System and method for intelligent sales engagement

1. A system for intelligent sales engagement, comprising:

a pre-integrated graph server comprising at least a processor, a memory, and a plurality of programming instructions stored in the memory and operating on the processor, wherein the programming instructions, when operating on the processor, cause the processor to:

(a) monitor and capture events from source systems and constructs an event graph of multichannel interactions and attributes including firm demographics and sales rep attributes;

(b) automatically reduce the graph to a plurality of state transitions;

(c) run in modes where reducing the graph happens either periodically or continuously; and

(d) support different pre-defined topologies of funnel, decision circle and journey;

a machine learning server comprising at least a processor, a memory, and a plurality of programming instructions stored in the memory and operating on the processor, wherein the programming instructions, when operating on the processor, cause the processor to:

(e) train a family of predictive machine-learning models for any transition of interest of the reduced graph and performs validation of the accuracy of each predictive machine learning model;

(f) choose 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) accept case determined numbers of input attributes of different types on each state transitions;

an optimization server comprising at least a processor, a memory, and a plurality of programming instructions stored in the memory and operating on the processor, wherein the programming instructions, when operating on the processor, cause the processor to:

(h) create a set of visualizations showing the various resulting performance metrics including conversion rate, representative utilization percentage, and total value in the pipeline;

(i) use the trained predictive models as input to an automated optimization phase which recommends specific actions to take to optimize the business outcome of prospects flowing through the reduced graph subject to constraints;

(j) support optimization under uncertainty;

(k) schedule interactions between agents and prospects to maximize an objective; and

(l) configure, 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 business-to-business flows and business-to-consumer flows.

4. The system of claim 1 wherein the machine learning module may learn 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. 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 a plurality of state transition occurrences, expressing the resultant graph in one of a plurality of pre-defined topologies, the plurality of pre-defined topologies including at least of: funnel, decision circle and engagement journey using the pre-integrated graph module;

(d) training a family of predictive machine-learning models for any transition of interest of the reduced graph and performs validation of the accuracy 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 to take to optimize the business outcome of prospects flowing through the reduced graph subject to constraints using the optimization module.

9. The method of claim 8 , wherein the expected sales process may be entered as input to guide graph reduction and to highlight deviations from expected flows.

10. The method of claim 8 , wherein the graph may also represent business-to-business flows and business-to-consumer flows.

11. The method of claim 8 , wherein the machine learning module may learn a process based on historical data.

12. The method of claim 8 , wherein the machine learning module may account for the multi-dimensional nature of social influence, and the role of advocates who aren't customers.

13. The method of claim 8 , wherein the machine learning module may shift to ongoing relationships beyond individual transactions.

14. The method of claim 8 , wherein the machine learning module runs in an adaptive mode where retraining happens periodically or continuously.