Claims:SYSTEM AND METHOD FOR AUTOMATED SALES PIPELINE RISK ANALYSIS AND CORRECTIVE RECOMMENDATIONS

TECHNICAL FIELD
The present disclosure relates to a system 400 and a method for detecting risky sales pipeline opportunities 215A, those are stagnant or at the risk of loss, out of the on-process sales pipeline opportunities 122 in an online based sales or Customer Relationship Management (CRM) platform 120. More specifically, the disclosure relates to pipeline corrective recommendations 123 responsive to the risks attributed to the sales pipeline opportunities 122 currently being processed in an online based sales or CRM platform 120.
BACKGROUND
In sales / CRM environment, currently prediction systems are available either for analyzing historical sales pipeline data for intelligence on past performance or sales pipeline prediction categories such as predicting the conversion rate, winnability or loss of opportunities, pre-defined sales tasks or actions recommended on sales stages, thus helping sales managers or sales representatives to predict sales future (i.e winnability of a deal, churning rate, sales revenue forecasting etc) or identifying the sales process or sequence of steps might be needed to achieve the sales objectives.
These systems informs sales team on what has happened, what is going to happen on the sales pipeline or what predefined sales steps or actions may lead to achieve future predictions, but if the opportunities in the pipeline are stagnant or not progressing as expected line on the prediction, the sales team is stuck with no answers or intelligence on what factors are contributing to the stagnancy or required to achieve the predicted sales objectives.
Basically these systems do not provide any recommendations on what behavioral patterns on sales engagement or pipeline execution are holding back the sales opportunities. For example, known systems would help to learn the past conversion rate, predict what would be the conversion rate on the new incoming leads or the set of steps (i.e, making phone call using predefined call script followed by a welcome email and then an online demonstration etc...) to achieve predicted conversion, but does not provide recommendations such as what behavioral factors of sales team on sales engagement or pipeline execution can improve the convertibility rate or causing the loss of opportunities. Hence if the opportunities in the pipeline are stagnant or not progressing as expected in the line of prediction, the sales team is stuck with no answers on what to do with the past or future prediction intelligence or ways to clarify predictive data to improve sales.
Basically the sales team doesn't know what behavioral problems on sales engagement or pipeline execution (such as not returning unanswered calls in specific time, delayed first responses after the opportunity generated, not crossing a particular sales stage in specific time etc..) that could be holding back sales or what behavioral patterns on sales engagement or execution (such as the response time to be adhered for email responses, timeline between successive customer follow ups) has to be followed up to improve the convertibility rate or avoiding the loss of opportunities.
Until unless they get some intelligence on such patterns, problem causes and the remedy best practice or actions, they will not be able to optimize or tweak the current sales engagement or pipeline execution to keep the sales pipeline healthy and on track to achieve the better pipeline objective like more winnability or conversion rate. Thus either the sales persons are not able to break the stagnancy of the sales pipeline opportunities 122 that are at risk or avoid the risk of losing those most of the time or they do not get any guidance from their sales managers on time to handle these problems.
Added to this, these expected intelligence data has to be provided on real time to the sales team unlike on other prediction systems, where the prediction analysis are either in the form of post-mortem analysis or pre-forecast analysis. Also manual analysis and efforts to keep vigil on the sales pipeline all the time on various parameters not only found to be tedious but looks to be almost not possible considering the volume of the sales pipeline data being processed in an online based sales or CRM platform 120.
US20040064360A1 discloses a method and apparatus that includes assessing the capacity and effectiveness of sales pipelines by assigning values to a lead as it progresses through a sales pipeline. The assigned values can be based upon a number of influencing factors, wherein the influencing factor is selected from the group consisting of a close percentage, days to close, quota attainment and monetary value of an opportunity or group of opportunities. These values improve Sale's and Marketing's ability to efficiently process and manage lead flows, as they can be used to access the health, capacity and effectiveness of a sales pipeline.
US 20140207533 A1 discloses a method that teaches and enable the tracking of the number of new prospects, the number of phone calls made, the number of phone invites made, the number in-person invites made, the number of appointments made, the number of confirmations, the number of prospects who attended appointments, the number of second meetings Scheduled and held, and the number of new customers or distributors acquired. The orders of activities tracked is the same as those listed, and is displayed on a computer Screen or in paper in that specific order for each day of the week, with goals and total columns to track a user's actions and Success. A second area tracks telephone calls made and use a three legend key to track the number of calls made (represented by a slash) and the number of appointments set during calls (represented by an encircled slash).
US7546248B2 claims a method and system for managing sales activities of sales associates, the method comprising: (a) a server computer receiving sales information from a remote computer; (b) the server computer processing the sales information; and (c) generating content-based advice for individual sales associates based on respective sales information.
US 20100114663 A1 discloses a system that uses demographic data to generate a sales prospect recommendation that includes a product recommendation with a probability that the sale will close, and may include an estimated time to close the sale and projected revenue. The system imports customer data including past purchasing data and demographic data for a plurality of customers. The system can then generate a predictive model by training the model using the past purchasing data and the demographic data. When queried for a sales prospect recommendation, the system responds to the query with at least one sales prospect recommended by the predictive model.
But none of the prior art address the compliance risk on the sales pipeline opportunities 122 in the context of performance activities related to sales engagement and execution behavior such as responding customer email that expects response on time, immediate callback when customer calls are missed, quick or immediate first response for the new opportunities generated, frequent or adequate successive customer follow ups or engagements, on time stage to stage progression etc, in real time and provide pipeline corrective recommendations123 on on-process sales pipeline opportunities 122.
In the light of the aforementioned discussion, there exists a need for a system and method that would overcome or ameliorate the above mentioned limitations. What is needed is a system and method to automatically guide the sales team with recommended actions highlighting the problem cause or best practice pattern in specific to the sales engagement or execution behavior to keep all their live / current sales opportunities in the pipeline always on track to winnability.
SUMMARY
The following is a simplified summary of the disclosure in order to provide a basic understanding of some aspects of the disclosure. This summary is not an extensive overview of the disclosure. It is intended to neither identify key or critical elements of the disclosure, nor delineate any scope of the particular implementations of the disclosure or any scope of the claims. Its sole purpose is to present some concepts of the disclosure in a simplified form as a prelude to the more detailed description that is presented later.
Embodiments of the invention effectively provides a novel system and method to detect risky sales pipeline opportunities 215A (those are stagnant or at the risk of loss) out of on-process sales pipeline opportunities 122 in an online based sales or Customer Relationship Management (CRM) platform 120 and pipeline corrective recommendations123 to the users of an online based sales or CRM platform 120 to keep sales pipeline on track of winnability.
Briefly described, a method employing aspects of the invention detects access of sales pipeline opportunities 122 by their sales user for eg. users of an online based sales or CRM platform 120. The method includes analyzing the accessed sales pipeline opportunities 214A out of on-process sales pipeline opportunities 122 for the compliance of one or more performance standards in the context of sales engagement (i.e call, email, online session or in-person visit) and execution behavior (i.e customers follow ups, sales tasks progressions). Non-compliance of one or more performance standards are indicative of the risk nature (i.e stagnancy or at risk of loss) of the accessed sales pipeline opportunities 214A. The method also includes detecting the risky sales pipeline opportunities 215A out of accessed sales pipeline opportunities 214A as a function of analyzed performance standards in the context of sales engagement and execution behavior. The method further includes generating recommendations for pipeline corrective actions 215C to be performed in specific to the detected risky sales pipeline opportunities 215A which are at risk based on the non-compliance factors. The method also includes presenting the pipeline corrective recommendations 123 to the users of online based sales or CRM platform 120 that comprises of detected risky sales pipeline opportunities 215A and pipeline corrective actions 215C to be performed in specific to the detected risky sales pipeline opportunities 215A.
In additional embodiments, one or more processing devices for performing the operations of the above described embodiments are disclosed. In additional embodiments, a system 400 is disclosed, the system 400 comprising a memory; and a processing device, coupled to the memory, for performing operations comprising the method according to any one of the above described implementations.
BRIEF DESCRIPTION OF DRAWINGS
The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles, in which like reference numerals generally refer to the same parts throughout the drawings.
