System and method for intelligent sales engagement

Abstract

A system for automatically automatic workflow triggering using real-time analytics, comprising an analytics server that receives and analyzes interaction information and a workflow server that produces workflow events based on the analysis, sends workflow events to handlers for processing, retrieves workflow-related data, and produces workflow reports for review, and a method for automatically automatic workflow triggering using real-time analytics.

What is claimed is: 
     
         1 . A system for intelligent sales engagement, comprising:
 a pre-integrated graph module comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a network-connected computing device;   a machine learning module comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a network-connected computing device; and   an optimization module comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a network-connected computing device;   wherein, the pre-integrated graph module:
 (a) monitors and captures events from source systems and constructs an event graph of multichannel interactions and attributes including firm demographics and sales rep attributes; 
 (b) automatically reduces the graph to the significant state transitions, effectively reverse engineering the sales process from available actual event data; 
 (c) runs in an adaptive mode where reducing the graph happens periodically or continuously; and 
 (d) supports different pre-defined topologies of funnel, circle and journey; 
   wherein the machine learning module:
 (e) trains a family of predictive machine-learning models for any transition of interest (or for all transitions) of the reduced graph and performs validation of the accuracy (AUC) of each predictive machine learning model; 
 (f) chooses different model types for different transitions based on model with highest accuracy, model then estimates the conditional probability of the transition from the starting to ending state potentially including all known input attributes at the starting state; 
 (g) accepts arbitrary numbers of input attributes of different types on each state transitions; 
 (h) runs with either full state history with attributes, Markov approximation or hidden Markov model or a hybrid mode; and 
 (i) supports a hidden Markov model to represent the hidden “intent” state of the contact or lead; 
   wherein the optimization module:
 (j) creates a set of visualizations showing the various resulting performance metrics including conversion rate, representative utilization percentage, and total value in the pipeline; 
 (k) uses the trained predictive models as input to an automated optimization phase which recommends specific actions (interactions) to take to optimize the business outcome of prospects flowing through the reduced graph subject to constraints; 
 (l) supports optimization under uncertainty; 
 (m) schedules interactions between agents and prospects to maximize an objective; and 
 (n) configures, in addition to existing model optimization, optimization experiments that are executed and is then able to run experiments, analyze the results and self-learn giving rise to increased utility. 
   
     
     
         2 . The system of  claim 1 , whereas the expected sales process may be entered as input to guide graph reduction and/or highlight deviations from expected flows. 
     
     
         3 . The system of  claim 1 , wherein the graph may also represent B2B flows and B2C flows. 
     
     
         4 . The system of  claim 1  wherein the machine learning module may learn or reverse engineer a process based on historical data. 
     
     
         5 . The system of  claim 1  wherein, the machine learning module may account for the multi-dimensional nature of social influence, and the role of advocates who aren't customers. 
     
     
         6 . The system of  claim 1  wherein, the machine learning module may shift to ongoing relationships beyond individual transactions. 
     
     
         7 . The system of  claim 1  wherein, the machine learning module runs in an adaptive mode where retraining happens periodically or continuously. 
     
     
         8 . The system of  claim 1  wherein the optimization module can use the trained predictive models can be used to support a “what-if” user interface for human users to understand the effect of change of attributes or graph structure. 
     
     
         9 . A method for intelligent sales engagement, the method comprising the steps of:
 (a) monitoring and extracting sets of customer relationship sales data from source systems into a pre-integrated graph module comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a network-connected computing device;   (b) constructing an event driven relational graph of multichannel interactions and attributes including firm demographics and sales rep attributes using the pre-integrated graph module;   (c) reducing the graph to the significant state transition occurrences, effectively reverse engineering the sales process from available actual event data expressing the resultant graph in one of a plurality of pre-defined topologies such as: funnel, circle and journey using the pre-integrated graph module;   (d) training a family of predictive machine-learning models for any transition of interest (or for all transitions) of the reduced graph and performs validation of the accuracy (AUC) of each predictive machine learning model using a machine learning module comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a network-connected computing device;   (e) choosing different model types using the machine learning module for different transitions based on model with highest accuracy, chosen model then estimates conditional probability of the transition from the starting to ending state potentially including all known input attributes at the starting state;   (f) running either full state history with attributes, Markov approximation or hidden Markov model or a hybrid mode using the machine learning module;   (g) creating a set of visualizations showing the various resulting performance metrics including conversion rate, rep utilization %, and total value in the pipeline using the optimization module;   (h) using the trained predictive models as input to an automated optimization phase which recommends specific actions (interactions) to take to optimize the business outcome of prospects flowing through the reduced graph subject to constraints using the optimization module.   
     
     
         10 . The method of  claim 9 , wherein the expected sales process may be entered as input to guide graph reduction and to highlight deviations from expected flows. 
     
     
         11 . The method of  claim 9 , wherein the graph may also represent B2B flows and B2C flows. 
     
     
         12 . The method of  claim 9 , wherein the machine learning module may learn or reverse engineer a process based on historical data. 
     
     
         13 . The method of  claim 9 , wherein the machine learning module may account for the multi-dimensional nature of social influence, and the role of advocates who aren't customers. 
     
     
         14 . The method of  claim 9 , wherein the machine learning module may shift to ongoing relationships beyond individual transactions. 
     
     
         15 . The method of  claim 9 , wherein the machine learning module runs in an adaptive mode where retraining happens periodically or continuously. 
     
     
         16 . The method of  claim 9  wherein the optimization module can use the trained predictive models can be used to support a “what-if” user interface for human users to understand the effect of change of attributes or graph structure.