generally mean a digital collection of data or information. The present invention uses novel methods and processes to store, link, and modify information such as digital images and videos and user profile information. For the purposes of the present disclosure, a database may be stored on a remote server and accessed by a client device through the internet (i.e., the database is in the cloud) or alternatively, in some embodiments the database may be stored on the client device or remote computer itself (i.e., local storage). 
     The term “DEAL” shall generally refer to an agreement or compromise between a buyer, the entity seeking to make a purchase, and seller or user, the entity seeking to make a sale, to transact goods and/or services at an agreed upon price. 
     The term “key player” shall generally refer to any person representing the buyer entity of the DEAL and who are determined by the sales person as having a degree of influence over the success of completing the DEAL. 
     The phrase “normalizing values” means taking input, for example, RIPAA input, in one given range or unit of measure and scaling or converting it to a common range or numerical range. The common range is typically between 0 and 1 or between 0 and 10. The normalized data makes it easier to compare data and other statistical operations. 
     The term “PRESCOT” is a coined acronym for Predictive Sales Closing Tool to refer to aspects of the present invention that are being marketed by the patent owner DealCoachPro Inc. 
     The term “sales person” or “sales professional” generally refers to any person representing the seller entity of the DEAL and is the user of this system for the purpose of receiving aid and assistance towards the completion of one or more DEALS. 
     The term “sales attribute” includes one or more of Role, Impact, Priority, Advocacy and Access with the coined acronym (RIPAA). RIPAA is defined in the above-identified previous patent applications which have been incorporated by reference in the first paragraph. For convenience a summary of these terms are defined here: 1) a role of at least one key member in the set of the plurality of key members in the customer&#39;s organization in a sales decision process; 2) an impact score representing an influence of the key member in completing a sales transaction; 3) a priority score representing a level of lessor or greater importance being allocated by the key member to the sales transaction; 4) an advocacy score representing an amount of support by the key member for completion of the sale transaction; and 5) an access score representing an amount of direct Access with the key member. 
     The term “similarity measurement” is a measure on how close two sales attributes, such as the RIPAA values, are between a source key play and target key player. It is referred to as “costs” i.e., “similarity measurement costs” to be consistent with the language used in the literature for a combinatorial optimization algorithm. One measurement of similarity is by “calculating a distance” as described above. However other measurements of similarity between two values, especially those used in the field of statistics, can also be used. 
     The term “total similarity measurement costs” is a measure of assigning similarity costs such that one corresponding additional sales attribute of each target key member is assigned to one corresponding additional sales attribute of each source key member and vice-versa, that is, one corresponding additional sales attribute of each source key member is assigned to one corresponding additional sales attribute of each target key member. This assignment of costs is performed by permuting the rows and columns of a matrix of similarity measurement costs. 
     Overview 
     The present invention is a tool for salespeople to provide instant insights into key sales. These insights help each sales person or sales professional on the team provide valuable support without taking time away from the sales team to get real-time updates. The tool provides the ability to salespeople to manage and analyze customer interactions and data throughout the customer lifecycle, with the goal of improving customer relationships and assisting in driving sales. 
     The present invention provides a tool to help sales professional, optimize their chances of successfully completing more sale deals or DEALs. Sales information is provided to sales professionals. The sales information includes sales methodology, sales training, and sales optimization, to sales professionals. More specifically, the present invention improves the outcome of the “source” opportunity a salesrep is working on through the use of historical data which closest match the source opportunities. 
     The present invention provides actionable tips to guide sales professionals on eliminating vulnerabilities or weaknesses in closing a DEAL and leveraging strengths to improve the likelihood to successfully close more DEALs. 
     The present invention provides a consistent scoring system that illustrates the likelihood of successfully closing a DEAL. This allows sales professionals to allocate more time on strategy where it&#39;s needed most. 
     The present invention is computationally efficient in solving a combinatorial optimization algorithm. Being computationally efficient enables the present invention to be scalable in order to handle more data and transactions. The results of the present invention provide insights in which a sales organization can act upon by re-using knowledge of previous interactions with a buyer or potential buyer. 