FIG 1: Illustration of System Architecture 100
FIG 2: A block diagram of an exemplary embodiment of a sales pipeline risk analyzer 131
FIG 3: An exemplary diagram illustrating a process according to one embodiment of the invention
FIG.4: A block diagram illustrating one example of a suitable computing system 400
DETAILED DESCRIPTION
Exemplary embodiments are described with reference to the accompanying drawings. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope and spirit being indicated by the following claims. Additional illustrative embodiments are listed below.
FIG. 1 illustrates an example of system architecture 100, in accordance with one embodiment of the present disclosure.
The system architecture 100 (also referred to as "system" herein) includes online based sales or CRM platform 120, one or more server machines 130 through 140, a data store 102, and client devices 110A-110Z connected to a network 101.
In embodiments, network 101 may include a public network (e.g., the Internet), a private network (e.g., a local area network (LAN) or wide area network (WAN)), a wired network (e.g., Ethernet network), a wireless network (e.g., an 802.11 network or a Wi-Fi network), a cellular network (e.g., a Long Term Evolution (LTE) network), routers, hubs, switches, server computers, and/or a combination hereof.
In embodiments, data store 102 is a persistent storage that is capable of storing sales pipeline opportunities 122, which will be used throughout the specification to illustrate the embodiments, as well as data structures to tag, organize, and index the above data items. Data store 102 may be hosted by one or more storage devices, such as main memory, magnetic or optical storage based disks, tapes or hard drives, NAS, SAN, and so forth. In some embodiments, data store 102 may be a network-attached file server, while in other embodiments data store 102 may be some other type of persistent storage such as an object-oriented database, a relational database, and so forth, that may be hosted by online based sales or CRM platform 120 or one or more different machines coupled to the online based sales or CRM platform 120 via the network 101.
The client devices 110A-110Z may each include computing devices such as personal computers (PCs), laptops, mobile phones, smart phones, tablet computers, netbook computers, network-connected televisions, etc. In some embodiments, client devices 110A through 110Z may also be referred to as "user devices".
In embodiments, each client device 110A-110Z includes a client interface 111. In one embodiment, the client interface 111 may be applications that allow users to view or access content, such as sales pipeline opportunities 122, web pages, documents, etc. For example, the client interface 111 may be a web browser that can access, retrieve, present, and/or navigate content (e.g., web pages such as Hyper Text Markup Language (HTML) pages, sales pipeline opportunities 122, etc.) served by a web server. The client interface 111 may render, display, and/or present the content (e.g., a web page) to a user. In another example, the client interface 111 may be a standalone application (e.g., a mobile application or app) that allows users to view content (e.g., sales pipeline opportunities 122, web pages, documents, etc.). According to aspects of the disclosure, the client interface 111 may be an online based sales or CRM application for users to record, edit, and/or upload data specific to customers and sales transactions on online based sales or CRM platform 120. As such, the client interface 111 may be provided to the client devices 110A-110Z by the server machine 130-140 or online based sales or CRM platform 120.
In one embodiment, the online based sales or CRM platform 120 or server machines 130-140 may be one or more computing devices (such as a rackmount server, a router computer, a server computer, a personal computer, a mainframe computer, a laptop computer, a tablet computer, a desktop computer, etc.), data stores102 (e.g., hard disks, memories, databases), networks, software components, and/or hardware components that may be used to provide a user with access to content specific to customer, sales pipeline opportunities 122 and pipeline corrective recommendations 123. For example, the online based sales or CRM platform 120 may allow a sales user to view, add, upload, search for, or comment on customer data 121, sales pipeline opportunities 122 and pipeline corrective recommendations 123. The online based sales or CRM platform 120 may also include a website (e.g., a web page) or application back-end software that may be used to provide a user with access to the data related to customers, sales pipeline opportunities 122 and pipeline corrective recommendations 123.
In embodiments of the disclosure, a "user" may be represented as a single individual or a sales team. For example, the user could be a sales person or executive who makes use of the online based sales or CRM platform 120 for his day to day sales execution activities.
In some embodiments, online based sales or CRM platform 120 present pipeline corrective recommendations 123, to a user via client interface 111. The client interface 111 configured and has a designated screen space to present the pipeline corrective recommendations 123. Pipeline corrective recommendations 123 may be presented as a set of indicators (e.g., interface component, electronic message, recommendation feed, etc.) that includes the list of detected risky sales pipeline opportunities 215A and the recommended pipeline corrective actions 215C to be performed in specific to the risks identified on the risky sales pipeline opportunities 215A. In one embodiment, the client interface 111 can involve a home page of the client application accessed by the user of online based sales or CRM platform 120.
In one embodiment, the user when accesses or login to the client application, the list of risk identified sales opportunities 215A out of the current active sales pipeline opportunities 122 owned by the user along with their recommended pipeline corrective actions 215C is highlighted explicitly.
In one embodiment, the pipeline corrective recommendations 123 may be a recommendation for one or more sales pipeline opportunities 122 currently being accessed by the user in an online based sales or CRM platform 120.
In one embodiment, the server machine 130 includes a pipeline risk analyzer 131. The pipeline risk analyzer 131 analyzes and detects risky sales pipeline opportunities 215A (those are stagnant or at the risk of loss) out of the on-process sales pipeline opportunities 122 in an online based sales or Customer Relationship Management (CRM) platform 120. In general, pipeline risk analyzer 131 may include a pipeline risk detector module 204, which analyzes the accessed sales pipeline opportunities 214A for the pipeline compliance risk levels of one or more performance standards in the context of sales engagement and execution activities.
One embodiment of the invention advantageously utilizes predictive analytics techniques to create pipeline risk detector module 204. For example, a pipeline risk detector module 204 may be created to analyze the sales pipeline opportunities 122 for the pipeline compliance risk levels of one or more performance standards in the context of sales engagement (i.e call, email, online session or in-person visit) and execution behavior (i.e customers follow ups, sales tasks progressions).
Predictive analysis techniques may create a pipeline risk detector module 204 by using combination of statistical, optimization or machine language based regression, pattern and prediction modeling to perform the pipeline compliance risk analysis on the sales pipeline opportunities 122 in the context of performance activities related to sales engagement and execution behavior such as responding customer email that expects response on time, immediate callback when customer calls are missed, quick or immediate first response for the new opportunities generated, frequent or adequate successive customer follow ups or engagements, on time stage to stage progression etc.
Pipeline compliance risk analysis involves a set of procedures to assess the risk nature of the current pipeline opportunities risk nature by measuring the adoption level of certain performance standards being achieved linked to the engagement or execution activities related to the sales pipeline transactions. These performance standards would be pre-defined and predicted from historical sales pipeline opportunities data and usually indicate boundary, limit or timeline related to the activities execution. And these performance standards are usually represented in the numeric form specifying the optimized performance weightage. For example, considering the activity of providing immediate first response to the newly generated sales pipeline opportunities 122, the risk nature would be assessed based on the timeline taken by the sales users to engage the pipeline opportunities for the first time. The first attention could be made via any communication channel like email, voice, online session or in-person visits. And the performance standard that would be predicted for this activity context would be the timeline within which the activity has to be executed. Hence for a specific sales pipeline opportunity 122, at first the exact time at which the first engagement happened via any of the channels has to be traced and then has to be validated whether the timeline falls in the ambit of the optimized value recommended by the performance standard predicted related to this specific activity. Based on the result of validation, the risk nature will be determined.
Pipeline compliance risk analysis being implemented to surface out the risky sales pipeline opportunities 215A and predict the risk factors behind those detected risky sales pipeline opportunities 215A is not a straightforward process such as one followed in regular prediction systems (i.e Past performance predictions, Future forecast, Conversion rate predictions etc) where the readily available historical data points are directly analyzed to predict the results such as winnability of deals, churning rate, sales revenue forecasting etc. Pipeline Compliance risk analysis is a multi stage process involving first the identification of right sales engagement or execution activities that are likelihood source or cause for better or poor performance of the sales opportunities. And then the performance standard in which ambit these identified sales engagement or execution activity has to be carried out to achieve the performance objective (i.e to move out of the risk stage and to be on track to achieve the winnability) has to be predicted.
