Systems and methods for determining a likelihood of a lead conversion event
Systems and methods are provided for generating, processing and distributing leads, the system comprising a leads processing engine for receiving customer requests, creating leads based upon the customer requests, determining a best available agent or agents for each lead from a pool of available agents based upon one or more selected factors, and offering and/or sending each lead to the best available agent or agents.
1. A system for determining a likelihood of a lead conversion event, the system comprising:
one or more physical processors configured to execute machine-readable instructions to:
obtain lead attribute information associated with a lead that was received from one or more customers submitting an incoming request, the lead attribute information comprising individual lead factors including at least one of a lead type, a lead submission method, a lead submission urgency level, and a vendor class;
determine historic booking information associated with previously submitted leads by the one or more customers submitting the incoming request; and
determine a likelihood of a lead conversion event for the lead received using a machine learning model and based on the lead attribute information and the historic booking information.
2. The system of claim 1 , wherein the historic booking information associated with the previously submitted leads comprises leads having lead information including the vendor class associated with the lead received.
3. The system of claim 1 , wherein the previously submitted leads comprise a date of submission, and wherein the date of submission does not exceed 12 months form a current date.
4. The system of claim 1 , wherein determining the likelihood of the lead conversion event for the lead received comprises assigning a weight to each of the individual lead factors.
5. The system of claim 1 , wherein determining the likelihood of the lead conversion event for the lead received comprises using a predictive model.
6. The system of claim 5 , wherein the predictive model includes Bayesian statistics.
7. The system of claim 1 , wherein the lead conversion event for the lead comprises a sales transaction.
8. The system of claim 1 , further comprising obtaining online behavior information associated with the one or more customers submitting the incoming request, the online behavior information including time spent on a website by the one or more customers prior to submitting the request.
9. A method for determining a likelihood of a lead conversion event, the method comprising:
obtaining lead attribute information associated with a lead that was received from one or more customers submitting an incoming request, the lead attribute information comprising individual lead factors including at least one of a lead type, a lead submission method, a lead submission urgency level, and a vendor class;
determining historic booking information associated with previously submitted leads by the one or more customers submitting the incoming request; and
determining a likelihood of a lead conversion event for the lead received using a machine learning model and based on the lead attribute information and the historic booking information.
10. The method of claim 9 , wherein the determining the historic booking information associated with the previously submitted leads comprises obtaining leads having lead information including the vendor class associated with the lead received.
11. The method of claim 9 , wherein the previously submitted leads comprise a date of submission, and wherein the date of submission does not exceed 12 months form a current date.
12. The method of claim 9 , wherein determining the likelihood of the lead conversion event for the lead received comprises assigning a weight to each of the individual lead factors.
13. The method of claim 9 , wherein determining the likelihood of the lead conversion event for the lead received comprises using a predictive model.
14. The method of claim 13 , wherein the predictive model includes Bayesian statistics.
15. The method of claim 9 , wherein the lead conversion event for the lead comprises a sale transaction.
16. The method of claim 9 , further comprising obtaining online behavior information associated with the one or more customers submitting the incoming request, the online behavior information including time spent on a website by the one or more customers prior to submitting the request.
17. A non-transitory computer-readable storage medium storing a plurality of instructions executable by one or more processors, the plurality of instructions when executed by the one or more processors cause the one or more processors to:
obtain lead attribute information associated with a lead that was received from one or more customers submitting an incoming request, the lead attribute information comprising individual lead factors including at least one of a lead type, a lead submission method, a lead submission urgency level, and a vendor class;
determine historic booking information associated with previously submitted leads by the one or more customers submitting the incoming request; and
determine a likelihood of a lead conversion event for the lead received using a machine learning model and based on the lead attribute information and the historic booking information.
18. The non-transitory computer-readable storage medium of claim 17 , wherein the one or more processors further to:
obtain leads having lead information including the vendor class associated with the lead received.
19. The non-transitory computer-readable storage medium of claim 17 , wherein the previously submitted leads comprise a date of submission, and wherein the date of submission does not exceed 12 months form a current date.
20. The non-transitory computer-readable storage medium of claim 17 , wherein determining the likelihood of the lead conversion event for the lead received comprises assigning a weight to each of the individual lead factors.