     Client Server Environment 
     Turning now to  FIG.  1    illustrates a high-level client-server environment, according to one aspect of the present invention. Shown are some of the physical components which may comprise a system for providing sales information to sales professionals (“the system”)  100  according to some embodiments is presented. The system  100  is configured to facilitate the transfer of data and information between one or more access points  170 ,  172 , client devices  120 ,  122 ,  124 ,  126 , and servers  160  over a data network  150 . Each client device  120 ,  122 ,  124 ,  126  may send data to and receive data from the data network  150  through a network connection  140 ,  142 ,  144 ,  146  with access points  170 ,  172 . A data store  162  accessible by the server  160  may contain one or more databases with database records such as sales attribute values including RIPAA for target key players, as described further below. The data may comprise any information pertinent to one or more users  130 ,  132 ,  134 ,  136  input into the system  100 , including information on or describing one or more users  130 ,  132 ,  134 ,  136 , information on or describing one or more seller entities, information on or describing one or more buyer entities, information on or describing one or more DEALS, information requested by one or more users  130 ,  132 ,  134 ,  136 , information supplied by one or more users  130 ,  132 ,  134 ,  136 , and any other information which a user  130 ,  132 ,  134 ,  136  may be provided such as for training and educational purposes. 
     In this example, the system  100  comprises at least one client device  120 ,  122 ,  124 ,  126  (but preferably more than two client devices  120 ,  122 ,  124 ,  126 ) configured to be operated by one or more users  130 ,  132 ,  134 ,  136 . Client devices  120 ,  122 ,  124 ,  126  can be mobile devices, such as laptops, tablet computers, personal digital assistants, smartphones, and the like, that are equipped with a wireless network interface capable of sending data to one or more servers  160  with Access to one or more data stores  162  over a network  150  such as a wireless local area network (WLAN)  172 . Additionally, client devices  120 ,  122 ,  124 ,  126  can be fixed devices, such as desktops, workstations, and the like, that are equipped with a wireless or wired network interface capable of sending data to one or more servers  160  with Access to one or more data stores (no shown) over a wireless  170  or wired local area network  150 . The present invention may be implemented on at least one client device  120 ,  122 ,  124 ,  126  and/or server  160  programmed to perform one or more of the steps described herein. In some embodiments, more than one client device  120 ,  122 ,  124 ,  126  and/or server  160  may be used, with each being programmed to carry out one or more steps of a method or process described herein. 
     In some embodiments, the system  100  may be configured to facilitate the communication of information to and from one or more users  130 ,  132 ,  134 ,  136 , through their respective client devices  120 ,  122 ,  124 ,  126 , and servers  160  of the system  100 . Users  130 ,  132 ,  134 ,  136  of the system  100  may include one or more sales professionals and any other individual associated with a seller entity. Typically, users  130 ,  132 ,  134 ,  136  describe individuals that desire to create or facilitate the formation of a DEAL with a buyer entity in order to transact goods and/or services at an agreed-upon price. The user  130 ,  132 ,  134 ,  136  may provide data and information describing a DEAL and data and information describing one or more key players to the system  100  and the system  100  may provide sales information to the user  130 ,  132 ,  134 ,  136  in real-time which may be used to create or facilitate the formation of the DEAL and which may otherwise not be available to the user  130 ,  132 ,  134 ,  136 . 
     RIPAA Slider Assist 
       FIG.  2    illustrates examples of a novel interface  200  for providing input of a sales attribute of a customer according to one aspect of the present invention. The sales attributes can include a Role (not shown), an Impact score  260 , a Priority score  262 , an Advocacy score  264 , and an Access score  266  (RIPAA). In this example a user is not sure how to assign a slider value to a given sales attribute ( 262 ,  264 ,  266 ). More specifically, the user is not sure how to assign an Impact score  260  using a slider. The user selects slider assist  218  to be ON. Once the slider assist is set to ON, a series of qualifying questions  210  thru  250  are presented related to the specific sales attribute. Depending on the answers from the user, e.g., Yes  212 , No  214 , and Not Sure  216 , the sale attribute, shown as a slider, is automatically set. 
     RIPAA to PRESCOT 
       FIG.  3    illustrates the evolution of the sales process from managing structured sales conversations using RIPAA to accelerating sales by using data across an organization by uncovering areas of improvements with sales professionals, according to one aspect of the present invention. The present invention builds on the sales attributes previously disclosed (RIPAA)  304  to provide PRECSOT (Predictive Sales Closing Tool)  312 . As further described below, PRESCOT provides insights to other similar DEALs through using computational efficiency, namely a combination of Euclidean geometry and linear optimization. Insights are gained from other DEALS. 