Again the prediction of these performance standards are not a straightforward analysis, as the data points will not be readily available and hence the new or additional data points that need to be collected specific to these identified performance activities has to be derived. These data points may be singular or plural based on the complexity of the activity related. All the identified data points that are either readily available or newly added will be provisioned to be automatically captured and tracked with no manual feed required. And then the right prediction systems have to be applied on these auto captured data patterns to arrive at the performance weightage or the standard to finally surface the risky sales pipeline opportunities 215A along with predicting the source risk factors behind them. For an example of the sales call engagement, reaching back customers whose calls are missed or unanswered due to some reasons in a specific turnaround time would avoid loss of opportunities. Likewise for an example of sales email engagement, responding back to an important customer's email that expects response within a specific time frame will improve the chance of winning those sales opportunities. The data points necessary to measure the timeframe taken to answer the missed calls and timeline in which the response expected emails are replied have to be introduced if not readily available within the sales data with provisions to be auto captured. And then the optimization values of these measured data points from the historical won and lost sales opportunities data will be predicted to arrive at the performance weightage. Once derived, these optimized performance weightages, will serve as a future guidelines for the salesperson on engagement activities involving the missed call response and important email responses related to any sales opportunities.
Without such performance weightages known, the salesperson would not be able to proactively be alert to attend the sales pipeline opportunities 122 which need immediate response on the calls or emails on stipulated time. Accordingly, the present invention advantageously utilizes a number of factors to identify such behavioral patterns on sales engagement or pipeline execution that can improve the convertibility rate or cause the loss of sales pipeline opportunities 122 along with predicting the optimized performance standards that need to be followed up. Based on its pipeline compliance risk analysis on the accessed sales pipeline opportunities 214A out of the on-process sales pipeline opportunities 122, pipeline risk analyzer 131 may determine the risky sales pipeline opportunities 215A and generate recommendations for pipeline corrective actions 215C to be performed in specific to the risk detected sales pipeline opportunities 215A. An exemplary operation of pipeline risk analyzer 131 in accordance with one embodiment of the invention is discussed hereinafter in connection with Figures 2 and 3.
Server machine 140 includes pipeline compliance module 141 that provides pipeline risk analysis input data 214 (that includes accessed sales pipeline opportunities 214A along with their related engagement and execution timeline data 214B) as input to the pipeline risk analyzer module 131 and obtain output pipeline corrective recommendations 123 that includes the list of detected risky sales pipeline opportunities 215A and the recommended pipeline corrective actions 215C to be performed in specific to the risks identified on the risky sales pipeline opportunities 215A. The pipeline compliancer module 141 further causes the online based sales or CRM platform 120 to present the pipeline corrective recommendations 123 to the users of online based sales or CRM platform 120 via client interface 111. Some operations of pipeline compliancer module 141 are described in detail below with respect to Figure 3.
It should be noted that in some other embodiments, the functions of server machines 130, 140 or online based sales or CRM platform 120 may be provided by a fewer number of machines. For example, in some embodiments server machines 130 and 140 may be integrated into a single machine. In addition, in some embodiments one or more server machines 130 and 140 may be integrated into the online based sales or CRM platform 120.
In general, the functions described in one embodiment as being performed by the online based sales or CRM platform 120, server machine 130, or server machine 140 can also be performed on the client devices 110A through 110Z in other embodiments, if appropriate. In addition, the functionality attributed to a particular component can be performed by different or multiple components operating together. The online based sales or CRM platform 120, server machine 130, or server machine 140 can also be accessed as a service provided to other systems or devices through appropriate application programming interfaces, and thus is not limited to use in websites.
Referring now to FIG. 2, an exemplary embodiment of a pipeline risk analyzer 131 adapted to detect the risky sales pipeline opportunities 215A out of the accessed sales pipeline opportunities 214A according to one embodiment of the invention is shown. FIG. 2 shows pipeline risk analyzer 131 receives the pipeline risk analysis input data 214 as input that includes accessed sales pipeline opportunities 214A and their related sales engagements and execution timelines data 214B.
FIG. 2 shows that the pipeline risk analysis input data 214 received as input are sent to a pipeline risk detector module 204, as indicated by an arrow 202. In general, the pipeline risk detector module 204 analyzes the accessed sales pipeline opportunities 214A for the pipeline compliance risk levels of one or more performance standards in the context of sales engagement and execution activities. For example, the pipeline risk detector module 204 may analyze the accessed sales pipeline opportunities 214A for the pipeline compliance risk levels of one or more performance standards in the context of sales engagement (i.e call, email, online session or in-person visit) and execution behavior (i.e customers follow ups, sales tasks progressions) covering the activities such as responding customer email that expects response on time, immediate callback when customer calls are missed, quick or immediate first response for the new opportunities generated, frequent or adequate successive customer follow ups or engagements, on time stage to stage progression etc. Non-compliance of one or more performance standards are indicative of the risk nature (i.e stagnancy or at risk of loss) of the accessed sales pipeline opportunities 214A.
In one exemplary embodiment of the invention, pipeline risk detector module 204 may be a database or a text file representing a set of pipeline performance rules comprising all the identified sales engagement or execution activities and their performance weightage. Each rule will represent a specific likelihood sales engagement or execution activity and will comprise a text representing the sales engagement or execution activity and a numerical value representing the expected performance weightage or standard. For example, if maintaining earliest response time for the first time response or engagement with customer is identified as one of the sales engagement or execution activities and the predicted weightage is 1 hr (i.e the response time within which the first response should happen), then the rule is created something similar to {“First Response Time”, “1 Hour”}.
A wide variety of training techniques may be utilized to create pipeline risk detector module 204. As one particular example, predictive analytics techniques may be utilized to create sales pipeline risk detector module 204. Predictive analytics techniques may internally make use of combination of statistical, optimization or machine language based regression, pattern and prediction modeling to perform the pipeline compliance risk analysis on the sales pipeline opportunities 122, analyzing the pipeline compliance risk levels of one or more performance standards in the context in the context of performance activities related to sales engagement and execution behavior.
The pipeline risk detector module 204 trained with training samples out of historical sales pipeline opportunities data both won and lost by predictive analytics techniques may be able to predict the performance weightage for each identified engagement or sales execution activities. The performance weightage predicted serves as a performance standard in which ambit the specific sales engagement or execution activity has to be carried out to achieve the performance objective (i.e to be on track to achieve the winnability). And this performance weightage is an optimization value usually in the numeric form defining the performance standard (i.e boundary, limit or timeline related to the activity execution).Some examples of pipeline performance activities are responding customer email that expects response on time, immediate callback when customer calls are missed, quick or immediate first response for the new opportunities generated, frequent or adequate successive customer follow ups or engagements, on time stage to stage progression etc. And as an example, for a sales engagement or execution activity such as responding customer emails on time, the performance weightage can be predicted something similar to 30 minutes, where it denotes that it would be a better practice or standard to respond to every customer email that expects a response within 30 minutes to keep that sales opportunity in better track of winnability. And as an example for adequate successive customer follow ups, the performance weightage can be predicted something similar to 3 days, where it denotes that it would be a better performance standard to regularly keep in touch with every active customer in an interval of 3 days to avoid losing them.
In one embodiment of the invention, pipeline risk detector module 204 may examine each of the accessed sales pipeline opportunities 214A along with their related sales engagements and execution timelines data 214B against the predicted set of performance standard rules set to assess the pipeline compliance risk level in the context sales engagement & execution. If any non-compliance of one or more performance standards are detected for an accessed sales pipeline opportunity 214A, then pipeline risk detector module 204 identifies the specific sales pipeline opportunity as a risky one.
All the identified risk natured sales pipeline opportunities 215A out of the accessed sales pipeline opportunities 214A due to the non-compliance of one or more performance standards (in the context of sales engagement& execution) by the pipeline risk detector module 204 are sent to the recommendation generator module 208, as indicated by an arrow 206. The pipeline risk detector module 204 also sends details on the non-compliant performance factors 215B (inclusive of related sales engagements& execution timelines data 214B) for each risk identified sales pipeline opportunities 215A to the recommendation generator module 208.
Based on the data received on the non-compliant performance factors 215B (inclusive of related sales engagements & execution timelines data 214B) for each of the risk identified sales pipeline opportunities 215A, the recommendation generator module 208, generates the pipeline corrective actions 215C. In embodiments, output pipeline corrective recommendations 123 of the recommendation generator module 208 includes the list of detected risky sales pipeline opportunities 215A and the recommended pipeline corrective actions 215C to be performed in specific to the risks identified on the risky sales pipeline opportunities 215A. The pipeline corrective actions 215C are in the form of simple sentences that can be easily understood by the users of an online based sales or CRM platform 120. For example, for a risk identified sales opportunity due to non-compliance of performance standards related to email engagement, such as an email has not been responded on suggested timeframe, then a recommendation provided will be something similar to “Intelligence recommends responding to customer emails within 1 hour increases the lead conversion probability. Please follow-up (Email arrived: 30 minutes back)”. And in another example, for a newly generated opportunity which has been identified as a risky one, if the first engagement has not been attended on suggested timeframe leading to non-compliance of performance standard related to first engagement, then the recommendations provided will be something similar to “Intelligence recommends reaching customer for the first engagement within 2 hours increases the lead conversion probability. Please follow-up (Lead arrived: 14 hours 8 minutes back).”