     PRESCOT—Step 1 of Selecting or Filter a Number of Key Players 
       FIG.  4    is a graphical illustration of matching source and target deals as step 1, according to one aspect of the present invention. In this example the source deal is been selected or filter to use four (4) key players in a DEAL. Only those other previous DEALS that also have four (4) key players for each target deal (Target Deal1, Target Deal2, Target Deal3, Target Deal 4) is selected as a target. The target deals, Target Deal1, Target Deal2, Target Deal3, Target Deal 4, as part of the information in a database with RIPAA and other information previously stored for a sales organization with sales professionals. Note that the number of key players can, and often change, over time for a given DEAL—Target Deal1, Target Deal2, Target Deal3, Target Deal 4. For example, Target Deal1 shows 2 key players on a first date, four key players on a second date, five key players on a third date, and eight key players on a fourth date. Also note that each Target Deal1, Target Deal2, and Target Deal3 can have multiple instances based on time i.e., snapshots. Where Target Deal4 is shown with only one snapshot. In one example the system defaults to using the most recent match of five key players in Target Deal3 as shown. However, other filters, such deals within a certain time period can be identified. 
     Turning now  FIG.  5    shown is another graphical illustration of matching source and target deals as step 1 with five (5) key players in comparison to four (4) key players in  FIG.  4   . Note that the information for the given deals DEAL—Target Deal1, Target Deal2, Target Deal3, Target Deal 4 are identical as that of  FIG.  4   . However, because a filter or choice was made to use five (5) key players to compare a source deal to the target deals, the target deals selected are different than the target deals selected in  FIG.  4   . 
     PRESCOT—Step 2 of Placing RIPPA Values in Matrix 
       FIG.  6    is an example of a matrix  602  of the source key players and target key player of  FIG.  4    as step 2, according to one aspect of the present invention. In this matrix representation  602 , the RIPAA values and other values of the Source Team Key Players (S-KP) are shown across the top of the matrix as S-KP 1, S-KP 2, S-KP 3, S-KP 4. The top row of each cell  610  thru  646  is the RIPAA values for each Source Team Key Player  650  thru  656 . Going down the matrix are the Target Key Players (T-KP) as T-KP 1, T-KP 2, T-KP 3, T-KP 4. The bottom row of each cell  610  thru  646  is the RIPAA values for each Target Team Key Player  660  thru  666 . 
     PRESCOT—Step 3 of First Order Normalization 
       FIG.  7    is an example of a matrix  702  of the source and target with a first order normalization of  FIG.  6    as step 3, according to one aspect of the present invention. Note all the values from  FIG.  6    (top row and bottom row) for each cell  710  thru  746  are normalized within a given range. In this example the range is 1 to 10. A decision maker is assigned a value of 10, whereas a decision influencer is assigned a value of 5 however it is important to note that other values and ranges could be used within the true scope and spirit of the present invention. 
     PRESCOT—Step 4 of Second Order Normalization 
       FIG.  8    is an example of a matrix  802  of the source and target with a second order normalization of  FIG.  7    as step 4, according to one aspect of the present invention. The second order normalization in this example uses a version of the distance formula. Here each RIPAA set of values (Role, Impact, Priority, Advocacy and Access with the coined acronym) or vector a Source Team Key Player is compared against each RIPAA set of value or vector of each Target Team Key Player 
     
       
         
           
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       850 . The top and bottom values of each cell  710  thru  746  shown in  FIG.  7    is placed in the formula  850  above. The results of each cell  810  thru  8   46  shown in  FIG.  8   . The values  852  are Xrs=Role Source, Xrt=Role Target, Xis=Impact Source, Xit=Impact Target, Xps=Priority Source, Xpt=Priority Target, Xads=Advocacy Source, Xadt=Advocacy Target, Xacs=Accessibility Source, and Xact=Accessibility target. This results in a RIPAA proximity score or distance score for each cell as shown. 
     PRESCOT—Step 5 of Combinatorial Optimization Algorithm Normalization 
       FIG.  9    is an example of a matrix  902  of the source and target using a combinatorial optimization algorithm for solving an assignment problem of the similarity measurement costs of  FIG.  8    as step 5, according to one aspect of the present invention. In this example, the Hungarian Algorithm is run across all cells cell  810  thru  846  in the matrix  802  of  FIG.  8   . The results of the Hungarian Algorithm are placed in each cell  910  thru  946  as shown. Examples of how the Hungarian Algorithm works in a matrix formulation is found here at online URLs (https://en.wikipedia.org/wiki/Hungarian_algorithm) and (https://www.youtube.com/watch?v=rrfFTdO2Z7I) and (https://www.youtube.com/watch?v=ezSx8OyBZVc), the teachings of which, are hereby incorporated by reference in their entirety. This is further described below with reference to  FIG.  11    and  FIG.  12   . 