FIG. 2 further shows an exemplary update module 210 which provides update data 212 to pipeline risk analyzer 131. Particularly, the update module 210 may update pipeline risk analyzer 131 with an updated pipeline risk detector module 204. Updating pipeline risk analyzer 131 might be necessary because the derived performance standards (i.e the predicted performance weightage in which ambit the specific sales engagement or execution activity expected to be carried out to achieve the performance objective) of the identified performance activities related to sales engagement and execution behavior may change over time. In today’s rapidly changing sales environment, customers constantly look out for instant or immediate response or attention on their queries, problems faced, thus there will be definitely change in the expected sales engagement and pipeline execution behavioral standards. If pipeline risk analyzer 131 does not have updated information on the updated expectation levels of modern day customers, pipeline risk analyzer 131 might not be able to accurately surface out the risky sales pipeline opportunities 215A out of the accessed sales pipeline opportunities 214A. By periodically e.g., once in six months updating pipeline risk analyzer 131 with an updated pipeline risk detector module 204, pipeline risk analyzer 131 may be able to remain accurate in detecting the risky sales pipeline opportunities 215A out of the accessed sales pipeline opportunities 214A.
FIG. 3 is an exemplary flow diagram illustrating process flow for detecting risky sales pipeline opportunities 215A (those are stagnant or at the risk of loss) out of on-process sales pipeline opportunities 122 in an online based sales or Customer Relationship Management (CRM) platform 120 and recommend pipeline corrective actions 215C to be performed by the users of an online based sales or CRM platform 120 in specific to the risks detected on the risky sales pipeline opportunities 215A according to one embodiment of the invention. At 302, a pipeline compliance module 141 receives a notification on access of one or more of sales pipeline opportunities 122 being processed in an online based sales or CRM platform 120 by its sales user for eg. an user of online based sales or CRM platform 120. At 304, the pipeline compliancer module 141 collects the pipeline risk analysis input data 214 from the online based sales or CRM platform 120 that includes accessed sales pipeline opportunities 214A and their related sales engagements and execution timelines data 214B. At 306, the pipeline compliancer module 141 provides the collected pipeline risk analysis input data 214 as input to the pipeline risk analyzer 131 to detect the risky sales pipeline opportunities 215A out of accessed sales pipeline opportunities 214A. At 308, the pipeline compliancer module 141 obtains pipeline corrective recommendations 123 as output from the pipeline risk analyzer 131 that comprises of (i) the list of detected risky sales pipeline opportunities 215A and (ii) the recommended pipeline corrective actions 215C to be performed in specific to the risks identified on the risky sales pipeline opportunities 215A. Proceeding to 310, in response to the output received, the pipeline compliancer module 141 present pipeline corrective recommendations 123 to the users of online based sales or CRM platform 120 that comprises of list of detected risky sales pipeline opportunities 215A and recommended pipeline corrective actions 215C to be performed in specific to the risks detected on the risky sales pipeline opportunities 215A.
FIG. 4 is a block diagram illustrating an exemplary computer system 400, in accordance with an embodiment of the present disclosure. The computer system 400 executes one or more sets of instructions that cause the machine to perform any one or more of the methodologies discussed herein. Set of instructions, instructions, and the like may refer to instructions that, when executed cause computer system 400 to perform one or more operations of pipeline risk analyzer 131 or pipeline compliancer module 141. The machine may operate in the capacity of a server 130-140 or a client device 110A-110Z in client-server network 101 environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a personal digital assistant (PDA), a mobile telephone, a web application, a server, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term "machine" shall also be taken to include any collection of machines that individually or jointly execute the sets of instructions to perform any one or more of the methodologies discussed herein.
The computer system 400 includes a processing device 402, a main memory 404 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM), etc.), a static memory 406 (e.g., flash memory, static random access memory (SRAM), etc.), and a data storage device 416, which communicate with each other via a bus 408.
The processing device 402 represents one or more processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device 402 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processing device implementing other instruction sets or processing devices implementing a combination of instruction sets. The processing device 402 may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device 402 is configured to execute instructions of the system architecture 100 and the pipeline risk analyzer 131 or pipeline compliancer module 141 for performing the operations discussed herein.
The computer system 400 may further include a network interface device 422 that provides communication with other machines over a network 101, such as a local area network (LAN), an intranet, an extranet, or the Internet. The computer system 400 also may include a display device 410 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device 412 (e.g., a keyboard), a cursor control device 414 (e.g., a mouse), and a signal generation device 420 (e.g., a speaker).
The data storage device 416 may include a non-transitory computer-readable storage medium 424 on which is stored the sets of instructions of the system architecture 100 and of pipeline risk analyzer 131 or pipeline compliancer module 141 embodying any one or more of the methodologies or functions described herein. The sets of instructions of the system architecture 100 and of pipeline risk analyzer 131 or of pipeline compliancer module 141 may also reside, completely or at least partially, within the main memory 404 and/or within the processing device 402 during execution thereof by the computer system 400, the main memory 404 and the processing device 402 also constituting computer-readable storage media. The sets of instructions may further be transmitted or received over the network 101 via the network interface device 422.
While the example of the computer-readable storage medium 424 is shown as a single medium, the term "computer-readable storage medium" can include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the sets of instructions. The term "computer-readable storage medium" can include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure. The term "computer-readable storage medium" can include, but not be limited to, solid-state memories, optical media, and magnetic media.
In the foregoing description, numerous details are set forth. It will be apparent, however, to one of ordinary skill in the art having the benefit of this disclosure, that the present disclosure may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the present disclosure.
It may be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise, it is appreciated that throughout the description, discussions utilizing terms such as "providing", "receiving", "adjusting", "generating", "obtaining", "determining", “detecting”, or the like, refer to the actions and processes of a computer system 400, or similar electronic computing device, that manipulates and transforms data represented as physical (e.g., electronic) quantities within the computer system 400 memories or registers into other data similarly represented as physical quantities within the computer system 400 memories or registers or other such information storage, transmission or display devices.
The present disclosure also relates to a computer system 400 for performing the operations herein. This system 400 may be specially constructed for the required purposes, or it may include a general purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but not limited to, any type of disk including a floppy disk, an optical disk, a compact disc read-only memory (CD-ROM), a magnetic-optical disk, a read-only memory (ROM), a random access memory (RAM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic or optical card, or any type of media suitable for storing electronic instructions.
The words "example" or "exemplar}'" are used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as "example' or "exemplar^'" is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, use of the words "example" or "exemplary" is intended to present concepts in a concrete fashion. As used in this application, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless specified otherwise, or clear from context, "X includes A or B" is intended to mean any of the natural inclusive permutations. That is, if X includes A; X includes B; or X includes both A and B, then "X includes A or B" is satisfied under any of the foregoing instances. In addition, the articles "a" and "an" as used in this application and the appended claims may generally be construed to mean "one or more" unless specified otherwise or clear from context to be directed to a singular form. Moreover, use of the term "an embodiment" or "one embodiment" throughout is not intended to mean the same embodiment or embodiment unless described as such.
For simplicity of explanation, methods herein are depicted and described as a series of acts or operations. However, acts in accordance with this disclosure can occur in various orders and/or concurrently, and with other acts not presented and described herein. Furthermore, not all illustrated acts may be required to implement the methods in accordance with the disclosed subject matter. In addition, those skilled in the art will understand and appreciate that the methods could alternatively be represented as a series of interrelated states via a state diagram or events. Additionally, it should be appreciated that the methods disclosed in this specification are capable of being stored on an article of manufacture to facilitate transporting and transferring such methods to computing devices. It is to be understood that the above description is intended to be illustrative, and not restrictive. Other embodiments will be apparent to those of skill in the art upon reading and understanding the above description. The scope of the disclosure may, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.