     In the matrix formulation, we are given a nonnegative n×n matrix, where the element in the i-th row and the j-th column represents the RIPAA values of the j-th Source Team Key Player compared, by the distance formula above, to the RIPAA values of the i-th Target Team Key Player. The Hungarian Algorithm finds a combination of source to target to source RIPAA values, such that RIPAA vector for each Source Team Key Player compared with RIPAA value of each Target Key Player is minimum. 
     Stated differently, note that the combinatorial optimization algorithm is to find the lowest comparison value using each column of the matrix. It is the overall value not the minimum value per column or per row that is found. The values that are circled in the cell on the matrix on show combination of values found from a finite set of objects. Here is shown that the minimum combination of values (circled in each column) across the matrix  902  is given by 1.41+7.55+4.24+2.24=15.44. It is important to note that the Hungarian does not find the smallest value per row or per column but rather the lowest value of values across the entire matrix for each row. This will be further described below in the section entitled Combinatorial Optimization Algorithm. 
     Proximity of DEALS Analyzed 
       FIG.  10    is an example of the results of lowest sub-set of proximity deals analyzed in  FIG.  9    as step 6, according to one aspect of the present invention. Shown is a ranking  1002  of five of the results target key players of the Hungarian Algorithm with the lowest scores (i.e. the highest proximity of the RIPAA values of source key players compared with the RIPPA values of target key players. Note the lower the score, the more closely match the deal. Shown are Company R, Company J2, Company K, University MM, Company DZ. 
     Combinatorial Optimization Algorithm Example 
       FIG.  11    is an example of using a combinatorial optimization algorithm for solving an assignment problem of  FIG.  8   , according to one aspect of the present invention. For simplicity, the values in cell for table  1102  match the values in cells  810  thru  846  in table  802  of  FIG.  8   . Values. In step 1, the minimum is identified for each row. These minimum values are shown in the right most column of table  1102 . This minimum value for each row is subtracted for each cell value in that row resulting in table  1104  as shown. Next, the minimum value for each column is identified. The minimum value for each column is shown in the bottom row of table  1104 . The minimum value for each column is subtracted from each cell value in that column resulting in table  1106  as shown in step 2. Next in step 3, as shown in table  1108 , the minimum number of vertical and horizontal lines are identified to cover each zero in the matrix a shown in table  1108 . The total number of lines required to cover each zero is compared against the dimension of the matrix. In the event the number of lines is less than the dimension of the matrix, the optimal solution is not yet identified. Otherwise, if the number of lines is equal to the dimension of the matrix, the optimal solution is identified. In this example, there are four (4) lines needed. The dimension m of the matrix is 4. Since m=four (4) lines, the optimal solution is found. If Step 4 is required, the process follows these sub-steps of:
         a. Find the lowest value from the list of values that are uncovered (no lines running through them).   b. Subtract the lowest value from the uncovered values.   c. Add the lowest value to the values where the lines intersect   d. Replay Step 3, whereby the goal is to find a scenario where the minimum number of vertical/horizontal lines match the matrix value m=4 in this scenario. Once achieved, the process continues to step 5.       

     Step 5 is shown in  FIG.  12    matrix  1202 . The process begins with the row or column that has the minimum number of zeros. The number of zeros for each column is shown in the bottom row and the number of zeros for each column is shown in the right most column. Note that it is possible for a row or column to have more than one zero. In this example there are a few rows or columns with only one zero. In one example the process begins randomly from the row or column with the minimum numbers of zeros. It does not matter where the process starts the assignment as long as the row or column has the minimum number of zeros. Note the allocated cell match the cells identified in matrix  902  in  FIG.  9    above. 
     Generating Reports 
       FIG.  13    illustrates filtering of matching source and target deals of  FIG.  13   , according to an example based on one more characteristics. The characteristics status of the deal state  1310 . The deal state  1310  shown are open  1312 , won  1314 , and lost  1316 . Another characteristic is Competitors  1320 . Competitors are companies  1322  through  1330 . The characteristics of stages of deals  1340 . In one example the Sales Stages are setup by the company. The sales stages can be thought of as a company&#39;s funnel. Different companies typically setup these stages differently. The Deal state is either open, won or lost. The characteristic of stages of deals are identifying  1342 , validating  1344 , building consensus  1346 , closing  1348 , closed won  1350 , and closed lost  1352 . 