, Description:SYSTEM AND METHOD FOR AUTOMATED SALES PIPELINE RISK ANALYSIS AND CORRECTIVE RECOMMENDATIONS

TECHNICAL FIELD
The present disclosure relates to a system 400 and a method for detecting risky sales pipeline opportunities 215A, those are stagnant or at the risk of loss, out of the on-process sales pipeline opportunities 122 in an online based sales or Customer Relationship Management (CRM) platform 120. More specifically, the disclosure relates to pipeline corrective recommendations 123 responsive to the risks attributed to the sales pipeline opportunities 122 currently being processed in an online based sales or CRM platform 120.
BACKGROUND
In sales / CRM environment, currently prediction systems are available either for analyzing historical sales pipeline data for intelligence on past performance or sales pipeline prediction categories such as predicting the conversion rate, winnability or loss of opportunities, pre-defined sales tasks or actions recommended on sales stages, thus helping sales managers or sales representatives to predict sales future (i.e winnability of a deal, churning rate, sales revenue forecasting etc) or identifying the sales process or sequence of steps might be needed to achieve the sales objectives.
These systems informs sales team on what has happened, what is going to happen on the sales pipeline or what predefined sales steps or actions may lead to achieve future predictions, but if the opportunities in the pipeline are stagnant or not progressing as expected line on the prediction, the sales team is stuck with no answers or intelligence on what factors are contributing to the stagnancy or required to achieve the predicted sales objectives.
Basically these systems do not provide any recommendations on what behavioral patterns on sales engagement or pipeline execution are holding back the sales opportunities. For example, known systems would help to learn the past conversion rate, predict what would be the conversion rate on the new incoming leads or the set of steps (i.e, making phone call using predefined call script followed by a welcome email and then an online demonstration etc...) to achieve predicted conversion, but does not provide recommendations such as what behavioral factors of sales team on sales engagement or pipeline execution can improve the convertibility rate or causing the loss of opportunities. Hence if the opportunities in the pipeline are stagnant or not progressing as expected in the line of prediction, the sales team is stuck with no answers on what to do with the past or future prediction intelligence or ways to clarify predictive data to improve sales.
Basically the sales team doesn't know what behavioral problems on sales engagement or pipeline execution (such as not returning unanswered calls in specific time, delayed first responses after the opportunity generated, not crossing a particular sales stage in specific time etc..) that could be holding back sales or what behavioral patterns on sales engagement or execution (such as the response time to be adhered for email responses, timeline between successive customer follow ups) has to be followed up to improve the convertibility rate or avoiding the loss of opportunities.
Until unless they get some intelligence on such patterns, problem causes and the remedy best practice or actions, they will not be able to optimize or tweak the current sales engagement or pipeline execution to keep the sales pipeline healthy and on track to achieve the better pipeline objective like more winnability or conversion rate. Thus either the sales persons are not able to break the stagnancy of the sales pipeline opportunities 122 that are at risk or avoid the risk of losing those most of the time or they do not get any guidance from their sales managers on time to handle these problems.
Added to this, these expected intelligence data has to be provided on real time to the sales team unlike on other prediction systems, where the prediction analysis are either in the form of post-mortem analysis or pre-forecast analysis. Also manual analysis and efforts to keep vigil on the sales pipeline all the time on various parameters not only found to be tedious but looks to be almost not possible considering the volume of the sales pipeline data being processed in an online based sales or CRM platform 120.
US20040064360A1 discloses a method and apparatus that includes assessing the capacity and effectiveness of sales pipelines by assigning values to a lead as it progresses through a sales pipeline. The assigned values can be based upon a number of influencing factors, wherein the influencing factor is selected from the group consisting of a close percentage, days to close, quota attainment and monetary value of an opportunity or group of opportunities. These values improve Sale's and Marketing's ability to efficiently process and manage lead flows, as they can be used to access the health, capacity and effectiveness of a sales pipeline.
US 20140207533 A1 discloses a method that teaches and enable the tracking of the number of new prospects, the number of phone calls made, the number of phone invites made, the number in-person invites made, the number of appointments made, the number of confirmations, the number of prospects who attended appointments, the number of second meetings Scheduled and held, and the number of new customers or distributors acquired. The orders of activities tracked is the same as those listed, and is displayed on a computer Screen or in paper in that specific order for each day of the week, with goals and total columns to track a user's actions and Success. A second area tracks telephone calls made and use a three legend key to track the number of calls made (represented by a slash) and the number of appointments set during calls (represented by an encircled slash).
US7546248B2 claims a method and system for managing sales activities of sales associates, the method comprising: (a) a server computer receiving sales information from a remote computer; (b) the server computer processing the sales information; and (c) generating content-based advice for individual sales associates based on respective sales information.
US 20100114663 A1 discloses a system that uses demographic data to generate a sales prospect recommendation that includes a product recommendation with a probability that the sale will close, and may include an estimated time to close the sale and projected revenue. The system imports customer data including past purchasing data and demographic data for a plurality of customers. The system can then generate a predictive model by training the model using the past purchasing data and the demographic data. When queried for a sales prospect recommendation, the system responds to the query with at least one sales prospect recommended by the predictive model.
But none of the prior art address the compliance risk on the sales pipeline opportunities 122 in the context of performance activities related to sales engagement and execution behavior such as responding customer email that expects response on time, immediate callback when customer calls are missed, quick or immediate first response for the new opportunities generated, frequent or adequate successive customer follow ups or engagements, on time stage to stage progression etc, in real time and provide pipeline corrective recommendations123 on on-process sales pipeline opportunities 122.
In the light of the aforementioned discussion, there exists a need for a system and method that would overcome or ameliorate the above mentioned limitations. What is needed is a system and method to automatically guide the sales team with recommended actions highlighting the problem cause or best practice pattern in specific to the sales engagement or execution behavior to keep all their live / current sales opportunities in the pipeline always on track to winnability.
SUMMARY
The following is a simplified summary of the disclosure in order to provide a basic understanding of some aspects of the disclosure. This summary is not an extensive overview of the disclosure. It is intended to neither identify key or critical elements of the disclosure, nor delineate any scope of the particular implementations of the disclosure or any scope of the claims. Its sole purpose is to present some concepts of the disclosure in a simplified form as a prelude to the more detailed description that is presented later.
Embodiments of the invention effectively provides a novel system and method to detect risky sales pipeline opportunities 215A (those are stagnant or at the risk of loss) out of on-process sales pipeline opportunities 122 in an online based sales or Customer Relationship Management (CRM) platform 120 and pipeline corrective recommendations123 to the users of an online based sales or CRM platform 120 to keep sales pipeline on track of winnability.
Briefly described, a method employing aspects of the invention detects access of sales pipeline opportunities 122 by their sales user for eg. users of an online based sales or CRM platform 120. The method includes analyzing the accessed sales pipeline opportunities 214A out of on-process sales pipeline opportunities 122 for the compliance of one or more performance standards in the context of sales engagement (i.e call, email, online session or in-person visit) and execution behavior (i.e customers follow ups, sales tasks progressions). Non-compliance of one or more performance standards are indicative of the risk nature (i.e stagnancy or at risk of loss) of the accessed sales pipeline opportunities 214A. The method also includes detecting the risky sales pipeline opportunities 215A out of accessed sales pipeline opportunities 214A as a function of analyzed performance standards in the context of sales engagement and execution behavior. The method further includes generating recommendations for pipeline corrective actions 215C to be performed in specific to the detected risky sales pipeline opportunities 215A which are at risk based on the non-compliance factors. The method also includes presenting the pipeline corrective recommendations 123 to the users of online based sales or CRM platform 120 that comprises of detected risky sales pipeline opportunities 215A and pipeline corrective actions 215C to be performed in specific to the detected risky sales pipeline opportunities 215A.
In additional embodiments, one or more processing devices for performing the operations of the above described embodiments are disclosed. In additional embodiments, a system 400 is disclosed, the system 400 comprising a memory; and a processing device, coupled to the memory, for performing operations comprising the method according to any one of the above described implementations.
BRIEF DESCRIPTION OF DRAWINGS
The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles, in which like reference numerals generally refer to the same parts throughout the drawings.
FIG 1: Illustration of System Architecture 100
FIG 2: A block diagram of an exemplary embodiment of a sales pipeline risk analyzer 131
FIG 3: An exemplary diagram illustrating a process according to one embodiment of the invention
FIG.4: A block diagram illustrating one example of a suitable computing system 400
DETAILED DESCRIPTION
Exemplary embodiments are described with reference to the accompanying drawings. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope and spirit being indicated by the following claims. Additional illustrative embodiments are listed below.
FIG. 1 illustrates an example of system architecture 100, in accordance with one embodiment of the present disclosure.
The system architecture 100 (also referred to as "system" herein) includes online based sales or CRM platform 120, one or more server machines 130 through 140, a data store 102, and client devices 110A-110Z connected to a network 101.
In embodiments, network 101 may include a public network (e.g., the Internet), a private network (e.g., a local area network (LAN) or wide area network (WAN)), a wired network (e.g., Ethernet network), a wireless network (e.g., an 802.11 network or a Wi-Fi network), a cellular network (e.g., a Long Term Evolution (LTE) network), routers, hubs, switches, server computers, and/or a combination hereof.
In embodiments, data store 102 is a persistent storage that is capable of storing sales pipeline opportunities 122, which will be used throughout the specification to illustrate the embodiments, as well as data structures to tag, organize, and index the above data items. Data store 102 may be hosted by one or more storage devices, such as main memory, magnetic or optical storage based disks, tapes or hard drives, NAS, SAN, and so forth. In some embodiments, data store 102 may be a network-attached file server, while in other embodiments data store 102 may be some other type of persistent storage such as an object-oriented database, a relational database, and so forth, that may be hosted by online based sales or CRM platform 120 or one or more different machines coupled to the online based sales or CRM platform 120 via the network 101.
The client devices 110A-110Z may each include computing devices such as personal computers (PCs), laptops, mobile phones, smart phones, tablet computers, netbook computers, network-connected televisions, etc. In some embodiments, client devices 110A through 110Z may also be referred to as "user devices".
In embodiments, each client device 110A-110Z includes a client interface 111. In one embodiment, the client interface 111 may be applications that allow users to view or access content, such as sales pipeline opportunities 122, web pages, documents, etc. For example, the client interface 111 may be a web browser that can access, retrieve, present, and/or navigate content (e.g., web pages such as Hyper Text Markup Language (HTML) pages, sales pipeline opportunities 122, etc.) served by a web server. The client interface 111 may render, display, and/or present the content (e.g., a web page) to a user. In another example, the client interface 111 may be a standalone application (e.g., a mobile application or app) that allows users to view content (e.g., sales pipeline opportunities 122, web pages, documents, etc.). According to aspects of the disclosure, the client interface 111 may be an online based sales or CRM application for users to record, edit, and/or upload data specific to customers and sales transactions on online based sales or CRM platform 120. As such, the client interface 111 may be provided to the client devices 110A-110Z by the server machine 130-140 or online based sales or CRM platform 120.
In one embodiment, the online based sales or CRM platform 120 or server machines 130-140 may be one or more computing devices (such as a rackmount server, a router computer, a server computer, a personal computer, a mainframe computer, a laptop computer, a tablet computer, a desktop computer, etc.), data stores102 (e.g., hard disks, memories, databases), networks, software components, and/or hardware components that may be used to provide a user with access to content specific to customer, sales pipeline opportunities 122 and pipeline corrective recommendations 123. For example, the online based sales or CRM platform 120 may allow a sales user to view, add, upload, search for, or comment on customer data 121, sales pipeline opportunities 122 and pipeline corrective recommendations 123. The online based sales or CRM platform 120 may also include a website (e.g., a web page) or application back-end software that may be used to provide a user with access to the data related to customers, sales pipeline opportunities 122 and pipeline corrective recommendations 123.
In embodiments of the disclosure, a "user" may be represented as a single individual or a sales team. For example, the user could be a sales person or executive who makes use of the online based sales or CRM platform 120 for his day to day sales execution activities.
In some embodiments, online based sales or CRM platform 120 present pipeline corrective recommendations 123, to a user via client interface 111. The client interface 111 configured and has a designated screen space to present the pipeline corrective recommendations 123. Pipeline corrective recommendations 123 may be presented as a set of indicators (e.g., interface component, electronic message, recommendation feed, etc.) that includes the list of detected risky sales pipeline opportunities 215A and the recommended pipeline corrective actions 215C to be performed in specific to the risks identified on the risky sales pipeline opportunities 215A. In one embodiment, the client interface 111 can involve a home page of the client application accessed by the user of online based sales or CRM platform 120.
In one embodiment, the user when accesses or login to the client application, the list of risk identified sales opportunities 215A out of the current active sales pipeline opportunities 122 owned by the user along with their recommended pipeline corrective actions 215C is highlighted explicitly.
In one embodiment, the pipeline corrective recommendations 123 may be a recommendation for one or more sales pipeline opportunities 122 currently being accessed by the user in an online based sales or CRM platform 120.
In one embodiment, the server machine 130 includes a pipeline risk analyzer 131. The pipeline risk analyzer 131 analyzes and detects risky sales pipeline opportunities 215A (those are stagnant or at the risk of loss) out of the on-process sales pipeline opportunities 122 in an online based sales or Customer Relationship Management (CRM) platform 120. In general, pipeline risk analyzer 131 may include a pipeline risk detector module 204, which analyzes the accessed sales pipeline opportunities 214A for the pipeline compliance risk levels of one or more performance standards in the context of sales engagement and execution activities.
One embodiment of the invention advantageously utilizes predictive analytics techniques to create pipeline risk detector module 204. For example, a pipeline risk detector module 204 may be created to analyze the sales pipeline opportunities 122 for the pipeline compliance risk levels of one or more performance standards in the context of sales engagement (i.e call, email, online session or in-person visit) and execution behavior (i.e customers follow ups, sales tasks progressions).
Predictive analysis techniques may create a pipeline risk detector module 204 by using combination of statistical, optimization or machine language based regression, pattern and prediction modeling to perform the pipeline compliance risk analysis on the sales pipeline opportunities 122 in the context of performance activities related to sales engagement and execution behavior such as responding customer email that expects response on time, immediate callback when customer calls are missed, quick or immediate first response for the new opportunities generated, frequent or adequate successive customer follow ups or engagements, on time stage to stage progression etc.
Pipeline compliance risk analysis involves a set of procedures to assess the risk nature of the current pipeline opportunities risk nature by measuring the adoption level of certain performance standards being achieved linked to the engagement or execution activities related to the sales pipeline transactions. These performance standards would be pre-defined and predicted from historical sales pipeline opportunities data and usually indicate boundary, limit or timeline related to the activities execution. And these performance standards are usually represented in the numeric form specifying the optimized performance weightage. For example, considering the activity of providing immediate first response to the newly generated sales pipeline opportunities 122, the risk nature would be assessed based on the timeline taken by the sales users to engage the pipeline opportunities for the first time. The first attention could be made via any communication channel like email, voice, online session or in-person visits. And the performance standard that would be predicted for this activity context would be the timeline within which the activity has to be executed. Hence for a specific sales pipeline opportunity 122, at first the exact time at which the first engagement happened via any of the channels has to be traced and then has to be validated whether the timeline falls in the ambit of the optimized value recommended by the performance standard predicted related to this specific activity. Based on the result of validation, the risk nature will be determined.
Pipeline compliance risk analysis being implemented to surface out the risky sales pipeline opportunities 215A and predict the risk factors behind those detected risky sales pipeline opportunities 215A is not a straightforward process such as one followed in regular prediction systems (i.e Past performance predictions, Future forecast, Conversion rate predictions etc) where the readily available historical data points are directly analyzed to predict the results such as winnability of deals, churning rate, sales revenue forecasting etc. Pipeline Compliance risk analysis is a multi stage process involving first the identification of right sales engagement or execution activities that are likelihood source or cause for better or poor performance of the sales opportunities. And then the performance standard in which ambit these identified sales engagement or execution activity has to be carried out to achieve the performance objective (i.e to move out of the risk stage and to be on track to achieve the winnability) has to be predicted.