       FIG.  14    is a novel graphical display of matching source and target deals of using aspects of  FIG.  4    thru  FIG.  13   , according to an example. Note that some of the selections for filters from  FIG.  13    are shown at top portions  1410 ,  1420 . The selection of filters shown are deal owner  1412 , competitors  1414 , close date  1422 , sales stage  1424 , value  1426 . In section  1430 . Shown are two graphs deal legitimacy  1440  and deal position  1450 . Shown are each of the target key players  1442  thru  1448  with their RIPAA values graphed. For convenience  1442 ,  1452  matches  1462 ,  1444 ,  1454  matches  1464 ,  1446 ,  1456  matches  1466  and  1448 ,  1458  matches  1468 . 
     Note that each of the two graphs deal legitimacy  1440  and deal position  1450  are formed with at least three visually distinct regions. In the case of the deal legitimacy graph the deal legitimacy graph is presented with at least three visually distinct regions indicating a value of the impact score and a value of the priority score  1440 . Similarly, in the case of the deal position graph  1450  the three visually distinct regions indicate a value of the advocacy score and a value of the access score. 
     The three visually distinct regions in one example is based on shape, color, gradients, pattern, shading, or a combination thereof. In another example, the two graphs deal legitimacy  1440  and deal position  1450  are shown in the shape of as a combination of three or more overlapping arcuate colored shapes. 
     Also shown in the area in  1470  in strengths  1472  and vulnerabilities  1474  listed from notes for source key player. 
     Overall Process Flow 
       FIG.  15    is an overall process flow  1500  of managing sales with an organization, according to an example. The process begins in step  1502  and immediately proceeds to step  1504 . In step  1504  a list of previous deals (i.e., target deals) is retrieved from more databases with database records such as sales attribute values, including RIPAA for target key players. This list or set of target key players is based on the number of key players to match in the source deal. Two examples of a number of key players, one for four (4) key members, is shown in  FIG.  4   , and one for five (5) key members is shown in  FIG.  5   . The process proceeds to step  1506 . 
     In step  1506  the RIPPA values for the source deal with the given number of key plays is compared to each target deal identified in step  1504  each with the same number of the given number of key players. This is described in Step 1 through Step 6 above with reference to  FIG.  5    thru  FIG.  10    above. More details are described in  FIG.  16    below. The process continues to step  1508 . 
     In step  1508 , a test is made to see if all the target deals identified in step  1504  are processed. In response to more target deals identified to be processed, then the process continues back to step  1506  to be processed. Otherwise, in response to no more target deals to be processed, then the flow continues to step  1510 . 
     In step  1510 , target deals found that have the lowest of the combinatorial optimization algorithm as compared with a settable threshold are identified. The settable threshold allows use to define how close of comparison of RIPAA values of the source to the target is identified. In one example, just the lowest scores of the combinatorial optimization algorithm are identified for the source compared with the target deals. The identified targets are used to create a report in step  1512  (described further in  FIG.  16    below), and the process ends in step  1514 . Otherwise, if source deals compared with target deals within a given threshold are not found, the process ends in step  1514 . 
       FIG.  16    is an overall process flow  1600  of finding matching deal attributes of step  1506  in  FIG.  15   , according to an example. The process starts in step  1600  and immediately proceeds to step  1602 . 
     In step  1602 , the number of key players in source and target are searched. As described above with reference to  FIG.  4    and  FIG.  5   , there may be one or more snapshots in time that have a number of key players that match the source key members. Stated differently each target deal may, over time, have more than one instance or revision of key players that match to source key members. The process continues to step  1604 . 
     In step  1604  a test is made to see if any target deals have revisions that match the number of key players in the source deal. In response to a target deal, if no deal revisions is found, the process continues to step  1610  to end. Otherwise, there are revisions in target deals in which the number of key players matches the source deal. The process continues to step  1606 . 
     In step  1606  all the revisions in the target deal with the same number of key players as the source key player are identified and the process flows to step  1608 . 
     In step  1608 , the RIPPA values for the source deal with the given number of key plays is compared to each target deal revision identified in step  1606  each with the same number of given number of key players. This is described in Step 1 through Step 6 above with reference to  FIG.  5    thru  FIG.  10    above. The process continues to step  1610  to end. 