Again the prediction of these performance standards are not a straightforward analysis, as the data points will not be readily available and hence the new or additional data points that need to be collected specific to these identified performance activities has to be derived. These data points may be singular or plural based on the complexity of the activity related. All the identified data points that are either readily available or newly added will be provisioned to be automatically captured and tracked with no manual feed required. And then the right prediction systems have to be applied on these auto captured data patterns to arrive at the performance weightage or the standard to finally surface the risky sales pipeline opportunities 215A along with predicting the source risk factors behind them. For an example of the sales call engagement, reaching back customers whose calls are missed or unanswered due to some reasons in a specific turnaround time would avoid loss of opportunities. Likewise for an example of sales email engagement, responding back to an important customer's email that expects response within a specific time frame will improve the chance of winning those sales opportunities. The data points necessary to measure the timeframe taken to answer the missed calls and timeline in which the response expected emails are replied have to be introduced if not readily available within the sales data with provisions to be auto captured. And then the optimization values of these measured data points from the historical won and lost sales opportunities data will be predicted to arrive at the performance weightage. Once derived, these optimized performance weightages, will serve as a future guidelines for the salesperson on engagement activities involving the missed call response and important email responses related to any sales opportunities.
Without such performance weightages known, the salesperson would not be able to proactively be alert to attend the sales pipeline opportunities 122 which need immediate response on the calls or emails on stipulated time. Accordingly, the present invention advantageously utilizes a number of factors to identify such behavioral patterns on sales engagement or pipeline execution that can improve the convertibility rate or cause the loss of sales pipeline opportunities 122 along with predicting the optimized performance standards that need to be followed up. Based on its pipeline compliance risk analysis on the accessed sales pipeline opportunities 214A out of the on-process sales pipeline opportunities 122, pipeline risk analyzer 131 may determine the risky sales pipeline opportunities 215A and generate recommendations for pipeline corrective actions 215C to be performed in specific to the risk detected sales pipeline opportunities 215A. An exemplary operation of pipeline risk analyzer 131 in accordance with one embodiment of the invention is discussed hereinafter in connection with Figures 2 and 3.
Server machine 140 includes pipeline compliance module 141 that provides pipeline risk analysis input data 214 (that includes accessed sales pipeline opportunities 214A along with their related engagement and execution timeline data 214B) as input to the pipeline risk analyzer module 131 and obtain output pipeline corrective recommendations 123 that includes the list of detected risky sales pipeline opportunities 215A and the recommended pipeline corrective actions 215C to be performed in specific to the risks identified on the risky sales pipeline opportunities 215A. The pipeline compliancer module 141 further causes the online based sales or CRM platform 120 to present the pipeline corrective recommendations 123 to the users of online based sales or CRM platform 120 via client interface 111. Some operations of pipeline compliancer module 141 are described in detail below with respect to Figure 3.
It should be noted that in some other embodiments, the functions of server machines 130, 140 or online based sales or CRM platform 120 may be provided by a fewer number of machines. For example, in some embodiments server machines 130 and 140 may be integrated into a single machine. In addition, in some embodiments one or more server machines 130 and 140 may be integrated into the online based sales or CRM platform 120.
In general, the functions described in one embodiment as being performed by the online based sales or CRM platform 120, server machine 130, or server machine 140 can also be performed on the client devices 110A through 110Z in other embodiments, if appropriate. In addition, the functionality attributed to a particular component can be performed by different or multiple components operating together. The online based sales or CRM platform 120, server machine 130, or server machine 140 can also be accessed as a service provided to other systems or devices through appropriate application programming interfaces, and thus is not limited to use in websites.
Referring now to FIG. 2, an exemplary embodiment of a pipeline risk analyzer 131 adapted to detect the risky sales pipeline opportunities 215A out of the accessed sales pipeline opportunities 214A according to one embodiment of the invention is shown. FIG. 2 shows pipeline risk analyzer 131 receives the pipeline risk analysis input data 214 as input that includes accessed sales pipeline opportunities 214A and their related sales engagements and execution timelines data 214B.
FIG. 2 shows that the pipeline risk analysis input data 214 received as input are sent to a pipeline risk detector module 204, as indicated by an arrow 202. In general, the pipeline risk detector module 204 analyzes the accessed sales pipeline opportunities 214A for the pipeline compliance risk levels of one or more performance standards in the context of sales engagement and execution activities. For example, the pipeline risk detector module 204 may analyze the accessed sales pipeline opportunities 214A for the pipeline compliance risk levels of one or more performance standards in the context of sales engagement (i.e call, email, online session or in-person visit) and execution behavior (i.e customers follow ups, sales tasks progressions) covering the activities such as responding customer email that expects response on time, immediate callback when customer calls are missed, quick or immediate first response for the new opportunities generated, frequent or adequate successive customer follow ups or engagements, on time stage to stage progression etc. Non-compliance of one or more performance standards are indicative of the risk nature (i.e stagnancy or at risk of loss) of the accessed sales pipeline opportunities 214A.
In one exemplary embodiment of the invention, pipeline risk detector module 204 may be a database or a text file representing a set of pipeline performance rules comprising all the identified sales engagement or execution activities and their performance weightage. Each rule will represent a specific likelihood sales engagement or execution activity and will comprise a text representing the sales engagement or execution activity and a numerical value representing the expected performance weightage or standard. For example, if maintaining earliest response time for the first time response or engagement with customer is identified as one of the sales engagement or execution activities and the predicted weightage is 1 hr (i.e the response time within which the first response should happen), then the rule is created something similar to {“First Response Time”, “1 Hour”}.
A wide variety of training techniques may be utilized to create pipeline risk detector module 204. As one particular example, predictive analytics techniques may be utilized to create sales pipeline risk detector module 204. Predictive analytics techniques may internally make use of combination of statistical, optimization or machine language based regression, pattern and prediction modeling to perform the pipeline compliance risk analysis on the sales pipeline opportunities 122, analyzing the pipeline compliance risk levels of one or more performance standards in the context in the context of performance activities related to sales engagement and execution behavior.
The pipeline risk detector module 204 trained with training samples out of historical sales pipeline opportunities data both won and lost by predictive analytics techniques may be able to predict the performance weightage for each identified engagement or sales execution activities. The performance weightage predicted serves as a performance standard in which ambit the specific sales engagement or execution activity has to be carried out to achieve the performance objective (i.e to be on track to achieve the winnability). And this performance weightage is an optimization value usually in the numeric form defining the performance standard (i.e boundary, limit or timeline related to the activity execution).Some examples of pipeline performance activities are responding customer email that expects response on time, immediate callback when customer calls are missed, quick or immediate first response for the new opportunities generated, frequent or adequate successive customer follow ups or engagements, on time stage to stage progression etc. And as an example, for a sales engagement or execution activity such as responding customer emails on time, the performance weightage can be predicted something similar to 30 minutes, where it denotes that it would be a better practice or standard to respond to every customer email that expects a response within 30 minutes to keep that sales opportunity in better track of winnability. And as an example for adequate successive customer follow ups, the performance weightage can be predicted something similar to 3 days, where it denotes that it would be a better performance standard to regularly keep in touch with every active customer in an interval of 3 days to avoid losing them.
In one embodiment of the invention, pipeline risk detector module 204 may examine each of the accessed sales pipeline opportunities 214A along with their related sales engagements and execution timelines data 214B against the predicted set of performance standard rules set to assess the pipeline compliance risk level in the context sales engagement & execution. If any non-compliance of one or more performance standards are detected for an accessed sales pipeline opportunity 214A, then pipeline risk detector module 204 identifies the specific sales pipeline opportunity as a risky one.
All the identified risk natured sales pipeline opportunities 215A out of the accessed sales pipeline opportunities 214A due to the non-compliance of one or more performance standards (in the context of sales engagement& execution) by the pipeline risk detector module 204 are sent to the recommendation generator module 208, as indicated by an arrow 206. The pipeline risk detector module 204 also sends details on the non-compliant performance factors 215B (inclusive of related sales engagements& execution timelines data 214B) for each risk identified sales pipeline opportunities 215A to the recommendation generator module 208.
Based on the data received on the non-compliant performance factors 215B (inclusive of related sales engagements & execution timelines data 214B) for each of the risk identified sales pipeline opportunities 215A, the recommendation generator module 208, generates the pipeline corrective actions 215C. In embodiments, output pipeline corrective recommendations 123 of the recommendation generator module 208 includes the list of detected risky sales pipeline opportunities 215A and the recommended pipeline corrective actions 215C to be performed in specific to the risks identified on the risky sales pipeline opportunities 215A. The pipeline corrective actions 215C are in the form of simple sentences that can be easily understood by the users of an online based sales or CRM platform 120. For example, for a risk identified sales opportunity due to non-compliance of performance standards related to email engagement, such as an email has not been responded on suggested timeframe, then a recommendation provided will be something similar to “Intelligence recommends responding to customer emails within 1 hour increases the lead conversion probability. Please follow-up (Email arrived: 30 minutes back)”. And in another example, for a newly generated opportunity which has been identified as a risky one, if the first engagement has not been attended on suggested timeframe leading to non-compliance of performance standard related to first engagement, then the recommendations provided will be something similar to “Intelligence recommends reaching customer for the first engagement within 2 hours increases the lead conversion probability. Please follow-up (Lead arrived: 14 hours 8 minutes back).”
FIG. 2 further shows an exemplary update module 210 which provides update data 212 to pipeline risk analyzer 131. Particularly, the update module 210 may update pipeline risk analyzer 131 with an updated pipeline risk detector module 204. Updating pipeline risk analyzer 131 might be necessary because the derived performance standards (i.e the predicted performance weightage in which ambit the specific sales engagement or execution activity expected to be carried out to achieve the performance objective) of the identified performance activities related to sales engagement and execution behavior may change over time. In today’s rapidly changing sales environment, customers constantly look out for instant or immediate response or attention on their queries, problems faced, thus there will be definitely change in the expected sales engagement and pipeline execution behavioral standards. If pipeline risk analyzer 131 does not have updated information on the updated expectation levels of modern day customers, pipeline risk analyzer 131 might not be able to accurately surface out the risky sales pipeline opportunities 215A out of the accessed sales pipeline opportunities 214A. By periodically e.g., once in six months updating pipeline risk analyzer 131 with an updated pipeline risk detector module 204, pipeline risk analyzer 131 may be able to remain accurate in detecting the risky sales pipeline opportunities 215A out of the accessed sales pipeline opportunities 214A.
FIG. 3 is an exemplary flow diagram illustrating process flow for detecting risky sales pipeline opportunities 215A (those are stagnant or at the risk of loss) out of on-process sales pipeline opportunities 122 in an online based sales or Customer Relationship Management (CRM) platform 120 and recommend pipeline corrective actions 215C to be performed by the users of an online based sales or CRM platform 120 in specific to the risks detected on the risky sales pipeline opportunities 215A according to one embodiment of the invention. At 302, a pipeline compliance module 141 receives a notification on access of one or more of sales pipeline opportunities 122 being processed in an online based sales or CRM platform 120 by its sales user for eg. an user of online based sales or CRM platform 120. At 304, the pipeline compliancer module 141 collects the pipeline risk analysis input data 214 from the online based sales or CRM platform 120 that includes accessed sales pipeline opportunities 214A and their related sales engagements and execution timelines data 214B. At 306, the pipeline compliancer module 141 provides the collected pipeline risk analysis input data 214 as input to the pipeline risk analyzer 131 to detect the risky sales pipeline opportunities 215A out of accessed sales pipeline opportunities 214A. At 308, the pipeline compliancer module 141 obtains pipeline corrective recommendations 123 as output from the pipeline risk analyzer 131 that comprises of (i) the list of detected risky sales pipeline opportunities 215A and (ii) the recommended pipeline corrective actions 215C to be performed in specific to the risks identified on the risky sales pipeline opportunities 215A. Proceeding to 310, in response to the output received, the pipeline compliancer module 141 present pipeline corrective recommendations 123 to the users of online based sales or CRM platform 120 that comprises of list of detected risky sales pipeline opportunities 215A and recommended pipeline corrective actions 215C to be performed in specific to the risks detected on the risky sales pipeline opportunities 215A.
FIG. 4 is a block diagram illustrating an exemplary computer system 400, in accordance with an embodiment of the present disclosure. The computer system 400 executes one or more sets of instructions that cause the machine to perform any one or more of the methodologies discussed herein. Set of instructions, instructions, and the like may refer to instructions that, when executed cause computer system 400 to perform one or more operations of pipeline risk analyzer 131 or pipeline compliancer module 141. The machine may operate in the capacity of a server 130-140 or a client device 110A-110Z in client-server network 101 environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a personal digital assistant (PDA), a mobile telephone, a web application, a server, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term "machine" shall also be taken to include any collection of machines that individually or jointly execute the sets of instructions to perform any one or more of the methodologies discussed herein.
The computer system 400 includes a processing device 402, a main memory 404 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM), etc.), a static memory 406 (e.g., flash memory, static random access memory (SRAM), etc.), and a data storage device 416, which communicate with each other via a bus 408.
The processing device 402 represents one or more processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device 402 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processing device implementing other instruction sets or processing devices implementing a combination of instruction sets. The processing device 402 may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device 402 is configured to execute instructions of the system architecture 100 and the pipeline risk analyzer 131 or pipeline compliancer module 141 for performing the operations discussed herein.
The computer system 400 may further include a network interface device 422 that provides communication with other machines over a network 101, such as a local area network (LAN), an intranet, an extranet, or the Internet. The computer system 400 also may include a display device 410 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device 412 (e.g., a keyboard), a cursor control device 414 (e.g., a mouse), and a signal generation device 420 (e.g., a speaker).
The data storage device 416 may include a non-transitory computer-readable storage medium 424 on which is stored the sets of instructions of the system architecture 100 and of pipeline risk analyzer 131 or pipeline compliancer module 141 embodying any one or more of the methodologies or functions described herein. The sets of instructions of the system architecture 100 and of pipeline risk analyzer 131 or of pipeline compliancer module 141 may also reside, completely or at least partially, within the main memory 404 and/or within the processing device 402 during execution thereof by the computer system 400, the main memory 404 and the processing device 402 also constituting computer-readable storage media. The sets of instructions may further be transmitted or received over the network 101 via the network interface device 422.
While the example of the computer-readable storage medium 424 is shown as a single medium, the term "computer-readable storage medium" can include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the sets of instructions. The term "computer-readable storage medium" can include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure. The term "computer-readable storage medium" can include, but not be limited to, solid-state memories, optical media, and magnetic media.
In the foregoing description, numerous details are set forth. It will be apparent, however, to one of ordinary skill in the art having the benefit of this disclosure, that the present disclosure may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the present disclosure.
It may be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise, it is appreciated that throughout the description, discussions utilizing terms such as "providing", "receiving", "adjusting", "generating", "obtaining", "determining", “detecting”, or the like, refer to the actions and processes of a computer system 400, or similar electronic computing device, that manipulates and transforms data represented as physical (e.g., electronic) quantities within the computer system 400 memories or registers into other data similarly represented as physical quantities within the computer system 400 memories or registers or other such information storage, transmission or display devices.
The present disclosure also relates to a computer system 400 for performing the operations herein. This system 400 may be specially constructed for the required purposes, or it may include a general purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but not limited to, any type of disk including a floppy disk, an optical disk, a compact disc read-only memory (CD-ROM), a magnetic-optical disk, a read-only memory (ROM), a random access memory (RAM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic or optical card, or any type of media suitable for storing electronic instructions.
The words "example" or "exemplar}'" are used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as "example' or "exemplar^'" is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, use of the words "example" or "exemplary" is intended to present concepts in a concrete fashion. As used in this application, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless specified otherwise, or clear from context, "X includes A or B" is intended to mean any of the natural inclusive permutations. That is, if X includes A; X includes B; or X includes both A and B, then "X includes A or B" is satisfied under any of the foregoing instances. In addition, the articles "a" and "an" as used in this application and the appended claims may generally be construed to mean "one or more" unless specified otherwise or clear from context to be directed to a singular form. Moreover, use of the term "an embodiment" or "one embodiment" throughout is not intended to mean the same embodiment or embodiment unless described as such.
For simplicity of explanation, methods herein are depicted and described as a series of acts or operations. However, acts in accordance with this disclosure can occur in various orders and/or concurrently, and with other acts not presented and described herein. Furthermore, not all illustrated acts may be required to implement the methods in accordance with the disclosed subject matter. In addition, those skilled in the art will understand and appreciate that the methods could alternatively be represented as a series of interrelated states via a state diagram or events. Additionally, it should be appreciated that the methods disclosed in this specification are capable of being stored on an article of manufacture to facilitate transporting and transferring such methods to computing devices. It is to be understood that the above description is intended to be illustrative, and not restrictive. Other embodiments will be apparent to those of skill in the art upon reading and understanding the above description. The scope of the disclosure may, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.