       FIG.  17    is an overall process flow  1700  of generating reports of  FIG.  17   , according to an example. The process starts in step  1702  and proceeds to step  1704 . In step  1704  the top n matching deals is identified. For example for n=5 the top five target deals as shown in  FIG.  10   . The number n is settable by the user. 
     Deal History/Replay 
     Turning now to  FIG.  18   , shown is a graphical display  1800  of matching source deals and target deals using aspects of  FIG.  4    thru  FIG.  13   , similar to  FIG.  14 A , according to an example. In this example, as well as  FIG.  20    and  FIG.  21   . Initials shown in italics denote a decision maker, whereas non-italic initials denote decision influencers. 
     Upon first glance at this deal, all that can be gleaned is that this deal is at risk from the Deal Health status  1802 . There is no ability to uncover what is behind this Deal Health  1802  status alone. Selecting the Deal History Icon  1804  brings up the Deal Health Over Time shown in  FIG.  19   . The Replay Deal icon  1806 , provides a series of graphical displays  1800  with the value at a given time. This allows a viewer to see how the deal progresses overtime using this interface so that the numbers for each player, their RIPPA value, and positions are displayed on the graph  1800 . 
     Also, notice that deal legitimacy  1840  and deal position  1850  are not identical to the deal legitimacy  1440  and deal position  1450  graphs of  FIG.  14   . More specifically, it is observed that impact and priority are weighted equally. Stated differently, they&#39;re equally important. This is shown by the symmetric overlapping arcuate regions  1842 ,  1844 , and  1846 . On the other hand, deal position  1850  is not the same as deal position  1450 . It is found that Advocacy is a little more important than Access. Therefore the three overlapping arcuate regions  1852 ,  1854 ,  1856  are shifted along the access axis X to the right. Other callouts, specifically  1822  through  1826  and  1860  through  1872  are identical to the features described above for  FIG.  14 A , specifically items  1422  through  1426  and  1460  through  1469 . 
     Referring to  FIG.  19    is a graph  1900  of the Deal Health status over time. As shown in the graph  1900 , the deal was trending in a positive direction. A series of connector dots 1 through 7 are shown, illustrating a change, above a certain threshold, of the Deal Health. In one example, the dots are colored to reflect the status of the Deal Health status change. For example, the color red means “Poor”, the color yellow means “Fair” and the color green means “Good”. Something clearly happened to move it to a risky deal right after January 16 on connector dot 3. What happened? The answer from this graph, when hovering over connector dot number 3 reveals the details shown in  FIG.  20   . 
     Turning now  FIG.  20    illustrates the January 18 details that the seller added a decision maker Alex W. Alex W. has high priority (7) but low Advocacy (2). His low advocacy is resolved in  FIG.  21   . The system scoring algorithm moved the Deal Health status from “Fair” down to “Poor.” 
     Returning to graph  1900 , on April 8, the deal has moved in a positive direction. What happened? Selecting connector dot 4 brings up  FIG.  21   . 
       FIG.  21    illustrates that the seller was able to turn three of the key player decision makers into advocates. The system scoring algorithm moved the Deal Health status from “Poor” up to “Fair.” The scoring algorithm takes into account that there are six decision makers. The scoring algorithm is conservative by design. With so many decision makers it is challenging to identify the ultimate authority. The high number of decision makers is probably because the scoring algorithm did not increase the Deal Health rating from “Fair” to “Good.” 
     Using this graphical option as shown in  FIG.  19   , the Deal History makes is easier to visualize the results and analytics performed by the scoring algorithm. In addition, this type of visualization helps sales teams and sales managers document the sales process. The system helps to identify both weaknesses or vulnerabilities and strengths. The goal is to minimize these vulnerabilities and maximize strengths and visualize the results. The entire sales team can now able to review the status of a deal. 
     Analogous to a checklist that a flight crew may use, this system helps to ensure that all required actions are performed without omission and in an orderly manner. The system allows sales teams to document the sales process and remove any subjectivity from the sales process. The team can review the Deal Health status over time in order to collaborate. 
     NON-LIMITING EXAMPLES 
     Although specific embodiments of the subject matter have been disclosed, those having ordinary skill in the art will understand that changes are made to the specific embodiments without departing from the spirit and scope of the disclosed subject matter. The scope of the disclosure is not to be restricted, therefore, to the specific embodiments, and it is intended that the appended claims cover any and all such applications, modifications, and embodiments within the scope of the present disclosure.